211 results about "Failure diagnosis" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a fault diagnosis and recovery verification method and device, equipment and a medium. Inputting the candidate event and the context state into an intelligent diagnosis model to obtain a diagnosis result and a fault type, selecting a recovery path in a recovery strategy library based on the diagnosis result, generating a recovery execution instruction, creating a verification experiment based on the instruction, and injecting drill data to obtain verification data, and forming a verification conclusion according to the verification data, associating a recovery execution instruction, and issuing the instruction in the execution environment to complete recovery operation. According to the method, through multi-source monitoring data processing, intelligent diagnosis model reasoning, strategy library path selection, verification experiment verification and execution environment automatic recovery, a full-link automatic process from detection, diagnosis, recovery to verification is constructed, and accurate diagnosis and dynamic recovery in a complex environment are achieved.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention relates to the technical field of equipment fault intelligent diagnosis, in particular to an equipment fault intelligent diagnosis method and system based on a Modbus protocol. The method comprises the following steps: acquiring real-time operation data from target industrial equipment through a Modbus protocol, dynamically adjusting an initial sampling frequency based on an equipment operation state, and performing multiple verification and compensation correction on the acquired data to obtain a stable data stream; performing multi-scale decomposition and feature enhancement processing on the stable data stream, extracting a time-frequency domain mixed feature set, and constructing a feature evolution trajectory; inputting the feature evolution trajectory into a double-branch diagnosis model integrating equipment state prediction and fault classification, and outputting an equipment health degree score and fault type probability distribution; and constructing a dynamic fault threshold curved surface, carrying out multi-dimensional fusion decision by combining the equipment health degree score and the fault type probability distribution, and generating a graded fault early warning and maintenance strategy. According to the invention, the accuracy, timeliness and adaptability of industrial equipment fault diagnosis can be greatly improved.

The invention discloses a cascade fault diagnosis method based on a root cause intensity power set belief rule base. According to the technical scheme, the method comprises the steps of 1, constructing a power set belief rule base; the conclusion hypothesis unit can clearly describe a single-point fault state, a composite fault state and a causal chain fault state. And 2, introducing a root cause strength parameter: introducing the root cause strength parameter into the conclusion hypothesis unit constructed in the step 1 for quantifying the causal influence capability of the fault state as the root cause. And step 3, reasoning fusion: calculating the matching degree and the activation degree of each rule, and applying an evidence theory-based reasoning fusion algorithm to an output result to obtain power set confidence distribution of each fault hypothesis. And step 4, fault diagnosis and fault root cause positioning are carried out, and a main fault root cause is output, and the main fault root cause is the root cause with the maximum root cause strength. The method is mainly used for realizing accurate diagnosis of cascade faults in a complex system by popularizing a traditional BRB model to power set reasoning and introducing a root cause strength concept.

The invention discloses a punching machine bearing fault diagnosis method and equipment. The method comprises the following steps: establishing a diagnosis mechanism fusing time-frequency domain feature extraction and multi-branch depth feature modeling; original vibration signals are respectively converted into GADF images to capture time sequence dependence, CWT is adopted to extract transient frequency change, STFT is utilized to obtain frequency spectrum characteristics under a stable working condition, and joint input of a multi-view time-frequency graph is realized. On a model structure, a Swin Transform branch is designed for local space detail modeling, a global perception branch is constructed by combining a CNN module and a GAM module, and multi-category fault classification is completed through self-adaptive pooling and a full connection layer on fusion output. User-defined data loading and a standardized process guarantee the consistency of training data, multi-core maximum mean difference is introduced for feature distribution alignment, and the migration ability of the model under different working conditions and the decoupling recognition ability of composite faults are improved.

The invention discloses a fault diagnosis method and device and a computer readable storage medium. The fault diagnosis method comprises the steps that hardware layer data, system layer data and application layer data are acquired; based on the hardware layer data, the system layer data and the application layer data, constructing fusion feature information; performing fault diagnosis based on the fused feature information to obtain fault diagnosis information; and generating a fault processing strategy based on the fault diagnosis information. According to the method, the multi-dimensional data of the hardware layer, the system layer and the application layer are acquired, the fusion feature information is constructed to perform fault diagnosis and generate the processing strategy, so that the performance of the fault at different levels can be comprehensively captured, the accuracy of positioning the fault reason is improved, missed diagnosis and misdiagnosis are reduced, the comprehensiveness and accuracy of fault diagnosis are improved, and the fault diagnosis efficiency is improved. Meanwhile, a user can conveniently and autonomously solve faults.

The invention discloses a fault diagnosis method and system for a rotating machine bearing, and belongs to the technical field of fault diagnosis. The method comprises the following steps: acquiring a to-be-diagnosed vibration signal of the rotary mechanical bearing; inputting the to-be-diagnosed vibration signal into a pre-constructed bearing fault diagnosis model, and outputting a fault diagnosis result of the rotating machine bearing; the bearing fault diagnosis model comprises a feature extraction network, a GMM feature enhancement module, a projection distillation module and a dynamic extensible classifier which are connected in sequence. According to the method, catastrophic forgetting can be effectively relieved, continuous diagnosis of newly-occurring accidental faults can be achieved, in the initial task, the model learns and identifies different fault types through a fault bearing data set collected in advance, a basis is provided for the subsequent increment task, the increment task is composed of a plurality of stages, and in each stage, the fault bearing data set is subjected to fault detection. The model enhances playback of old knowledge through historical category pseudo features generated by a Gaussian mixture model, and aligns feature representations of new and old models by using a projection distillation module.

The invention discloses a carrier roller fault diagnosis method based on aliasing period fusion features, and particularly relates to the field of roller fault diagnosis. According to the scheme, the operation data of the carrier roller of the conveyor are collected through the sensor, and the collected original sensing data are preprocessed; the method comprises the following steps: dividing carrier roller sensing data into samples based on sliding window sampling, and dividing into a training set, a verification set and a test set according to 6: 3: 1; a carrier roller fault diagnosis model based on the Times-SCConv network is constructed, and parameters are initialized; training and verifying the Times-SCConv network based on the training set and the verification set, optimizing network parameters by using an optimization algorithm, and storing an optimal weight; and inputting the test set into the optimal Times-SCConv network model to carry out carrier roller fault diagnosis, and finally obtaining a carrier roller fault classification result and test accuracy. According to the technical scheme, the problem that fault feature extraction is difficult when sensing data signals of the carrier roller are subjected to aliasing coupling is solved, and modeling extraction of carrier roller data periodic change features is achieved.

The invention discloses a composite fault diagnosis method for motor overload and pressure abnormity of a wind power water cooling system. The method comprises the following steps: S1, data acquisition; the collected original signal data is preprocessed; s2, constructing a lightweight system performance model, and calculating a pressure residual error; s3, extracting time domain and frequency domain features from the original signal data and the residual signals to form feature vectors; s4, performing fault diagnosis based on a machine learning model, and inputting the feature vector into a trained model; the model outputs a diagnosis result and gives a fault confidence coefficient; and S5, outputting a result, and performing early warning. Predictive maintenance is achieved, early warning can be given out at the early stage of a fault when shutdown or secondary damage is not caused, a maintenance plan can be made in advance, passive first-aid repair is changed into active planning, the non-planned shutdown time is shortened, and the availability of a wind field is improved; the unnecessary on-site inspection frequency is reduced, the excessive dependence on the qualification operation and maintenance experts is reduced, and the troubleshooting time is shortened.

The invention relates to a harvester fault diagnosis method and device, electronic equipment and a storage medium. The method comprises the steps that fault information input by a user is acquired; carrying out vectorization coding on the fault information to obtain a query vector; according to the query vector, a preliminary retrieval result is obtained from a pre-constructed knowledge base, and the preliminary retrieval result comprises at least two fault-related historical cases corresponding to the query vector; and obtaining a fault diagnosis report through a pre-trained retrieval enhancement model according to the at least two historical cases and the query vector. By means of the method, the generated fault diagnosis report is comprehensive in content and clear in structure, explainable basis based on real cases can be provided, the reliability and operability of maintenance suggestions are enhanced, and finally the fault processing efficiency is improved, the misdiagnosis rate is reduced, and the downtime is shortened.

Embodiments of the present disclosure provide a fault diagnosis method and apparatus for service failure, and a storage medium. The method includes: acquiring abnormal information to be diagnosed described in a natural language; retrieving, from a fault knowledge base, similar target known fault knowledge based on the abnormal information to be diagnosed, where the fault knowledge base is used to maintain a plurality of sets of known fault knowledge; constructing a first fault analysis instruction of the abnormal information to be diagnosed according to the target known fault knowledge and the abnormal information to be diagnosed; and invoking a language model to take the first fault analysis instruction as an input, and output a fault analysis result of the abnormal information to be diagnosed.

The invention provides a ballast water emergency receiving and processing fault diagnosis method and system based on a knowledge graph, and the method comprises the steps: obtaining the fault performance, real-time operation and medium characteristic information of a ballast water emergency receiving and processing system, constructing a dynamic association link in a pre-configured knowledge association system, the knowledge association system comprises fault performance, causes, component association, medium influence nodes and dynamic association relationships, and is adjusted in real time along with the change of medium characteristics. And performing multipath fault source deduction along the dynamic association link, positioning fault cause nodes, and calling association processing operation node information to generate a fault diagnosis scheme including fault source description, processing flow and medium regulation and control suggestions. And finally, integrating implementation feedback information and fault data, and updating a node association relationship. According to the invention, the accuracy and comprehensiveness of fault diagnosis are improved.

The invention discloses a new energy vehicle fault diagnosis training system based on digital twinning, and the system comprises a data collection and assimilation module which is used for collecting vehicle operation data, and constructing a digital twinning body; the mechanism map construction module is used for constructing a causal failure mechanism map and generating a failure scene; the sample generation and management module is used for generating a virtual domain sample set, a disturbance domain sample set and a real domain sample set, and combining to form a training data set; the residual fusion diagnosis model module is used for setting a physical residual branch and a disturbance residual branch and outputting a fault diagnosis result; the consistency alignment training module is used for constructing joint loss and optimizing the residual fusion diagnosis model; and the intelligent diagnosis and simulation module is used for executing real-time diagnosis and risk prediction, generating a simulation drilling script and training maintenance personnel. According to the invention, through digital twinning, residual fusion modeling and three-dimensional consistency training, accurate diagnosis and safe and efficient training of new energy vehicle faults are realized.

The invention discloses a mobile application function exception processing method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the parallel monitoring of a system resource state and business behavior data through a buried point probe, generating a structured log with an exception type identifier, and storing the structured log in a local database; when an exception is responded, triggering an associated diagnostor based on an exception type identifier in the log, and analyzing log data through a predefined rule to judge an exception level; and executing the sub-path response according to the grade. According to the method, the problems of slow response and fuzzy positioning of manual operation and maintenance are solved through probe collaborative collection, dynamic grading of the diagnostor and automatic response closed loop, the key anomaly recognition accuracy is improved, and automatic fault diagnosis and user consultation processing are achieved.

The invention discloses a CAN card control method and system based on fault diagnosis, and relates to the technical field of electronic information, the system is composed of a plurality of functional modules, and the system comprises a feature sensing prediction module which dynamically adjusts the sampling frequency based on a bus load, collects multi-dimensional data, fuses the data into time-space correlation features, and sends the features to a server; inputting the space-time correlation characteristics into a lightweight adaptive attention LSTM model, and outputting a real-time fault prediction probability and time delay; the optimization control module obtains a fault prediction probability, constructs a multi-objective optimization strategy based on different intervals of the fault prediction probability, and adaptively adjusts CAN card control parameters according to the multi-objective optimization strategy, and the CAN card control parameters comprise a CAN Carport rate, a data priority and a working mode; collecting the bit error rate, the data transmission time delay deviation and the channel equipment temperature of a channel, performing normalization processing, and performing weighted summation according to a preset weight to obtain a channel health degree; and a closed loop adaptation module.

The invention relates to the technical field of oil-gas field oil well fault diagnosis, in particular to an oil-gas field oil well intelligent fault diagnosis method and device based on a swan-gap system. According to the method, the multi-mode equipment is connected through the swan-Mongolia distributed soft bus to collect data, and the edge computing nodes are deployed to perform local diagnosis, so that the dependence on the network is reduced, and the real-time performance of fault diagnosis is improved; a generalized Choquet integral synthesis model is used for fusing load displacement, acoustic emission and motor electrical parameter sub-risk values, the fault risk is comprehensively evaluated, the diagnosis accuracy is improved, and the missed diagnosis and misdiagnosis probability is reduced; self-inspection data such as hardware state and signal quality are collected through the sensor self-inspection layer, a fusion instruction is generated through the data credibility analysis layer to adjust data weight, a fault sensor is isolated, and data reliability is guaranteed; based on hardware and communication parameter analysis of network transmission data, a network adaptation execution instruction is dynamically generated, stable data transmission is ensured, and the anti-interference capability of the system is improved.

The invention discloses a medical equipment remote fault diagnosis, maintenance and guidance system based on a cloud platform. According to the invention, the deep reconstruction of the medical equipment maintenance ecology is realized by constructing the digital service network covering the whole process. The AR cooperation platform of the maintenance engineer end and the real-time data intercommunication of the hospital end break through the space-time limitation of technical support in a traditional maintenance mode, so that remote experts can directly participate in field fault diagnosis and cooperate with historical maintenance archives and fault tree analysis provided by the intelligent work order system, and the first repair rate is greatly improved. An intelligent order sending algorithm of the management background is combined with an order grabbing mechanism of the mobile terminal, so that the geographical distribution efficiency of engineer resources is optimized, precise docking is realized through skill label matching, the industrial pain point of scarcity of special equipment maintenance resources is solved, and the service life of the mobile terminal is prolonged. Modular design and data interconnection characteristics bring systematic improvement to medical equipment operation and maintenance management, and fine control of equipment management cost and continuous improvement of service quality are realized.

The embodiment of the invention provides an agent reflection method and device. The method comprises the following steps: determining a failure diagnosis result corresponding to a cooperation failure trajectory of a plurality of agents, wherein the failure diagnosis result comprises a key failure step in the cooperation failure trajectory and a corresponding responsible agent; based on the failure diagnosis result, determining a correction result corresponding to the key failure step through anti-factual reasoning; and generating a reflection for the responsibility agent based on the failure diagnosis result and the correction result. According to the technical scheme provided by the embodiment of the invention, the targeted correction suggestions can be generated only for the responsibility agent in the decisive error step and the reflection is carried out, so that the reflection efficiency and the reflection quality of the multi-agent system are improved.

The invention discloses a fault diagnosis method and system for hydraulic element general equipment, and relates to the technical field of hydraulic element fault diagnos.The method comprises the steps that control channels of the general equipment are recognized based on hydraulic elements, transient signals are collected in all the control channels in real time, preset interference constraints are introduced, and fault diagnosis is conducted on the general equipment; generating an interference factor through a high-entropy random disturbance source, performing disturbance analysis on the amplitude, the frequency and the waveform state of the transient signal, and generating an interference identifier; a health state vector is formed in combination with the operation working state, the deviation degree of the health state vector and the health baseline characteristic spectrum is compared, if the deviation degree is smaller than a standard deviation threshold value, a state evaluation strategy is executed in the first diagnosis channel, and a health stability trend is recognized; if the deviation degree is greater than or equal to the standard deviation threshold value, executing a fault diagnosis strategy in the second diagnosis channel, and identifying a fault sign; according to the invention, efficient division of labor of health assessment and fault diagnosis is realized, and the effect of accurately identifying fault signs is achieved.

The invention discloses a rolling bearing fault diagnosis method and device based on characteristic mode decomposition and a medium, and relates to the technical field of bearing diagnosis. The method comprises the following steps: acquiring an original vibration signal of a rolling bearing, and obtaining a modal component through characteristic modal decomposition; a comprehensive fitness function is constructed, an optimal parameter combination of characteristic modal decomposition is calculated through a hunting optimization algorithm, and the comprehensive fitness function comprises a kurtosis value of a modal component, an envelope spectrum Gini coefficient and an autocorrelation function peak value; decomposing the vibration signal by using the optimal input parameter combination to obtain a plurality of modal components, and taking a kurtosis value as a screening index; selecting the modal signal with the maximum kurtosis value as a target modal component; and performing envelope demodulation on the target modal component, and extracting fault feature information to obtain the fault type of the rolling bearing. According to the method, the problem that multiple parameters of feature mode decomposition depend on artificial experience is solved, the fault recognition result is accurate and efficient, and certain noise robustness is achieved.

The invention discloses an intelligent diagnosis and maintenance decision-making method and system for power equipment, and the method comprises the steps: carrying out the fault diagnosis of different diagnosis targets for the monitoring data of the power equipment through a plurality of professional models, and obtaining a plurality of diagnosis results; calling a state comprehensive evaluation agent constructed based on a large model to perform equipment state diagnosis on the plurality of diagnosis results to obtain a comprehensive state diagnosis result of the power equipment; calling a maintenance strategy generation agent constructed based on a large model to carry out maintenance decision on the comprehensive state diagnosis result to obtain a maintenance strategy of the power equipment; according to the invention, comprehensive monitoring and intelligent analysis of the equipment state can be realized, the diagnosis efficiency and accuracy of the power equipment state are improved, and the increasingly complex intelligent diagnosis requirement of the power equipment state can be met, so that the stable operation of a power system is ensured.

The invention relates to the field of equipment diagnosis, and discloses a submersible fault time sequence causal chain diagnosis method and system, and the method comprises the steps: obtaining multi-dimensional operation monitoring data in the operation process of a submersible; performing exception association analysis on the multi-dimensional operation monitoring data to obtain a cross-dimensional collaborative exception coefficient of an association unit group under each monitoring dimension; performing threshold comparison on all the cross-dimension collaborative anomaly coefficients, and determining each anomaly association unit group; and determining a diagnosis dimension trigger signal of the submersible according to the number of the abnormal association unit groups and the evolution characteristics of the cross-dimension collaborative abnormal coefficients of all the abnormal association unit groups. And a diagnosis dimension trigger signal is dynamically generated through the change and evolution characteristics of the cross-dimension collaborative abnormal coefficient, so that fault diagnosis scheduling of the submersible is triggered, and the response mechanism ensures the flexibility and adaptability in the diagnosis process.

In some implementations, a fault diagnosis system may track information associated with a first execution of a code pipeline to deploy an updated version of software associated with the code pipeline. The fault diagnosis system may receive an indication of a failure in a second execution of the code pipeline, wherein the second execution of the code pipeline is triggered by a commit of source code to a source code repository. The fault diagnosis system may determine whether the updated version of the software associated with the code pipeline or the source code committed to the source code repository is a cause of the failure in the second execution of the code pipeline. The fault diagnosis system may send a request to plan code development to resolve the cause of the failure in the second execution of the code pipeline.

The embodiment of the application discloses a kind of diagnostic process abnormality monitoring method, device, medium, OBD module and vehicle;Based on abnormality monitoring method, the leakage failure that can occur in fault diagnosis process is included in the main object of monitoring, by the signal monitoring of the object to be monitored, especially the signal monitoring of the evaporation system of fuel vehicle, on the basis of analyzing signal characteristics, with existing hardware structure, the abnormality monitoring of fault diagnosis system is realized;In addition, the related scheme is also applicable to the development of computer storage medium, OBD module and intelligent vehicle, and the upgrading of original system can be conveniently realized by the upgrading of control software.

The application discloses an auditing method and device for power transmission line fault reports and equipment thereof, relates to the technical field of report auditing, and comprises the following steps: obtaining an original fault report obtained after fault diagnosis on a power transmission line; performing data consistency auditing on the original fault report; when it is determined that the report data in the original fault report is consistent with the attachment materials corresponding to the original fault report, performing suggestion auditing on a preliminary fault report that passes the data consistency auditing; when it is determined that the treatment suggestion in the preliminary fault report meets the requirements of a preset judgment standard, performing multi-dimensional comprehensive auditing on a second-round fault report that passes the suggestion auditing, and obtaining a target fault report with procedure traceability. The method avoids the false parameters or misuse of professional terms caused by the pursuit of fluent text by a large model, reduces the burden of artificial auditing and the probability of misjudgment, and improves the auditing accuracy.

The invention discloses a combined electric appliance state diagnosis method based on SF6 pressure data, and belongs to the technical field of power equipment state detection, and the method comprises the following steps: S1, obtaining an SF6 pressure monitoring data sequence of a target combined electric appliance gas chamber in a preset time period; s2, performing normalization processing on the SF6 pressure monitoring data sequence to obtain a normalized pressure data sequence; s3, performing linear fitting on the normalized pressure data sequence to obtain a slope value K representing a pressure change trend; and S4, comparing the slope value K with a preset reference slope threshold value, and diagnosing the state of the gas chamber of the target combined electric appliance according to a comparison result. According to the method, whether the internal state of the air chamber is abnormal or not is diagnosed by analyzing the long-term, weak trend and slope of the pressure data along with time change, the fault diagnosis problem is converted into quantitative analysis of the long-term trend slope of the data sequence, the fitting slope K is used as a core diagnosis index, the defect of slow development can be captured, and early warning is achieved.

This invention relates to the field of vehicle fault diagnosis technology, and discloses a method, apparatus, and computer-readable storage medium for diagnosing vehicle gear shift switches. The method includes: using two Hall effect sensors corresponding to each gear shift switch contact in the vehicle to acquire a first state signal and a second state signal for each gear shift switch contact at the current moment; determining a first integrated state value based on each first state signal, and determining a second integrated state value based on each second state signal; and performing fault diagnosis on the vehicle's gear shift switch based on the first integrated state value and the second integrated state value. Applying the technical solution of this invention can meet the current functional safety requirements for gear shift switch state acquisition, achieve accurate diagnosis of different fault types of vehicle gear shift switches, and improve the overall safety performance of the vehicle.

The embodiment of the invention discloses a fault diagnosis method and device for a heating ventilation air-conditioning system and a medium, belongs to the technical field of fault diagnosis, and solves the problem that the diagnosis efficiency and accuracy are difficult to consider when fault diagnosis is carried out on the heating ventilation air-conditioning system. Inputting the constructed fault diagnosis prompt function into the large language model to obtain the diagnosis accuracy of the large language model in various fault diagnosis tasks; under the condition that the diagnosis accuracy does not meet the preset condition, generating a correction response based on the correction prompt function and the misdiagnosis response; based on the original prompt containing the misdiagnosis response and the correction response, constructing a fine tuning data set, and performing fine tuning on the large language model through the fine tuning data set; performing diagnosis accuracy detection on the fine-tuned large language model again through a fault diagnosis prompt function; and under the condition that a detection result meets a preset condition, fault diagnosis is carried out on the current heating ventilation air conditioning system through the fine-tuned large language model.

The system comprises a data access module, a power grid flood disaster field knowledge graph, a digital twin body environment, and a digital twin body agent (DTA), a monitoring and early warning agent (MWA), a fault diagnosis agent (FDA) and a case handling agent (CHA). The DTA provides simulation deduction services. The MWA generates a flood field and a device-level risk list using a physical model chain. The FDA predicts device failure probability using a GWO-BPNN model. The CHA retrieves cases based on the knowledge graph to generate a scheme, and requests the DTA to perform virtual deduction and iterative optimization. The application interaction layer integrates visual output. The application realizes a full-process closed-loop intelligent decision from disaster perception, risk diagnosis, scheme generation to virtual pre-performance, solves the problems of insufficient early warning accuracy, static risk evaluation and lack of forward-looking verification of the disposal scheme, and improves the emergency response capability and decision scientificity of the power grid in response to flood disasters.

The invention relates to a multistage pump fault diagnosis method, and relates to the technical field of multistage pumps, and the method comprises the following steps: S1, collecting a pump vibration signal and / or a sound signal, and carrying out the preprocessing; s2, according to the structural parameters and the real-time rotating speed of the pump, theoretical characteristic frequencies corresponding to different faults are calculated, a self-adaptive weight vector is constructed based on the calculated characteristic frequencies, and a sample is generated through the weight; s3, inputting the sample into the CNN model for training to obtain a result; and S4, outputting the classification result of the CNN. The method has the advantages that the diagnosis accuracy and efficiency can be improved for the key fault frequency.