A Fault Detection Method, Device, Equipment and Medium for a Rolling Bearing

Abstract

The present invention is applicable to the technical field of fault detection, and particularly relates to a fault detection method, device, equipment and medium for a rolling bearing. The operating state of a target rolling bearing is initially judged based on the collected state data and a preset data threshold. When it is judged that there is an abnormal situation with the target rolling bearing, the state data of the target rolling bearing between any two adjacent preset time periods is predicted based on the collected M state data sequences and a trained data prediction model, so as to complete the state data of the target rolling bearing within a target time interval, and a gradient sequence and the current fault degree are obtained by analyzing from the completed reference data sequence, and the real-time fault state and the development state of the fault of the target rolling bearing are analyzed, providing rich data in the real-time dimension and the development dimension for obtaining the target fault degree, thereby improving the accuracy of the fault detection result and helping to provide early warning information in a timely manner to improve the reliability and safety of the bearing system.

Description

Technical Field

[0001] The present invention is applicable to the technical field of fault detection, and particularly relates to a fault detection method, device, equipment and medium for rolling bearings. Background Art

[0002] As a key component widely used in mechanical equipment, the running state stability of a rolling bearing is directly related to the performance and reliability of the entire equipment. Therefore, it is crucial to accurately and timely detect the faults of rolling bearings.

[0003] Currently, in the field of rolling bearing fault detection, it is possible to determine whether a bearing has a fault by monitoring state data such as the vibration amplitude, vibration frequency, and bearing temperature generated during the operation of the rolling bearing. However, in actual application scenarios, the state data is usually collected at fixed time intervals, unable to accurately capture the subtle changes of the rolling bearing over time, and usually based on historical state data for fault diagnosis, lacking the ability to predict future states and comprehensive analysis of state data, making it difficult to timely evaluate the accurate degree of faults and unable to meet the accuracy requirements for bearing fault detection in various application fields.

[0004] Therefore, when performing fault detection on rolling bearings, how to improve the accuracy of fault detection results has become an urgent problem to be solved. Summary of the Invention

[0005] In view of this, embodiments of the present invention provide a fault detection method, device, equipment and medium for rolling bearings to solve the problem of low accuracy of fault detection results for rolling bearings.

[0006] In a first aspect, embodiments of the present invention provide a fault detection method for a rolling bearing, and the fault detection method for the rolling bearing includes:

[0007] Obtain a state data sequence of a target rolling bearing within M preset time periods in a target time interval, where each state data sequence includes state data corresponding to N preset time points, and M and N are integers greater than 1.

[0008] Compare the M state data sequences with a preset data threshold to obtain a predicted state corresponding to the target rolling bearing, where the predicted state includes a normal state and a state to be analyzed.

[0009] If the predicted state is a state to be analyzed, then according to the M state data sequences and a trained data prediction model, obtain a reference data sequence of the target rolling bearing within the target time interval, where the reference data sequence includes reference data corresponding to K preset time points, and K > M × N.

[0010] According to the reference data sequence, obtain the gradient sequence corresponding to the reference data sequence and the current fault degree.

[0011] According to the gradient sequence corresponding to the reference data sequence, obtain the fault development degree of the target rolling bearing.

[0012] According to the current fault degree and the fault development degree of the target rolling bearing, obtain the target fault degree of the target rolling bearing.

[0013] In a second aspect, an embodiment of the present invention provides a fault detection device for a rolling bearing. The fault detection device for the rolling bearing includes:

[0014] A data acquisition module, configured to acquire a state data sequence of a target rolling bearing within M preset time periods in a target time interval, where each state data sequence includes state data corresponding to N preset time points, and M and N are integers greater than 1.

[0015] A state prediction module, configured to compare the M state data sequences with a preset data threshold to obtain a predicted state of the target rolling bearing, where the predicted state includes a normal state and a state to be analyzed.

[0016] A data prediction module, configured to, if the predicted state is the state to be analyzed, obtain a reference data sequence of the target rolling bearing within the target time interval according to the M state data sequences and a trained data prediction model, where the reference data sequence includes reference data corresponding to K preset time points, and K > M × N.

[0017] A data analysis module, configured to obtain a gradient sequence corresponding to the reference data sequence and the current fault degree according to the reference data sequence.

[0018] A fault analysis module, configured to obtain the fault development degree of the target rolling bearing according to the gradient sequence corresponding to the reference data sequence.

[0019] A fault detection module, configured to obtain the target fault degree of the target rolling bearing according to the current fault degree and the fault development degree of the target rolling bearing.

[0020] In a third aspect, an embodiment of the present invention provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the fault detection method for a rolling bearing as in the first aspect is implemented.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the fault detection method for a rolling bearing as in the first aspect is implemented.

[0022] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: The operating state of the target rolling bearing is initially judged based on the collected state data and the preset data threshold. When it is judged that there is an abnormal situation with the target rolling bearing, the state data of the target rolling bearing between any two adjacent preset time periods is predicted based on the collected M state data sequences and the trained data prediction model, so as to complete the state data of the target rolling bearing within the target time interval, and the gradient sequence and the current fault degree are obtained by analyzing the completed reference data sequence, and the real-time fault state and the development state of the fault of the target rolling bearing are analyzed, providing rich data in the real-time dimension and the development dimension for obtaining the target fault degree, thereby improving the accuracy of the fault detection result. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain other drawings without creative efforts based on these drawings.

[0024] Figure 1 It is a schematic diagram of an application environment of a fault detection method for a rolling bearing provided in Embodiment 1 of the present invention;

[0025] Figure 2 It is a schematic flowchart of a fault detection method for a rolling bearing provided in Embodiment 1 of the present invention;

[0026] Figure 3 It is a schematic structural diagram of a fault detection device for a rolling bearing provided in Embodiment 2 of the present invention;

[0027] Figure 4 It is a schematic structural diagram of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. However, those skilled in the art should clearly understand that the present invention can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present invention.

[0029] It should be understood that when used in the specification of the present invention and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their combinations.

[0030] It should also be understood that the term "and / or" as used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0031] As used in the specification of the present invention and the appended claims, the term "if" may be construed, depending on the context, as "when", "once", "in response to determining", or "in response to detecting". Similarly, the phrases "if determined" or "if [the described condition or event] is detected" may be construed, depending on the context, as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]".

[0032] In addition, in the description of the specification of the present invention and the appended claims, the terms "first", "second", "third", etc. are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0033] Reference to "one embodiment" or "some embodiments" or the like described in the specification of the present invention means that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of the present invention. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification are not necessarily all referring to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in another way. The terms "comprising", "including", "having" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in another way.

[0034] Embodiments of the present invention may acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results.

[0035] The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0036] It should be understood that the magnitudes of the sequence numbers of the steps in the following embodiments do not mean the order of execution, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0037] In order to illustrate the technical solution of the present invention, the following will be described through specific embodiments.

[0038] A fault detection method for a rolling bearing provided in the first embodiment of the present invention can be applied in an application environment such as Figure 1 where the client communicates with the server. The client includes, but is not limited to, computer devices such as a palm computer, a desktop computer, a notebook computer, an ultra-mobile personal computer (UMPC), a netbook, a cloud computer device, and a personal digital assistant (PDA). The server can be implemented by an independent server or a server cluster composed of multiple servers.

[0039] See Figure 2 , which is a schematic flowchart of a fault detection method for a rolling bearing provided in the first embodiment of the present invention. The above-mentioned fault detection method for a rolling bearing can be applied to the Figure 1 client in, and the fault detection method for the rolling bearing may include the following steps:

[0040] S201, obtain a state data sequence of a target rolling bearing within M preset time periods in a target time interval, where each state data sequence includes state data corresponding to N preset time points, and M and N are integers greater than 1.

[0041] Wherein, the target rolling bearing refers to the rolling bearing that needs to be subjected to fault detection.

[0042] The target time interval refers to a specific time period during which the state of the rolling bearing needs to be monitored. For example, it can be 1 hour, 1 day, or 1 week. There are multiple preset time periods within the target time interval, which can be set by the implementer according to the actual situation to comprehensively capture the state changes of the rolling bearing at different operating stages.

[0043] The status data can be data such as vibration amplitude, vibration frequency, bearing temperature, bearing pressure, bearing speed, etc., which can characterize the working status of the rolling bearing, and can be collected by acquisition devices such as vibration acquisition devices, temperature acquisition devices, pressure acquisition devices, speed acquisition devices, etc. based on a certain time interval.

[0044] Optionally, the time interval between any two adjacent preset time periods is the first preset time interval T1, and the time interval between any two adjacent preset time points is the second preset time interval T2, where T1 = Q × T2, and Q is an integer greater than 1.

[0045] Among them, the selected acquisition device can execute the data acquisition task multiple times within the target time interval based on the first preset time interval, and when executing the data acquisition task each time, collect multiple status data within each preset time period based on the second preset time interval, as the data basis for fault detection of the rolling bearing.

[0046] The specific values of the first preset time interval T1 and the second preset time interval T2 can be set by the implementer according to the actual situation.

[0047] S202. Compare the M status data sequences with the preset data threshold to obtain the predicted status corresponding to the target rolling bearing, where the predicted status includes a normal status and a status to be analyzed.

[0048] Among them, the preset data threshold is a value determined through comprehensive consideration of various status data characteristics during the normal operation of the rolling bearing, through a large number of experiments, data analysis, and actual operation experience, etc., as a reference value for judging whether the operation status of the rolling bearing is normal. Correspondingly, when the status data exceeds the preset data threshold, it indicates that the operation status of the bearing may have abnormal conditions.

[0049] The predicted status is a preliminary judgment and classification of the current operation status of the target rolling bearing based on the comparison result between the M status data sequences and the preset data threshold. The normal status indicates that the operation condition of the target rolling bearing basically conforms to the characteristics of normal operation, and the status to be analyzed indicates that there are partial abnormal conditions in the operation condition of the target rolling bearing, and further analysis is required to clarify the fault condition of the target rolling bearing.

[0050] Optionally, comparing the M status data sequences with the preset data threshold to obtain the predicted status corresponding to the target rolling bearing includes:

[0051] Determine the time label corresponding to the i-th preset time period as i, where i = 1, 2,..., M.

[0052] For the i-th state data sequence, if the largest state data in the i-th state data sequence is greater than or equal to a preset data threshold, then determine the time label i corresponding to the i-th state data sequence as the target label.

[0053] Traverse all state data sequences to obtain all target labels.

[0054] If there are adjacent time labels among all the target labels, then determine the predicted state of the target rolling bearing as the state to be analyzed; otherwise, determine the predicted state of the target rolling bearing as the normal state.

[0055] Among them, if the largest state data in the i-th state data sequence is greater than or equal to the preset data threshold, it means that there is an abnormal situation of the target rolling bearing in the i-th preset time period.

[0056] Furthermore, if there are adjacent time labels among all the target labels, it means that there are abnormal situations of the target rolling bearing continuously for a period of time. Then, set the predicted state of the target rolling bearing as the state to be analyzed to further analyze and clarify the fault situation of the target rolling bearing.

[0057] As described above, based on the collected state data and the preset data threshold, the operating state of the target rolling bearing can be quickly and preliminarily judged, providing a basis for subsequent fault detection.

[0058] S203. If the predicted state is the state to be analyzed, then according to the M state data sequences and the trained data prediction model, obtain the reference data sequence of the target rolling bearing in the target time interval, where the reference data sequence includes the reference data corresponding to K preset time points, and K > M × N.

[0059] Among them, the trained data prediction model is trained through a large amount of historical state data of the rolling bearing. During the training process, various state data of the rolling bearing under normal operation and fault states can be used to enable the data prediction model to learn the internal laws and change patterns between different state data, so as to improve the generalization and accuracy of the trained data prediction model. For the state data sequence in the time series mode in this embodiment, the data prediction model can be a model structure such as a recurrent neural network or a long short-term memory network.

[0060] Since there is a first preset time interval between any two adjacent preset time periods, that is, the acquisition device does not acquire the state data of the target rolling bearing within the corresponding first preset time interval, it is difficult to characterize the complete working condition of the target rolling bearing within the target time interval only through the M state data sequences. Therefore, based on the trained data prediction model, this embodiment predicts the state data of the target rolling bearing between any two adjacent preset time periods based on the acquired M state data sequences, so as to complete the state data of the target rolling bearing within the target time interval, and obtain a reference data sequence composed of the reference data corresponding to K preset time points, which serves as the basis for fault detection of the target rolling bearing to improve the accuracy of the fault detection result.

[0061] Optionally, obtaining the reference data sequence of the target rolling bearing within the target time interval according to the M state data sequences and the trained data prediction model includes:

[0062] Input the M state data sequences into the trained data prediction model to obtain a prediction data sequence within any two adjacent preset time periods, where the preset data sequence includes the prediction data corresponding to Q preset time points.

[0063] Integrate the state data sequences within the M preset time periods and the prediction data sequences within any two adjacent preset time periods to obtain the reference data sequence of the target rolling bearing within the target time interval.

[0064] Wherein, since the first preset time interval T1 is equal to Q second preset time intervals T2, each prediction data sequence includes the prediction data corresponding to Q preset time points. Further, the M state data sequences correspond to M - 1 prediction data sequences. Therefore, the reference data sequence is integrally obtained from M×N state data and (M - 1)×Q prediction data in chronological order, that is, K = M×N+(M - 1)×Q.

[0065] As described above, based on the trained data prediction model, the state data of the target rolling bearing between any two adjacent preset time periods is predicted based on the acquired M state data sequences, so as to complete the state data of the target rolling bearing within the target time interval, providing richer and more detailed data support for more accurately judging the fault degree of the rolling bearing in the subsequent process and improving the accuracy of the fault detection result.

[0066] S204. Obtain the gradient sequence and the current fault degree corresponding to the reference data sequence according to the reference data corresponding to the K preset time points in the reference data sequence.

[0067] Among them, the gradient sequence can reflect the change rate of the reference data between adjacent time points, that is, it can reflect the change trend of the state of the target rolling bearing within the current time range. The current fault degree reflects the fault situation of the target rolling bearing obtained by intuitive analysis based on the collected state data, providing an important data basis for subsequent judgment of the target fault degree of the target rolling bearing.

[0068] Optionally, according to the reference data corresponding to K preset time points in the reference data sequence, obtaining the gradient sequence and the current fault degree corresponding to the reference data sequence includes:

[0069] Obtaining the 0th reference data, where the 0th reference data is equal to the 1st reference data.

[0070] According to the (i - 1)th reference data and the ith reference data in the reference data sequence, obtaining the ith data gradient corresponding to the reference data sequence, where i = 1, 2,..., M.

[0071] According to the 1st, 2nd,..., Mth data gradients corresponding to the reference data sequence, obtaining the gradient sequence corresponding to the reference data sequence.

[0072] Among them, the ith data gradient D i =(A i -A i-1 ) / T2, A i is the ith reference data, and A i-1 is the (i - 1)th reference data.

[0073] Arranging the calculated 1st to Mth data gradients in chronological order to form the gradient sequence corresponding to the reference data sequence. This gradient sequence records the change rate information of the reference data between adjacent time points, providing a data basis for analyzing the fault development of the target rolling bearing after the current time point.

[0074] Optionally, according to the reference data sequence, obtaining the gradient sequence and the current fault degree corresponding to the reference data sequence further includes:

[0075] Determining the average value of the reference data from the (K - P)th reference data to the Kth reference data in the reference data sequence as the target state data corresponding to the target rolling bearing.

[0076] According to the target state data and the preset data threshold, obtaining the current fault degree corresponding to the target rolling bearing.

[0077] Among them, in order to analyze the working state of the target rolling bearing at the current time point, this embodiment focuses on the reference data near the current time point. Therefore, the reference data from the (K - P)-th reference data to the K-th reference data in the reference data sequence is selected for average calculation to obtain the target state data to characterize the comprehensive state of the target rolling bearing in the recent period of time, so as to reduce the influence brought by data fluctuations and more accurately reflect the current operating state of the target rolling bearing.

[0078] Calculate the difference between the target state data and the preset data threshold, and determine the ratio of the difference to the preset data threshold as the current fault degree corresponding to the reference data sequence. Correspondingly, the larger the target state data, the greater the current fault degree.

[0079] Among them, the value of P determines the time range considered for analyzing the current fault degree, and the specific value of P can be set by the implementer according to the actual situation.

[0080] As described above, analyzing the gradient sequence and the current fault degree from the reference data sequence not only reflects the change rate of the reference data between adjacent time points, but also can characterize the working state of the target rolling bearing near the current time point, providing rich data in the real-time dimension and development dimension for judging the target fault degree of the target rolling bearing, thereby improving the accuracy of the fault detection result.

[0081] S205. Obtain the fault development degree corresponding to the target rolling bearing according to the gradient sequence corresponding to the reference data sequence.

[0082] Among them, a data gradient greater than 0 indicates that the corresponding reference data has an upward trend, and a data gradient less than 0 indicates that the corresponding reference data has a downward trend. Therefore, according to the data gradients in the gradient sequence, the change trend of the state data of the target rolling bearing can be analyzed, so as to measure the fault development degree of the target rolling bearing. Correspondingly, the larger the data gradient, the greater the fault severity trend of the target rolling bearing, that is, the more serious the fault condition of the target rolling bearing.

[0083] Optionally, obtaining the fault development degree corresponding to the target rolling bearing according to the gradient sequence corresponding to the reference data sequence includes:

[0084] Determine the (M - E)-th data gradient to the M-th data gradient in the gradient sequence as the 1st to (E + 1)-th reference gradients respectively, where E is an integer greater than 1.

[0085] Obtain the preset development weights corresponding to the 1st to (E + 1)-th reference gradients respectively.

[0086] Perform a weighted calculation on the first to the (E + 1)-th reference gradients and the preset development weights corresponding to the first to the (E + 1)-th reference gradients respectively to obtain the degree of fault development corresponding to the target rolling bearing.

[0087] Among them, in order to analyze the development of the working state of the target rolling bearing after the current time point, this embodiment focuses on the reference data near the current time point. Therefore, the data gradients from the (M - E)-th to the M-th in the gradient sequence are selected for analysis, and the corresponding preset development weights are assigned to perform a weighted calculation on the first to the (E + 1)-th reference gradients and the preset development weights corresponding to the first to the (E + 1)-th reference gradients respectively to obtain the degree of fault development corresponding to the target rolling bearing.

[0088] As time progresses, the preset development weights corresponding to the reference gradients gradually increase. The specific values of the preset development weights for each reference gradient can be set by the implementer according to the actual situation.

[0089] The value of E determines the time range considered for analyzing the degree of fault development, and the specific value of E can be set by the implementer according to the actual situation.

[0090] As described above, analyzing the degree of fault development corresponding to the target rolling bearing according to the gradient sequence corresponding to the reference data sequence characterizes the fault development situation of the target rolling bearing and provides a data basis for accurately measuring the severity of the fault of the target rolling bearing.

[0091] S206. Obtain the target fault degree corresponding to the target rolling bearing according to the current fault degree and the degree of fault development corresponding to the reference data sequence.

[0092] Among them, by the current fault degree and the degree of fault development corresponding to the reference data sequence, analyze the real-time fault state and the development state of the fault of the target rolling bearing, so as to more accurately characterize the fault situation of the target rolling bearing. Correspondingly, the greater the target fault degree, the higher the current fault degree and the subsequent fault severity of the rolling bearing. Then, relevant warning information of the target rolling bearing can be provided in a timely manner according to the target fault degree, further improving the reliability and safety of the bearing system.

[0093] As described above, by analyzing the real-time fault state and the development state of the fault of the target rolling bearing through the current fault degree and the gradient sequence corresponding to the reference data sequence, the detection accuracy of the target fault degree of the target rolling bearing is improved.

[0094] In the embodiment of the present invention, the operating state of the target rolling bearing is preliminarily judged based on the collected state data and the preset data threshold. When it is judged that there is an abnormal situation in the target rolling bearing, the state data between any two adjacent preset time periods of the target rolling bearing is predicted based on the collected M state data sequences and the trained data prediction model, so as to complete the state data of the target rolling bearing in the target time interval, and the gradient sequence and the current fault degree are obtained by analyzing from the completed reference data sequence, and the real-time fault state and the development state of the fault of the target rolling bearing are analyzed, providing rich data in the real-time dimension and the development dimension for obtaining the target fault degree, thereby improving the accuracy of the fault detection result.

[0095] Corresponding to the fault detection method of the rolling bearing in the above embodiment, Figure 3 The structural block diagram of the fault detection device of the rolling bearing provided in the second embodiment of the present invention is given. For the sake of convenience of description, only the parts related to the embodiment of the present invention are shown.

[0096] See Figure 3 , the fault detection device of the rolling bearing includes:

[0097] A data acquisition module 31, configured to acquire M state data sequences of the target rolling bearing within M preset time periods in the target time interval, where each state data sequence includes state data corresponding to N preset time points, and M and N are integers greater than 1.

[0098] A state prediction module 32, configured to compare the M state data sequences with the preset data threshold to obtain the predicted state corresponding to the target rolling bearing, where the predicted state includes a normal state and a state to be analyzed.

[0099] A data prediction module 33, configured to, if the predicted state is a state to be analyzed, obtain a reference data sequence of the target rolling bearing in the target time interval according to the M state data sequences and the trained data prediction model, where the reference data sequence includes reference data corresponding to K preset time points, and K > M×N.

[0100] A data analysis module 34, configured to obtain the gradient sequence and the current fault degree corresponding to the reference data sequence according to the reference data sequence.

[0101] A fault analysis module 35, configured to obtain the fault development degree corresponding to the target rolling bearing according to the gradient sequence corresponding to the reference data sequence.

[0102] A fault detection module 36, configured to obtain the target fault degree corresponding to the target rolling bearing according to the current fault degree and the fault development degree corresponding to the target rolling bearing.

[0103] Optionally, the state prediction module 32 includes:

[0104] A time label determination sub-module, configured to determine the time label corresponding to the i-th preset time period as i, where i = 1, 2, ……, M.

[0105] A target label determination sub-module, for the i-th state data sequence, if the maximum state data in the i-th state data sequence is greater than or equal to a preset data threshold, then determine the time label i corresponding to the i-th state data sequence as the target label.

[0106] A data sequence traversal sub-module, configured to traverse all state data sequences to obtain all target labels.

[0107] A state prediction sub-module, configured to if there are adjacent time labels among all target labels, then determine the predicted state of the target rolling bearing as the state to be analyzed, otherwise, determine the predicted state of the target rolling bearing as the normal state.

[0108] Optionally, the data prediction module 33 includes:

[0109] A data prediction sub-module, configured to input M state data sequences into a trained data prediction model to obtain predicted data sequences within any two adjacent preset time periods, where the preset data sequence includes predicted data corresponding to Q preset time points.

[0110] A data integration sub-module, configured to integrate the state data sequences within M preset time periods and the predicted data sequences within any two adjacent preset time periods to obtain a reference data sequence of the target rolling bearing within the target time interval.

[0111] Optionally, the data analysis module 34 includes:

[0112] A reference data setting sub-module, configured to obtain the 0-th reference data, where the 0-th reference data is equal to the 1-st reference data.

[0113] A data gradient calculation sub-module, configured to obtain the i-th data gradient corresponding to the reference data sequence according to the (i - 1)-th reference data and the i-th reference data in the reference data sequence, where i = 1, 2, ……, M.

[0114] A gradient sequence acquisition sub-module, configured to obtain the gradient sequence corresponding to the reference data sequence according to the 1st, 2nd, ……, M-th data gradients corresponding to the reference data sequence.

[0115] Optionally, the data analysis module 34 further includes:

[0116] A target status data determination sub-module, configured to determine the average value of the (K - P)-th to K-th reference data in the reference data sequence as the target status data corresponding to the target rolling bearing, where P is an integer greater than 1.

[0117] A current fault degree acquisition sub-module, configured to obtain the current fault degree corresponding to the target rolling bearing according to the target status data and a preset data threshold.

[0118] Optionally, the fault analysis module 35 includes:

[0119] A reference gradient acquisition sub-module, configured to determine the (M - E)-th to M-th data gradients in the gradient sequence as the 1st to (E + 1)-th reference gradients respectively, where E is an integer greater than 1.

[0120] A preset development weight acquisition sub-module, configured to respectively obtain the preset development weights corresponding to the 1st to (E + 1)-th reference gradients.

[0121] A fault development degree acquisition sub-module, configured to perform weighted calculation on the 1st to (E + 1)-th reference gradients and the preset development weights respectively corresponding to the 1st to (E + 1)-th reference gradients, to obtain the fault development degree corresponding to the target rolling bearing.

[0122] It should be noted that for the information interaction, execution process, etc. among the above modules, since they are based on the same concept as the method embodiment of the present invention, for their specific functions and the technical effects brought, reference can be specifically made to the method embodiment part, and details are not described herein again.

[0123] Figure 4 This is a schematic structural diagram of a computer device provided in Embodiment 3 of the present invention. As Figure 4 shown, the computer device of this embodiment includes: at least one processor ( Figure 4 only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor. When the processor executes the computer program, it implements the steps in any of the above method embodiments for fault detection of rolling bearings.

[0124] This computer device may include, but is not limited to, a processor and a memory. Those skilled in the art can understand that Figure 4 this is only an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than shown in the figure, or combine certain components, or different components. For example, it may also include a network interface, a display screen, and an input device, etc.

[0125] The so-called processor may be a CPU, and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0126] The memory includes a readable storage medium, internal memory, etc. Among them, the internal memory may be the memory of the computer device, and the internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be the hard disk of the computer device, and in some other embodiments, it may also be an external storage device of the computer device. For example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Further, the memory may also include both the internal storage unit of the computer device and the external storage device. The memory is used to store the operating system, application programs, boot loaders, data, and other programs, such as the program code of computer programs. The memory may also be used to temporarily store data that has been output or will be output.

[0127] Those skilled in the art can clearly understand that, for the convenience and conciseness of description, only the above division of each functional unit and module is used as an example. In practical applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiments can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present invention. The specific working processes of the units and modules in the above device can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above method embodiments of the present invention, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The computer-readable medium can at least include: any entity or device capable of carrying the computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0128] All or part of the processes in the above method embodiments of the present invention can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can be made to execute the steps in the above method embodiments.

[0129] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] One of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0131] In the embodiments provided by the present invention, it should be understood that the disclosed device / computer equipment and method can be implemented in other ways. For example, the device / computer equipment embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be electrical, mechanical or other forms.

[0132] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0133] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, one of ordinary skill in the art should understand that: it is still possible to modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A fault detection method for a rolling bearing, characterized in that The fault detection method of the rolling bearing includes: Obtaining state data sequences of a target rolling bearing within M preset time periods in a target time interval, where each state data sequence includes state data corresponding to N preset time points, and M and N are integers greater than 1; the time interval between any two adjacent preset time periods is a first preset time interval T1, and the time interval between any two adjacent preset time points is a second preset time interval T2, where T1 = Q × T2, and Q is an integer greater than 1; Comparing the M state data sequences with a preset data threshold to obtain a predicted state corresponding to the target rolling bearing, where the predicted state includes a normal state and a state to be analyzed; If the predicted state is the state to be analyzed, then according to the M state data sequences and a trained data prediction model, a reference data sequence of the target rolling bearing within the target time interval is obtained, where the reference data sequence includes reference data corresponding to K preset time points, K > M × N; the reference data sequence is the state data of the target rolling bearing between any two adjacent preset time periods; K = M × N+(M - 1)×Q; According to the reference data sequence, a gradient sequence and a current fault degree corresponding to the reference data sequence are obtained; According to the gradient sequence corresponding to the reference data sequence, a fault development degree corresponding to the target rolling bearing is obtained; According to the current fault degree and the fault development degree corresponding to the target rolling bearing, a target fault degree corresponding to the target rolling bearing is obtained.

2. The fault detection method for a rolling bearing according to claim 1, characterized in that, The comparing the M state data sequences with a preset data threshold to obtain a predicted state corresponding to the target rolling bearing includes: Determining the time label corresponding to the i-th preset time period as i, where i = 1, 2,..., M; For the i-th state data sequence, if the maximum state data in the i-th state data sequence is greater than or equal to the preset data threshold, then determining the time label i corresponding to the i-th state data sequence as a target label; Traversing all the state data sequences to obtain all the target labels; If there are adjacent time labels among all the target labels, then determining the predicted state corresponding to the target rolling bearing as the state to be analyzed, otherwise, determining the predicted state corresponding to the target rolling bearing as the normal state.

3. The fault detection method for a rolling bearing according to claim 1, wherein The obtaining a reference data sequence of the target rolling bearing within the target time interval according to the M state data sequences and a trained data prediction model includes: Inputting the M state data sequences into the trained data prediction model to obtain prediction data sequences within any two adjacent preset time periods, where the prediction data sequences include prediction data corresponding to Q preset time points; Integrating the state data sequences within the M preset time periods and the prediction data sequences within any two adjacent preset time periods to obtain the reference data sequence of the target rolling bearing within the target time interval.

4. The fault detection method for a rolling bearing according to claim 1, wherein The obtaining a gradient sequence and a current fault degree corresponding to the reference data sequence according to the reference data sequence includes: The 0th reference data is obtained, where the 0th reference data is equal to the 1st reference data; According to the (i - 1)th reference data and the ith reference data in the reference data sequence, the ith data gradient corresponding to the reference data sequence is obtained, where i = 1, 2, ……, M; According to the 1st, 2nd, ……, Mth data gradients corresponding to the reference data sequence, the gradient sequence corresponding to the reference data sequence is obtained.

5. The fault detection method of a rolling bearing according to claim 4, characterized in that The step of obtaining the gradient sequence and the current fault degree corresponding to the reference data sequence according to the reference data sequence further includes: The average value of the (K - P)th reference data to the Kth reference data in the reference data sequence is determined as the target state data corresponding to the target rolling bearing, where P is an integer greater than 1; According to the target state data and the preset data threshold, the current fault degree corresponding to the target rolling bearing is obtained.

6. The fault detection method of the rolling bearing according to claim 4, wherein, The step of obtaining the fault development degree corresponding to the target rolling bearing according to the gradient sequence corresponding to the reference data sequence includes: The (M - E)th data gradient to the Mth data gradient in the gradient sequence are respectively determined as the 1st to the (E + 1)th reference gradients, where E is an integer greater than 1; The preset development weights corresponding to the 1st to the (E + 1)th reference gradients are respectively obtained; The weighted calculation is performed on the 1st to the (E + 1)th reference gradients and the preset development weights corresponding to the 1st to the (E + 1)th reference gradients respectively, to obtain the fault development degree corresponding to the target rolling bearing.

7. A fault detection device for a rolling bearing, characterized in that, The fault detection device of the rolling bearing includes: A data acquisition module, configured to acquire a state data sequence of a target rolling bearing within M preset time periods in a target time interval, where each state data sequence includes state data corresponding to N preset time points, and M and N are integers greater than 1; the time interval between any two adjacent preset time periods is a first preset time interval T1, and the time interval between any two adjacent preset time points is a second preset time interval T2, where T1 = Q × T2, and Q is an integer greater than 1; A state prediction module, configured to compare the M state data sequences with a preset data threshold to obtain the predicted state corresponding to the target rolling bearing, where the predicted state includes a normal state and a state to be analyzed; A data prediction module, configured to, if the predicted state is the state to be analyzed, obtain a reference data sequence of the target rolling bearing within the target time interval according to the M state data sequences and a trained data prediction model, where the reference data sequence includes reference data corresponding to K preset time points, K > M × N; the reference data sequence is the state data of the target rolling bearing between any two adjacent preset time periods; K = M × N+(M - 1)×Q; A data analysis module, configured to obtain the gradient sequence and the current fault degree corresponding to the reference data sequence according to the reference data sequence; A fault analysis module, configured to obtain the fault development degree corresponding to the target rolling bearing according to the gradient sequence corresponding to the reference data sequence; A fault detection module, configured to obtain a target fault level corresponding to the target rolling bearing according to the current fault level and the fault development level corresponding to the target rolling bearing.

8. A computer device, characterized in that, The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the fault detection method of the rolling bearing according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, the fault detection method of the rolling bearing according to any one of claims 1 to 6 is implemented.

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