CN114418238A

CN114418238A - Oil temperature abnormity prediction method and device and working machine

Info

Publication number: CN114418238A
Application number: CN202210152015.4A
Authority: CN
Inventors: 张琪琪; 杨晓茹; 贺群
Original assignee: Shengjing Intelligent Technology Jiaxing Co ltd
Current assignee: Shengjing Intelligent Technology Jiaxing Co ltd
Priority date: 2022-02-18
Filing date: 2022-02-18
Publication date: 2022-04-29

Abstract

The invention provides a method and a device for predicting abnormal oil temperature and an operating machine, and relates to the technical field of engineering machinery, wherein the method comprises the following steps: acquiring target data, wherein the target data comprises the oil temperature of hydraulic oil in a hydraulic system of the working machine in a first time period; inputting the target data into an oil temperature abnormity prediction model, and acquiring a prediction result of the oil temperature abnormity of the hydraulic system in a second time interval output by the oil temperature abnormity prediction model; wherein the ending time of the first time interval is earlier than the starting time of the second time interval; the oil temperature abnormity prediction model is constructed based on the oil temperature of hydraulic oil in a hydraulic system of the sample operation machine in a first sample time period and whether the oil temperature of the hydraulic system of the sample operation machine in a second sample time period is abnormal; the end time of the first sample period is earlier than the start time of the second sample period. The oil temperature abnormity prediction method, the device and the working machine provided by the invention can predict the oil temperature abnormity of the hydraulic system in advance and improve the practicability of oil temperature abnormity prediction.

Description

Oil temperature abnormity prediction method and device and working machine

Technical Field

The invention relates to the technical field of engineering machinery, in particular to a method and a device for predicting abnormal oil temperature and operating machinery.

Background

The hydraulic oil refers to a hydraulic medium in a hydraulic system of the working machine, and can play roles in energy transfer, wear resistance, system lubrication, corrosion resistance, rust resistance, cooling and the like in the hydraulic system. The temperature of the hydraulic oil may be referred to as an oil temperature, and the oil temperature anomaly may refer to the oil temperature being too high or too low. If the oil temperature is abnormal, the normal operation of the hydraulic system and thus the working machine is affected, for example: when the oil temperature is too high, the viscosity of hydraulic oil is reduced, the oxidation speed is accelerated, and thermal deformation of hydraulic components in a hydraulic system can be caused, so that the hydraulic system fails; the oil temperature is too low, which causes the viscosity of the hydraulic oil to increase and the fluidity to be poor, and influences the normal operation of the hydraulic system.

The existing oil temperature abnormity prediction method can determine that the oil temperature is abnormal under the condition that the real-time monitored oil temperature exceeds the oil temperature threshold interval according to the predetermined oil temperature threshold interval. However, the oil temperature abnormality is predicted based on the oil temperature abnormality prediction method, and when it is determined that the oil temperature monitored in real time exceeds the oil temperature threshold interval, the oil temperature is often abnormal, it is difficult to reserve enough time for overhauling or controlling the working machine, and the practicability of predicting the oil temperature abnormality is weak.

Disclosure of Invention

The invention provides a method and a device for predicting abnormal oil temperature and an operating machine, which are used for solving the defect that the practicability of predicting the abnormal oil temperature is weaker in the prior art and realizing the improvement of the practicability of predicting the abnormal oil temperature.

The invention provides a method for predicting abnormal oil temperature, which comprises the following steps:

acquiring target data, wherein the target data comprises the oil temperature of hydraulic oil in a hydraulic system of a working machine in a first time period;

inputting the target data into an oil temperature abnormity prediction model, and acquiring a prediction result of the oil temperature abnormity of the hydraulic system in a second time interval output by the oil temperature abnormity prediction model;

wherein an end time of the first time period is earlier than a start time of the second time period; the oil temperature abnormity prediction model is constructed based on the oil temperature of hydraulic oil in a hydraulic system of the sample operation machine in a first sample time period and whether the oil temperature of the hydraulic system of the sample operation machine is abnormal in a second sample time period; the end time of the first sample period is earlier than the start time of the second sample period.

According to the oil temperature abnormity prediction method provided by the invention, the oil temperature abnormity prediction model comprises the following steps: a feature extraction layer and an anomaly prediction layer;

correspondingly, the inputting the target data into an oil temperature abnormality prediction model, and obtaining a prediction result of the oil temperature abnormality of the hydraulic system of the working machine in a second time period output by the oil temperature abnormality prediction model specifically includes:

inputting the target data into the feature extraction layer, and performing feature extraction on the target data by the feature extraction layer to obtain feature data corresponding to the target data output by the feature extraction layer;

inputting the characteristic data into the abnormity prediction layer, and obtaining the prediction result output by the abnormity prediction layer, wherein the prediction result comprises a first probability that the oil temperature of the hydraulic system of the working machine is abnormal in the second time interval.

According to the oil temperature abnormality prediction method provided by the invention, the characteristic data comprises the following steps: the temperature deviation between the maximum value and the average value of the oil temperature in the first period, the total duration of the oil temperature being higher than a first preset value and the longest duration of the oil temperature being higher than a second preset value.

According to the oil temperature abnormality prediction method provided by the invention, whether the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample time period is determined based on the time when the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample time period;

correspondingly, the inputting the characteristic data into the abnormality prediction layer, and obtaining the prediction result output by the abnormality prediction layer, where the prediction result includes a first probability that an oil temperature of the hydraulic system of the working machine is abnormal in the second period, specifically includes:

and inputting the feature data into the abnormal prediction layer, acquiring a second probability corresponding to each type of feature data by the abnormal prediction layer based on the feature data, acquiring the first probability based on each second probability, and further acquiring the prediction result output by the abnormal prediction layer.

According to the oil temperature abnormality prediction method provided by the invention, the prediction result further comprises the following steps: the first probability corresponds to a risk level.

According to the oil temperature abnormality prediction method provided by the invention, the first probability comprises the probability of the oil temperature abnormality of the hydraulic system in a target time interval in the second time interval.

According to the oil temperature abnormality prediction method provided by the invention, the acquiring of the target data specifically comprises the following steps:

acquiring the oil temperature of hydraulic oil in a hydraulic system of the working machine in the first time period based on a preset sampling frequency to serve as original data;

and performing data processing on the original data, and taking the original data subjected to data processing as the target data.

The present invention also provides an oil temperature abnormality prediction device, including:

the data acquisition module is used for acquiring target data, wherein the target data comprises the oil temperature of hydraulic oil in a hydraulic system of the working machine in a first time period;

the abnormal prediction module is used for inputting the target data into an oil temperature abnormal prediction model and acquiring a prediction result of the oil temperature abnormality of the hydraulic system in a second time interval output by the oil temperature abnormal prediction model;

The present invention also provides a work machine comprising: the oil temperature abnormality prediction device described above.

The invention also provides an electronic device, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the program, the oil temperature abnormity prediction method is realized according to any one of the methods.

The present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements any of the oil temperature abnormality prediction methods described above.

The invention also provides a computer program product comprising a computer program which, when executed by a processor, implements any of the above-described oil temperature anomaly prediction methods.

The invention provides an oil temperature abnormity prediction method, a device and an operating machine, which can predict the oil temperature abnormity of a hydraulic system in advance by acquiring target data comprising the oil temperature of hydraulic oil in an operating machine hydraulic system in a first time interval, inputting the target data into an oil temperature abnormity model, acquiring the prediction result of the oil temperature abnormity of the hydraulic system in a second time interval output by the oil temperature abnormity model, wherein the ending time of the first time is earlier than the starting time of the second time interval, the oil temperature abnormity model is constructed based on the oil temperature of a sample operating machine in the first sample time interval and whether the oil temperature abnormity occurs in the sample operating machine in the second sample time interval, the ending time of the first sample time interval is earlier than the starting time of the second sample time interval, can predict the oil temperature abnormity of the hydraulic system in advance, can improve the practicability of the oil temperature abnormity prediction, and reserve enough time to overhaul and maintain the operating machine before the oil temperature abnormity occurs in the hydraulic system by predicting the oil temperature abnormity in advance, the oil temperature abnormality can be effectively avoided, the usability of the working machine can be improved, the maintenance workload can be reduced, and the maintenance cost can be reduced.

Drawings

In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.

FIG. 1 is a schematic flow chart of a method for predicting an abnormal oil temperature according to the present invention;

FIG. 2 is a data diagram of raw data in the oil temperature abnormality detection method according to the present invention;

FIG. 3 is a data diagram illustrating target data in the method for detecting an abnormal oil temperature according to the present invention;

FIG. 4 is a schematic structural diagram of an oil temperature anomaly prediction model in the oil temperature anomaly prediction method provided by the present invention;

FIG. 5 is a data schematic diagram of a second probability corresponding to characteristic data in the oil temperature anomaly prediction method provided by the present invention;

FIG. 6 is a data schematic of a confidence interval corresponding to a first probability in the oil temperature anomaly prediction method provided by the present invention;

FIG. 7 is a schematic diagram of a device for predicting an abnormal oil temperature according to the present invention;

fig. 8 is a schematic structural diagram of an electronic device provided in the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

In the description of the invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.

It should be noted that, based on the conventional intelligent diagnosis technology, it is possible to diagnose components such as an engine, a power head, and a bearing of a work machine, and determine whether the components have a fault, and there are few intelligent diagnosis technologies for the oil temperature of hydraulic oil in a hydraulic system of the work machine, especially in the hydraulic system.

Under the normal condition, whether the oil temperature of the hydraulic oil is abnormal or not can be judged based on the real-time monitoring of the oil temperature of the hydraulic oil, when the real-time monitored oil temperature exceeds the oil temperature threshold interval, the oil temperature is possibly abnormal, enough time is difficult to reserve to overhaul or control the operation machinery, the timeliness is poor, and the limitation is strong.

Therefore, the invention provides an oil temperature abnormity prediction method, based on which whether the oil temperature abnormity occurs in the hydraulic system can be predicted in advance, so that enough time can be reserved for overhauling or controlling the working machine, the damage of the working machine caused by the oil temperature abnormity of the hydraulic system is avoided, the usability of the working machine is improved, the maintenance workload is reduced, and the operation monitoring cost is reduced.

Fig. 1 is a schematic flow chart of a method for predicting an abnormal oil temperature according to the present invention. The oil temperature abnormality prediction method of the present invention is described below with reference to fig. 1. As shown in fig. 1, the method includes: 101, acquiring target data, wherein the target data comprises the oil temperature of hydraulic oil in a hydraulic system of an operating machine in a first time period; the end time of the first period is earlier than the start time of the second period.

Specifically, the first period is earlier than the second period and the first period does not intersect the second period. Based on the oil temperature abnormity prediction method provided by the invention, the oil temperature abnormity of the hydraulic system in the second time period can be predicted based on the oil temperature of the hydraulic oil in the hydraulic system of the working machine in the first time period, and the prediction result of the oil temperature abnormity of the hydraulic system in the second time period is obtained.

Alternatively, the end time of the first period may be the start time of the second period. The duration of the first period may be the same as the duration of the second period. For example: the first time period can be 08:10:57 in 11-19 months in 2019 to 08:10:56 in 11-20 months in 2019, and the second time period can be 08:10:56 in 11-20 months in 2019 to 08:10:56 in 21 months in 2019, on the basis of the oil temperature abnormity prediction method provided by the invention, whether the oil temperature abnormity occurs in the hydraulic system in 11-19 months in 2019 or in a ratio of 08:10:56 in 11-20 months in 2019 or in a ratio of 10:57 in 11-21 months in 2019 or in a ratio of 08:10:56 in 11-20 months in 2019 or in a ratio of 10:56 in 2019 is obtained, and the prediction result of the oil temperature abnormity occurs in the hydraulic system in 11-20 months in 2019, 08:10:57 in 11-21 months in 2019 is obtained.

The oil temperature of the hydraulic oil in the hydraulic system of the working machine in the first period of time may be obtained based on preset rules, for example: the temperature sensor may be used to obtain the oil temperature of the hydraulic oil in the hydraulic system of the work machine at preset time intervals during the first period.

After the oil temperature of the hydraulic oil in the hydraulic system of the working machine in the first period is acquired, the oil temperature of the hydraulic oil can be directly used as target data.

Optionally, due to the complex working environment of the working machine, a large amplitude vibration may be generated during the working of the working machine, so that the data acquired by the temperature sensor has a large noise, a low signal-to-noise ratio and includes many abnormal values and blank values. In order to improve the accuracy of oil temperature anomaly prediction, after the oil temperature of the hydraulic oil in the first time period is obtained, the data can be used as original data, data processing can be performed on the original data, and the original data after the data processing is used as target data.

The raw data can be processed based on mathematical statistics, linear interpolation, down-sampling and other manners. For example: normal distribution test can be carried out on the oil temperature of the hydraulic oil in the first time period by utilizing the D statistic of Kolmogorov-Smirnov, for data which do not meet the assumption of normal distribution, a reasonable interval of the oil temperature of the hydraulic oil can be determined based on priori knowledge, and the data which exceed the reasonable interval are taken as abnormal values to be removed; and the upper quartile, the lower quartile, the median, the maximum value and the minimum value of the assumed data of the approximate normal distribution can be obtained through numerical calculation, and the maximum value and the minimum value are taken as abnormal values to be removed.

Fig. 2 is a data schematic diagram of raw data in the oil temperature abnormality detection method provided by the present invention. As shown in fig. 2, the oil temperature of the hydraulic oil that is not subjected to data processing in the first period is noisy, and there are a plurality of abnormal points where the fluctuation is large.

Fig. 3 is a data schematic diagram of target data in the oil temperature abnormality detection method provided by the present invention. As shown in fig. 3, the oil temperature of the hydraulic oil subjected to data processing in the first period is approximately normally distributed, and the noise is small, so that the oil temperature of the hydraulic oil subjected to data processing in the first period can be used as the target data.

It should be noted that "first" and "second" in the embodiments of the present invention are used for distinguishing similar objects, and are not used for describing or indicating a specific order or sequence.

102, inputting target data into an oil temperature abnormity prediction model, and acquiring a prediction result of oil temperature abnormity of the hydraulic system in a second time interval output by the oil temperature abnormity prediction model; the oil temperature abnormity prediction model is constructed based on the oil temperature of hydraulic oil in a hydraulic system of the sample operation machine in a first sample time period and whether the oil temperature of the hydraulic system of the sample operation machine in a second sample time period is abnormal; the end time of the first sample period is earlier than the start time of the second sample period.

Specifically, before the target data is input into the oil temperature abnormality prediction model, the oil temperature of the hydraulic oil in the hydraulic system of the sample working machine in the first sample period may be acquired as sample data, whether the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample period is acquired as a sample label, and the oil temperature abnormality prediction model is constructed based on the sample data and the sample label. Wherein the first sample period is earlier than the second sample period, and the first sample period does not intersect the second sample period.

Alternatively, the end time of the first sample period may be the start time of the second sample period. The duration of the first sample period may be the same as the duration of the second sample period.

It should be noted that the first sample period and the second sample period have a corresponding relationship with the first period and the second period, that is, the duration of the first period is the same as the duration of the first sample period, the duration of the second period is the same as the duration of the second sample period, and the duration of the interval between the first period and the second period is the same as the duration of the interval between the first sample period and the second sample period.

The sample work machine is a work machine in a normal operation state. The work machine described above is the same type and model as the sample work machine.

The oil temperature of the hydraulic oil in the hydraulic system of the sample working machine in the first sample period may be obtained based on a preset rule as sample data, for example: the temperature of the hydraulic oil in the hydraulic system of the sample working machine may be acquired as sample data at preset time intervals within a first sample period by using a temperature sensor.

Whether the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample period may be determined based on the prior knowledge.

After the sample data and the sample label are obtained, an oil temperature abnormity prediction model can be constructed based on the sample data and the sample label.

After the oil temperature abnormity prediction model is constructed, target data can be input into the oil temperature abnormity prediction model, and a prediction result of the oil temperature abnormity of the hydraulic system in a second time interval output by the oil temperature abnormity prediction model is obtained.

It should be noted that, the predicted result may include that the oil temperature of the hydraulic system is abnormal in the second period, or the oil temperature is not abnormal; the prediction result can also comprise the probability and/or risk level of the oil temperature abnormity of the hydraulic system in the second period; the predicted result may further include a probability and/or a risk level of the oil temperature abnormality of the hydraulic system at the target time in the second period. The prediction result is not particularly limited in the embodiment of the present invention.

The embodiment of the invention obtains the target data comprising the oil temperature of the hydraulic oil in the hydraulic system of the working machine in the first time interval, inputs the target data into the oil temperature abnormity model, obtains the prediction result of the abnormal oil temperature of the hydraulic system in the second time interval output by the oil temperature abnormity model, the ending time of the first time is earlier than the starting time of the second time interval, the oil temperature abnormity model is constructed based on the oil temperature of the sample working machine in the first sample time interval and whether the abnormal oil temperature of the sample working machine occurs in the second sample time interval, the ending time of the first sample time is earlier than the starting time of the second sample time interval, can predict the abnormal oil temperature of the hydraulic system in advance, can improve the practicability of the abnormal oil temperature prediction, can reserve enough time to overhaul and maintain the working machine before the abnormal oil temperature occurs in the hydraulic system by predicting the abnormal oil temperature of the hydraulic system in advance, the oil temperature abnormality can be effectively avoided, the usability of the working machine can be improved, the maintenance workload can be reduced, and the maintenance cost can be reduced.

Fig. 4 is a schematic structural diagram of an oil temperature abnormality prediction model in the oil temperature abnormality prediction method provided by the present invention. As shown in fig. 4, the oil temperature abnormality prediction model includes: a feature extraction layer and an anomaly prediction layer.

Correspondingly, the target data is input into the oil temperature abnormality prediction model, and the prediction result of the oil temperature abnormality of the hydraulic system of the working machine in the second time interval output by the oil temperature abnormality prediction model is obtained, which specifically comprises the following steps: inputting the target data into the feature extraction layer, and performing feature extraction on the target data by the feature extraction layer to obtain feature data corresponding to the target data output by the feature extraction layer.

Before the target data is input into the feature extraction layer in the oil temperature abnormality prediction model, the feature extraction layer may be trained based on the sample data to obtain a trained feature extraction layer.

Specifically, feature extraction may be performed on the sample data, and sample feature data corresponding to the sample data may be obtained.

And training the feature extraction layer based on the sample data and the sample feature data corresponding to the sample data to obtain the trained feature extraction layer.

After the trained feature extraction layer is obtained, the target data can be input into the feature extraction layer, and feature data corresponding to the target data output by the feature extraction layer is obtained.

The feature extraction layer may perform feature extraction on the target data, and acquire and input the feature data.

And inputting the characteristic data into an abnormal prediction layer, and acquiring a prediction result output by the abnormal prediction layer, wherein the prediction result comprises a first probability of the oil temperature abnormality of a hydraulic system of the working machine in a second time period.

Before the characteristic data is input into the abnormal prediction layer in the oil temperature abnormal prediction model, the abnormal prediction layer may be trained based on the sample data and the sample label, so as to obtain a trained first prediction layer.

Specifically, the sample data is subjected to feature extraction, and sample feature data corresponding to the sample data can be obtained

Optionally, the sample data may be input into the feature extraction layer, and sample feature data corresponding to the sample data output by the feature extraction layer may be acquired.

Based on the priori knowledge and whether the oil temperature of the hydraulic system of the sample working machine is abnormal in the second sample period, a first sample probability that the oil temperature of the hydraulic system of the sample working machine is abnormal in the second sample period can be obtained, for example: if the oil temperature of the hydraulic system of the sample working machine is not abnormal in the second sample time period, the first sample probability is 0.

Based on the sample feature data and the first sample probability, the abnormal prediction layer can be trained, and the trained abnormal prediction layer is obtained.

After the trained abnormality prediction layer is obtained, the characteristic data may be input into the trained abnormality prediction layer, and a first probability that an oil temperature abnormality occurs in a hydraulic system of the working machine in a second period of time output by the abnormality prediction layer is obtained.

It should be noted that the first probability may include a probability that the oil temperature of the hydraulic system is abnormal in the second period, and may further include a probability that the oil temperature of the hydraulic system is abnormal at the target time in the second period.

The embodiment of the invention inputs the target data into the characteristic extraction layer in the oil temperature abnormity prediction model, the characteristic extraction layer carries out characteristic extraction on the target data, obtains the characteristic data corresponding to the target data output by the characteristic extraction layer, inputs the characteristic data into the abnormity prediction layer, obtains the first probability of the abnormal oil temperature of the hydraulic system of the operating machinery in the second time interval output by the abnormity prediction layer, can more intuitively obtain the prediction result of the abnormal oil temperature of the hydraulic system of the operating machinery in the second time interval without depending on too many sensors, can reduce the use cost of the sensors, can avoid the excessive generation of redundant noise and interference of the sensors, can carry out more targeted measures based on the prediction result, more effectively avoid the abnormal oil temperature of the hydraulic system, can avoid the viscosity reduction of hydraulic oil caused by the abnormal oil temperature and the volume efficiency reduction of the hydraulic pump, accelerated aging of the rubber sealing element, aggravation of abrasion of the hydraulic element and the like can reduce maintenance workload and reduce maintenance cost investment.

Based on the content of the foregoing embodiments, the feature data includes: a temperature deviation between a maximum value and an average value of the oil temperature in the first period, a total duration of the oil temperature being higher than a first preset value, and a maximum duration of the oil temperature being higher than a second preset value.

Specifically, after the target data is input into the feature extraction layer, the feature extraction layer may perform feature extraction on the target data by using methods such as mathematical statistics and numerical calculation, and obtain and output feature data corresponding to the target data.

The characteristic extraction layer can quantize the target data into the characteristic data, so that the fluctuation situation of the oil temperature of the hydraulic oil in the first time interval can be better described based on the characteristic data.

Alternatively, the feature extraction layer may obtain the maximum value of the oil temperature of the hydraulic oil in the first period based on a mathematical statistic method, obtain the average value of the oil temperature of the hydraulic oil in the first period based on a numerical calculation method, and use the temperature deviation between the maximum value and the average value as the feature data.

Optionally, the feature extraction layer may obtain, as the feature data, a total duration of the oil temperature in the first period that is higher than the first preset value based on a mathematical statistic method.

Optionally, the feature extraction layer may obtain the longest duration of the oil temperature in the first period, which is higher than the second preset value, as the feature data based on a mathematical statistics method. Wherein, the first preset value can be the same as or different from the second preset value.

Optionally, the feature extraction layer may obtain an average value of the oil temperature of the hydraulic oil in the first period based on a numerical calculation method, and obtain a historical quantile of the average value as the feature data based on the average value of the oil temperature of the hydraulic oil in the first period and the sample data.

Optionally, the feature extraction layer may obtain a maximum value of the oil temperature of the hydraulic oil in the first period based on a mathematical statistics method, and obtain a historical quantile of the maximum value as the feature data based on the maximum value of the oil temperature of the hydraulic oil in the first period and the sample data.

Optionally, after the feature extraction layer obtains a total duration in which the oil temperature of the hydraulic oil in the first period is higher than the first preset value, a ratio of the total duration to the duration of the first period may be obtained as the feature data.

Optionally, after the feature extraction layer obtains the longest duration that the oil temperature of the hydraulic oil in the first period is higher than the second preset value, a ratio of the longest duration to the duration of the first period may be obtained as the feature data.

According to the embodiment of the invention, the target data is input into the characteristic extraction layer to obtain the characteristic data output by the characteristic extraction layer, wherein the characteristic data comprises but is not limited to the temperature deviation between the maximum value and the average value of the oil temperature in the first period, the total duration of the oil temperature higher than the first preset value and the longest duration of the oil temperature higher than the second preset value, so that the characteristic of the target data can be obtained more accurately and efficiently, the fluctuation condition of the oil temperature of hydraulic oil in a hydraulic system of an operating machine in the first period can be further accurately described based on the characteristic data, and a data basis is provided for more accurately obtaining the prediction result of the oil temperature abnormity.

Based on the contents of the above embodiments, whether the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample period is determined based on the time when the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample period.

Correspondingly, inputting the characteristic data into an abnormal prediction layer, and obtaining a prediction result output by the abnormal prediction layer, wherein the prediction result comprises a first probability that the oil temperature of a hydraulic system of the working machine is abnormal in a second time period, and the method specifically comprises the following steps: and inputting the feature data into an anomaly prediction layer, acquiring second probabilities corresponding to each type of feature data by the anomaly prediction layer based on the feature data, acquiring first probabilities based on the second probabilities, and further acquiring prediction results output by the anomaly prediction layer.

Before the characteristic data is input into the abnormality prediction layer, the abnormality prediction layer may be constructed based on the sample data and the time when the oil temperature of the hydraulic system of the sample working machine is abnormal in the second sample period.

Specifically, sample feature data corresponding to the sample data may be acquired based on the sample data.

Optionally, the sample data is input into the feature extraction layer, and sample feature data corresponding to the sample data output by the feature extraction layer may be acquired. The sample characteristic data may include a temperature deviation between a maximum value and an average value of the oil temperature of the hydraulic oil in the hydraulic system of the sample working machine in the first sample period, a total duration of the oil temperature higher than a first preset value in the first sample period, a maximum duration of the oil temperature higher than a second preset value in the first sample period, a historical quantile of the average value, a historical quantile of the maximum value, a ratio of the total duration to a duration of the first sample period, and a ratio of the maximum duration to the duration of the first sample period.

Based on the priori knowledge and the time when the oil temperature of the hydraulic system of the sample working machine is abnormal in the second sample period, the probability density function and the accumulated probability density function of each type of sample characteristic data at the time when the oil temperature is abnormal in the second sample period can be obtained. Based on the probability density function and the cumulative probability density function, a probability model can be constructed. Based on the probability model, a second sample probability corresponding to each type of sample feature data can be obtained. The second sample probability may be used to describe a probability that the corresponding sample feature data in the second sample period is abnormal.

Based on the second sample probabilities and the priori knowledge, the first sample probability that the oil temperature of the hydraulic system of the sample operation machine is abnormal in the second sample time period can be obtained.

And constructing an abnormal prediction layer based on the probability model, the second sample probabilities and the first sample probability.

After the abnormality prediction layer is constructed, the characteristic data can be input into the abnormality prediction layer, and a prediction result output by the abnormality prediction layer and including a first probability that the oil temperature of the hydraulic system of the working machine is abnormal in a second time interval is obtained.

The abnormal prediction layer may be configured to obtain a second probability corresponding to each type of feature data based on the feature data and the probability model, and obtain a first probability that the oil temperature of the hydraulic system of the work machine is abnormal in a second time period based on each second probability. The second probability may be used to describe the probability of the corresponding type of feature data being abnormal in the second time period.

Optionally, the prediction result may further include a second probability corresponding to each type of feature data.

Fig. 5 is a data schematic diagram of a second probability corresponding to the characteristic data in the oil temperature abnormality prediction method provided by the present invention. In the case where the characteristic data is a temperature deviation between the maximum value and the average value of the oil temperature of the hydraulic oil in the first period, the second probability corresponding to the above type of characteristic data acquired by the abnormality prediction layer is as shown in fig. 5. As shown in fig. 5, the second probability corresponding to the characteristic data is 83.4% when the temperature deviation is 26 ℃, which indicates that the probability of abnormality in the temperature deviation is 83.4% when the temperature deviation is 26 ℃.

It should be noted that, based on the probability model, the second sample probabilities, and the first sample probability, a threshold corresponding to the first sample probability and a confidence interval of the first sample probability may also be obtained.

Accordingly, the anomaly prediction layer may be constructed based on the probability model, the second sample probabilities, the first sample probability, the threshold corresponding to the first sample probability, and the confidence interval of the first sample probability.

And inputting the characteristic data into the constructed abnormal prediction layer, wherein the abnormal prediction layer can acquire a second probability corresponding to each second characteristic data, acquire a first probability of abnormal oil temperature of a hydraulic system of the working machine in a second time interval based on each second probability, and acquire a threshold corresponding to the first probability and a confidence interval of the first probability.

Optionally, the prediction result output by the anomaly prediction layer may further include a threshold corresponding to the first probability and a confidence interval of the first probability.

Fig. 6 is a data schematic diagram of a confidence interval corresponding to a first probability in the oil temperature abnormality prediction method provided by the present invention. As shown in fig. 6, the first probability is 85% when the confidence interval is 0.5 or the like.

According to the embodiment of the invention, the characteristic data is input into the abnormity prediction layer, the abnormity prediction layer obtains the second probability corresponding to each type of characteristic data based on the characteristic data, obtains the first probability of the oil temperature abnormity of the hydraulic system of the operating machine in the second time period based on each second probability, and further obtains the prediction result comprising the first probability output by the abnormity prediction layer, so that the interpretability, the calculation efficiency and the accuracy of the oil temperature abnormity prediction model can be improved, the false alarm rate of the oil temperature abnormity prediction model is reduced, and the oil temperature abnormity can be predicted in advance more accurately and more efficiently.

Based on the content of the foregoing embodiments, the prediction result further includes: the first probability corresponds to a risk level.

Alternatively, the characteristic data may be input to an abnormality prediction layer, and the abnormality prediction layer may acquire a first probability that an oil temperature of a hydraulic system of the work machine is abnormal in a second period, and acquire a threshold corresponding to the first probability and a confidence interval of the first probability. The abnormality prediction layer may determine a risk level of the oil temperature abnormality of the hydraulic system of the working machine in the second period based on the first probability, the threshold value corresponding to the first probability, and the confidence interval of the first probability.

Optionally, the characteristic data is input into an abnormality prediction layer, and the abnormality prediction layer may obtain a first probability that the oil temperature of the hydraulic system of the working machine is abnormal in the second period, and determine a risk level of the oil temperature abnormality of the hydraulic system of the working machine in the second period based on the first probability and a third preset value. For example: the third preset value may include 80%, 60%, 40%, and 20%, and if the first probability is greater than 80%, the risk level is determined to be a high risk, if the first probability is greater than 60%, the risk level is determined to be a medium-high risk, if the first probability is greater than 40%, the risk level is determined to be a medium-low risk, if the first probability is greater than 20%, the risk is determined to be a medium-low risk, and if the first probability is less than 20%, the risk level is determined to be a low risk.

It should be noted that the risk level of the oil temperature abnormality occurring in the hydraulic system of the working machine in the second time period may be an average risk level of the oil temperature abnormality occurring in the hydraulic system of the working machine in the second time period, for example: the risk level of the oil temperature abnormity of the hydraulic system of the working machine in the second time interval is a medium-high risk; the risk level of the hydraulic system of the working machine with abnormal oil temperature in the second time period may also be a risk level of the hydraulic system of the working machine with abnormal oil temperature at a target time in the second time period, wherein a time interval between any two adjacent target times may be a preset time length. In the embodiment of the present invention, the risk level of the oil temperature abnormality occurring in the hydraulic system of the working machine in the second period is not specifically limited.

Based on the acquired risk level of the hydraulic system of the working machine with abnormal oil temperature in the second time period, relevant measures can be taken in a targeted manner, and the hydraulic system of the working machine is prevented from having abnormal oil temperature, for example: and in the case that the risk level of the oil temperature abnormality of the hydraulic system of the working machine in the second time interval is high, the working machine needs to be stopped and overhauled.

According to the embodiment of the invention, after the first probability of the oil temperature abnormity of the hydraulic system of the working machine in the second time interval is obtained through the abnormity prediction layer based on the characteristic data, the risk level of the oil temperature abnormity of the hydraulic system of the working machine in the second time interval is obtained based on the first probability, and the prediction result output comprising the risk level is output, so that the prediction result of the oil temperature abnormity of the hydraulic system of the working machine in the second time interval can be displayed more flexibly and more intuitively, more targeted measures can be carried out based on the risk level included by the prediction result, the oil temperature abnormity of the hydraulic system is avoided, the maintenance workload can be reduced, and the maintenance cost investment can be reduced.

Based on the content of the above embodiments, the first probability includes the probability that the oil temperature of the hydraulic system is abnormal in the target period in the second period.

Specifically, based on the time and prior knowledge of the occurrence of the abnormal oil temperature of the hydraulic system of the sample working machine in the second sample period, the first sample probability of the occurrence of the abnormal oil temperature of the hydraulic system of the sample working machine at the sample time in the second sample period can be obtained, where the time intervals of any two adjacent sample times are equal. For example: if the oil temperature of the hydraulic system of the sample operation machine at a certain sample moment is not abnormal, the first sample probability that the oil temperature of the hydraulic system of the sample operation machine at the sample moment is abnormal is 0; if the oil temperature of the hydraulic system of the sample working machine at a certain sample time is abnormal, the probability that the oil temperature of the hydraulic system of the sample working machine at the sample time is abnormal is 100%.

Based on the sample characteristic data corresponding to the sample data and the sample probability of the abnormal oil temperature of the hydraulic system of the sample operation machine at the sample time in the second sample period, the abnormal prediction layer can be trained, and the trained abnormal prediction layer can be obtained.

The characteristic data corresponding to the target data is input into the trained abnormality prediction layer, a first probability output by the abnormality prediction layer can be obtained, the first probability can include a probability that an oil temperature of a hydraulic system of the working machine is abnormal at a target time in a second time period, and a time interval between any two adjacent target times can be preset time length. For example: the probability of the oil temperature abnormality occurring in the hydraulic system of the working machine at the first target time in the second period is 1%, the probability of the oil temperature abnormality occurring in the hydraulic system of the working machine at the second target time is 2%, …, and the probability of the oil temperature abnormality occurring in the hydraulic system of the working machine at the target time in the nth target period is 85%.

The abnormality prediction layer in the embodiment of the invention obtains the first probability of the abnormal oil temperature of the hydraulic system of the working machine in the second time interval based on the characteristic data corresponding to the target data, wherein the first probability comprises the probability of the abnormal oil temperature of the hydraulic system of the working machine at the target moment in the second time interval, so that the prediction result of the abnormal oil temperature of the hydraulic system of the working machine in the second time interval can be obtained more accurately and more intuitively, and the abnormal oil temperature of the hydraulic system can be avoided more effectively based on the mechanical more targeted measures of the prediction result.

Based on the content of the foregoing embodiments, acquiring target data specifically includes: the oil temperature of hydraulic oil in a hydraulic system of the working machine in a first period is acquired as original data based on a preset sampling frequency.

And performing data processing on the original data, and taking the original data subjected to the data processing as target data.

Specifically, the oil temperature of hydraulic oil in a hydraulic system of the working machine may be acquired at regular time intervals in a first period based on a preset sampling frequency, and the acquired oil temperature is used as original data, and the original data may be subjected to data processing based on mathematical statistics, linear interpolation, down-sampling and other manners, and the original data after the data processing is used as target data.

According to the embodiment of the invention, the oil temperature of the hydraulic oil in the hydraulic system of the working machine is acquired at regular intervals in the first time interval as the original data based on the preset sampling frequency, and after the data processing is carried out on the original data, the original data after the data processing is used as the target data, so that the accuracy of oil temperature abnormity prediction can be improved.

Fig. 7 is a schematic structural diagram of an oil temperature abnormality prediction device according to the present invention. The oil temperature abnormality prediction device provided by the present invention will be described below with reference to fig. 7, and the oil temperature abnormality prediction device described below and the oil temperature abnormality prediction method provided by the present invention described above may be referred to in correspondence with each other. As shown in fig. 7, the apparatus includes: a data acquisition module 701 and an anomaly prediction module 702.

The data acquiring module 701 is configured to acquire target data, where the target data includes an oil temperature of hydraulic oil in a hydraulic system of the work machine in a first period.

And the abnormal prediction module 702 is configured to input the target data into the oil temperature abnormal prediction model, and obtain a prediction result of the oil temperature abnormality of the hydraulic system in the second time period output by the oil temperature abnormal prediction model.

Wherein the ending time of the first time interval is earlier than the starting time of the second time interval; the oil temperature abnormity prediction model is constructed based on the oil temperature of hydraulic oil in a hydraulic system of the sample operation machine in a first sample time period and whether the oil temperature of the hydraulic system of the sample operation machine in a second sample time period is abnormal; the end time of the first sample period is earlier than the start time of the second sample period.

Specifically, the data acquisition module 701 and the anomaly prediction module 702 are electrically connected.

The data obtaining module 701 may obtain the oil temperature of the hydraulic oil in the hydraulic system of the working machine in a first time period based on a preset rule, for example: the temperature sensor may be used to obtain the oil temperature of the hydraulic oil in the hydraulic system of the work machine at preset time intervals during the first period.

After the data obtaining module 701 obtains the oil temperature of the hydraulic oil in the hydraulic system of the working machine in the first period, the oil temperature of the hydraulic oil may be directly used as the target data.

In order to improve the accuracy of oil temperature abnormality prediction, after the data acquisition module 701 acquires the oil temperature of the hydraulic oil in the first time period, the data may be used as original data, and data processing may be performed on the original data, and the original data after the data processing is used as target data.

The abnormality prediction module 702 may input the target data into the constructed oil temperature abnormality prediction model, and obtain a prediction result of the oil temperature abnormality of the hydraulic system in the second time period output by the oil temperature abnormality prediction model.

Optionally, the oil temperature anomaly prediction model includes: a feature extraction layer and an anomaly prediction layer; correspondingly, the anomaly prediction module 702 may be specifically configured to input the target data into the feature extraction layer, perform feature extraction on the target data by the feature extraction layer, and obtain feature data corresponding to the target data output by the feature extraction layer; and inputting the characteristic data into an abnormal prediction layer, and acquiring a prediction result output by the abnormal prediction layer, wherein the prediction result comprises a first probability of the oil temperature abnormality of a hydraulic system of the working machine in a second time period.

Optionally, whether the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample period is determined based on the time when the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample period; correspondingly, the anomaly prediction module 702 may be further specifically configured to input the feature data into an anomaly prediction layer, obtain, by the anomaly prediction layer, the second probability corresponding to each type of feature data based on the feature data, obtain, based on each second probability, the first probability, and further obtain the prediction result output by the anomaly prediction layer.

Optionally, the data obtaining module 701 may be specifically configured to obtain, as the original data, the oil temperature of hydraulic oil in a hydraulic system of the work machine in the first time period based on the preset sampling frequency; and performing data processing on the original data, and taking the original data subjected to the data processing as target data.

Based on the content of the foregoing embodiments, a working machine includes the oil temperature abnormality prediction device described above.

Specifically, the work machine may include a piling machine, an excavator, an automobile, and the like. The piling machinery refers to a working machine for drilling, piling and sinking piles in various pile foundation constructions, such as a pile driver, a vibration pile sinking machine, a cast-in-place pile drilling machine and a rotary drilling rig.

The working machine includes the oil temperature abnormality prediction device described above, and can predict the oil temperature abnormality of the hydraulic system in advance, thereby improving the practicality of oil temperature abnormality prediction. The structure and the specific working process of the oil temperature abnormality prediction device can be referred to the contents of the above embodiments, and are not described in detail in the embodiments of the present invention.

Fig. 8 illustrates a physical structure diagram of an electronic device, and as shown in fig. 8, the electronic device may include: a processor (processor)810, a communication Interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication Interface 820 and the memory 830 communicate with each other via the communication bus 840. The processor 810 may call logic instructions in the memory 830 to perform a method of oil temperature anomaly prediction, the method comprising: acquiring target data, wherein the target data comprises the oil temperature of hydraulic oil in a hydraulic system of the working machine in a first time period; inputting the target data into an oil temperature abnormity prediction model, and acquiring a prediction result of the oil temperature abnormity of the hydraulic system in a second time interval output by the oil temperature abnormity prediction model; wherein the ending time of the first time interval is earlier than the starting time of the second time interval; the oil temperature abnormity prediction model is constructed based on the oil temperature of hydraulic oil in a hydraulic system of the sample operation machine in a first sample time period and whether the oil temperature of the hydraulic system of the sample operation machine in a second sample time period is abnormal; the end time of the first sample period is earlier than the start time of the second sample period.

In addition, the logic instructions in the memory 830 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

In another aspect, the present invention also provides a computer program product, the computer program product including a computer program, the computer program being stored on a non-transitory computer readable storage medium, wherein when the computer program is executed by a processor, the computer is capable of executing the oil temperature abnormality prediction method provided by the above methods, the method including: acquiring target data, wherein the target data comprises the oil temperature of hydraulic oil in a hydraulic system of the working machine in a first time period; inputting the target data into an oil temperature abnormity prediction model, and acquiring a prediction result of the oil temperature abnormity of the hydraulic system in a second time interval output by the oil temperature abnormity prediction model; wherein the ending time of the first time interval is earlier than the starting time of the second time interval; the oil temperature abnormity prediction model is constructed based on the oil temperature of hydraulic oil in a hydraulic system of the sample operation machine in a first sample time period and whether the oil temperature of the hydraulic system of the sample operation machine in a second sample time period is abnormal; the end time of the first sample period is earlier than the start time of the second sample period.

In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program, which when executed by a processor, implements a method for predicting an oil temperature abnormality provided by the above methods, the method comprising: acquiring target data, wherein the target data comprises the oil temperature of hydraulic oil in a hydraulic system of the working machine in a first time period; inputting the target data into an oil temperature abnormity prediction model, and acquiring a prediction result of the oil temperature abnormity of the hydraulic system in a second time interval output by the oil temperature abnormity prediction model; wherein the ending time of the first time interval is earlier than the starting time of the second time interval; the oil temperature abnormity prediction model is constructed based on the oil temperature of hydraulic oil in a hydraulic system of the sample operation machine in a first sample time period and whether the oil temperature of the hydraulic system of the sample operation machine in a second sample time period is abnormal; the end time of the first sample period is earlier than the start time of the second sample period.

The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. An oil temperature abnormality prediction method is characterized by comprising the following steps:

2. The oil temperature abnormality prediction method according to claim 1, wherein the oil temperature abnormality prediction model includes: a feature extraction layer and an anomaly prediction layer;

3. The oil temperature abnormality prediction method according to claim 2, characterized in that the characteristic data includes: the temperature deviation between the maximum value and the average value of the oil temperature in the first period, the total duration of the oil temperature being higher than a first preset value and the longest duration of the oil temperature being higher than a second preset value.

4. The oil temperature abnormality prediction method according to claim 3, characterized in that whether or not an oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample period is determined based on a time at which the oil temperature abnormality occurs in the hydraulic system of the sample working machine in the second sample period;

5. The oil temperature abnormality prediction method according to claim 4, characterized in that the prediction result further includes: the first probability corresponds to a risk level.

6. The oil temperature abnormality prediction method according to claim 4, characterized in that the first probability includes a probability that an oil temperature abnormality occurs in the hydraulic system for a target period within the second period.

7. The oil temperature abnormality prediction method according to any one of claims 1 to 6, wherein the acquiring target data specifically includes:

8. An oil temperature abnormality prediction device characterized by comprising:

9. A work machine, comprising: the oil temperature abnormality prediction device according to claim 8.

10. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the oil temperature abnormality prediction method according to any one of claims 1 to 7 when executing the program.