38results about How to "Troubleshooting Troubleshooting Issues" patented technology

The invention discloses a fault diagnosis device and a fault diagnosis method based on a multi-agent system and wavelet analysis. The device comprises a mutual inductor group, a data acquisition module, a control and man-machine interaction module, a multi-agent system module, and a database module. Active electronic voltage and a current transformer are adopted by the mutual inductor group; and the data acquisition module comprises a follower circuit, an amplification circuit, a biasing circuit and an alternate-current/direct-current (A/D) convertor. The control and man-machine interaction module comprises a protocol conversion module, a 485 bus, an Ethernet network cable, and an upper computer. The multi-agent system module comprises a task decomposition agent, a task distribution agent, a diagnosis agent, an assisting agent and a decision-making agent. The running of the device is controlled by a control program, the running state of a primary side of a power grid is displayed in real time, and historical data is called by a database; and the acquired signal is sent to the task decomposition agent, the fault diagnosis result of the decision-making agent is received for alarming, and a user is assisted in making a final decision.

A rolling bearing fault on-line detection and state assessment method is disclosed. The method comprises the following steps: twelve dimensional dimensionless parameters are extracted; the twelve dimensional dimensionless parameters comprise six dimensional time domain statistical parameters, three dimensional frequency domain statistical parameters and three dimensional dimensionless parameters in a small wave envelope spectrum; standardized reconstruction characteristic vectors can be obtained; whether a rolling bearing malfunctions is determined, and a state of the rolling bearing is assessed. Via the rolling bearing fault on-line detection and state assessment method, the twelve dimensional dimensionless parameters which can be used for effectively representing the state of the rolling bearing can be automatically extracted, the twelve dimensional dimensionless parameters are subjected to decorrelation and standardization operation, standardized reconstruction characteristic vectors that are distributed to form a hypersphere with an original point being a sphere center, and fault detection and state assessment of the rolling bearing can be realized via 2-norms of the standardized reconstruction characteristic vectors; difficult problems of long on line training time, low efficiency, and hard-to-obtain fault samples and the like of a rolling bearing state assessing model can be solved.

The invention discloses a permanent magnetic direct-drive wind power generation system integrated fault diagnosis method. The permanent magnetic direct-drive wind power generation system integrated fault diagnosis method comprises the steps of conducting sampling and data pre-processing on multiple types of signals of a permanent magnetic direct-drive wind power generation system, utilizing a multi-wavelet-packet decomposition technology to extract sampling signal transient-state components of different frequency bands, calculating wavelet time entropies of sampling signals, training a support vector machine fault diagnosis model, and enabling the trained fault diagnosis model to output fault parts and fault type information corresponding to the wind power generation system. The permanent magnetic direct-drive wind power generation system integrated fault diagnosis method adopts a wavelet theory and the fault diagnosis model formed by multiple 'binary tree' support vector mechanisms, effectively improves the training speed and identification accuracy and is especially suitable for solution of the fault diagnosis problem of a small-sample, nonlinear and high-dimensional large-scale electromechanical system.

The invention discloses a method for diagnosing the fault of intelligent traffic capturing equipment based on an image abnormal characteristic. An aim of diagnosing the fault of the capturing equipment is fulfilled by a method for intelligently identifying an abnormal image by a computer, so that the complicated and low-efficiency manual diagnosis method is avoided. The method comprises the following steps of: firstly, establishing a mapping relation from the abnormal image to the fault of the equipment, and implementing a high-practicability license plate positioning method based on a color characteristic and a character texture characteristic according to the requirements of normal characteristics; secondly, adopting an abnormal image identification method based on multi-characteristic combination, and researching the adaptability of an identification algorithm; and finally, performing high-observability visualization processing on fault information of complicated comprehensive equipment. According to an equipment fault diagnosis system, fault diagnosis for the capturing equipment can meet requirements on high instantaneity, high accuracy and high efficiency; and a humanized and scientific fault diagnosis method is supplied.

The invention relates to a Bayesian network (BN)-based rolling bearing fault diagnosis method. According to a common rolling bearing fault diagnosis method, a mathematical model is required to be established, and an initial diagnosis effect is unsatisfactory; problems of the selection of a wavelet base function are unsolved; and the interpretability of a deduction process is low. The method comprises the following steps of: sampling a vibration signal of a bearing, acquiring a sample, performing N-point rapid Fourier transformation processing to convert a time-domain signal into a frequency-domain signal, calculating a fault characteristic vector, discretizing the fault characteristic vector, establishing a fault diagnosis reasoning BN model, setting a fault sample to be diagnosed, acquiring an observational evidence of the bearing, finishing updating the reliability Theta of a fault diagnosis type node Bearing in the BN model, calculating a fault diagnosis type node, and outputting a result. A complex mathematical modeling process for the vibration signal is avoided, an obtained diagnosis reasoning model has the advantages of a few characteristic parameters, prominent fault characteristics, high interpretability and the like, and an effective way for solving the problems of the rolling bearing fault diagnosis is provided.

The invention relates to an industrial process fault diagnosis method based on a direction kernel partial least square. The method is characterized in that historical normal data of an input variable and an output variable of an industrial process is acquired, wherein a fault is easily generated in the industrial process; an operation based on the direction kernel partial least square is performed on the historical normal data; a control limit of Hotelling statistics of the historical normal data and a control limit of a squared prediction error of the historical normal data are calculated; sampling data of the input variable of the industrial process is collected and the operation based on the direction kernel partial least square is performed on the sampling data so as to acquire process monitoring statistics of the sampling data and a squared prediction error of the sampling data are obtained; when the process monitoring statistics control limit of the sampling data or the squared prediction error of the sampling data exceeds the control limit, the sampling data possesses one kind of fault; historical fault data of a known fault type is acquired; reconstruction based on the Hotelling statistics and reconstruction based on the squared prediction error are performed on the historical fault data of the known fault type and a fault type of the sampling data is determined.

The invention provides a fault detection and fault locating method based on a differential geometry theory for an actuator of a flight control system. The actuator faults include damage, loose floating, stuck situations and saturation. In a non-linear flight control system, homeomorphic transformation can be performed in state and output space so that the original system is transformed into a plurality of observable quotient subsystems influenced by a single channel fault input. Design problems of residual error generators in the original system are transformed into the design problems of observers of the observable quotient subsystems in each channel so that residual errors can decouple from faults and structural interferences of other channels. A reasonable fault detection threshold is arranged. A channel has faults when the threshold output by the residual error generator exceeds the detection threshold. Fault detection and fault location can be realized and multiple faults can be effectively diagnosed. In the invention, a plurality of faults in the actuator can be timely detected; flight safety can be guaranteed; hardware structures of the flight control system can be simplified; and system costs can be reduced.

The invention discloses a fault diagnosis method for a deep adversarial migration network based on a Wasserstein distance. The distance of the feature distribution of the two fields in the feature space is measured through the wassertein distance, the feature distribution is adapted, the difference between two fields is reduced, field-independent features are learned to train an effective classifier, the classifier is responsible for mapping the field-independent features to a category space to complete a classification task, and the problem of unsupervised transfer learning of vibration datawithout labels in a target domain is solved.

The invention discloses a rolling bearing variable-work-condition fault diagnosis method based on visual cognition, and relates to a rolling bearing variable-work-condition fault diagnosis technology. The method comprises the following steps of converting rolling bearing vibration signals under the variable work conditions into a two-dimensional image by using a recurrence plot technology; performing feature extraction on the two-dimensional image by utilizing an SURF (speed up robust features) algorithm to obtain the vision invariability high-dimension fault feature vector; performing dimension reduction processing on the high-dimension feature vector by using an equal-distance mapping Isomap algorithm to obtain the low-dimension stable feature vector; using an SVD (singular value decomposition) algorithm for extracting the feature matrix singular value built by the low-dimension stable feature vector to form the final feature vector; performing fault classification on the final feature vector by using the trained classifier; performing fault diagnosis on the rolling bearing under the variable work conditions. The invention provides a novel solution for the rolling bearing fault diagnosis.

The invention discloses an electrochemical impedance spectrum prediction method for a high-power proton exchange membrane fuel cell stack. The method comprises the following steps: testing operating parameters and impedance spectrums of a high-power proton exchange membrane fuel cell under various working conditions by using a fuel cell test platform and an impedance spectrum analyzer; setting parameters of a long-short-term neural network according to the measured operation parameters and the impedance spectrum, and modeling the impedance of the proton exchange membrane fuel cell through thelong-short-term neural network; and predicting the impedance of the high-power proton exchange membrane fuel cell under various working conditions through the constructed model. According to the invention, the electrochemical impedance spectrum of the high-power proton exchange membrane fuel cell under each working condition can be accurately predicted, so that the fault diagnosis problem of the high-power proton exchange membrane fuel cell is effectively solved.

The invention discloses an electromechanical control system fault diagnosis method based on an iterative learning filter, and relates to the field of iterative learning control. The method takes the electromechanical control system which contains executor faults, uncertainty disturbance and output sensor non-uniform sampling as a research object, the own characteristics of a non-uniform sampling system are combined to give a novel fault diagnosis method based on the iterative learning filter, and the state-space equation of an executor fault non-uniform sampling electromechanical control system is constructed to equivalently transform an executor fault signal. An iterative learning fault diagnosis filter can be used for solving the fault diagnosis problem of the non-uniform sampling electromechanical control system, fault diagnosis efficiency and accuracy is high so as to be easy in engineering realization, various types of executor faults can be detected and estimated in real time, and meanwhile, executor fault diagnosis frequencies can be effectively reduced.

The invention discloses an equipment fault diagnosis method based on an improved 1DCNN-BiLSTM, and the method comprises the following steps: S1, preprocessing an original vibration acceleration signal by a self-adaptive white noise complete empirical mode decomposition (CEEMDAN) technology, and taking the preprocessed signal as input of a model; S2, constructing a 1DCNN-BiLSTM dual-channel model, inputting the preprocessed signal into two channels of a bidirectional LSTM model and a one-dimensional CNN model, and fully extracting the time sequence correlation characteristics of the signal, the non-correlation characteristics of the local space and the weak periodicity rule; S3, improving a SENet module and acting on two different model channels aiming at the problem that the signal is mixed with strong noise; and S4, fusing the two-channel extraction characteristics in a full connection layer, and realizing accurate identification of equipment faults by means of a Softmax classifier. To solve the problems of time sequence and noise inclusion of fault data in the industrial field, filtering and denoising preprocessing is carried out on original signals, a 1DCNN-BiLSTM dual-channel feature extraction module is constructed, a modified SENet module is integrated to realize weighting of feature channels, and the fault diagnosis efficiency of mechanical equipment is effectively improved.

The invention discloses an intelligent substation fault diagnosis method considering control center fault information tampering. The method comprises the steps of: firstly searching suspected fault elements through employing a fuzzy C-means clustering algorithm; evaluating the uncertainty degree of remote measurement of each suspected fault element through calculating a voltage sampling value clustering coefficient of each suspected fault element, thereby determining the input data of an fault element identification model; and finally, providing a fault element identification model based on a multivariate pulse neural membrane system and a corresponding multivariate pulse matrix reasoning algorithm, and solving to obtain fault diagnosis results of the suspected fault elements. According to the method, the problem of intelligent substation fault diagnosis under the condition that the fault information of a control center is tampered can be effectively solved, and the problems of high uncertainty and inaccuracy of the fault information caused by tampering of the fault information which cannot be handled by an existing method can also be solved.

The invention relates to an intelligent continuous caster control platform. The intelligent continuous caster control platform comprises a database, wherein the database is in bidirectional connection with an open unified standard heterogeneous data management platform, the open unified standard heterogeneous data management platform is in bidirectional connection with a private cloud system, an output end of the private cloud system is connected with an input end of an intelligent analysis decision system, the real-time data of the database is transmitted to the open unified standard heterogeneous data management platform, and the information outputted by the open unified standard heterogeneous data management platform is transmitted through the private cloud system to the intelligent analysis decision system. The intelligent continuous caster control platform is advantaged in that monitoring and management on a development process of each subsystem are realized, and transparent heterogeneous data exchange, intelligent decision, cooperative work and concentrated data storage and management are realized.

The invention discloses a garbage incinerator fault risk assessment method based on a fuzzy Petri network. The garbage incinerator fault risk assessment method comprises the steps that fault state risks possibly existing in the operation process of all subsystems of a garbage incinerator are assessed; the condition event and the relative probability of each fault state are evaluated; a fuzzy Petri net graph model is constructed; generating an input matrix, an output matrix, a place credibility vector and a transition confidence vector by combining the fault state, the fault state possibility, the condition event and the condition event relative probability of the garbage incinerator; converting the graph model into a mathematical model and carrying out iterative operation; and finally, the possibility of the key fault of the garbage incinerator is obtained. According to the method, the defect that an existing risk assessment method focuses on qualitative analysis is overcome, the possibility of various faults can be quantitatively obtained, the structure of the fuzzy Petri network is effectively utilized, concurrent faults among the subsystems are considered, and the reliability of fault diagnosis is improved.

The invention relates to a fault diagnosis method and system for a complementary classification regression tree based on differential evolution. The method comprises the steps of obtaining a sample set, wherein the sample set comprises sample signals corresponding to various fault types, and each sample signal is an operation signal of the equipment under the corresponding fault type; analyzing each sample signal in the sample set to obtain a sample feature vector set composed of all sample feature vectors; obtaining a complementary classification regression tree model by taking a genetic algorithm as a differential evolution basis according to the sample feature vector set; wherein the complementary classification regression tree model comprises an original classification regression treeand a complementary classification regression tree; determining an optimal classification regression tree in the complementary classification regression tree model based on the sum of Gini indexes ofall leaf nodes of the classification regression tree and the number of the leaf nodes to obtain a fault diagnosis model of the equipment; and carrying out fault diagnosis on the equipment by adoptinga fault diagnosis model of the equipment based on the operation signal of the equipment. According to the invention, the equipment fault diagnosis performance can be improved.