59results about How to "High accuracy of fault diagnosis" patented technology

The invention discloses a rolling bearing fault diagnosis method based on improved variational model decomposition and an extreme learning machine. The method comprises: vibration signals of a rollingbearing under different types of faults are collected, the vibration signals are filtered by means of maximum correlation kurtosis deconvolution, parameter optimization is carried out on the maximumcorrelation kurtosis deconvolution method by using a particle swarm algorithm, and an enveloped energy entropy after signal deconvolution is used as a fitness function; the mode number of variationalmodel decomposition is improved by an energy threshold and improved variational model decomposition of the filtered vibration signals is realized to obtain mode matrixes of the corresponding vibrationsignals; singular value decomposition is carried out on the mode matrixes to obtain a singular value vector and a rolling bearing fault feature set is constructed; and the fault feature set is trained by using an extreme learning machine and a rolling bearing fault diagnosis model is established. Therefore, stable feature extraction of the complex vibration signal of the rolling bearing is realized, so that the diagnostic accuracy is improved.

The invention relates to a centralized and remote control monitoring, and fault diagnosis system of wind turbine. Various sensors sample an original signal from a wind turbine; the signal obtained by the various sensors is converted into a digital signal by a data conversion module and conveyed to a site controller, the data processed is conveyed to a host computer; and two manners including centralizing and remote controlling are included. Accident alerting program software in the computer analytically analyzes and judges testing data, and draws a conclusion to alert an accident; and fault diagnosis specialist program software in the computer analyzes the testing data, draws a diagnosis conclusion and displays the conclusion in text on the display. Online monitoring on comprehensiveness of key parameters of all important parts of the whole wind turbine is realized; the accident alerting program software is used to analyze and judge the testing data to draw a conclusion and alert an accident; and the fault diagnosis specialist program software is used to diagnose operation condition, thereby improving operation safety reliability and service life of a large-sized wind turbine and enhancing the operation quality of a wind turbine.

The invention belongs to the field of maintenance decision-making of hydraulic equipment, and relates to a method for the optimal maintenance decision-making of hydraulic equipment with risk control. The method mainly comprises three steps: 1) judging whether a system is in a status of defect by using a variable-weight association rule algorithm, if so, calculating the probability values of occurrences of latent faults of the system; 2) calculating the comprehensive evaluation value for the consequence of each latent fault by using a BP neural network; and 3) multiplying the probability values obtained in step 1 by the comprehensive evaluation values obtained in step 2 so as to obtain the VaRs (values-at-risk) of the latent faults, judging whether the VaRs are more than a specified threshold, if so, ranking the VaRs in descending order so as to determine the maintenance sequence; otherwise, returning to the step of monitoring. The method can judge whether a device is in a status of defect, judge the type of the latent fault and calculate the probability values of occurrences of latent faults only through a calculation; and compared with traditional risk maintenance methods, the method of the invention improves the accuracy of fault diagnosis, speeds up the diagnosis speed, and provides a better reference for online decision-making.

The invention discloses an aero-generator fault feature extraction method based on iSDAE (improved Stacked Denoising Auto-Encoderes), and is mainly to solve the problem of not high diagnosis accuracy since the existing fault diagnosis technology is limited by artificial feature extraction. The method has the following specific steps: 1) fault analysis; 2) data acquisition; 3) data pre-processing; 4) training of the iSDAE (improved Stacked Denoising Auto-Encoderes); and 5) feature output. The method can learn data features automatically and obtains distributed feature representations of original data, has a certain noise immunity and good robustness and effectively improves aero-generator fault diagnosis correct rate.

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.

The invention belongs to the field of petroleum engineering, and particularly relates to a fault diagnosis method and system for a digital twin-driven offshore oil underwater production system. The fault diagnosis method for the digital twin-driven offshore oil underwater production system comprises three steps of digital twin body establishment, digital twin body tracking and updating and fault diagnosis reasoning model establishment. The fault diagnosis system for the digital twin-driven offshore oil underwater production system comprises seven parts: a data collection and analysis subsystem, a water control module data acquisition and processing subsystem, a hydraulic power unit data acquisition and processing subsystem, an electric power unit data acquisition and processing subsystem,an underwater control module data acquisition and processing subsystem, an underwater control module electronic module group data acquisition and processing subsystem and an underwater Christmas treedata acquisition and processing subsystem.

The invention provides an aviation power converter fault diagnosis method based on an IWO (Invasive Weed Optimization, IWO for short) and an ELM (Extreme Learning Machine, ELM for short). The aviation power converter fault diagnosis method belongs to the field of circuit fault diagnosis, and comprises the steps of: 1) acquiring output signals of measurable nodes of an aviation power converter in a normal mode and a failure mode; 2) extracting key features of the signals by utilizing a PCA (Principal Component Analysis, PCA for short) method, and constructing a feature sample set; 3) dividing the feature sample set into a training sample set and a test sample set which are separately used for training and evaluation of the ELM; 4) dividing the training sample set into training data and test data, training the ELM by utilizing the training data, and optimizing hidden layer node number, input weight and hidden layer node bias by adopting the IWO, so that the ELM has an optimized classifier structure; 5) and applying the test sample set to evaluate the fault diagnostic function of the optimized ELM.

The invention discloses a diagnosis method for an active power filter switching device open-circuit fault based on model reference analysis. The diagnosis method includes the steps that on the basis of building an actual operation model of active power filter main circuits, a reference model is constructed; measurable active power filters in all sampling periods are used for outputting three-phase compensating currents, and voltage residual errors are generated according to reference analysis of the model; by setting a threshold value and two judgment flag bits, the residual errors are evaluated to detect whether the open-circuit fault happens on an APF switching device or not in real time; if the fault happens, the specific position where the open-circuit fault of the switching device happens is diagnosed according to the two judgment flag bits. The diagnosis method is effectively used for monitoring the active power filters on line and diagnosing faults, and electric, electronic and electric energy quality control devices such as the active power filters and reactive compensators operate reliably and safely.

The invention discloses a discrete Bayesian network water chilling unit fault diagnosis method based on information entropy, and the method comprises the steps: obtaining the historical data of a fault through the historical data stored in an experiment or on site, and carrying out the steady-state screening and feature selection; carrying out discretization processing on historical data by utilizing an information entropy-based discretization algorithm, carrying out statistics on frequency to determine conditional probability, and constructing a network model; and verifying the performance ofthe model. According to the method, the main limitation of traditional Bayesian network water chilling unit fault diagnosis based on expert discretization is effectively overcome, and the possibilityof field application of a fault diagnosis system is greatly improved.

The invention relates to an intelligent diagnosis diagnosis method and device based on an ensemble learning framework, equipment and a medium, and the method comprises the steps of collecting multi-source operation data of a target metrological verification automatic system, and carrying out the preprocessing of the multi-source operation data to obtain a corresponding fault data sample set; constructing a readable target data set of a preset fault diagnosis model according to the fault data sample set, wherein the preset fault diagnosis model adopts an XGBoost model, training the XGBoost model according to the target data set, and taking the corresponding XGBoost model after training as a target fault diagnosis model; and acquiring online operation data of the target metrological verification automatic system in real time, and analyzing the online operation data according to the target fault diagnosis model to predict the fault condition of the target metrological verification automatic system. According to the invention, the fault condition of the metrological verification automatic system can be accurately predicted on line in real time, and the fault diagnosis efficiency and accuracy are improved.

The invention discloses a WSN wireless communication module fault diagnosis method based on a fuzzy neural network. A fuzzy neural network current model is established by using emission consumption parameters corresponding to a DHT11 temperature and humidity sensor under different temperatures and voltages for the fault diagnosis of a wireless communication module. For data subjected to normalization processing, firstly an initial structure and parameters of the fuzzy neural network are adaptively determined by using subtraction clustering, then parameter optimization and adjustment are carried out on the model by using a hybrid learning method combining the particle swarm optimization algorithm with the least square method, and finally fault diagnosis is carried out on a test sample by using a trained diagnosis model. According to the WSN wireless communication module fault diagnosis method disclosed by the invention, the advantages of fuzzy reasoning and the neural network are integrated, an improved learning algorithm is adopted, the fuzzy neural network current model of the wireless communication module is established for the relation among the current, the voltage and the faults of a WSN, and the model is short in training time, high in convergence speed and high in fault diagnosis efficiency.

The invention discloses a transformer fault diagnosis method for optimizing a multi-granularity cascade forest model based on a particle swarm algorithm, and the method comprises the steps: taking a non-coding ratio of characteristic gas dissolved in transformer oil as a characteristic parameter of the model, carrying out the normalization of the characteristic parameter, and dividing a training set and a test set; then constructing a multi-granularity cascade forest model, optimizing two key parameters of the multi-granularity cascade forest through a particle swarm algorithm, and obtaining two optimal parameters; and finally, establishing a multi-granularity cascade forest model based on particle swarm optimization for identifying the fault category of the transformer, so that the faultdiagnosis accuracy of the transformer is effectively improved, and a reliable basis is provided for operation and maintenance personnel to correctly judge the operation condition of the transformer.

The invention discloses a transformer fault diagnosis method based on a deep forest model. The method comprises the steps that a non-coding ratio of analysis data of dissolved gas in transformer oil is taken as a characteristic parameter of the deep forest model, and sample data is divided into a training set and a test set; and then a deep forest model DF is constructed, the deep forest model DFextracts more feature information from multi-dimensional data of a transformer fault through multi-granularity scanning, and the effect of diagnosing and identifying the fault type of the transformeris optimal through cascade forest training. According to the method, the fault diagnosis accuracy of the transformer is effectively improved, and a reliable basis is provided for operation and maintenance personnel to correctly judge the operation condition of the transformer.

The invention discloses a cross-working-condition fault diagnosis method based on open set joint transfer learning. The method comprises the steps: training a feature extraction model and a feature classification model by extracting and recognizing the fault types of cross-working-condition source domain sample data and target domain sample data; building a fault diagnosis model by using the trained feature extraction model and feature classification model; and inputting target domain data needing fault diagnosis into the fault diagnosis model, and diagnosing a fault type corresponding to the target domain data.

An absolute photoelectric encoder decoding circuit is suitable for communication field and consists of a coarse code comparison amplification circuit, a precise code differential amplification circuit, a coarse code adaptive comparison level circuit, and an adaptive gain amplifier control circuit, and is high reliability, high in fault diagnosis accuracy, good in adaptability, low in precision loss and low in bit error rate.

The invention provides a ship refrigeration system fault diagnosis method and device and a storage medium. The method comprises the steps: collecting ship refrigeration system data including system operation data and working condition types corresponding to the operation data; and performing fault identification on the ship refrigeration system data by using the trained fault identification model,wherein the fault identification model is a support vector machine model and is used for classifying the system operation data to obtain a working condition type corresponding to the operation data,and a penalty factor and a kernel parameter of the support vector machine model are obtained based on particle swarm optimization. The method for optimizing the support vector machine based on the principal component analysis and the particle swarm optimization performs fault diagnosis on the ship refrigeration system so as to improve the fault diagnosis accuracy of the ship refrigeration system,shorten the diagnosis time and improve the fault diagnosis sensitivity.

The invention relates to a water chilling unit fault diagnosis method based on a support vector regression model. The fault type of a water chilling unit is determined; sensitive characteristic parameters and fault diagnosis rules of faults of the water chilling unit are determined; training a support vector regression model of each sensitive characteristic parameter by using the operation data of the water chilling unit, wherein parameters required to be selected by the support vector regression model are obtained through a wolf pack algorithm; determining a fault threshold value of each sensitive characteristic parameter; and predicting each current sensitive characteristic parameter value by using the support vector regression model, and judging the fault of the water chilling unit according to the sensitive parameter values and the fault threshold. According to the invention, the fault diagnosis prediction precision and accuracy can be effectively improved.

The invention discloses a plunger pump fault diagnosis method and diagnosis system. A diagnosis system is included and comprises a fault parameter platform, a cloud server, a circuit diagnosis module and a warning module, and the fault parameter platform provides information storing fault parameters and provides a function of changing equipment parameters; the cloud server is used for summarizing and storing the fault parameters and realizing online real-time sharing; the circuit diagnosis module is in contact with an electricity measuring instrument to eliminate measurement circuit faults; and according to instrument fault diagnosis, interconnection and intercommunication of monitoring and tracking, online monitoring, predictive maintenance, fault early warning, diagnosis and repair, operation optimization and remote upgrading are carried out on automatic hardware, instruments, sensors and controllers, and the fault parameter platform, the cloud server, the circuit diagnosis module and the warning module are communicated with one another. An intelligent detection method is adopted, the diagnosis efficiency is greatly improved, equipment maintenance is facilitated, and the method is relatively practical.

A transformer failure diagnosis method and system based on an integrated deep belief network are provided. The disclosure relates to the fields of electronic circuit engineering and computer vision. The method includes the following: obtaining a plurality of vibration signals of transformers of various types exhibiting different failure types, retrieving a feature of each of the vibration signals, and establishing training data through the retrieved features; training a plurality of deep belief networks exhibiting different learning rates through the training data and obtaining a failure diagnosis correct rate of each of the deep belief networks; and keeping target deep belief networks corresponding to the failure diagnosis correct rates that satisfy requirements, building an integrated deep belief network through each of the target deep belief networks, and performing a failure diagnosis on the transformers through the integrated deep belief network.

The invention discloses an opening and closing coil current analysis method combining Bayesian update and a DS evidence theory. The Bayesian update compensates the influence of the environment on parameter estimation by performing feature residual extraction on the difference between an actual feature and an ideal feature and taking the difference as a new classification feature; and on the basis of a Bayesian parameter estimation result, an uncertainty probability and DS evidence reasoning method is introduced to realize DS data fusion. The method provided by the invention has relatively low knowledge dependency and relatively weak subjective degree, and trust distribution does not need to construct a complex membership function. According to the method, the method is low in degree of dependence on fault samples, and is suitable for occasions such as high-voltage circuit breakers where the reliability requirement is high and the fault samples are relatively difficult to obtain; and in practical application, the method can introduce characteristics of signals such as vibration, displacement and pressure in the fusion process, so that the circuit breaker state monitoring system which is more comprehensive and higher in fault diagnosis accuracy is realized.