Fault prediction method and device, equipment and storage medium

Abstract

The invention discloses a fault prediction method and device, equipment and a storage medium, and relates to the technical field of power electronics, and the method comprises the steps: obtaining the real-time operation data of the electronic equipment, and determining a real-time operation curve corresponding to the real-time operation data; the normal operation curve and the real-time operation curve corresponding to the real-time operation data are compared; when it is determined that the real-time operation curve deviates from the normal operation curve, a target fault curve is determined according to the similarity between the real-time operation curve and each fault curve in a fault curve library; and determining a current fault component according to the target fault curve. According to the technical scheme, under the condition that it is determined that the real-time operation curve of the electronic equipment deviates from the normal operation curve, the target fault curve corresponding to the real-time operation curve is determined in the fault curve library, and the current fault component is determined according to the target fault curve; the current fault component is determined on the premise of considering the influence of the component fault on other components, and the fault prediction accuracy is improved.

Description

Technical Field

[0001] Embodiments of the present invention relate to the field of power electronics technology, and in particular to a fault prediction method, device, equipment and storage medium. Background Art

[0002] Wind turbines are used to convert wind kinetic energy into electrical energy. When generating electricity based on wind turbines, the output frequency needs to be constant. Therefore, the stable operation of wind turbines needs to be ensured. In order to ensure the stable operation of wind turbines, fault prediction of wind turbines is required.

[0003] In the prior art, the vibration signals of various components of the wind turbine generator set, oil parameters, temperature parameters, current and voltage at the generator output end and other parameters are usually measured, and then fault prediction is performed based on the measured data.

[0004] However, the accuracy of existing fault prediction methods is not high. Summary of the invention

[0005] The present invention provides a fault prediction method, device, equipment and storage medium to improve the accuracy of fault prediction.

[0006] In a first aspect, an embodiment of the present invention provides a fault prediction method, comprising:

[0007] Acquire real-time operation data of the electronic device, and determine a real-time operation curve corresponding to the real-time operation data;

[0008] Acquire real-time operation data of the electronic device, and determine a real-time operation curve corresponding to the real-time operation data;

[0009] Comparing a normal operating curve corresponding to the real-time operating data with the real-time operating curve;

[0010] In the case where it is determined that the real-time operation curve deviates from the normal operation curve, determining a target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library;

[0011] A current faulty component is determined according to the target fault curve.

[0012] The technical solution of the embodiment of the present invention provides a fault prediction method, including: obtaining real-time operation data of an electronic device, and determining a real-time operation curve corresponding to the real-time operation data; comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve; in the case of determining that the real-time operation curve deviates from the normal operation curve, determining a target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library; and determining the current fault component according to the target fault curve. The above technical solution can firstly obtain the real-time operation data of the electronic device, and determine the real-time operation curve corresponding to the real-time operation data, so as to determine the change trend of the real-time operation data; secondly, the normal operation curve corresponding to the real-time operation data and the real-time operation curve can be compared to determine whether the normal operation curve corresponding to the real-time operation data and the real-time operation curve are consistent; in the case of determining that the real-time operation curve deviates from the normal operation curve, the target fault curve is determined according to the similarity between the real-time operation curve and each fault curve in the fault curve library, so as to determine the target fault curve corresponding to the real-time operation curve deviating from the normal operation curve in the fault curve library, and then the current fault component can be determined according to the target fault curve, and the current fault component can be determined under the premise of considering the impact of the component failure on other components, thereby improving the accuracy of fault prediction.

[0013] Furthermore, the real-time operation data includes operation data of each component of the electronic device, and the operation data includes at least vibration signals, electrical signals, temperature data and pressure data.

[0014] Furthermore, it also includes:

[0015] Determining the fault type of each of the components according to the historical fault records;

[0016] For each of the components, the fault curve library is constructed based on the operating data of the component and the associated components corresponding to the component when various fault types of faults occur in the component, wherein the fault curve library includes fault curves and the fault components and fault types corresponding to each of the fault curves.

[0017] Furthermore, before comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve, the method further includes:

[0018] The operating data of each component when the electronic device is operating normally is determined according to the historical operating records, and the normal operating curve of each component is determined according to the operating data of each component when the electronic device is operating normally.

[0019] Further, comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve includes:

[0020] Determine a normal maximum value and a normal minimum value according to the normal operating curve, and determine an operating maximum value and an operating minimum value according to the real-time operating curve;

[0021] Determine a first deviation value according to the operation maximum value and the normal maximum value, and determine a second deviation value according to the operation minimum value and the normal minimum value;

[0022] When it is determined that both the first deviation value and the second deviation value are greater than a first preset threshold, determining the similarity between the normal operation curve and the real-time operation curve;

[0023] When it is determined that the similarity is less than a second preset threshold, it is determined that the real-time operation curve deviates from the normal operation curve.

[0024] Further, determining a target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library includes:

[0025] Determining the similarity between the real-time operation curve and each of the fault curves in the fault curve library;

[0026] The fault curve corresponding to the similarity that meets the preset condition is determined as the target fault curve.

[0027] Further, determining the current faulty component according to the target fault curve includes:

[0028] Determine the faulty component corresponding to the target fault curve as the current faulty component;

[0029] Correspondingly, the method further includes: determining the fault type of the current faulty component according to the fault type corresponding to the target fault curve.

[0030] In a second aspect, an embodiment of the present invention further provides a fault prediction device, comprising:

[0031] An acquisition module, used to acquire real-time operation data of the electronic device and determine a real-time operation curve corresponding to the real-time operation data;

[0032] A comparison module, used for comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve;

[0033] A determination module, configured to determine a target fault curve according to a similarity between the real-time operation curve and each fault curve in a fault curve library when it is determined that the real-time operation curve deviates from the normal operation curve;

[0034] An execution module is used to determine a current faulty component according to the target fault curve.

[0035] In a third aspect, an embodiment of the present invention further provides an electronic device, the electronic device comprising:

[0036] at least one processor; and a memory communicatively coupled to the at least one processor;

[0037] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fault prediction method as described in any one of the first aspects.

[0038] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, wherein the computer executable instructions, when executed by a computer processor, are used to execute the fault prediction method as described in any one of the first aspects.

[0039] In a fifth aspect, the present application provides a computer program product, which includes computer instructions. When the computer instructions are executed on a computer, the computer executes the fault prediction method provided in the first aspect.

[0040] It should be noted that the above computer instructions may be stored in whole or in part on a computer-readable storage medium, wherein the computer-readable storage medium may be packaged together with the processor of the fault prediction device, or may be packaged separately from the processor of the fault prediction device, which is not limited in this application.

[0041] The description of the second, third, fourth and fifth aspects in this application can refer to the detailed description of the first aspect; and the beneficial effects of the description of the second, third, fourth and fifth aspects can refer to the beneficial effect analysis of the first aspect, which will not be repeated here.

[0042] In this application, the name of the above-mentioned fault prediction device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear with other names. As long as the functions of each device or functional module are similar to those of this application, they fall within the scope of the claims of this application and their equivalent technologies.

[0043] These and other aspects of the present application will become more apparent from the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 A flowchart of a fault prediction method provided by an embodiment of the present invention;

[0046] Figure 2 A flowchart of another fault prediction method provided by an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of the structure of a fault prediction device provided by an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0050] The term "and / or" in this article is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0051] The terms "first" and "second" and the like in the specification and drawings of this application are used to distinguish different objects, or to distinguish different processing of the same object, rather than to describe a specific order of objects.

[0052] In addition, the terms "including" and "having" and any variations thereof mentioned in the description of the present application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products or devices.

[0053] It should be mentioned before discussing exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe various operations (or steps) as sequential processes, many operations therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of various operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to methods, functions, procedures, subroutines, subprograms, etc. In addition, the embodiments in the present invention and the features in the embodiments can be combined with each other without conflict.

[0054] It should be noted that, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0055] In the description of the present application, unless otherwise specified, “plurality” means two or more.

[0056] When any component of a wind turbine generator fails, it will affect its associated components. Therefore, the accuracy of fault prediction based on vibration signals, electrical signals, temperature data, etc. of each component of the wind turbine generator is not high.

[0057] Therefore, the present application proposes a fault prediction method, which combines the operating data of each component and the operating data of the associated components corresponding to each component to perform fault prediction, thereby improving the accuracy of fault prediction.

[0058] The fault prediction method proposed in the present application will be described in detail below with reference to diagrams and embodiments.

[0059] Figure 1 A flowchart of a fault prediction method provided by an embodiment of the present invention is provided. This embodiment is applicable to situations where the accuracy of fault prediction needs to be improved. The method can be executed by a fault prediction device, such as Figure 1 As shown, the specific steps include:

[0060] Step 110: Acquire real-time operation data of the electronic device, and determine a real-time operation curve corresponding to the real-time operation data.

[0061] The real-time operation data of the electronic device can be understood as the real-time operation data of each component of the electronic device.

[0062] Specifically, real-time operating data of each component is obtained when the electronic device is running. Specifically, the real-time vibration signal, real-time electrical signal, real-time temperature data and real-time pressure data of each component can be obtained in real time, and the corresponding real-time operating curve is determined according to the acquired real-time operating data. That is, the real-time vibration curve, real-time current curve, real-time temperature change curve and real-time pressure change curve of each component can be determined according to the real-time vibration signal, real-time electrical signal, real-time temperature data and real-time pressure data of each component.

[0063] In the embodiment of the present invention, the acquisition of real-time operation data of the electronic device and the construction of a real-time operation curve are achieved, thereby determining the change trend of the real-time operation data.

[0064] Step 120: Compare the normal operation curve corresponding to the real-time operation data with the real-time operation curve.

[0065] The normal operating curve can be understood as the normal operating curve of each component when the electronic device is operating normally, that is, the normal vibration curve, normal current curve, normal temperature change curve and normal pressure change curve of each component.

[0066] Specifically, the normal operating curve of each component can be determined first. Specifically, the vibration signal, electrical signal, temperature data and pressure data of each component when the electronic equipment is operating normally can be determined based on the historical operation records. The normal vibration curve, normal current curve, normal temperature change curve and normal pressure change curve of each component can be determined based on the vibration signal, electrical signal, temperature data and pressure data of each component when the electronic equipment is operating normally.

[0067] Furthermore, by comparing the normal vibration curve and the real-time vibration curve, the normal current curve and the real-time current curve, the normal temperature change curve and the real-time temperature change curve, the normal pressure change curve and the real-time pressure change curve corresponding to each component, the comparison result of each component is determined.

[0068] In the embodiment of the present invention, the comparison result is determined by comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve.

[0069] Step 130: When it is determined that the real-time operation curve deviates from the normal operation curve, a target fault curve is determined according to the similarity between the real-time operation curve and each fault curve in a fault curve library.

[0070] Among them, the fault curve library includes fault curves and the fault components and fault types corresponding to the fault curves. The fault curves may include the vibration curves, current curves, temperature change curves and pressure change curves of the associated components corresponding to various types of faults occurring in each component, fully considering the impact of component failures on other components.

[0071] Specifically, if it is determined that the real-time operation curve deviates from the normal operation curve, then there is a faulty component in the electronic device. When it is determined that any real-time operation curve of any component deviates from the corresponding normal operation curve, it can be determined that there is a faulty component in the electronic device. At this time, a search can be performed in the fault curve library according to the real-time operation curve that deviates from the normal operation curve, and specifically, the similarity between the real-time operation curve that deviates from the normal operation curve and each fault curve in the fault curve library can be determined, and the fault curve corresponding to the maximum similarity is determined as the target fault curve.

[0072] In the embodiment of the present invention, it is achieved to determine the target fault curve corresponding to the real-time operation curve deviating from the normal operation curve in the fault curve library.

[0073] Step 140: Determine the current faulty component according to the target fault curve.

[0074] Specifically, since the fault curve library is composed of fault curves and the fault components and fault types corresponding to each fault curve, after determining the target fault curve, the fault component and fault type corresponding to the target fault curve can be determined in the fault curve library, and then the fault component corresponding to the target fault curve can be determined as the current fault component.

[0075] In the embodiment of the present invention, the current faulty component is determined under the premise of considering the impact of component failure on other components, thereby improving the accuracy of fault prediction.

[0076] The fault prediction method provided by the embodiment of the present invention includes: obtaining the real-time operation data of the electronic device, and determining the real-time operation curve corresponding to the real-time operation data; comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve; in the case of determining that the real-time operation curve deviates from the normal operation curve, determining the target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library; and determining the current fault component according to the target fault curve. The above technical scheme can firstly obtain the real-time operation data of the electronic device, and determine the real-time operation curve corresponding to the real-time operation data, so as to determine the change trend of the real-time operation data; secondly, the normal operation curve corresponding to the real-time operation data and the real-time operation curve can be compared to determine whether the normal operation curve corresponding to the real-time operation data and the real-time operation curve are consistent; in the case of determining that the real-time operation curve deviates from the normal operation curve, the target fault curve is determined according to the similarity between the real-time operation curve and each fault curve in the fault curve library, so as to determine the target fault curve corresponding to the real-time operation curve deviating from the normal operation curve in the fault curve library, and then the current fault component can be determined according to the target fault curve, and the current fault component can be determined under the premise of considering the impact of the component failure on other components, thereby improving the accuracy of fault prediction.

[0077] Figure 2 This is a flowchart of another fault prediction method provided by an embodiment of the present invention. This embodiment is specific based on the above embodiment. Figure 2 As shown, in this embodiment, the method may further include:

[0078] Step 210: Determine the fault type of each component according to the historical fault records.

[0079] The electronic device may be a wind turbine generator set, and the components constituting the generator set include gears, blades, yaw systems, towers, main shafts, bearings, generators, hydraulic motors, brake systems, etc.

[0080] Specifically, the historical fault records of the wind turbine generator set within a preset time period before the current moment can be obtained, and the fault types of the components constituting the generator set can be determined based on the historical fault records. It can be determined that the fault types of the gear include broken teeth, pitting, bonding, and wear; the fault types of the blades include cracks and fractures; the fault types of the yaw system include gear ring wear, tooth surface wear, and jamming; the fault types of the tower include vibration; the fault types of the main shaft include wear, broken shaft, and vibration; the fault types of the bearings include vibration, wear, and fatigue; the fault types of the generator include aging; the fault types of the hydraulic motor include excessive hydraulic oil temperature, hydraulic oil leakage, low hydraulic pressure, hydraulic motor overload, excessive lubricating oil temperature, and lubricating oil pollution; the fault types of the brake system include failure.

[0081] In the embodiment of the present invention, the fault type of each component constituting the wind turbine generator set is determined according to the historical fault records.

[0082] Step 220: For each of the components, construct the fault curve library according to the operation data of the component and the associated components corresponding to the component when various fault types of faults occur in the component.

[0083] The fault curve library includes fault curves and fault components and fault types corresponding to each fault curve.

[0084] The associated components corresponding to each component can be determined based on the association relationship between the components in the wind turbine generator set. Specifically, the component whose operating data produces abnormal fluctuations when the component fails can be determined as the associated component corresponding to the component. For example, when a blade fails, it will affect the bearing and the generator, and the operating data of the bearing and the generator will produce abnormal fluctuations, that is, the associated components corresponding to the blade can be determined to be the bearing and the generator. The operating data here can include at least one of vibration signals, electrical signals, temperature data, pressure data, displacement data, sound data, infrared data, color data, ultrasonic data, image data, hydraulic data, and lubricating oil data.

[0085] Specifically, the historical fault records also include the operating data of other components when various types of faults occur in each component. Therefore, the operating data of the components and the associated components corresponding to the components when various types of faults occur in each component can be determined based on the historical fault records, and the fault curve can be determined based on the operating data. Furthermore, a fault curve library can be constructed based on the fault curves and the fault components and fault types corresponding to each fault curve.

[0086] In an embodiment of the present invention, a fault curve library is constructed based on the operating data of the components and the associated components corresponding to the components when various fault types occur in each component, and the changes in the operating data of the components and the associated components corresponding to the components when various fault types occur in each component are recorded and stored based on the fault curve library.

[0087] Step 230: determining the operating data of each component when the electronic device is operating normally according to the historical operating records, and determining the normal operating curve of each component according to the operating data of each component when the electronic device is operating normally.

[0088] The historical operation records include the operation data of each component when the wind turbine generator set operates normally.

[0089] Specifically, the historical operation records within a preset time period before the current moment can be obtained, and the operation data of each component when the wind turbine generator set is operating normally can be determined in the historical operation records. Furthermore, the normal operation curve of each component can be determined based on the operation data of each component when the wind turbine generator set is operating normally.

[0090] In an embodiment of the present invention, the normal operating curve of each component is determined according to the operating data of each component when the wind turbine generator set is operating normally determined in the historical operating record of the wind turbine generator set, thereby determining the changing trend of the operating data when the wind turbine generator set is operating normally.

[0091] Step 240: Acquire real-time operation data of the electronic device, and determine a real-time operation curve corresponding to the real-time operation data.

[0092] Specifically, when the wind turbine generator set is in operation, the real-time operation data of each component is obtained. Specifically, at least one of the real-time vibration signal, real-time electrical signal, real-time temperature data, real-time pressure data, real-time displacement data, real-time sound data, real-time infrared data, real-time color data, real-time ultrasonic data, real-time image data, real-time hydraulic data, and real-time lubricating oil data of each component can be obtained in real time. According to the obtained real-time operation data, the corresponding real-time operation curve is determined, that is, the real-time vibration curve corresponding to the real-time vibration signal, the real-time current curve corresponding to the real-time electrical signal, the real-time temperature change curve corresponding to the real-time temperature data, the real-time pressure change curve corresponding to the real-time pressure data, the real-time displacement change curve corresponding to the real-time displacement data, the real-time sound curve corresponding to the real-time sound data, the real-time infrared curve corresponding to the real-time infrared data, the real-time color change curve corresponding to the real-time color data, the real-time ultrasonic curve corresponding to the real-time ultrasonic data, the real-time image change curve corresponding to the real-time image data, the real-time hydraulic change curve corresponding to the real-time hydraulic data, and the real-time lubricating oil data change curve corresponding to the real-time lubricating oil data.

[0093] In the embodiment of the present invention, the acquisition of real-time operation data of the wind turbine generator set and the construction of a real-time operation curve are achieved, thereby determining the change trend of the real-time operation data.

[0094] Step 250: Compare the normal operation curve corresponding to the real-time operation data with the real-time operation curve.

[0095] In one implementation, step 250 may specifically include:

[0096] Determine a normal maximum value and a normal minimum value according to the normal operating curve, and determine an operating maximum value and an operating minimum value according to the real-time operating curve; determine a first deviation value according to the determined operating maximum value and the normal maximum value, and determine a second deviation value according to the operating minimum value and the normal minimum value; when it is determined that the first deviation value and the second deviation value are both greater than a first preset threshold, determine the similarity between the normal operating curve and the real-time operating curve; when it is determined that the similarity is less than a second preset threshold, determine that the real-time operating curve deviates from the normal operating curve.

[0097] Specifically, for each real-time operation data, firstly, a normal maximum value and a normal minimum value can be determined in the normal operation curve corresponding to the real-time operation data, and the operation maximum value and the operation minimum value can be determined in the real-time operation curve corresponding to the real-time operation data to achieve the determination of the normal extreme value and the real-time extreme value. Secondly, a first deviation value between the operation maximum value and the normal maximum value, and a second deviation value between the operation minimum value and the normal minimum value can be determined, and the magnitude relationship between the first deviation value and the second deviation value and the first preset threshold value can be determined. If both the first deviation value and the second deviation value are greater than the first preset threshold value, it is determined that the real-time operation curve may deviate from the normal operation curve. In order to determine again whether the real-time operation curve deviates from the normal operation curve, the similarity between the real-time operation curve and the normal operation curve can be determined. Specifically, a curve similarity calculation method such as a cosine similarity algorithm and a Pearson correlation coefficient can be used to determine the similarity between the real-time operation curve and the normal operation curve, and the magnitude relationship between the similarity and the second preset threshold value can be determined. If the similarity is greater than the second preset threshold value, it is determined that the real-time operation curve deviates from the normal operation curve.

[0098] In addition, it is also possible to determine whether the real-time operation curve deviates from the normal operation curve based on the trend change of the curve, that is, when it is determined that there is an obvious difference between the trend change of the real-time operation curve and the trend change of the normal operation curve, it is determined that the real-time operation curve deviates from the normal operation curve.

[0099] In the embodiment of the present invention, the comparison result of the real-time operation curve and the normal operation curve is determined by comparing the extreme values ​​of the real-time operation curve and the normal operation curve and determining the similarity between the real-time operation curve and the normal operation curve.

[0100] Step 260: When it is determined that the real-time operation curve deviates from the normal operation curve, a target fault curve is determined according to the similarity between the real-time operation curve and each fault curve in the fault curve library.

[0101] In one implementation, step 260 may specifically include:

[0102] Determine the similarity between the real-time operation curve and each of the fault curves in the fault curve library; and determine the fault curve corresponding to the similarity that meets a preset condition as the target fault curve.

[0103] Specifically, when it is determined that any real-time operation curve of any component of the wind turbine generator set deviates from the corresponding normal operation curve, it can be determined that there is a faulty component in the wind turbine generator set. At this time, the similarity between the real-time operation curve that deviates from the normal operation curve and each fault curve in the fault curve library can be determined. Specifically, the similarity between the real-time operation curve and each fault curve in the fault curve library can be determined based on a curve similarity calculation method such as a cosine similarity algorithm and a Pearson correlation coefficient, and the fault curve corresponding to the maximum similarity is determined as the target fault curve.

[0104] In addition, when a component fails, it may affect multiple components, causing the real-time operation curves of the multiple components to deviate from the normal operation curve. Therefore, the target failure curves corresponding to the multiple real-time operation curves can be determined.

[0105] In the embodiment of the present invention, it is achieved to determine the target fault curve corresponding to the real-time operation curve deviating from the normal operation curve in the fault curve library.

[0106] Step 270: Determine the current faulty component according to the target fault curve.

[0107] In one implementation, step 270 may specifically include:

[0108] The faulty component corresponding to the target fault curve is determined as the current faulty component.

[0109] Step 280: Determine the fault type of the current faulty component according to the fault type corresponding to the target fault curve.

[0110] Specifically, after determining the target fault curve, the fault component and the fault type corresponding to the target fault curve may be determined in the fault curve library, and then the fault component corresponding to the target fault curve may be determined as the current fault component.

[0111] Of course, if the faulty components determined according to the target fault curves corresponding to the multiple real-time operation curves are different, the faulty components determined by the target fault curves corresponding to the majority of the real-time operation curves may be determined as the current faulty components.

[0112] In practical applications, the fault type corresponding to the target fault curve may also be determined as the fault type of the current faulty component.

[0113] In the embodiment of the present invention, the current faulty component and the fault type of the current faulty component are determined under the premise of considering the impact of the component failure on other components, thereby improving the accuracy of fault prediction and the efficiency of fault judgment.

[0114] The fault prediction method provided by an embodiment of the present invention includes: determining the fault type of each component according to historical fault records; for each component, constructing the fault curve library according to the operation data of the component and the associated components corresponding to the component when various fault types of faults occur in the component; determining the operation data of each component when the electronic device is operating normally according to the historical operation records, and determining the normal operation curve of each component according to the operation data of each component when the electronic device is operating normally; acquiring the real-time operation data of the electronic device, and determining the real-time operation curve corresponding to the real-time operation data; comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve; when it is determined that the real-time operation curve deviates from the normal operation curve, determining the target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library; determining the current faulty component according to the target fault curve. The above technical scheme determines the fault type of each component constituting the wind turbine generator set according to the historical fault records, builds a fault curve library according to the operating data of the components and the associated components corresponding to the components when various fault types of faults occur in each component, realizes the recording and storage of the change of the operating data of the components and the associated components corresponding to the components when various fault types of faults occur in each component based on the fault curve library, determines the normal operating curve of each component according to the operating data of each component when the wind turbine generator set is operating normally determined in the historical operating records of the wind turbine generator set, realizes the determination of the change trend of the operating data when the wind turbine generator set is operating normally, obtains the real-time operating data of the wind turbine generator set, determines the real-time operating curve corresponding to the real-time operating data, realizes the determination of the change trend of the real-time operating data, and compares the extreme values ​​of the real-time operating curve and the normal operating curve and determines the real-time operating curve. The similarity between the real-time operation curve and the normal operation curve is determined to determine the comparison result between the real-time operation curve and the normal operation curve. By performing curve fitting on the real-time operation data and comparing the real-time operation curve with the normal operation curve, it is possible to preliminarily judge whether the wind turbine generator set may fail, realize fault prediction, and improve the efficiency and accuracy of fault judgment. When it is determined that the real-time operation curve deviates from the normal operation curve, the target fault curve is determined according to the similarity between the real-time operation curve and each fault curve in the fault curve library, the fault component corresponding to the target fault curve is determined as the current fault component, and the fault type corresponding to the target fault curve is determined as the fault type of the current fault component. By determining the similarity between the real-time operation curve and each fault curve in the fault curve library, the fault component is located and the fault type is determined, thereby improving the accuracy and efficiency of fault location.

[0115] In addition, it provides a data basis for fault prevention, facilitates timely maintenance of wind turbines, and increases the service life of wind turbines.

[0116] Figure 3This is a schematic diagram of the structure of a fault prediction device provided by an embodiment of the present invention, which can be applied to improve the accuracy of fault prediction. The device can be implemented by software and / or hardware and is generally integrated in an electronic device, such as a computer device.

[0117] like Figure 3 As shown, the device comprises:

[0118] The acquisition module 310 is used to acquire real-time operation data of the electronic device and determine a real-time operation curve corresponding to the real-time operation data;

[0119] A comparison module 320, used to compare the normal operation curve corresponding to the real-time operation data with the real-time operation curve;

[0120] A determination module 330, configured to determine a target fault curve according to a similarity between the real-time operation curve and each fault curve in a fault curve library when it is determined that the real-time operation curve deviates from the normal operation curve;

[0121] The execution module 340 is used to determine the current fault component according to the target fault curve.

[0122] The fault prediction device provided in this embodiment obtains the real-time operation data of the electronic device, and determines the real-time operation curve corresponding to the real-time operation data; compares the normal operation curve corresponding to the real-time operation data with the real-time operation curve; when it is determined that the real-time operation curve deviates from the normal operation curve, determines the target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library; and determines the current fault component according to the target fault curve. The above technical scheme can firstly obtain the real-time operation data of the electronic device, and determine the real-time operation curve corresponding to the real-time operation data, so as to determine the change trend of the real-time operation data; secondly, it can compare the normal operation curve corresponding to the real-time operation data with the real-time operation curve, and determine whether the normal operation curve corresponding to the real-time operation data and the real-time operation curve are consistent; when it is determined that the real-time operation curve deviates from the normal operation curve, it can determine the target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library, so as to determine the target fault curve corresponding to the real-time operation curve deviating from the normal operation curve in the fault curve library, and then determine the current fault component according to the target fault curve, and determine the current fault component under the premise of considering the impact of the component failure on other components, so as to improve the accuracy of fault prediction.

[0123] In one embodiment, the real-time operating data includes operating data of each component of the electronic device, and the operating data includes at least vibration signals, electrical signals, temperature data and pressure data.

[0124] Based on the above embodiment, the device further includes:

[0125] A construction module is used to determine the fault type of each component according to historical fault records; for each component, the fault curve library is constructed according to the operating data of the component and the associated components corresponding to the component when various fault types of faults occur in the component, wherein the fault curve library includes fault curves and the fault components and fault types corresponding to each fault curve.

[0126] Based on the above embodiment, the comparison module 320 is specifically used for:

[0127] Determine the operating data of each component when the electronic device is operating normally according to the historical operating records, and determine the normal operating curve of each component according to the operating data of each component when the electronic device is operating normally; determine the normal maximum value and the normal minimum value according to the normal operating curve, and determine the operating maximum value and the operating minimum value according to the real-time operating curve; determine a first deviation value according to the operating maximum value and the normal maximum value, and determine a second deviation value according to the operating minimum value and the normal minimum value; when it is determined that the first deviation value and the second deviation value are both greater than a first preset threshold, determine the similarity between the normal operating curve and the real-time operating curve; when it is determined that the similarity is less than a second preset threshold, determine that the real-time operating curve deviates from the normal operating curve.

[0128] Based on the above embodiment, the determination module 330 is specifically used for:

[0129] When it is determined that the real-time operation curve deviates from the normal operation curve, the similarity between the real-time operation curve and each of the fault curves in the fault curve library is determined; and the fault curve corresponding to the similarity that meets the preset conditions is determined as the target fault curve.

[0130] Based on the above embodiment, the execution module 340 is specifically used to:

[0131] The faulty component corresponding to the target fault curve is determined as the current faulty component.

[0132] Based on the above embodiment, the execution module 340 is further used for:

[0133] The fault type of the current faulty component is determined according to the fault type corresponding to the target fault curve.

[0134] The fault prediction device provided in the embodiment of the present invention can execute the fault prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the fault prediction method.

[0135] It is worth noting that in the embodiment of the above-mentioned fault prediction device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0136] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 4 A block diagram of an exemplary electronic device 4 suitable for implementing embodiments of the present invention is shown. Figure 4 The electronic device 4 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0137] like Figure 4 As shown, the electronic device 4 is in the form of a general purpose computing electronic device. The components of the electronic device 4 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 connecting different system components (including the system memory 28 and the processing unit 16).

[0138] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0139] The electronic device 4 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 4, including volatile and non-volatile media, removable and non-removable media.

[0140] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 4 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0141] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0142] The electronic device 4 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), one or more devices that enable a user to interact with the electronic device 4, and / or any device that enables the electronic device 4 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 4 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 20. Figure 4 As shown, the network adapter 20 communicates with other modules of the electronic device 4 via the bus 18. It should be understood that although Figure 4 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 4, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0143] The processing unit 16 executes various functional applications and page displays by running the program stored in the system memory 28, for example, implementing the fault prediction method provided by the embodiment of the present invention, which includes:

[0144] Acquire real-time operation data of the electronic device, and determine a real-time operation curve corresponding to the real-time operation data;

[0145] Comparing a normal operating curve corresponding to the real-time operating data with the real-time operating curve;

[0146] In the case where it is determined that the real-time operation curve deviates from the normal operation curve, determining a target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library;

[0147] A current faulty component is determined according to the target fault curve.

[0148] Of course, those skilled in the art can understand that the processor can also implement the technical solution of the fault prediction method provided by any embodiment of the present invention.

[0149] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, for example, a fault prediction method provided by an embodiment of the present invention is implemented. The method includes:

[0150] Acquire real-time operation data of the electronic device, and determine a real-time operation curve corresponding to the real-time operation data;

[0151] Comparing a normal operating curve corresponding to the real-time operating data with the real-time operating curve;

[0152] In the case where it is determined that the real-time operation curve deviates from the normal operation curve, determining a target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library;

[0153] A current faulty component is determined according to the target fault curve.

[0154] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0155] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0156] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0157] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0158] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0159] In addition, the acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of national laws and regulations.

[0160] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A fault prediction method, characterized in that: include: Acquire real-time operation data of the electronic device, and determine a real-time operation curve corresponding to the real-time operation data; Comparing a normal operating curve corresponding to the real-time operating data with the real-time operating curve; In the case where it is determined that the real-time operation curve deviates from the normal operation curve, determining a target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library; A current faulty component is determined according to the target fault curve.

2. The fault prediction method according to claim 1, characterized in that: The real-time operation data includes operation data of each component of the electronic device, and the operation data includes at least vibration signals, electrical signals, temperature data and pressure data.

3. The fault prediction method according to claim 2, characterized in that: Also includes: Determining the fault type of each of the components according to the historical fault records; For each of the components, the fault curve library is constructed based on the operating data of the component and the associated components corresponding to the component when various fault types of faults occur in the component, wherein the fault curve library includes fault curves and the fault components and fault types corresponding to each of the fault curves.

4. The fault prediction method according to claim 2, characterized in that: Before comparing the normal operating curve corresponding to the real-time operating data with the real-time operating curve, the method further includes: The operating data of each component when the electronic device is operating normally is determined according to the historical operating records, and the normal operating curve of each component is determined according to the operating data of each component when the electronic device is operating normally.

5. The fault prediction method according to claim 1, characterized in that: Comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve includes: Determine a normal maximum value and a normal minimum value according to the normal operating curve, and determine an operating maximum value and an operating minimum value according to the real-time operating curve; Determine a first deviation value according to the operation maximum value and the normal maximum value, and determine a second deviation value according to the operation minimum value and the normal minimum value; When it is determined that both the first deviation value and the second deviation value are greater than a first preset threshold, determining the similarity between the normal operation curve and the real-time operation curve; When it is determined that the similarity is less than a second preset threshold, it is determined that the real-time operation curve deviates from the normal operation curve.

6. The fault prediction method according to claim 1, characterized in that: Determining a target fault curve according to the similarity between the real-time operation curve and each fault curve in the fault curve library includes: Determining the similarity between the real-time operation curve and each of the fault curves in the fault curve library; The fault curve corresponding to the similarity that meets the preset condition is determined as the target fault curve.

7. The fault prediction method according to claim 2, characterized in that: Determining a current faulty component according to the target fault curve includes: Determine the faulty component corresponding to the target fault curve as the current faulty component; Correspondingly, the method further includes: determining the fault type of the current faulty component according to the fault type corresponding to the target fault curve.

8. A fault prediction device, characterized in that: include: An acquisition module, used to acquire real-time operation data of the electronic device and determine a real-time operation curve corresponding to the real-time operation data; A comparison module, used for comparing the normal operation curve corresponding to the real-time operation data with the real-time operation curve; A determination module, configured to determine a target fault curve according to a similarity between the real-time operation curve and each fault curve in a fault curve library when it is determined that the real-time operation curve deviates from the normal operation curve; An execution module is used to determine a current faulty component according to the target fault curve.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the fault prediction method as described in any one of claims 1-7.

10. A storage medium containing computer executable instructions, characterized in that: The computer executable instructions are used to perform the fault prediction method according to any one of claims 1 to 7 when executed by a computer processor.

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