CN113111585A - Intelligent cabinet fault prediction method and system and intelligent cabinet - Google Patents

Intelligent cabinet fault prediction method and system and intelligent cabinet Download PDF

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CN113111585A
CN113111585A CN202110407115.2A CN202110407115A CN113111585A CN 113111585 A CN113111585 A CN 113111585A CN 202110407115 A CN202110407115 A CN 202110407115A CN 113111585 A CN113111585 A CN 113111585A
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别如国
史红
王恩峰
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Dezhou Ourui Electronic Communication Equipment Manufacturing Co ltd
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Abstract

The invention provides an intelligent cabinet fault prediction method, a system and an intelligent cabinet, wherein the scheme comprises the steps of acquiring the running states of various devices of the intelligent cabinet; constructing a fault type prediction model based on an LSTM network, and constructing a fault time prediction model based on a wavelet neural network; combining a pre-trained fault type prediction model with a time prediction model, and predicting the fault type and the fault occurrence time of the intelligent cabinet based on the operation state data; according to the scheme, the LSTM network and the wavelet neural network are used for predicting the fault type and the fault time respectively, the relation between the fault type and the fault time can be effectively processed, and therefore the prediction accuracy is improved.

Description

Intelligent cabinet fault prediction method and system and intelligent cabinet
Technical Field
The disclosure belongs to the technical field of equipment fault prediction, and particularly relates to an intelligent cabinet fault prediction method and system and an intelligent cabinet.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
As a protection device for various computer devices, the intelligent cabinet not only needs to provide multi-dimensional protection for internal devices thereof, but also needs to be responsible for monitoring the running state of the devices, thereby facilitating user management. The inventor finds that, in the past, intelligent cabinets focus on developing physical protection functions, and neglects fault handling of equipment inside the cabinets, so that the pressure of operation and maintenance of the intelligent cabinets is increased.
Disclosure of Invention
In order to solve the above problems, the present disclosure provides an intelligent cabinet fault prediction method, system and intelligent cabinet, and the scheme can obtain prediction information of cabinet equipment fault types and also prediction information of fault time, thereby providing an important basis for maintenance of the intelligent cabinet.
According to a first aspect of the embodiments of the present disclosure, there is provided an intelligent cabinet fault prediction method, including:
acquiring the running states of various equipment of the intelligent cabinet;
constructing a fault type prediction model based on an LSTM network, and constructing a fault time prediction model based on a wavelet neural network; combining a pre-trained fault type prediction model with a time prediction model, and predicting the fault type and the fault occurrence time of the intelligent cabinet based on the operation state data;
the method comprises the steps of training a fault type prediction model and a time prediction model, performing data preprocessing on historical fault data of the intelligent cabinet to obtain a key influence factor and fault occurrence time data set, training the fault type prediction model by using the key influence factor data set, and training the time prediction model by using the fault occurrence time data set.
Further, the data preprocessing comprises the following steps:
marking the state monitoring data as a normal state and an abnormal state and storing the state monitoring data as historical fault data;
performing clustering analysis on the historical fault data by using a K-means clustering algorithm;
and screening out key influence factors influencing the fault of the intelligent cabinet to form a key influence factor data set for fault type prediction model training.
Furthermore, different fault occurrence time data sets are obtained according to the fault type corresponding to the specific situation of the intelligent cabinet fault type prediction, and the fault occurrence time is sent to the wavelet neural network to respectively establish a fault time prediction model.
Further, the combination of the pre-trained fault type prediction model and the time prediction model to predict the fault type and the fault occurrence time of the intelligent cabinet specifically includes:
taking key influence factors in real-time state monitoring data of the intelligent cabinet as input of a pre-training fault type prediction model to obtain fault type prediction;
and taking the running time of the intelligent cabinet equipment as the input of a pre-trained time prediction model to obtain the fault occurrence time.
According to a second aspect of the embodiments of the present disclosure, there is provided an intelligent cabinet fault prediction system, including:
the data acquisition unit is used for acquiring the running states of various types of equipment of the intelligent cabinet;
the model building unit is used for building a fault type prediction model based on an LSTM network and building a fault time prediction model based on a wavelet neural network;
the result prediction unit is used for predicting the fault type and the fault occurrence time of the intelligent cabinet based on the operation state data;
the method comprises the steps of training a fault type prediction model and a time prediction model, performing data preprocessing on historical fault data of the intelligent cabinet to obtain a key influence factor and fault occurrence time data set, training the fault type prediction model by using the key influence factor data set, and training the time prediction model by using the fault occurrence time data set.
According to a third aspect of the embodiments of the present disclosure, an intelligent cabinet is provided, which includes a cabinet body, a memory, a processor, and a computer program stored in the memory for running, and when the processor executes the program, the intelligent cabinet fault prediction method is implemented.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an intelligent cabinet fault prediction method as described.
Compared with the prior art, the beneficial effect of this disclosure is:
the scheme of the disclosure provides an LSTM-WNN-based intelligent cabinet for fault prediction, a fault type prediction model is established through an LSTM network, and a fault time prediction model is established through a wavelet neural network. Firstly, the scheme fills the gap that the existing intelligent cabinet lacks equipment fault prediction; secondly, compared with the traditional single type fault prediction method, the scheme utilizes the LSTM network and the wavelet neural network to predict the fault type and the fault time respectively, and can effectively process the relation between the fault type and the fault time, thereby improving the prediction accuracy.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
Fig. 1 is a flowchart of an intelligent cabinet fault prediction method according to a first embodiment of the present disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
The first embodiment is as follows:
the embodiment aims to provide an intelligent cabinet fault prediction method.
According to the scheme, a fault type prediction model and a fault time prediction model are respectively established based on key influence factors and fault time data sets in historical fault data of the intelligent cabinet. Firstly, establishing a fault type prediction model by using an LSTM network, sending key influence factors into the LSTM network for training, and taking the key influence factors in real-time state monitoring data of an intelligent cabinet as the input of the LSTM network prediction model so as to obtain fault type prediction information; and then, establishing a fault time prediction model by using the wavelet neural network, and sending the running time of the intelligent cabinet into the wavelet neural network for prediction so as to obtain fault time prediction information. And finally, combining a fault type prediction model established by using an LSTM network with a fault time prediction model established by using a wavelet neural network, namely, firstly predicting the fault type, then acquiring different fault occurrence time data sets corresponding to the fault type, sending the fault occurrence time into the wavelet neural network to respectively establish the fault time prediction models, and taking the running time of the intelligent cabinet equipment as input and the obtained output as the fault occurrence time.
Specifically, as shown in fig. 1, a flowchart of the intelligent cabinet fault prediction method according to the present disclosure is provided, and with reference to fig. 1, the prediction method includes:
s1: acquiring the running states of various equipment of the intelligent cabinet;
s2: constructing a fault type prediction model based on an LSTM network, and constructing a fault time prediction model based on a wavelet neural network;
constructing a fault type prediction model:
the fault type prediction model adopts an LSTM network model, and the specific structure is as follows: the input of the fault type prediction model is a key influence factor of cabinet operation, the output is a fault type, and the structure is three common doors in a classical LSTM network model; in general, the key influence factors of the cabinet are numerous in types and large in quantity, the LSTM network model is suitable for constructing a large-scale deep neural network, and in addition, the LSTM network model improves the long-term dependence problem in the RNN and can effectively prevent the problems of gradient explosion, gradient disappearance and the like.
The types of failures of intelligent cabinets are various, and the key influencing factors causing the failures are determined. Different action strengths and different types of key influence factors cause different fault types of the intelligent cabinet. Therefore, the key influence factors are sent to the LSTM network for training, and the key influence factors in the real-time state monitoring data of the intelligent cabinet are used as the input of the LSTM network prediction model, so that the fault type prediction information is obtained.
Constructing a fault time prediction model:
the fault time prediction model adopts a wavelet neural network model, wherein the input of the wavelet neural network model is fault occurrence time (historical data), and the output of the wavelet neural network model is fault future occurrence time. The wavelet neural network integrates the advantages of an artificial neural network and wavelet analysis, and has the characteristics of high network convergence speed, avoidance of falling into local optimization and sometimes frequency local analysis.
Firstly, acquiring different fault occurrence time data sets corresponding to fault types according to the specific situation of intelligent cabinet fault type prediction; and sending the fault occurrence time to a wavelet neural network to respectively establish a fault time prediction model, namely: depending on the type of fault, different fault detection sensors are used, for example dedicated to detecting the fault voltage, and then the sensors detect the fault and send a signal to the processor, which marks the type of fault and at the same time the time when the fault occurred. Respectively acquiring data sets of different fault types and training different prediction models; and finally, the running time of the intelligent cabinet equipment is used as input, and the obtained output is the fault occurrence time.
S3: combining a pre-trained fault type prediction model with a time prediction model, and predicting the fault type and the fault occurrence time of the intelligent cabinet based on the operation state data;
the method comprises the steps of training a fault type prediction model and a time prediction model, performing data preprocessing on historical fault data of the intelligent cabinet to obtain a key influence factor and fault occurrence time data set, training the fault type prediction model by using the key influence factor data set, and training the time prediction model by using the fault occurrence time data set.
The data preprocessing specifically comprises: firstly, the running states of various equipment of the intelligent cabinet are monitored, the state monitoring data are correctly marked as a normal state and an abnormal state and are stored as historical fault data. And then, analyzing historical fault data of the intelligent cabinet by using a K-means clustering algorithm, carrying out clustering analysis on the fault types possibly occurring in the intelligent cabinet and screening out key influence factors with certain values on different fault types.
Among them, the K-means algorithm is the most commonly used clustering algorithm, and the input of the algorithm is a sample set (or called point set), by which samples can be clustered, and samples with similar characteristics are grouped into a class. The method comprises the following specific steps: for each point, the center point of the point closest to all the center points is calculated, and then the point is classified as the cluster represented by the center point. After one iteration is finished, the central point is recalculated for each cluster class, and then the central point closest to the cluster is searched for each point again. And circulating until the cluster class of the two previous and next iterations is not changed.
Example two:
the purpose of this embodiment is to provide an intelligent rack failure prediction system.
An intelligent cabinet fault prediction system, comprising:
the data acquisition unit is used for acquiring the running states of various types of equipment of the intelligent cabinet;
the model building unit is used for building a fault type prediction model based on an LSTM network and building a fault time prediction model based on a wavelet neural network;
the result prediction unit is used for combining a pre-trained fault type prediction model and a time prediction model and predicting the fault type and the fault occurrence time of the intelligent cabinet based on the operation state data;
the method comprises the steps of training a fault type prediction model and a time prediction model, performing data preprocessing on historical fault data of the intelligent cabinet to obtain a key influence factor and fault occurrence time data set, training the fault type prediction model by using the key influence factor data set, and training the time prediction model by using the fault occurrence time data set.
Further, the result prediction unit combines the pre-trained fault type prediction model and the time prediction model to predict the fault type and the fault occurrence time of the intelligent cabinet, and specifically includes:
taking key influence factors in real-time state monitoring data of the intelligent cabinet as input of a pre-training fault type prediction model to obtain fault type prediction;
and taking the running time of the intelligent cabinet equipment as the input of a pre-trained time prediction model to obtain the prediction result of the occurrence time of the type of fault.
Example three:
the purpose of this embodiment is to provide an intelligent rack.
An intelligent cabinet comprises a cabinet body, a memory, a processor and a computer program stored in the memory for running, wherein the processor executes the program to realize the intelligent cabinet fault prediction method.
The intelligent cabinet for fault prediction mainly performs fault prediction on the intelligent cabinet, so that equipment in the cabinet is ensured to obtain good environmental protection. The intelligent cabinet includes: power module, constant temperature system, drying system, display system, access control system etc.. For example: the power module may have an overheat failure, and the thermostat system may have some failures related to the motor depending on the specific device (e.g., fan), which are only typical examples, because the intelligent cabinet itself is not well defined.
Further, acquiring the state operation data of the cabinet: for possible faults of different modules, corresponding physical quantity sensors are used; such as an overheat sensor for power module overheat faults, etc.
Further, the determination process of the key influence factor: the fault types of the cabinet are classified through cluster analysis, after the fault types are definite, detection is manually carried out on various fault types, and key influence factors are determined, namely the key influence factors are screened.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In further embodiments, there is also provided:
a computer readable storage medium storing computer instructions which, when executed by a processor, perform the method of embodiment one.
The method in the first embodiment may be directly implemented by a hardware processor, or may be implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The intelligent cabinet fault prediction method, the intelligent cabinet fault prediction system and the intelligent cabinet provided by the embodiment can be realized, and have wide application prospects.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims (10)

1. An intelligent cabinet fault prediction method is characterized by comprising the following steps:
acquiring the running states of various equipment of the intelligent cabinet;
constructing a fault type prediction model based on an LSTM network, and constructing a fault time prediction model based on a wavelet neural network; combining a pre-trained fault type prediction model with a time prediction model, and predicting the fault type and the fault occurrence time of the intelligent cabinet based on the operation state data;
the method comprises the steps of training a fault type prediction model and a time prediction model, performing data preprocessing on historical fault data of the intelligent cabinet to obtain a key influence factor and fault occurrence time data set, training the fault type prediction model by using the key influence factor data set, and training the time prediction model by using the fault occurrence time data set.
2. The intelligent cabinet fault prediction method of claim 1, wherein the data preprocessing comprises the steps of:
marking the state monitoring data as a normal state and an abnormal state and storing the state monitoring data as historical fault data;
performing clustering analysis on the historical fault data by using a K-means clustering algorithm;
and screening out key influence factors influencing the fault of the intelligent cabinet to form a key influence factor data set for fault type prediction model training.
3. The intelligent cabinet fault prediction method according to claim 2, wherein the screening out key influence factors that influence the intelligent cabinet fault specifically includes: classifying the fault types of the cabinet through clustering analysis; and detecting various fault types, and judging key influence factors based on expert experience.
4. The intelligent cabinet fault prediction method according to claim 1, wherein different fault occurrence time data sets are obtained according to the fault type according to the specific situation of the intelligent cabinet fault type prediction, and the fault occurrence time is sent to the wavelet neural network to respectively establish the fault time prediction model.
5. The intelligent cabinet fault prediction method according to claim 1, wherein the prediction of the fault type and the fault occurrence time for the intelligent cabinet by combining the pre-trained fault type prediction model and the time prediction model specifically comprises:
taking key influence factors in real-time state monitoring data of the intelligent cabinet as input of a pre-training fault type prediction model to obtain fault type prediction;
and taking the running time of the intelligent cabinet equipment as the input of a pre-trained time prediction model to obtain the prediction result of the occurrence time of the type of fault.
6. An intelligent cabinet fault prediction system, comprising:
the data acquisition unit is used for acquiring the running states of various types of equipment of the intelligent cabinet;
the model building unit is used for building a fault type prediction model based on an LSTM network and building a fault time prediction model based on a wavelet neural network;
the result prediction unit is used for combining a pre-trained fault type prediction model and a time prediction model and predicting the fault type and the fault occurrence time of the intelligent cabinet based on the operation state data;
the method comprises the steps of training a fault type prediction model and a time prediction model, performing data preprocessing on historical fault data of the intelligent cabinet to obtain a key influence factor and fault occurrence time data set, training the fault type prediction model by using the key influence factor data set, and training the time prediction model by using the fault occurrence time data set.
7. The intelligent cabinet fault prediction system of claim 6, wherein the data preprocessing comprises the steps of:
marking the state monitoring data as a normal state and an abnormal state and storing the state monitoring data as historical fault data;
performing clustering analysis on the historical fault data by using a K-means clustering algorithm;
and screening out key influence factors influencing the fault of the intelligent cabinet to form a key influence factor data set for fault type prediction model training.
8. The intelligent cabinet fault prediction system of claim 6, wherein the result prediction unit combines a pre-trained fault type prediction model and a pre-trained time prediction model to predict the fault type and the fault occurrence time of the intelligent cabinet, and specifically comprises:
taking key influence factors in real-time state monitoring data of the intelligent cabinet as input of a pre-training fault type prediction model to obtain fault type prediction;
and taking the running time of the intelligent cabinet equipment as the input of a pre-trained time prediction model to obtain the prediction result of the occurrence time of the type of fault.
9. An intelligent cabinet comprising a cabinet body, a memory, a processor and a computer program stored and run on the memory, wherein the processor executes the program to implement an intelligent cabinet fault prediction method as claimed in any one of claims 1-7.
10. A non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements an intelligent cabinet failure prediction method as recited in any one of claims 1-7.
CN202110407115.2A 2021-04-15 2021-04-15 Intelligent cabinet fault prediction method and system and intelligent cabinet Pending CN113111585A (en)

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