412 results about "Failure diagnosis" patented technology

The present invention discloses an aero engine bearing fault diagnosis method based on STFT-IncepNext, which includes the following steps: step 1: collect acceleration signals under various working conditions and fault conditions of the bearing as sample data; step 2: determine the number of sampling points of each sensor first, and perform data merging by permuting and combining data collected by sensors at different positions in the same time series, and then use short-time Fourier transform to convert the fused vibration signal into a time-frequency diagram; step 3: data preprocessing: divide into training set, test set and validation set according to fault type, and normalize the converted time-frequency diagram using average value and standard difference; step 4: build an IncepNext model, train and validate it; and step 5: use the trained IncepNext model to diagnose aero engine bearing faults and obtain a diagnosis result. The present invention can effectively identify aero engine bearing faults, can guarantee the fault identification rate, and has good accuracy.

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 provides an equipment health examination method and system based on multi-agent collaboration. The method comprises the following steps: a data acquisition step: acquiring target data to generate a multi-dimensional feature vector; the target data comprises frequency data, process data and control data; an equipment monitoring step: monitoring the equipment based on the generated multi-dimensional feature vector, and outputting a monitoring result; and a deployment processing step: deploying and implementing a corresponding processing scheme according to a monitoring result. The invention provides an equipment early warning and diagnosis method and system based on multi-agent cooperation, and the method comprises the steps: combining the advantages of a professional mechanism model, an AI model and a large language model through the multi-agent cooperation of feature extraction, threshold adjustment, fault diagnosis, decision generation, summary review and closed-loop feedback; the problems that a traditional method is single in model, high in false alarm rate, lagged in decision and the like are solved.

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention relates to the technical field of medical related information and communication, and discloses a hospital logistics one-stop service inspection and repair platform. The hospital logistics one-stop inspection and repair method comprises the following steps: processing equipment operation data through a multi-modal data fusion network, and generating a state feature vector set; constructing an equipment association graph model by using the space-time diagram attention network in combination with the topological information; detecting the abnormity of a single device and predicting a propagation path through a hierarchical time sequence abnormity detection network, and generating a minimum intervention strategy; an abnormal root cause analysis report is generated by means of a multi-stage interpretability engine, and decision making is supported; routing inspection routes and repair task allocation are optimized based on reports and strategies; according to the invention, integrated management of the whole process from abnormity prediction, inspection planning, fault diagnosis to maintenance dispatching can be realized, the hospital logistics pain point is solved, and the system has significant technical innovation and practical value.

The invention discloses a transformer multi-dimensional state sensing and diagnosing system, which comprises a multi-dimensional state sensing module for acquiring transformer multi-dimensional data in real time through various sensors; the data acquisition and processing module performs processing such as synchronous sampling and filtering on the data and extracts features; the state evaluation and fault diagnosis module evaluates a state and diagnoses a fault by using a three-dimensional evaluation model and a CNN-LSTM fusion model; the self-adaptive early warning module dynamically adjusts a threshold value according to an evaluation result and sends out multi-level early warning; the communication and interaction module realizes data encryption transmission, has a visual interface, and supports interaction diagnosis and remote configuration updating. The system can comprehensively collect data, accurately diagnose faults and efficiently early warn, is convenient to operate and maintain, and improves the operation reliability and the operation and maintenance efficiency of the transformer.

The invention discloses an IMU (Inertial Measurement Unit) fault diagnosis and repair method and device, vehicle-mounted navigation equipment and a storage medium, and the IMU fault diagnosis and repair method comprises the following steps: obtaining IMU time domain data, and analyzing and processing the IMU time domain data to obtain an initial measurement value; performing periodic analysis on the initial measurement value, and performing calculation and statistics to obtain missing measurement points; diagnosing and analyzing the missing measurement points to obtain fault feature description; a fault data mapping relation is established, and fault judgment parameters are obtained through characteristic decomposition; calculating to obtain a fault classification value by using the fault judgment parameter; data accuracy measurement is carried out based on the fault classification value, and a fault degree value is obtained through threshold value dynamic comparison; calculating a correction parameter by using the fault degree value, and obtaining an adjustment sequence through parameter conversion; performing extreme value decomposition, and obtaining a steady-state quantity through waveform recombination; and inputting a quantization equilibrium equation, and performing frequency domain modulation to obtain a final repair result. According to the technical scheme, the fault diagnosis accuracy and the repair comprehensiveness are improved, and the fault can be accurately diagnosed and repaired under the complex working condition.

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 an equipment preventive maintenance and diagnosis method based on multi-dimensional data verification, relates to the technical field of equipment preventive diagnosis, and solves the technical problems that fault reasons are difficult to comprehensively and deeply analyze, and maintenance measures are lack of pertinence due to the fact that the fault reasons are often misjudged or cannot be positioned. According to the method, the fault diagnosis model is constructed, the adaptation algorithm is adopted for different types of data, the diagnosis accuracy of the model to equipment faults and the adaptability of the model to complex working conditions are improved, the equipment fault type and the abnormal state can be rapidly and accurately recognized, and the fault diagnosis accuracy is improved through the methods of fault phenomenon matching, parameter correlation analysis, historical data deep backtracking and the like. According to the method, fault reasons can be accurately determined, the performance degradation trend and potential risks of the equipment can be effectively identified in combination with a hybrid similarity measurement method, correlation among data is further mined through a secondary analysis mechanism, the potential risks are accurately pre-judged in combination with historical data, and real preventive maintenance is achieved.

The invention relates to the technical field of aircraft fault diagnosis, in particular to a vertical take-off and landing aircraft diagnosis generalization enhancement method based on causal weight. Firstly, a directed acyclic graph between flight features and fault types is established through an information flow causal discovery method, the causal relationship between the flight features and the fault types can be clearly presented, and a basis is provided for subsequent analysis. Then, mutual information between nodes is calculated and converted into causal strength for causal enhancement, and expression of the causal relationship in the data is enhanced; in the convolutional neural network, causal weights are used for dynamic weighting to extract local features, causal features of a directed acyclic graph are combined, and feature information of different types is fully utilized. According to the method, features are fused through a graph convolutional network to obtain high-order features, adversarial training is completed through a full-connection network, the constructed fault prediction model integrates various technical advantages, the generalization ability of the model is effectively improved, the model can better adapt to different working conditions and data changes, and aircraft faults are accurately diagnosed.

The invention discloses an aircraft intelligent maintenance and diagnosis system, and relates to the technical field of aircraft maintenance and diagnosis, and the system comprises a data collection module which is used for collecting various data in the actual operation of an aircraft. According to the invention, a digital twin model construction module is utilized to accurately simulate the operation state and fault condition of the aircraft, early discovery and accurate diagnosis of potential faults are realized, the pertinence and effectiveness of maintenance are improved, immersive fault experience and maintenance guidance are provided for maintenance personnel through fusion of VR / AR technologies, and the maintenance efficiency is improved. The maintenance accuracy and efficiency are improved, pre-diagnosis and maintenance scheme verification are carried out on the digital twin model, unnecessary disassembly and detection work is avoided, the maintenance time and labor cost are reduced, potential safety hazards can be eliminated in time through accurate fault diagnosis and an effective maintenance scheme, the flight safety of an aircraft is improved, and the safety of the aircraft is improved. And the maintenance scheme is comprehensively evaluated and optimized, so that the performance and the structural integrity of the maintained aircraft are ensured.

The utility model discloses a multifunctional operating platform for bearing fault diagnosis, which relates to the field of bearing detection and comprises a diagnosis operating platform, a through hole arranged on the upper surface of the diagnosis operating platform, a positioning component arranged on the surface of the diagnosis operating platform and an electronic stethoscope arranged on the upper surface of the diagnosis operating platform. The positioning assembly comprises a first electric push rod fixedly installed on the lower surface of the diagnosis operation platform, through arrangement of the positioning assembly, the bearing is placed on the upper surface of the rotating plate, then the electric push rod is started to enable the positioning plate on the peripheral side to slide outwards and extrude the interior of the bearing, and therefore the bearing is fixed; and meanwhile, the pressing disc on the upper portion moves downwards to drive the pressing plate to slide downwards to fix the upper portion of the bearing, so that the bearing is limited and fixed in multiple directions, the device can fix and limit the bearings of different sizes, the application range of the operation platform is widened, the practicability of the operation platform is improved, and the bearing positioning effect is improved.

The invention discloses an automatic fault detection and diagnosis processing method and device based on a data platform and a terminal. The method comprises the following steps: collecting system resource data regularly according to a set time interval; analyzing and judging whether the collected system resource data is abnormal or not; when it is judged that the system resource data is abnormal, fault diagnosis is controlled to be carried out, a mode based on combination of preset rule diagnosis, log analysis diagnosis and / or correlation analysis diagnosis is adopted, a fault generation reason is analyzed, and fault generation reason data is generated; and based on the generated fault generation reason data, searching a preset automatic repair strategy corresponding to the generated fault generation reason data, performing fault repair according to a repair scheme recorded by the found automatic repair strategy, and outputting a fault repair result. The invention provides the method capable of automatically detecting the fault, accurately diagnosing the fault reason and automatically executing the repair measure, so that the stable operation of the data platform can be ensured, and the reliability and availability of the data platform can be improved.

The invention discloses an IT system fault diagnosis method and system based on a large language model, and the method comprises the steps: carrying out the vectorization of a troubleshooting guide TSG through the LLM technology, generating a corresponding TSG vector, and defining an automatic troubleshooting process according to the TSG; according to the received alarm event information, determining an associated IT component and an occurring alarm event by using an IT topological graph; performing vectorization on the alarm event by using an LLM technology to generate an alarm event vector, performing similarity comparison with a TSG vector, and determining a TSG of a similar fault; and executing an automatic troubleshooting process of similar fault TSG, determining a fault root cause according to the obtained diagnosis information, and generating a fault diagnosis report by using an LLM technology. Through the technical scheme of the invention, the fault root cause can be accurately and quickly positioned, automatic fault diagnosis is realized, and the fault handling efficiency is improved.

The invention relates to the technical field of fault diagnosis, in particular to an electromechanical equipment fault diagnosis method and system based on data analysis. The method comprises the following steps: extracting a historical operation log of a motor, analyzing a current overload state and constructing a current overload spectrogram; then, carrying out insulation layer performance loss gradient analysis based on the spectrogram, and calculating the failure probability of the motor; finally, a risk level is given to the overload current according to the failure probability, an equipment fault diagnosis framework is constructed, and the framework is sent to a control terminal to execute fault diagnosis. According to the invention, the electromechanical equipment fault diagnosis technology is optimized, so that the electromechanical equipment fault diagnosis technology is more accurate.

The invention provides a fault diagnosis method and system for a spindle box of a high-speed gear milling machine based on fault mechanism simulation and data fusion. The method mainly comprises the following steps: S1, calculating a tooth profile wear curve of a gear based on an Archard wear principle; s2, three-dimensional modeling software is used for building system models in different wear states, and simulation data sets under different wear degrees are obtained with the help of dynamics simulation software; s3, establishing a data acquisition system to obtain an experimental vibration data set under the actual working condition of the spindle box; s4, constructing a spindle box fault diagnosis model by using the analogue simulation data set obtained in the step S2; and S5, performing fault diagnosis on the experimental vibration data set obtained in the step S3 by using the spindle box fault diagnosis model constructed in the step S4, and identifying the health condition of the spindle box system. Important data support is provided for a main axle box fault diagnosis and health management system, and the diagnosis accuracy and reliability are improved.

The invention discloses a cable state monitoring and fault diagnosis device and method, equipment and a medium, and the device comprises a data collection module and an intelligent diagnosis unit, and the data collection module collects multi-source data used for diagnosing the state of a cable; the method comprises the following steps: converting multi-source data into a space-time matrix through an intelligent diagnosis unit, carrying out feature extraction, outputting a fault type and a corresponding prediction probability of a cable by adopting a preset multi-label classification and dynamic threshold decision-making mechanism according to the extracted features, and sending out early warning signals of different grades according to the prediction probability. The diagnosis device solves the problems that in the prior art, multi-dimensional data are not fully fused, so that the diagnosis result is not comprehensive and accurate enough; and new fault features caused by cable aging or operation environment change cannot be self-adapted, the model generalization ability is limited, and the model adaptability is poor.

The invention discloses a GMM-L2 fault diagnosis method, device and equipment based on a DBGSA algorithm and a medium, and relates to the technical field of engine fault detection, and the method comprises the steps: obtaining the original data of a wind power engine; processing the original data of the wind power engine to obtain second data; then calculating a first clustering center in the second data for generating a first Gaussian mixture model for calculating a health degree; setting an alarm limit according to the health degree, and if the second data is smaller than the alarm limit, determining that the second data is in an abnormal state; processing the second data in the abnormal state to obtain third data; updating the clustering center of the third data by adopting a DBGSA algorithm of fuzzy clustering to obtain a third clustering center; and setting a membership threshold value of the fault type, and calculating the membership probability of the fault type to which each cluster belongs to obtain the fault type to which the third data belongs. According to the method, missing detection caused by classifying only one data point into one fault can be avoided.

The invention provides a wind turbine generator fault diagnosis method and system based on multiple types of symptoms, and the method comprises the steps: carrying out the structural expression of the obtained knowledge of a wind turbine generator based on an ontology theory, and storing data through a graph database, so as to generate a fault diagnosis knowledge base; real-time operation parameters of the unit are detected, and threshold type symptoms and trend type symptoms are extracted from the multiple target parameters through a preset GMM-based symptom extraction model and a BiLSTM network-based symptom extraction model; inputting the multiple fault symptoms into a knowledge base to carry out deterministic reasoning based on a fault knowledge ontology, and carrying out uncertainty reasoning based on evidence theory fusion by utilizing the multiple fault symptoms; and determining a fault mode and a fault reason of the unit by combining diagnosis results of the deterministic reasoning and the uncertainty reasoning. According to the method, multiple fault symptoms and reasoning modes are combined, and the fault mode and the fault reason of the unit under the complex working condition can be quickly and accurately diagnosed.

The invention relates to the field of intelligent diagnosis of oil and gas field development, in particular to a fracturing wellhead device failure analysis method and system based on a large model and a knowledge graph. The system is helpful for solving the problem that the accuracy and timeliness of failure analysis and fault diagnosis of the fracturing wellhead device in the prior art are limited. The method comprises the steps of building a knowledge graph, setting a knowledge graph dynamic updating mechanism oriented to the fault evolution process, building an open source large model and introducing a closed loop feedback mechanism. According to the method, the large model, the knowledge graph and the RAG technology are applied to fracturing wellhead failure analysis, and the intelligent level of the oil and gas industry is remarkably improved; the problem that a large model is high in generalization but insufficient in specialty is optimized, and the accuracy of answer generation is 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 relates to the technical field of ship equipment operation and maintenance, and discloses an auxiliary decision-making method and system for operation and maintenance of ship equipment, and the method comprises the steps: collecting the historical state data of the ship equipment; preprocessing the historical state data to obtain preprocessed historical state data; based on the preprocessed historical state data, performing fault diagnosis by using a pre-trained fault diagnosis model, outputting a fault type diagnosis result, and generating a diagnosis report; making and executing a maintenance plan according to the diagnosis report, and recording maintenance data of the maintenance process; and iteratively updating the fault diagnosis model according to the maintenance data to obtain an optimal fault diagnosis model, and re-diagnosing the fault by using the optimal fault diagnosis model to improve the operation and maintenance auxiliary decision of the ship equipment. And iteratively updating the fault diagnosis model to ensure that the model can adapt to the change of the equipment state. By updating the fault diagnosis model, the prediction accuracy can be improved, so that the operation and maintenance decision is better supported, and the downtime of the equipment is reduced.

The invention relates to a method for diagnosing a flight fault of an aircraft, in which a quick access recorder (QAR) is mounted on the aircraft, in which raw flight data from various sensors of the aircraft are stored, the method comprising the steps of: optimizing the raw flight data, (S1) optimizing the unrelated parameters to obtain optimized data of which the unrelated parameters are removed; performing anomaly detection on the optimized data by using a long short-term memory (LSTM) model to identify an abnormal point (S2); and for the identified abnormal point, determining a fault type by using a decision tree (DT) fault diagnosis model (S3). According to the invention, the reliability and accuracy of flight fault diagnosis can be improved, so that flight accidents are prevented with higher reliability, flight potential safety hazards are eliminated, potential flight risks are reduced, and the maintenance cost is reduced.

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 photovoltaic data anomaly detection and fault diagnosis method and system based on a variational auto-encoder, and the method comprises the steps: carrying out the automatic processing of original production data through a data processing algorithm, enabling the original production data to become standardized input data, and selecting a part of samples to form a training set; constructing a variational auto-encoder model; obtaining information such as abnormal scores and hidden variables of the to-be-detected data according to the trained model; an anomaly diagnosis machine learning model is constructed to cluster hidden variables of the to-be-detected data, and a specific anomaly type is diagnosed; and establishing a manual annotation interface to annotate the test data, and evaluating the model anomaly detection and diagnosis effect by using the annotated data. According to the method, through algorithms such as data automatic processing and time sequence anomaly detection, collection, arrangement, labeling, detection and diagnosis automation of new energy power generation data mainly including photovoltaic power generation data can be realized, and a basic technical support for quantitative evaluation, anomaly detection and fault diagnosis of the photovoltaic industry is formed.

The invention discloses a semi-supervised learning-based direct-current charging pile health state evaluation method, and aims to solve the problems that a direct-current charging pile operation state comprehensive evaluation system is incomplete, data dimensions are not comprehensive enough, and data preprocessing is difficult in a large-scale real scene in the prior art. Through a semi-supervised learning method of data preprocessing, feature index design, feature index correlation analysis and hybrid consistency regularization and pseudo label generation, key health indexes are extracted from multi-dimensional data of the DC charging pile, and a comprehensive health state evaluation system is established. And an efficient operation state classification model is trained by using a semi-supervised learning method, so that the accuracy and reliability of fault diagnosis are improved. According to the method, the task load of labeling the equipment by operation and maintenance personnel can be effectively reduced, the operation and maintenance efficiency is improved, and stable operation and safe use of the charging pile are ensured.

The invention discloses a ship propulsion shafting fault diagnosis method based on multi-modal attention fusion, and relates to the technical field of ship fault diagnosis. According to the method, multi-modal data such as vibration parameters, lubricating oil parameters and cooling water parameters of key parts of a ship propulsion shafting are synchronously collected and preprocessed; a modal specific feature extraction strategy is adopted, a multi-modal attention fusion mechanism including intra-modal self-attention, inter-modal cross attention and adaptive dynamic weight distribution is designed, and heterogeneous modal features are effectively integrated. Compared with a single-mode method and a traditional feature splicing method, the method has the advantage that the diagnosis accuracy is remarkably improved. The method has high robustness, and can still maintain high diagnosis accuracy even under the condition that part of sensors fail.