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264results about How to "Improve the accuracy of fault diagnosis" patented technology

Dynamic layer diagnostic device and method of smart power grid fault

A dynamic layer diagnostic device for smart power grid fault comprises a data collection and monitoring unit, a data processing unit, a data base unit, a communication unit and a man-machine interaction unit. A dynamic layer diagnostic method includes: when a smart power grid breaks down, calculating fault diagnosis starting conditions to conform a diagnosis strategy, wherein the fault diagnosis strategy comprises switch layer diagnosis, feeder layer diagnosis and transformer substation layer diagnosis; starting the switch layer diagnosis when changes of switch motion information are remarkable before and after the fault; starting the transformer substation diagnosis when changes of electricity amount information are remarkable before and after the fault; stopping the diagnosis when a fault element is the only one element during the diagnosis of the switch layer; otherwise, retrieving the switch historical action recording, and starting the transformer substation diagnosis when recording matched with the current switch action exists; and otherwise, starting the feeder layer diagnosis. The dynamic layer diagnostic method performs layering analysis on the fault, fully utilizes various fault information and improves fault diagnosis accuracy according to different characteristics of multisource information after the power grid fault and difficulty layer in obtaining and processing of various information.
Owner:SHENYANG POWER SUPPLY LIAONING POWER +2

Rolling bearing fault probabilistic intelligent diagnosis method based on adaptive MRVM

ActiveCN107505133AOvercome the defect that it is impossible to evaluate the probability of occurrence of each rolling bearing failure typeRealize fault type diagnosisMachine bearings testingCharacter and pattern recognitionAlgorithmPrincipal component analysis
The invention discloses a rolling bearing probabilistic intelligent fault diagnosis method based on adaptive MRVM. The method comprises the steps that the original fault data of a rolling bearing are measured through an acceleration sensor; a vibration signal is segmented, and wavelet packet energy characteristics are extracted; principal component analysis and dimension reduction are used for normalization simultaneously; a training sample set and a test sample set are processed and divided; an algorithm is used to adaptively select nuclear parameters; the training sample set is used to train and test a multi-class correlation vector machine; and the test result is compared with the actual fault type to acquire the validity of a diagnosis model. According to the invention, the method overcomes the defect that a traditional intelligent fault diagnosis method cannot output the fault probability value; the fault diagnosis accuracy of the rolling bearing is improved; more fault type determining information of the rolling bearing can be provided; through the fault type probability value provided by the invention, the state of the rolling bearing can be further assessed; and method has the advantages of good engineering value and application prospect.
Owner:CHUZHOU UNIV

Fault diagnosis method for variable structure fuzzy system sensor and application thereof in flight control system

The invention provides a fault diagnosis method for a variable structure fuzzy system sensor and the application thereof in a flight control system. According to the method, an accurate analytical model of a flight control system sensor does not need to be established, and a sensor prediction model is established by means of the global approximation ability of a variable structure fuzzy system, the fault of one or more sensors can be detected, identified and tolerated based on errors between a real model and the prediction model, and the normal work of the flight control system is guaranteed; meanwhile, the variable structure fuzzy system can configure a fuzzy rule base online in real time according to collected sensor data, so that the fault diagnosis method not only has fault tolerance performance, but also has real-time performance and adaptivity.
Owner:XI AN JIAOTONG UNIV

Network fault diagnosis methods and systems

The invention provides network fault diagnosis methods and systems. A gradient boosting decision tree classifier prediction model is trained through adoption of a symptom data set and a fault data setin network history data. Network fault diagnosis is carried out through utilization of the trained gradient boosting decision tree classifier prediction model. Network fault diagnosis precision can be effectively improved. Network fault diagnosis time can be effectively reduced. The method and the system are applicable to diversified production scenes.
Owner:CHINA UNITECHS

Satellite power supply system online fault diagnosis method

The invention discloses a satellite power supply system online fault diagnosis method based on improved ensemble empirical mode decomposition (EEMD for short) and error correcting output codes-support vector machines (ECOC-SVM for short), and belongs to the field of circuit fault diagnosis. The method includes the following steps that first, a satellite power supply circuit to be tested is subjected to testability analysis, and testable points, fault levels, fault types and the number of faults are determined; second, off-line training is performed, and output signals of the circuit to be tested are collected in the testable points of the satellite power supply circuit; an improved EEMD method is adopted for performing fault characteristic extraction on collected circuit fault signals so that a sample can be formed and used for training the mode classifier SVM and the improved ECOC; third, on-line diagnosis is performed, the state of the circuit is judged through the trained mode classifier SVM, monitoring is performed again if the state of the circuit is healthy, diagnosis is performed through the improved ECOC if faults happen, and finally, the faults can be recognized and positioned.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Noise diagnosis algorithm for rolling bearing faults of rotary equipment

The invention discloses a noise diagnosis algorithm for rolling bearing faults of rotary equipment. Firstly, a sound pick-up device collects running noise signals of a rolling bearing, and the signalsare subjected to preliminary fault judgment through a bearing normality and anomaly pre-classification model based on an anomaly detection algorithm; secondly, according to a fault pre-judgment result, the abnormal signals (the faults occur) pass through a neural network filter to filter normal components in the signals of the bearing, the output net abnormal signals are connected to a subsequentfeature extraction module, and the normal signals (no faults occur) are directly connected to the feature extraction module; the feature extraction module extracts Mel-cepstrum coefficients (MFCC) ofthe signals to serve as eigenvectors, feature reconstruction is carried out by utilizing a gradient boosted decision tree (GBDT) to form composite eigenvectors, and principal component analysis (PCA)is used for carrying out dimensionality reduction on features; and finally, feature signals are input into an improved two-stage support vector machine (SVM) ensemble classifier for training and testing, and at last, high-accuracy fault type diagnosis is achieved. According to the algorithm, the bearing faults can be effectively detected and relatively high fault identification accuracy is kept;and the algorithm has relatively high effectiveness and robustness for detection and classification of the bearing faults.
Owner:CHINA UNIV OF MINING & TECH

Intelligent trouble diagnosis method for tractive power supply system and system thereof

The invention provides an intelligent trouble diagnosis method for a tractive power supply system, which comprises the following steps: firstly establishing a system description library for a system to be diagnosed; then collecting observed value data required by the diagnosis in real time, carrying out pretreatment, acquiring system predicted values in accordance with a system element action description library and the observed value data, and performing a fuzzy consistency check on the predicted values and the observed values to judge the difference, if not, performing a layering consistency diagnosis on the system to generate a fault candidate collection; selecting a fault action model to perform an abductive diagnosis to determine fault reasons and fault positions, thereby predicating relay protection action and breaker action; and finally comparing the consistency of a predicated action result and real action to obtain the fault reasons and the results, and carrying out alarm output. The invention also provides an intelligent trouble diagnosis system for a tractive power supply system. The invention is suitable for the tractive power supply system, can immediately and exactly find out the fault elements and the fault reasons, overcomes computational complexity and realizes real-time monitoring and fault diagnosis for the tractive power supply system.
Owner:暨南大学珠海学院

LightGBM fault diagnosis method based on improved Bayesian optimization

The invention discloses a LightGBM fault diagnosis method based on improved Bayesian optimization. The LightGBM fault diagnosis method comprises the following steps: 1) determining hyper-parameters needing to be optimized by a LightGBM model and a hyper-parameter value range; 2) improving the Bayesian optimization algorithm to obtain an improved Bayesian optimization algorithm GP-ProbHedge; 3) selecting an optimal hyper-parameter combination of the fault diagnosis model by using the method in the step 2) in combination with a five-fold cross validation mode; and 4) constructing an improved Bayesian optimization LightGBM fault diagnosis model, and giving a model iteration process and an optimization result. By adopting the technology, compared with the prior art, according to the invention,an improved Bayesian optimization algorithm is provided to carry out optimization selection on parameters of a fault model; by improving an acquisition function of a traditional Bayesian optimizationalgorithm and a covariance function of a Gaussian process of the traditional Bayesian optimization algorithm, an improved Bayesian optimization LightGBM fault diagnosis method is provided, and equipment faults are diagnosed and predicted.
Owner:ZHEJIANG UNIV OF TECH

Method for locating parameter type fault of analogue integrated circuit

The invention discloses a location method of the parameter type fault of the analog integrated circuit. The invention carries out the polyphase filter bank for the measured analog integrated circuit, then calculates the cohere function sequence which corresponds to the faultless sub band sequence for the fault polyphase filter bank sequence in the sub band of the highest fault resolution, and obtains the autocorrelation function sequence of the cohere function sequence, and takes the definite integral answers of the autocorrelation function sequence of the cohere function as the digital characteristic of the fault to realize the fault location. By comparing to the exist technology, the invention can realize the location of the parameter type fault of the analog integrated circuit, reach high accuracy of fault diagnosis, high fault resolution and high fault coverage, realize the multi-parameter type fault location, and easily realize the automation of the fault diagnosis by the digitized fault characteristic with the obvious difference.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Convolutional neural network adversarial transfer learning method based on Waserstein distance and application thereof

InactiveCN110414383AHigh fault judgment abilityImprove discrimination sensitivityCharacter and pattern recognitionNeural architecturesFeature setNetwork structure
The invention relates to a convolutional neural network adversarial transfer learning method based on Waserstein distance and application thereof and the method comprises the steps: employing a to-be-migrated convolutional neural network to obtain a source domain feature set and a source domain fault judgment set of a source domain mark sample set and a target feature set of a target domain sampleset; and with maximization of a Wasserstein distance between the source domain feature set and the target feature set and minimization of the sum of the Wasserstein distance and a judgment loss valueof the source domain fault judgment set as a target, realizing adversarial migration learning of the convolutional neural network based on a convergence criterion. According to the invention, the Wasserstein distance is introduced into the transfer learning of the convolutional neural network. The maximum Wasserstein distance is used as a target; the distinguishing sensitivity of the features extracted from the two sample sets is improved; and the minimum sum of the Wasserstein distance and the loss value of the source domain fault judgment set is taken as a target, so that the judgment precision of the convolutional neural network is improved, the requirements on sample data and a network structure are low while the fault diagnosis capability is ensured, and the invention can be suitablefor migration among multiple working conditions and is high in practical applicability.
Owner:HUAZHONG UNIV OF SCI & TECH

Intelligent electrical-network fault diagnosis method based on multilevel feedback adjustment

The invention provides an intelligent electrical-network fault diagnosis method based on multilevel feedback adjustment. The method comprises the steps that historical switching information and electrical quantity attribute information of different devices in an electrical network when the electrical network has faults are stored in a historical fault information base; electrical quantity information of the devices in the electrical network is obtained when the electrical network has faults; diagnosis at the rough identification level is carried out according to the obtained electrical quantity information of the devices in the electrical network, and suspected fault elements are determined in a minimal breaking zone defining method based on the equivalent network to form a candidate set of the suspected fault elements; diagnosis at the fuzzy decision making level is carried out; diagnosis at the accurate positioning level is carried out; and according to results of three levels of fault diagnosis, intersection elements are obtained from suspected fault element sets E1 and E2 to finally determine a diagnosis result of fault elements. The electrical network with faults is analyzed at different levels according to the different sources of the multi-source fault information and the obtaining and processing difficulty of different type of information, and different types of fault information is fully utilized and information is complementary to improve the accuracy of fault diagnosis.
Owner:STATE GRID CORP OF CHINA +2

FastRVM (fast relevance vector machine) wastewater treatment fault diagnosis method

The invention discloses a FastRVM(fast relevance vector machine) wastewater treatment fault diagnostic method. The method includes the following steps that: 1) samples with incomplete properties in samples to be recognized in wastewater data are removed, since the dimensions of the properties of the samples are different, the samples are normalized to an interval of [0, 1]; 2) based on a clustering fast relevance vector machine, the majority of types of data are compressed; 3) the synthetic minority over samplingtechnique is adopted to expand the minority of types of data; 4) a "one-to-one" fast relevance vector machine multi-classification model is established; and 5) fast relevance vector machine wastewater fault diagnosis modeling is carried out. According to the FastRVM wastewater treatment fault diagnosis method of the invention, the majority of types of data are compressed based on the clustering fast relevance vector machine, and the minority of types of data are expanded through the synthetic minority over sampling technique, and therefore, the imbalance of wastewater data can be decreased; and the fast RVM is adopted to establish a multi-classification model for a wastewater biochemical treatment process, and therefore, the accuracy of fault diagnosis on a wastewater biological wastewater treatment system can be effectively improved.
Owner:SOUTH CHINA UNIV OF TECH

Method of realizing intelligent fault operation and maintenance decision support system

The invention discloses a method of realizing an intelligent fault operation and maintenance decision support system, comprising the following steps: SS1, a fault operation and maintenance information model is built; SS2, a fault operation and maintenance decision support system is constructed; SS3, an intelligent module of field operation equipment carries out state diagnosis; SS4, an intelligent management unit carries out centralized management; SS5, a cloud server makes analysis and decision and provides a scheme; SS6, an operation and maintenance management terminal confirms and modifies the scheme and judges whether the scheme is feasible, if the scheme is feasible, a solution is provided, and maintenance spare parts or information are / is provided for the field operation equipment, or, the method goes to SS7; SS7, a manufacturer support terminal makes confirmation and provides a scheme; and SS8, intelligent order delivery is carried out. According to the invention, fault information records can be provided online, an analysis solution can be provided through an intelligent means, confirmation and judgment are made manually, and the accuracy of fault diagnosis is improved.
Owner:NARI TECH CO LTD +1

Diagnosis method for iterative learning fault of single-joint manipulator system

The invention discloses a diagnosis method for an iterative learning fault of a single-joint manipulator system. The diagnosis method comprises the following steps: firstly, establishing a single-joint nonlinear manipulator system model, and constructing a manipulator nonlinear state variable dynamic equation; secondly, performing expansion transformation on a state variable dynamic system, and designing a diagnosis method for an iterative learning fault of an expansion system; finally, analyzing the stability and parameter selecting conditions of a fault diagnosis algorithm, and realizing real-time fault diagnosis for the manipulator system. The diagnosis method has the advantages that the fault diagnosis algorithm is not only suitable for faults of difficult types, and has generality for respectively diagnosing faults of an executor and a sensor; the generation of the faults can be qualitatively detected, and online fault reconstruction and estimation can be realized, so the real-time property is good; an expanded equation is directly constructed by a system equation; an iterative algorithm is simple and highly efficient; no mass additional parameter variables are needed to be introduced or no complex matrix equations are required to be solved; the engineering realization is easily reached.
Owner:JIANGNAN UNIV

Portable bearing fault diagnosis device and method based on vibration detection

The invention provides a portable bearing fault diagnosis device and method based on vibration detection. The portable bearing fault diagnosis device comprises a vibrator sensor and a portable bearing fault diagnosis instrument, wherein the portable bearing fault diagnosis instrument comprises a shell and a circuit device; the circuit device comprises an A / D (Analogue / Digital) conversion module, a DSP (Digital Signal Processor) data analyzing module, an ARM (Advanced RISC Machines) data processing module, a storage module, a display module and a power supply module; the A / D conversion module is used for uploading an acquired and processed signal of a vibration sensor to the DSP data analyzing module for analysis and fault judgment; the ARM data processing module is used for receiving a processing result and distributing storage and display. The diagnosis method mainly comprises the following steps: a vibration signal is obtained by the vibration sensor; the signal is acquired by the A / D conversion module; the DSP data analyzing module is used for analyzing and processing; a fault analyzing result is stored and displayed. The portable bearing fault diagnosis device is simple and rapid to operate, and has high diagnosis precision and high automation degree; the real-time monitoring and the fault diagnosis of a rolling bearing fault can be effectively realized.
Owner:SHANXI LUAN ENVIRONMENTAL ENERGY DEV +2

Failure diagnosis chart clustering method based on network dividing

The invention discloses a fault diagnosis spectral clustering operation method based on network partition. The method comprises the steps that: the fault diagnosis is molded into a network partition by the characteristic of network description fault sample which consists of nodes and relations; the objective function of the partition is made by utilizing the smallest and the largest criterion of the comprehensive evaluation of larger similarity between classes and smaller similarity inside a class; the objective function is optimally solved by a method of spectral clustering based on the theory of spectrogram; the operation method can acquire the state characteristics more quickly and acquire a comparatively high diagnosis accurate rate. The fault diagnosis embodiment of a UCI standard data set and a four-grade compressor proves the quick and effective performance of the operation method.
Owner:XI AN JIAOTONG UNIV

Turbo generator set vibration fault diagnosis method based on forward reasoning

The invention discloses a turbo generator set vibration fault diagnosis method based on forward reasoning. After a diagnosed turbo generator set starts, a parameter detection device is employed to carry out real-time detection on related work parameters in a start and operation process of the diagnosed turbo generator set and synchronously sends the detection information to a vibration fault diagnosis device for automatic vibration fault diagnosis, vibration faults of the diagnosed turbo generator set are diagnosed level by level by the vibration fault diagnosis device according to the detection result transmitted by the parameter detection device in combination with the active power value of the diagnosed turbo generator set, and the fault diagnosis process comprises steps of 1, shaft vibration swinging value diagnosis; 2, start process vibration fault diagnosis; 3, zero load operation vibration fault diagnosis; and 4, loaded operation vibration fault diagnosis. The method is advantaged in that the steps are simple, and the method is reasonable in design, is convenient to realize, has good use effects, can conveniently and rapidly accomplish the online steam turbine vibration fault diagnosis process and can further realize accurate and reliable diagnosis results.
Owner:XIAN XIRE VIBRATION INST CO LTD

Locomotive motor bearing automatic diagnostic method based on vibration acceleration signal

The invention discloses a locomotive motor bearing automatic diagnostic method based on a vibration acceleration signal, comprising the steps of: acquiring a vibration acceleration signal and a speed key phase signal of a motor bearing; performing order tracking and constant-angle incremental sampling, and converting the vibration acceleration signal into an angular-domain vibration acceleration signal; calculating an FFT spectrum and an EEVS enhanced envelope spectrum; setting effective frequency bands in the FFT spectrum and the EEVS enhanced envelope spectrum according to a bearing fault frequency; calculating the fault probability of each component of the bearing according to the band energy in the FFT spectrum and the EEVS enhanced envelope spectrum; setting weights and calculating the weighted probability of each component of the bearing; comparing a preset fault reference probability with the weighted probability of the current bearing so as to automatically determine a fault type and representing the severity of the fault by the weighted probability.
Owner:湖北微特传感物联研究院有限公司

Multi-fault diagnosis method of spacecraft attitude control system

The invention provides a multi-fault diagnosis method of a spacecraft attitude control system. The multi-fault diagnosis method comprises the steps: establishing a spacecraft attitude dynamic model and an attitude kinematics model; establishing a spacecraft augmented attitude dynamics model and an augmented attitude kinematics model based on the above models; designing an RBF neural network interference observer based on the augmented attitude dynamics model and the augmented attitude kinematics model, and estimating an interference value in the system; designing a robust fault detection observer, calculating a residual evaluation value, and selecting a fault detection threshold; according to the residual evaluation value and a fault detection threshold value, determining fault occurrenceparts comprising an actuator, a gyroscope, an attitude sensor or a plurality of parts; designing an RBF neural network fault isolation observer, reconstructing actuator faults, gyroscope faults and attitude sensor faults on the three axes, and selecting a fault isolation threshold; and finally determining a specific part with a fault according to the reconstructed fault value and the fault isolation threshold, thereby finishing multi-fault diagnosis.
Owner:SHUNDE GRADUATE SCHOOL UNIV OF SCI & TECH BEIJING

Intelligent fault diagnosis method under small sample based on attention mechanism element learning model

The invention discloses an intelligent fault diagnosis method under a small sample based on an attention mechanism element learning model. According to the intelligent fault diagnosis method, an attention mechanism and a meta-learning method are used for establishing an association network model; short-time Fourier transform is carried out on mechanical signals to obtain a time-frequency spectrogram of the mechanical signals; feature extraction and operation state recognition are further carried out on the time-frequency spectrogram; and rich fault information contained in the mechanical signals can be effectively mined. According to the intelligent fault diagnosis method, a pseudo distance can be trained adaptively to evaluate the similarity between related data; clear mathematical formula definition is not needed; and high mechanical equipment fault diagnosis accuracy can be obtained. Therefore, the dependence of a feature extraction process on artificial experience and the dependence of an existing intelligent fault diagnosis algorithm on a large amount of training data in a traditional diagnosis method are eliminated, and the problem of mechanical equipment fault diagnosis under the condition of small sample data is practically solved.
Owner:XI AN JIAOTONG UNIV

Mechanical fault diagnosis method and system based on TJM transfer learning

ActiveCN110543860ASolve the problem of low efficiency of fault diagnosisSmall amount of calculationSustainable transportationCharacter and pattern recognitionDecompositionAlgorithm
The invention discloses a mechanical fault diagnosis method and system based on TJM transfer learning. According to the method, CEEMDAN decomposition is introduced. The algorithm calculation amount isreduced while the mode mixing problem is solved, and meanwhile the problems that according to a traditional machine learning method, when training and testing data distribution has a certain degree of difference, the established classification model is poor in popularization capacity, and even sometimes the classification model cannot be universally used are solved through a transfer learning method. And meanwhile, the problem of low fault diagnosis efficiency caused by data difference between different working conditions of the rotary machine is solved, and the problems that the fault stateis incomplete and the fault diagnosis cannot be correctly and completely carried out due to insufficient data acquisition quantity of the rotary machine in some working states are also solved. According to the method, the characteristic that cross-domain feature matching and instance reweighting are jointly executed in the TJM transfer learning method is utilized, the problem that the recognitionand diagnosis rate is not high due to the fact that the data difference between the source domain and the target domain is large is solved to the maximum degree, and the fault diagnosis precision is greatly improved.
Owner:YANSHAN UNIV

Fault diagnosis method and device based on device working condition

The invention relates to a fault diagnosis method and device based on device working condition. The method comprises the steps that feature extraction is conducted on diagnosis data of unknown samples of a device; whether the fault of the device can be recognized directly or not is judged according to features of the diagnosis data; if the fault is recognized directly, fault diagnosis recognition is conducted on the device directly; if not, working condition recognition and classification are conducted according to behavior parameters of the device; optical diagnosis algorithms corresponding to all the working conditions of the device are obtained through a Q-matrix by the adoption of the working condition classification result and the features of the diagnosis data, wherein the Q-matrix represents the corresponding relation between different working condition types and the optical diagnosis algorithms.
Owner:CHINA UNIV OF PETROLEUM (BEIJING)

Rolling bearing health assessment and fault diagnosis method and monitoring system

The invention discloses a rolling bearing health assessment and fault diagnosis method and a monitoring system. The problem that in the prior art, a large amount of known data is needed or too much human experience intervention is needed to guarantee the monitoring effect is solved, and the effect of accurately detecting and recognizing bearing faults through online real-time analysis on bearing vibration signals is achieved. According to the technical scheme, the method comprises the following steps: acquiring a vibration signal of a bearing, and processing the vibration signal to obtain a spectrogram; establishing a graph model for the spectrogram; performing similarity comparison on adjacent matrixes generated by the graph model to calculate an abnormity degree, and making a decision onan abnormity degree index; setting a threshold value to carry out hypothesis inspection, and carrying out fault inspection on the bearing; And carrying out fault diagnosis when a bearing signal has afault.
Owner:SHANDONG UNIV

Method for optimizing fault diagnosis rules based on ant colony optimization algorithm

The invention discloses a method for optimizing fault diagnosis rules based on an ant colony optimization algorithm, applied to intelligent fault diagnosis. In the invention, the ant colony optimization algorithm is adopted, the fault diagnosis rules (namely a fault pattern sample data vector) in a system fault characteristic pattern sample library are reduced and optimized by reducing the length of the fault pattern sample data vector, redundant condition entries in the fault diagnosis rules are eliminated, and optimal diagnosis rules with fewer condition entries and higher fault diagnosis accuracy are obtained, thus the accuracy rate for diagnosing the type of a fault in a diagnosis field can be improved. The invention also discloses a method for reducing the fault diagnosis rules.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS

Oil pumping unit fault diagnosis method based on multiscale convolutional neural network

The invention discloses an oil pumping unit fault diagnosis method based on a multiscale convolutional neural network. The conventional fault diagnosis method has problems of dependence on artificialselection of features, complicated calculation and poor accuracy rate, and the existing deep neural networks applied to the oil pumping unit fault diagnosis are all completed in a single path, size ofa filter is set singly at each layer, and flexibility of parameters is limited. A multiscale convolutional block is taken as a core structure, and the oil pumping unit fault diagnosis method based ona multiscale convolutional neural network is improved. The method avoids influence of complicated feature engineering and uncertainty of feature selection on fault identification accuracy rate in theconventional fault diagnosis method, meanwhile, the method can extract global and local features with more abundant and effect indicator diagrams, and can improve fault diagnosis accuracy rate.
Owner:SOUTHWEST PETROLEUM UNIV

Gear case fault diagnosis method based on blind source separation

The invention discloses a gear case fault diagnosis method based on blind source separation and relates to a mechanical fault diagnosis method, the method is applied to the blind source separation technology for solving the gear case fault diagnosis problem. The blind source separation technology is applied to the gear case diagnosis based on vibration analysis as the main tool for pre-processing the signal and extracting the fault feature and is capable of greatly strengthening the fault information, changing the conventional fault information strengthening thought by taking the noise reducing process as the main part, raising the diagnosis precision and solving the problems that the fault is difficult to locate and the early fault diagnosis rate is low. The blind source separation technology is applied to the gear case diagnosis based on vibration analysis as the main tool for pre-processing the signal and extracting the fault feature and is capable of greatly strengthening the fault information. The conventional fault information strengthening thought by taking the noise reducing process as the main part is changed for raising the diagnosis precision and solving the problems that the fault is difficult to locate and the early fault diagnosis rate is low.
Owner:SHENYANG INSTITUTE OF CHEMICAL TECHNOLOGY

Rotary machine intelligent fault diagnosis method based on integrated depth auto-encoder

The invention provides a rotary machine fault diagnosis method based on a depth automatic encoder, and aims to improve the fault diagnosis precision of a rotary machine, and the method comprises the steps: firstly collecting a vibration acceleration time domain signal of the rotary machine, and obtaining a training data set and a test data set; secondly, for each activation function, training a series of depth auto-encoders through different training sets by using a K-fold cross validation method; secondly, verifying the trained depth auto-encoder through a verification set, and obtaining theprecision of each fault label; thirdly, searching an optimal selection parameter by adopting a grid search method, screening the depth auto-encoder through the optimal selection parameter, and constructing an integrated depth auto-encoder model; and finally, obtaining a prediction label of the input sample, and mapping the prediction label back to the fault type of the rotating machine to realizefault diagnosis of the rotating machine.
Owner:XIDIAN UNIV

Online fault diagnosing method for Fast RVM (relevance vector machine) sewage treatment

The invention discloses an online fault diagnosing method for Fast RVM (relevance vector machine) sewage treatment. The method includes the steps of firstly, removing samples with incomplete attributes in sewage data, normalizing the samples into a [0, 1] interval, and determining a historical data set and an updating test set; secondly, using a relevance vector machine method based on clustering to compress the majority data of the historical data set; thirdly, using a virtual minority upward sampling method to extend the minority data of the historical data set; fourth, building a 'one-to-one' fast relevance vector machine multi-classification training model; fifthly, adding new samples from the updating test set into the model for testing, and updating the historical data set; sixthly, returning to the second step, reprocessing unbalanced historical data, training the model, and repeating the above process until online data testing is finished. By the online fault diagnosing method, the unbalance of the sewage data is lowered effectively, classification accuracy is increased, online updating speed is increased, operation faults can be diagnosed in real time, and the safety operation of a sewage treatment plant is guaranteed.
Owner:SOUTH CHINA UNIV OF TECH

Dual-model fault diagnosis method based on dynamic weighing

The invention discloses a dual-model fault diagnosis method based on dynamic weighing. The method comprises the steps that sensor vibration signals and fault recording texts collected by a motor driveend under the normal state and various fault states are selected; then,the sensor vibration signals and the fault recording texts are learned,then,a dynamic weighing combination algorithm is adoptedfor giving a weight to a model,sub-model SVM multi-classification voting results are combined,and a final classification result is obtained. Joint diagnosis for bearing fault data and bearing fault texts can be achieved. Non-balanced processing and valuable information extraction and classification are performed on equipment operation data,manual recording experience knowledges are effectively combined for text data mining,compared with a single diagnosis model,the method can remarkably improve the fault diagnosis precision,a better performance evaluation index is obtained,and the good theoretic and application value is obtained.
Owner:HEBEI UNIV OF TECH

Bearing fault diagnosis method based on improved ant lion algorithm and support vector machine

The invention discloses a bearing fault diagnosis method based on an improved ant lion algorithm and a support vector machine. The bearing fault diagnosis method comprises the following steps that vibration acceleration signals under a typical fault state are collected; the collected signals are extracted to obtain a data sample of a typical fault type; based on the improved escape mechanism ant lion optimization algorithm and the support vector machine, a bearing fault diagnosis model is established; the data sample is input into the bearing fault diagnosis model to optimize the bearing faultdiagnosis model; and bearing fault diagnosis is performed based on the optimized bearing fault diagnosis model. According to the bearing fault diagnosis method based on the improved ant lion algorithm and the support vector machine, the improved EALO algorithm is provided by introducing an escape mechanism and adaptive convergence conditions to improve the optimization performance of the algorithm, and the improved ant lion algorithm is combined with the support vector machine to realize the bearing fault diagnosis, so that important theoretical significance and practical value are achieved on improving the rolling bearing fault diagnosis accuracy, ensuring the safety of rolling bearings and stabilizing the operation.
Owner:HUNAN UNIV OF SCI & TECH
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