GB2619825A - A fault diagnosis method of blast blower and apparatus, electronic device thereof - Google Patents
A fault diagnosis method of blast blower and apparatus, electronic device thereof Download PDFInfo
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/008—Stop safety or alarm devices, e.g. stop-and-go control; Disposition of check-valves
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D29/00—Details, component parts, or accessories
- F04D29/40—Casings; Connections of working fluid
- F04D29/42—Casings; Connections of working fluid for radial or helico-centrifugal pumps
- F04D29/44—Fluid-guiding means, e.g. diffusers
- F04D29/46—Fluid-guiding means, e.g. diffusers adjustable
- F04D29/462—Fluid-guiding means, e.g. diffusers adjustable especially adapted for elastic fluid pumps
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D29/00—Details, component parts, or accessories
- F04D29/40—Casings; Connections of working fluid
- F04D29/52—Casings; Connections of working fluid for axial pumps
- F04D29/54—Fluid-guiding means, e.g. diffusers
- F04D29/56—Fluid-guiding means, e.g. diffusers adjustable
- F04D29/563—Fluid-guiding means, e.g. diffusers adjustable specially adapted for elastic fluid pumps
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
- G06N20/10—Machine learning using kernel methods, e.g. support vector machines [SVM]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2260/00—Function
- F05D2260/80—Diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2260/00—Function
- F05D2260/81—Modelling or simulation
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2260/00—Function
- F05D2260/82—Forecasts
- F05D2260/821—Parameter estimation or prediction
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/30—Control parameters, e.g. input parameters
- F05D2270/301—Pressure
- F05D2270/3013—Outlet pressure
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/70—Type of control algorithm
- F05D2270/709—Type of control algorithm with neural networks
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/70—Type of control algorithm
- F05D2270/71—Type of control algorithm synthesized, i.e. parameter computed by a mathematical model
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
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- Control Of Positive-Displacement Air Blowers (AREA)
Abstract
A fault diagnosis method for air blower equipment, includes obtaining current outlet flow data, current guide vane opening data, current guide vane opening change state data, and current outlet pressure data of the air blower equipment to be tested; inputting the current outlet flow data and the current guide vane opening data, the current guide vane opening change state data into an outlet pressure prediction model pre-trained to obtain an outlet pressure prediction value F1; adjusting an alarm threshold F0' based on the outlet pressure prediction value F1 and historical outlet pressure data that are pre-stored; adjusting the alarm threshold F0' based on the outlet pressure prediction value F1 and the historical outlet pressure data; judging whether the current outlet pressure data exceeds the adjusted alarm threshold F0' ; if the current outlet pressure data exceeds the adjusted alarm threshold F0' , determining that a blade breakage fault has occurred, and initiating an alarm.
Description
FAULT DIAGNOSIS METHOD AND DEVICE. ELECTRONIC DEVICE FOR
AIR BLOWER EQUIPMENT
Technical field
(0001] This invention relates to the technical field of air blower equipment, particularly to a fault diagnosis method and system, an electronic device, and a computer program product for air blower equipment.
Background
(0002] As a type of rotating machinery, blowers are widely used in energy, construction, chemical and other industries, playing a crucial role in modern industry. Because blowers operate for long periods, and run under harsh conditions like high temperature, high pressure, high load, and strong impact, their key components frequently malfunction. As the manufacturing level improves, the structure of air blower equipmentbecornes more and more complex. If one part fails, it can likely cause other related parts to fail simultaneously. Therefore, the safety and reliability of air blower equipmentattract great attention, (0003] Blowers not only have many types of faults but also complicated ones, such as insufficient air volume, motor overload, overheating, abnormal noise, lubricating oil leakage, aggravated bearing vibration, etc. Their repair costs are high, and the repair cycle is long. Air blower equipmentbelongs to large industrial equipment, which is expensive and takes up large space. Factories usually don't purchase spare equipment. Hence, if a fault occurs and cannot be predicted or diagnosed in time, it will force the entire industrial production line to stop production, causing substantial economic losses to the company and even endangering personal safety.
[0004] Currently, there are mainly two types of fault diagnosis for blowers. One is to train deep learning models with a large amount of fault data, but in actual production, faults are infrequent. Furthermore, different manufacturers might face different types of faults, so collecting fault data is challenging, not to mention the need for a large amount of fault data. The other type is to model multiple variables related to the faults separately. However, because of the complexity of the air blower equipment and its numerous and complex types, there are many variables, making it too complicated for industrial applications and not conducive to promotion.
[0005] Since pressure can be used to measure the state changes of fluid air, representing the vertical force acting on the air blower equipment, the industry monitors fan blade breakage faults by monitoring pressure. However, traditional monitoring methods mainly use fixed threshold (or static threshold) to monitor faults/ for example, if the outlet pressure exceeds this fixed threshold, it indicates that the blades of the device are broken. But using a fixed threshold is very challenging in capturing fault characteristics in real-time in actual engineering applications, hence, it lacks precision and often leads to fake alarms. Therefore/ it has not been widely used in factories. In actual production scenarios, judgment is primarily made through manual observation. However, manual observation depends on the observer's experience and requires the detector to have some experiential and professional knowledge, and there's certain latency.
Summary of Invention
[0006] The purpose of this invention is to provide a fault diagnosis method and device for air blower equipment, partially solve or alleviate the aforementioned deficiencies in existing technologies, and improve the accuracy of fault diagnosis to some extent without collecting a large mount of fault data.
0007] To solve the aforementioned technical problems. this invention adopts the following technical scheme: [0008] The first aspect of this invention is to provide a fault diagnosis method for air blower equipment, which includes the following steps: [0009] Obtain current outlet flow data, current guide vane opening data, current guide vane opening change state data, and current outlet pressure data of the air blower equipmentto be tested; [ 0 0 1 0] Input the current outlet flow data and the current guide vane opening data, the current guide vane opening change state data into an outlet pressure prediction model pre-trained, and obtain an outlet pressure prediction value.1:1; [0011] Adjust an alarm threshold in a preset dynamic threshold based on the outlet pressure prediction value and historical outlet pressure data that are pre-stored; [0012] Adjust the alarm threshold lc.' based on the outlet pressure prediction value i and the historical outlet pressure data; [0013] Determine whether the current outlet pressure data exceeds the alarm threshold that has been adjusted. If the current outlet pressure data exceeds the alarm threshold F.:, it is then determined that a blade breakage fault has occurred and an alarm is initiated or Decor triggered, The calculation formula for adjusting the alarm threshold Fo according to the outlet pressure prediction value.fi; and the historical outlet pressure data is; F =F ± ATer, wherein u is the standard deviation of ail historical outlet pressure data"v is an integer that satisfies -±"A [0014] In some embodiments, the step of training the outlet pressure prediction model specifically includes: constructing a training sample library, wherein the training sample library comprises historical outlet flow data, historical guide vane opening data, historical guide vane opening change state data, and historical outlet pressure data that are collected under normal working conditions/states of air blower equipment; inputting the historical outlet flow data, the historical guide vane opening data, the historical guide vane opening change state data, and the historical outlet pressure data into a support vector machine model or a neural network model for raining to obtain the outlet pressure prediction model.
NOM] In some embodiments, prior to the step o nputting the current guide vane opening data into the outlet pressure prediction model, there is also a step of discretizing the current guide vane opening data.
[0016] In some embodiments, the step of obtaining the current guide vane opening change state data specifically includes a step of calculating the current guide vane opening change estimate value according to the current guide vane opening data and the historical guide vane opening data that are pre-stored.
[0017] In some embodiments, the preset dynamic threshold also includes: a pre-warningthreshold, correspondingly, before the step of De," adjusting the alarm threshold, there is also a step of determining whether the current outlet pressure data exceeds the adjusted pre-c.varningthreshold f. If the current outlet pressure data exceeds the adjusted pre-warningthreshold F. then it is diagnosed that a blade of the air blower equipment may break. The calculation formula for adjusting the pre-warning thresholdF', according to the outlet pressure prediction value F, and the historical outlet pressure data is: F,±170-, where Cr is the standard deviation of all historical outlet pressure data, .el is an integer that satisfies i n '5; 5.
10018] The second aspect of the present invention provides a failure diagnosis device for an air blower equipment, which is configured to comprise: [0019] Database configured to store training sample libraries, the training sample library includes historical outlet flow data, historical guide vane opening data, historical guide vane opening state change data, and historical outlet pressure data collected under normal operating conditions of the air blower equipment; [00201 Data acquisition module configured to obtain current outlet flow data, current guide vane opening data, current guide vane opening state change data, and current outlet pressure data of the air blower equipment to be tested; [0021] Mode: training module configured to train a support vector machine or a neural network model according to the historical outlet flow data, the historical guide vane opening data, the historical guide vane opening state change data, and the historical outlet pressure data stored in the database, to obtain an outlet pressure prediction model; De," [0022] Fault diagnosis module configured to call or invoke the outlet pressure prediction model to calculate outlet pressure prediction value /7, based on the current outlet flow data, the current guide vane opening change state data, and the current guide vane opening data obtained by the data acquisition module, and to adjust alarm threshold r70 in a preset dynamic threshold based on the outlet pressure prediction value F and the historical outiet pressure data stored in the database, and to assess whether the current outlet pressure data exceeds the alarm threshold 17,,' that has been adjusted. If the current outlet pressure data exceeds the alarm threshold it is then determined that a blade breakage fault has occurred and an alarm is initiated/triggered; wherein the calculation.forrnula for adjusting the alarm threshold r based on the outlet pressure prediction value and the historical outlet pressure data is: 17, = F ± Na, wherein G. is the standard deviation of all the historical outlet pressure data, Ar is an integer that satisfies n = ±5 [ 0023] In some embodiments, the data acquisition module specifically inciudes: 0024] Data acquisition unit configured to obtain the current outlet guide vane opening data, and current outlet pressure data of the air blower equipment to be tested; [0025] Calculation unit configured to calculate the current guide vane opening change estimate value and change state based on the current guide vane opening data and the historical guide vane opening data stored in the database.
[0026] In some embodiments, the device also tides: a fault De," pre-warning module configured to adjust pre-warning threshold Po in the preset dynamic threshold based on the outlet pressure prediction value ic,' and the historical outlet pressure data stored, and to assess whether the current outlet pressure exceeds the ore-warning threshold in the preset dynamic threshold adjusted. If the current outlet pressure exceeds the pre-warning threshold if.), it is then predicted that a blade of the air blower equipment may break; wherein the calculation formula for adjusting the pre-warningthreshoid fil based on the outlet pressure prediction valuell'i and the historical outlet pressure data is: -F ± no-, u is the standard deviation of all the historical outlet pressure data, p is an integer, and I ii-, in 5 (00271 The third aspect of the present invention provides an electronic device for diagnosing an air blower equipment, including: a processor, a network interface, and a memory storing instructions, where the network interface is used to provide network communication functions, the memory is used to store program code, the processor is used to invoke the program code, to perform the steps of the above fault diagnosis method.
[0028] The fourth aspect of the present invention provides a computer program product for diagnosing an air blower equipment, the computer program product includes instructions, and the instructions make the electronic device perform the steps of the above method when being executed by the electronic device.
Technical Benefits: [0029.] Whether it is fault diagnosis or fault prediction, the inertia thinking in this field is to use a large amount of fault data for model De," training to obtain a fault diagnosis model, which requires pre--collection of a large amount of fault data for model training. However, on one hand, in actual engineering applications, such large-scale equipment has fewer faults and fewer types of faults. On the other hand, different engineering users and different application scenarios (for example, high-temperature environments and low-temperature environments, environments with a lot of sand and environments with little sand), make the types of faults that occur in different equipment different, which increases the difficulty of fault data coilection, and it is unrealistic to collect a large amount of fault data. The fault diagnosis method of the present invention, uses a completely contrary approach, that is, it uses data from the normal operation of the air blower equipment to train the model, and predicts the current outlet pressure according to the trained model, then adjusts the preset dynamic threshold (including the first preset dynamic threshold) according to the predicted outlet pressure value combined with the historical outlet pressure to get the preset dynamic threshold at the current moment, that is, by dynamically adjusting the preset dynamic threshold, and then diagnosing the fault according to this preset dynamic threshold, the automatic, real-time monitoring of blade breakage faults has been achieved.
[0030] Compared with the existing method of using fault data to train a fault classification model, because the model constructed is under the normal operation of the device, therefore, there is no need for fault data when constructing the pressure prediction model, which is consistent with the actual operation scenario of factory air blower equipment (i.e." the vast majority is normal data, and it is difficult to collect fault data).
[0031] Compared to the diagnostic methods using static thresholds, where the static threshold is simply decreased or increased, in the present invention, the preset dynamic threshold is calculated based on the current outlet pressure prediction value of the air blower equipment. This outlet pressure prediction value is obtained based on the current guide vane opening and the current outlet flow of the air blower equipment. In other words, the preset dynamic threshold changes with the actual working conditions of the air blower equipment, reducing the false negative and false positive rates of the static threshold method, and enabling the more accurate detection of guide vane breakage faults in a short period of time.
[0032] Description of Drawings
[0033] To clarify the technical solutions in the embodiments of the present invention or in the prior art, a simple introduction to the drawings needed for the descriptions of the embodiments or prior art will be given below. In aR drawings, similar components or parts are generally marked by similar reference symbols. In the drawings, the components or parts are not necessarily drawn to scale. Obviously, the drawings described below are some embodiments of the present invention, and for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without inventive labor.
[0034] Figure 1 is a flowchart of a fault diagnosis method for an air blower equipment in an exemplary embodiment of the present invention; [0035] Figure 21s a functional module diagram of a fault diagnosis device for an air blower equipment in an exemplary embodiment of the present invention; De," [0036] Figure 3 is a schematic diagram reflecting the real outlet pressure, preset dynamic threshold, and outlet pressure prediction value of the air blower equipment under normal working conditions; [0037] Figure 4 is a schematic diagram reflecting the omission caused by an overly large static threshold range; [ 0038] Figure 5 is a schematic diagram reflecting the blade breakage fault and alarm under the preset dynamic threshold; [0039] Figure 6 is a schematic diagram reflecting the false report caused by reducing the static threshold range.
Embodiments [0040] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will clearly and completely described with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of, but not all, embodiments of the present invention. Ail other embodiments obtained by these of ordinary skill in the art without making inventive labor based on the embodiments of the present invention belong to the scope of protection of the present invention.
[0041] In this document, the suffixes such as module, "component" or "unit" used to denote components are only for the convenience of describing the invention, and they themselves have no specific meaning. Therefore, "module", "component" or "unit" can be used intercha nge.ably.
[0042] Embodiment 1.: Refer to Figure 1, which is a fault diagnosis method in an exemplary embodiment of the present invention, specifically, this method includes steps: [0043] 5100, construct a training sample library, and input the training samples in the training sample library into a support vector machine model for training, to obtain an outlet pressure prediction model.
[0044] In some embodiments, the training sample library includes training samples, which include: historical outlet flow data, historical guide vane opening data, historical guide vane opening change state data, and historical outlet pressure data. Specifically, the historical outlet flow data, historical guide vane opening data, historical guide vane opening change state data, and historical outlet pressure data are collected under normal working conditions (La, no fault occurred) of the air blower equipment. Wherein, the historical guide vane opening change state data can be obtained based on the guide vane opening data at moment T and the guide vane opening data at moment T--At, for example, how much it has increased or decreased. Where, At is the data collection time interval.
[0045] Of course, in other embodiments, other models such as neural network models, etc, can also he used.
0046] 5101, obtain the current outlet flow data, current guide vane opening data, current guide vane opening change state data, and current outlet pressure data of the air blower equipment to be tested.
[0047] In some embodiments, outlet flow data and outlet pressure data can be collected by flow meters and pressure sensors, respectively.
[0048] In some embodiments, the outlet flow change estimate value and change state (such as increase or decrease) can be obtained based on the current outlet flow data and stored historical outlet flow data, then he guide vane opening change can be calculated based on this outlet flow change estimate value and change state, thus the current guide vane opening can be calculated based on this guide vane opening change and historical guide vane opening data.
[0049] Of course, in other embodiments, the current guide vane opening can also be directly obtained from the device (or measured directly with a measurement tool), correspondingly, the current guide vane opening change state data can be calculated based on this current guide vane opening data and the guide vane opening data at the last moment, for example, how much it has increased or decreased.
[0050] In some embodiments, because the current guide vane opening value is obtained from the increase or decrease from the previous moment, it will affect the outiet flow and pressure values (for example, the outlet pressure data corresponding to the current T moment guide vane opening of 37,5, and T-At moment is 36 (increased to 37.5), is different from the outlet pressure data corresponding to I-A t moment is 33 (reduced to 37.5)). Therefore, this embodiment also adds the guide vane opening change state data, specifically, this guide vane opening change state data includes: change state: increase or decrease, and the specific change value (for example, the absolute value difference of the guide vane opening values at the last moment and the current moment).
[0051] 5102, input the current outlet flow data, current guide vane opening data, and current guide vane opening change state data into the pre-trained outlet pressure prediction model to obtain the outlet pressure prediction value.
[0052] S103, adjust the pre-warning threshold in the preset De," dynamic threshold based on the outlet pressure prediction value and historical outlet pressure data.
[0053] In some embodiments, the step of adjusting the pre-warning threshold according to the outlet pressure prediction value and historical outlet pressure data specifically includes the steps of: (0054] Calculate, the standard deviation a based on the pre-stored all historical outlet pressure data; [0055] Calculate the pre-warning threshold according to the standard deviation and the outlet pressure prediction value: [ 0056] I1=t±na ( ) , [0057] where T, is the pre-warning threshoid, is the outlet pressure prediction value, a is the standard deviation of all historical outlet pressure data, JJ is an integer, and VI) .5; 5.
[0058] Of course, in some other embodiments, the value of can also be modified according to actual conditions.
[0059] Of course, in some other embodiments, when calculating the standard deviation rr, it can be calculated only based on the historical outlet pressure data of the air blower equipment to be tested (for example, from the moment t=ls to the moment T--1s).
[0050] 5104, assess whether the current outlet pressure data exceeds the adjusted pre-warning threshold, if it exceeds, execute step 5105, otherwise, re-acquire the outlet flow data, guide vane opening data, guide vane opening change state data and outlet pressure data of the air blower equipment to he tested, that is, re-execute step Slot [0051] In some embodiments, the initial stage of the fault causes a small range of outlet pressure fluctuations, but at the same time, environmental noise may also cause small fluctuations. However, in De," some cases, even if a small anomaly occurs (such as small fluctuations), considering factors such as maintenance costs, the equipment will still continue to operate, so it is necessary to carry out diagnosis for early warning prompts for human intervention.
(0062] 5105, predict that the blade of the air blower equipment may break.
0053 Further, the method also includes the step of: [0064] 5106, adjust the alarm threshold in the preset dynamic threshold based on the outlet pressure prediction value and the historical outlet pressure data of the air blower equipment to be tested.
[0065] In some embodiments, the calculation formula for adjusting the above-mentioned alarm threshold based on the outlet pressure prediction value 11, and historical outlet pressure data is: (0066] -1:Na (2) , where u is the standard deviation of all said historical outlet pressure data, N is an integer, and (n-fE) s N. Of course, the value of N can also be modified according to actual conditions.
[0067] 5107, assess whether the current outlet pressure data exceeds the alarm threshold r* adjusted by step 51.06. If so, assess that the blade has broken and issue an alarm. Otherwise! execute step 5101..
[0068] Of course, in some other embodiments, the above-mentioned steps S103-5105 and steps S106-5107 can also be executed simultaneously, or steps 5106-5107 can be directly executed after step 5102, that is, direct fault diagnosis can be performed without prior fault prediction and pre-warning.
[0069] In order to verify the fault diagnosis method embodiment, the results of fault alarms for the air blower equipment using static thresholds and preset dynamic thresholds were compared respectively: [0070] As shown in Figure 4, the fixed threshold interval in existing technology is [2 Kpa., 18 Kpa]. However, even after the blade breaks, the outlet pressure of the air blower equipment may be greater than 2 Kpa and less than 18 Kpa. Therefore, the blade breakage fault of the air blower equipment in this fixed threshold interval is far from detectable. If this fixed threshold is simply increased or decreased, the probability of false alarms is high. As shown in Figure 6, in order to reduce the occurrence of missed alarms, the fixed threshold range is directly reduced to 10 Kpa, 18 Kpa]. However; under normal conditions of the air blower equipment, due to the influence of other factors, the actual outlet pressure drops from between 11 Kpa to 12 Kpa down to 10 Kpa, and remains at 10 Kpa for a period of time, resulting in a large number of false alarms.
[0071] As shown in Figure 3, from September 1, 2020, to October 1, 2020, within this one month, most of the time, the warning threshold range in the preset dynamic threshold remains stable between [8 Kpa, 12 Kpa]; on September 3, 2020, September 6, 2020, and September 30, 2020, the warning threshold range is close to [4.5 Kpa, 5 Kpa]; on September 26, 2020, the warning threshold range is close to [5 Kpa, 6,3 Kpa]; and during September 28-29, 2020, the warning threshold range varies between [12 Kpa, 35 Kpa].
[0072] As shown in Figure 3, under normal conditions, the real outlet pressure of the air blower equipment always fluctuates within the alarm threshold range. For example, from September 1, 2020, to October 1, Des" 2020, within this one month, most of the time, the real outlet pressure of this air blower equipment fluctuates between [9 Kpa, 12 Kpa]; on September 3, September 6, and September 30, 2020, the decline is significant, and dose to the maximum value of the alarm threshold of 5 Kpa; on September 26, there is a large drop, close to the maximum value of the alarm threshold of 6.3 Kpa; and during September 28--29, 2020, there is a large increase, but it always stays within the alarm threshold range.
[ 0073] As shown in Figure 3, under normal conditions, the predicted outlet pressure of the air blower equipment does not vary much from the actual outlet pressure: From September 1, 2020, to October 1, 2020, within this one month, most of the time, the predicted outlet pressure fluctuates between [9 Kpa, 12 Kpa]; on September 3, September 6, and September 30, 2020, the decrease is large, and close to the maximum value of the alarm threshold of 5 Kpa; on September 26, there is a large drop, close to the maximum value of the alarm threshold of 6.3 Kpa; and during September 28-29, 2020, there is a large increase, but it always stays within the alarm threshold range.
[0074] As shown in Figures 4 and 6, they respectively show the schematic diagrams of missed alarms caused by simply increasing the static threshold when using the static threshold, and a large number of false alarms caused by simply reducing the static threshold. Specifically, as shown in Figure 4, in order to avoid false alarms, the static threshold (i.e., fixed threshold) is increased to [2 Kpa, 18 Kpa], thus making it impossible to detect faults and causing missed alarms; as shown in Figure 6, in order to avoid missed alarms, the static threshold is reduced to [10 Kpa, 18 Kpa], thereby causing the actual outlet pressure to overlap De," (or coincide with) the lower limit value of the static hreshold 10 Kpa, resulting in a large number of false alarms.
[0075] As shown in ire 5, the second preset dynamic threshold range changes with the actual outlet pressure. From September 2020 to March 2021, the air blower equipment diagnosed a fault alarm between October 2020 and November 2020 (as indicated by the dots in the figure). At this time, the actual outlet pressure is less than the minimum value of the dynamic threshold 2 Kpa. However, when using a static threshold, as shown in Figure 6, because the static threshold range is reduced (Le., the lower limit value of the static threshold is increased), the actual outlet pressure exceeds (or coincides with) the lower limit value of the static threshold 1.0 Kpa for a long time (from around September 15, 2020, to around September 28, 2020), resulting in a large number of false alarms. Comparing the results of Figures 5 and 6, the use of dynamic thresholds can reduce the amount of false alarm data by 96.33%.
[0076] As shown in Figure 2, it is a functional module diagram of a fault diagnosis device in an exemplary embodiment of the present invention. Specifically, the fault diagnosis device includes: [0077] A database for storing data; specifically, the database can be used to store a training sample library, including historical outlet flow data, historical guide vane opening data, historical guide vane opening change state data, and historical outlet pressure data, etc. [0078] A data acquisition module for obtaining current outlet flow data, current guide vane opening data, current guide vane opening change state data, and current outlet pressure data of the air blower equipment to be tested. De,"
[0079] A model training module; which trains a support vector machine or neural network model according to the historical outlet flow data, historical guide vane opening data, historical guide vane opening change state data, and historical outlet pressure data stored in the database, to obtain an outlet pressure prediction mode:.
[0080] A fault diagnosis module, which calls the above-mentioned outlet pressure prediction model to calculate the outlet pressure prediction value based on the current outlet flow data, current guide vane opening data; and current guide vane opening change state data obtained by the data acquisition module, and adjusts the pre-warning threshold in the preset dynamic threshold according to the calculated outlet pressure prediction value and the historical outlet pressure data stored in the database; and assesss whether the current outlet pressure data exceeds the adjusted pre-warning threshold. If it exceeds, it predicts that the blade of the air blower equipment may break.
[0081] In some embodiments, the data acquisition module specifically includes: [0082] A data acquisition unit for obtaining current outlet flow data and current outlet pressure data of the air blower equipment to be tested; [0083] A first calculation unit for calculating an outlet flow change estimate value based on current outlet flow data and the first historical outlet flow data of the air blower equipment to be tested stored in the database; [0084] A second calculation unit for calculating the current guide vane opening change value based on the outlet flow change estimate value;
Description
[0085] A third calculation unit for calculating the current guide vane opening based on the current guide vane opening change value and the first historical guide vane opening data of the air blower equipment to be tested stored in the database.
[0086] In other embodiments, the data acquisition unit is specifically used to directly obtain the current guide vane opening data of the air blower equipment to be tested.
[0087] In other embodiments, the data acquisition module is specifically used to calculate the outlet flow change estimate value (for example, how much it has increased or decreased) based on current guide vane opening data and historical guide vane opening data (for example, guide vane opening data from the previous moment).
[0088] In some embodiments, the device further includes: a fault pre-warning module, which, when the fault diagnosis module predicts that the air blower equipment may experience blade breakage faults, adjusts the warning threshold in the preset dynamic threshold according to the outlet pressure prediction value and the historical outlet pressure data of the air blower equipment to be tested, and assesss whether the current outlet pressure data exceeds the adjusted warning threshold p: If so, it determines a blade breakage fault has occurred and triggers an alarm.
[0089] In some embodiments, the calculation formula for adjusting the above-mentioned warning threshold pi.: according to the outlet pressure prediction value P and the historical outlet pressure data is: [0090] = bl ±Wa (2), where, C7 is the standard deviation of all the historical outlet pressure data, N is an integer; and (n N.
Description
[0091] On the other hand, the present invention also provides an electronic device, including: a processor, a network interface, and a storage device storing instructions, where the network interface provides network communication functions, the storage device stores program code, and the processor calls the program code to perform the. steps of the fault diagnosis method of the above embodiments. Exemplarily, the electronic device can be: a mobile phone, a tablet PC, a digital camera, a personal digital assistant (PDA), a navigation device, a mobile internet device (MID), a wearable device, and other devices capable of performing fault diagnosis. In addition, the fault diagnosis method of the embodiments of the present disclosure can also be implemented as a function of the operating system of the electronic device.
[0092] In the above embodiments, all or part can be realized through software, hardware, firmware, or any combination thereof. When implemented using software programs, it can appear in whole or in part in the form of a computer program product. The computer programn product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, they produce all or part of the process or function described in the embodiments of the present disclosure. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from a website, computer; server, or data center to another website, computer" server, or data center through wired (for example, coaxial cable, optical fiber, DSL) or De," wireless (for example, infrared, wireless, microwave, etc.) methods. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available media. The available medium can be a magnetic medium (for example, floppy disk, hard disk, tape), an optical medium (for example, END), or a semiconductor medium (for example, solid-state disk (SSD)).
[0093] Generally speaking, various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuits, software, logic, or any combination thereof Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software executed by a controller, microprocessor, or other computing device. For example, in some embodiments, various examples (e.g., methods, devices, or equipment) of this disclosure can be partially or wholly implemented on a computer-readable medium. When aspects of the embodiments of this disclosure are illustrated or described as block diagrams, flow charts, or using some other graphical representation, it should be understood that the boxes, devices, systems, techniques, or methods described here can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuits or logic, general hardware or controller or other computing devices, or some combination thereof.
[0094] Furthermore, the present invention also provides a computer program product stored on a non-transitory computer-readable storage medium, the computer program product includes computer executable instructions, the instructions when executed by an electronic device cause the electronic device to perform the fault diagnosis method according to the above embodiments. In general, program modules can include routines, programs, libraries, objects, classes, components, data structures, etc., which perform specific tasks or implement specific abstract data types. In various embodiments, the functions of program modules can he merged or split among the described program modules. Computer executable instructions for program modules can be executed locally or in distributed devices, in distributed devices, program modules can be located in both local and remote storage medium.
[ 0095] In the above embodiments, it can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, they produce all or part of the process or function described in the embodiments of this application. The computer can be a general-purpose compute"; a dedicated computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or can be transmitted from one computer-readable storage medium to another, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g." infrared, wireless, microwave) methods. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that integrates one or more available medium. The available medium can be a magnetic 2? medium (e.g., floppy disk, hard drive, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state drive).
[0096] A person skilled in the art can understand how to implement all or part of the processes in the above-described embodiments, which can be instructed by a computer program to complete the relevant hardware. The program can be stored in a computer-readable storage medium. When the program is executed, it may include the process of the above-mentioned method embodiment. The aforementioned storage medium includes: ROM or random access memory (RAM), magnetic disk or optical disk, and various medium that can store program codes. A computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer-readable medium can include but is riot limited to electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of machine-readable storage medium include electrical connections with one or more wires, portable computer disks, hard drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory ([PROM or flash), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof, [0097] It should be noted that in this document, terms such as "includes" and has or any other variation are intended to cover non-exclusive inclusions, so that processes, methods, articles, or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed, or also include elements De," inherent to such processes, methods, articles, or devices. In the absence of more limitations, an element defined by the statement "includes an... does not exclude the existence of other identical elements in the process, method, article, or device that includes the element.
[0098] The above embodiments are only used to illustrate the technical solutions of the present invention; rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical schemes recorded in the foregoing embodiments, or replace them partially or entirely with equivalent changes; and these modifications or replacements do not make the nature corresponding technical schemes depart from the scope of the technical schemes of the embodiments of the present invention, which should all be covered within the scope of the claims and the specification of the present invention.
Claims (9)
1. A fault diagnosis method for an air blower equipment, wherein comprising the following steps: obtaining current outlet flow data, current guide vane opening data, current guide vane opening change state data, and current outlet pressure data of the air blower equipment to be tested; inputting the current outlet flow data, the current guide vane opening data, and the current guide vane opening change state data into an outlet pressure prediction model pre-trained to get an outlet pressure prediction value 1;7; wherein training the outlet pressure prediction model comprises: constructing a training sample library, wherein the training sample library comprises historical outlet flow data, historical guide vane opening data, historical guide vane opening change state data, and historical outlet pressure data, which are collected under normal working state of the air blower equipment; Cr) inputting the historical outlet flow data, the historical guide vane opening data, the historical guide vane opening change state data, and the historical outlet pressure data into a support vector machine model or a neural network model for training to obtain the outlet pressure prediction C\J model; adjusting alarm threshold /7, in a preset dynamic threshold based on the outlet pressure prediction value I?, and historical outlet pressure data that are pre-stored; adjusting the alarm threshold F" based on the outlet pressure prediction value F, and the historical outlet pressure data; assessing whether the current outlet pressure data exceeds the alarm threshold Fa' that has been adjusted; if the current outlet pressure data exceeds the alarm threshold Fr; , determining that a blade breakage fault has occurred, and initiating an alarm; wherein, calculation formula for adjusting the alarm threshold F7,7 based on the outlet pressure prediction value F and the historical outlet pressure data is: F 0 F ±Na wherein a is the standard deviation of all the historical outlet pressure data, and N is an integer that satisfies ii +1 < N l< n < 5.
2. The method according to claim 1, wherein, prior to the step of inputting the current guide vane opening data into the outlet pressure prediction model, discretizing the current guide vane opening data.
3. The method according to claim 1, wherein the step of obtaining the current guide vane opening change state data comprises calculating a current guide vane opening change estimate value based on the current guide vane opening data and the historical guide vane opening data that are pre-stored.
4. The method according to anyone of claims 1 to 3, wherein the preset dynamic threshold further comprises a pre-warning threshold; prior to the step of Cr) adjusting the alarm threshold, further comprising: assessing whether the current outlet pressure data exceeds the pre-warning threshold Fo that has been adjusted; if the current outlet pressure data exceeds the pre-warning threshold Fo, diagnosing that a blade of the air blower C\J equipment may have a breakage fault; wherein, calculation formula for adjusting the pre-warning threshold Ea based on the outlet pressure prediction value /7 and the historical outlet pressure data is: Ft, = 17± no-, wherein a is the standard deviation of all the historical outlet pressure data, and 11 is an integer that satisfies n 5.S. A fault diagnosis device for an air blower equipment, comprising: a database configured to store a training sample library, wherein the training sample library comprises historical outlet flow data, historical guide vane opening data, historical guide vane opening change state data, and historical outlet pressure data that are collected under normal working state of the air blower equipment; a data acquisition module configured to obtain current outlet flow data, current guide vane opening data, current guide vane opening change state data, and current outlet pressure data of the air blower equipment to be tested; a model training module configured to train a support vector machine or a neural network model based on the historical outlet flow data, the historical guide vane opening data, the historical guide vane opening change state data, and the historical outlet pressure data stored in the database, to obtain an outlet pressure prediction model; and, a fault diagnosis module configured to: call the outlet pressure prediction model to calculate outlet pressure prediction value I; based on the current outlet flow data, the current guide vane opening change state data, and the current guide vane opening data obtained by the data acquisition module, and
COto adjust alarm threshold 170' in a preset dynamic threshold based on the outlet pressure prediction value 17, and the historical outlet pressure data stored in the database, and CO to determine whether the current outlet pressure data exceeds the C\I adjusted alarm threshold J'; if the current outlet pressure data exceeds the adjusted alarm threshold P, to determine that a blade breakage fault has occurred, and initiate an alarm; wherein, calculation formula for adjusting the alarm threshold ro.based on the outlet pressure prediction value Ft and the historical outlet pressure data is: Pic =1,1 ±No-, wherein a is the standard deviation of all the historical outlet pressure data, and N is an integer that satisfies n+1<N,1<n< 5.
6. The device according to claim 5, wherein the data acquisition module comprises: a data acquisition unit configured to obtain the current outlet flow data, the current guide vane opening data, and the current outlet pressure data of the air blower equipment to be tested; a calculation unit configured to calculate the current guide vane opening change estimate value and change state based on the current guide vane opening data and the historical guide vane opening data stored in the database.
7. The device according to claim 5, wherein further comprising a fault pre-warning module configured to: adjust pre-warning threshold 1 in the preset dynamic threshold based on the outlet pressure prediction value P; and the historical outlet pressure data stored, and determine whether the current outlet pressure exceeds the pre-warning threshold F, in the preset dynamic threshold adjusted; if the current outlet pressure exceeds the pre-warning threshold J, predict that a blade of the air CO blower equipment may break; wherein, calculation formula for adjusting the pre-warning threshold Fo o based on the outlet pressure prediction value F, and the historical outlet pressure data is: F°= F'± no-, wherein a is the standard deviation of all the
8. An electronic device, comprisinga processor, a network interface, and a memory storing instructions, wherein, the network interface is configured to provide network communication function, the memory is configured to store program code, and the processor is configured to call the program code to execute the steps of the fault diagnosis method as claimed in any one of claims 1 to 4.
9. A computer program product, the computer program product comprises instruction, which when being executed by an electronic device, cause the electronic device to implement the method as claimed in any one of claims 1 to C\I historical outlet pressure data, and Il is an integer that satisfies l< n < 5. 4.
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