524results about How to "Implement fault diagnosis" patented technology

The invention provides a universal circuit breaker mechanical fault diagnosis method based on feature fusion of vibration and sound signals. The method includes steps of 1, collecting machine vibration signals and machine sound signals during an engaging and disengaging process of a universal circuit breaker; 2, adopting an improved wavelet packet threshold value denoising algorithm for denoising; 3, adopting a complementary total average empirical mode decomposition algorithm for extracting a plurality of solid mode function components reflecting state information of engagement and disengagement actions of the circuit breaker from the denoising signals; 4, determining the number Z of the solid mode function components; 5, calculating the energy ratio, the sample ratio and the power spectrum entropy as three types of features; 6, adopting a combination core principal component analysis method for performing dimension reduction on a feature sample with unified three types of features of the vibration and the sound signals and obtaining M principle components; 7, establishing a related vector machine based sequence binary tree multiple classifier model.

The invention discloses a performance prediction and fault alarm method for a photovoltaic power station. The method comprises the following steps of: a, setting the station of the power station; b, setting the operation mode of the power station; c, judging whether required real-time data or historical data exists or not; d, predicting the performance of the power station through an experience model if the state in the step a is that a new photovoltaic power station is required to be designed and the required real-time data or historical data in the step c does not exist, and predicting the performance of the power station through a data drive performance model if the required real-time data or historical data in the step c exists; e, predicting the performance of the power station through the data drive performance model or a polynomial regression model if the photovoltaic power station in the step a is operated and the required real-time data or historical data in the step c exists, and predicting the performance of the power station through the experience model if the required real-time data or historical data in the step c does not exist; f, comparing actual performance with the predicted performance, and performing fault alarm; and g, correcting the models on line by a Kalman filtering method and returning to the step c, and otherwise, directly returning to the step e. By the method, solar energy resources can be utilized to the maximum extent, and power utilization cost can be reduced; and the accuracy of performance prediction and fault diagnosis is improved.

The invention discloses a power transmission line evaluation and diagnosis method, which includes the following steps: (A) acquiring the power data, abnormal power data and environmental data of a power transmission line on the sensing layer, including the voltage, current, electric quantity, power, frequency, power factor, phase, harmonic waves, power voltage loss, power phase failure, electricity theft, temperature, humidity, icing, wind deflection, lightning strike, power transmission tower side wind speed and rainfall, tension, stress, salt density, sag, displacement and acceleration of each node of the power transmission line; (B) transmitting the acquired data to the application layer via a network layer; (C) carrying out fluctuation analysis on the data on the application layer, carrying out evaluation and analysis on the power transmission line according to an analysis result, and remotely monitoring the site of the power transmission line via an Internet fixed port and the IP (Internet Protocol). The invention also discloses a power transmission line evaluation and diagnosis system. The invention realizes on-site monitoring and fault diagnosis on power transmission lines, and not only meets the requirement of remote information management, but also achieves an on-site equipment monitoring extension function.

The invention relates to a fault monitor and diagnosis method of a continuous annealing unit, in particular to a PCA (Principle Component Analysis) model based furnace temperature and tension monitoring of a continuous annealing unit, mainly comprising the following steps of firstly, according to process variable data obtained in the field, and establishing a temperature and tension monitor modelby utilizing a principle component analysis PCA method; secondly, establishing an off-line model and calculating the T2 statistics quantity and the SPE statistics quantity as well as contributed control limits thereof by utilizing the data, obtained in step one, when process variable is in a normal work condition; thirdly, applying an on-line model, calculating the T2 statistics quantity and the SPE statistics quantity of current data, monitoring whether a current state is normal or not according to information supplied by the off-line model, and giving alarm signals if abnormal; fourthly, determining a leading variable which causes a fault by utilizing contribution of the T2 statistics quantity and contribution of the SPE statistics quantity. The invention monitors the furnace temperature and tension in real time in the production process and traces back a fault reason for leading to system abnormality when the abnormality occurs.

An equipment fault diagnosis method successively comprises fault diagnosis training steps and fault diagnosis operation steps. The method focuses on a data form characteristic essence and combines a multi-dimensional segmentation fitting algorithm and an optimized dynamic time warping algorithm based on equipment fault data to carry out pattern expression of modeling and distance threshold extraction, and extract form characteristics from the found equipment abnormity data to carry out mode matching, so that an equipment data fault type can be identified and a reason diagnosis function is realized. In this way, the difficulties in current fault diagnosis technologies that similarity degrees among the fault data can hardly be described efficiently and accurately can be solved.

The invention discloses an equipment fault diagnosis method based on multidimensional segmentation fitting. The method successively comprises a fault diagnosis training step and a fault diagnosis operation step. The method realizes mode expression and distance threshold extraction functions of modeling through combining a multidimensional segmentation fitting algorithm and an optimized dynamic time bending algorithm by focusing on the essence of data form characteristics on the basis of equipment fault data, and realizes equipment data fault type identification and cause diagnosis functions by performing mode coupling through extracting form characteristics of discovered equipment abnormal data, thereby solving the problem of incapability of efficiently and accurately describing the similarity degree between fault data by use of a conventional fault diagnosis technology.

The invention discloses a satellite attitude control system failure diagnosis device based on a state observer and an equivalent space and a satellite attitude control system failure diagnosis method based on the state observer and the equivalent space, which belong to the field of aerospace and aim to solve the problems of high hardware complexity, low control accuracy and low failure diagnosis algorithm effectiveness of the conventional failure diagnosis method. The method provided by the invention comprises the following steps that: 1, a failure diagnosis observer outputs a satellite triaxial angular rate residual according to output signals of an actuator and a gyro sensor; 2, an equivalent vector space description module constructs equivalent space descriptions of the gyro sensor according to the output signal of the gyro sensor, wherein an output equivalent vector p is used for judging whether the gyro sensor fails or not; and 3, a failure diagnosis and isolation module outputs a failure separation result indicating that the actuator or the gyro sensor fails according to the satellite triaxial angular rate residual obtained by the step 1 and the equivalent vector p obtained by the step 2, and further judges which axis of the failing part fails.

The invention relates to an embedded type state monitoring information adaptor capable of operating under complex working conditions of a numerically-controlled machine tool and a method thereof. Theembedded type state monitoring information adaptor mainly comprises three modules of a PLC communication module, a standard information output module and a configuration module. The embedded type state monitoring information adaptor communicates with a monitor computer by the Modbus based transmission control protocol/internet protocol to realize the receiving and the transmission of the data information; the embedded type state monitoring information adaptor communicates with the control PLCs in real time and receives and transmits the data; and the embedded type state monitoring informationadaptor detects the communication states with all control PLCs in real time, and if errors occur, the embedded type state monitoring information adaptor stores the diagnostic information in a designated buffer memory area. In the adaptor, the isomorphism of the equipment in an isomerical environment is realized as the signals of equipment from different enterprises are uniformly converted, and the'data rather than people are transmitted' is realized by intelligently monitoring and diagnosing the numerically-controlled machine tool by the network, so the passive situation that the maintenancepersonnel is busied at maintaining the equipment is changed when the equipment is out of work.

The invention provides an opening-closing fault diagnosis method for an air circuit breaker based on vibration signals, wherein an acceleration sensor is used to collect machine body vibration signals generated during opening-closing courses of the air circuit breaker. The method comprises the steps that firstly, the acceleration sensor is used to collect the machine body vibration signals generated during opening-closing actions of the air circuit breaker and transform the vibration signals into digital signals, so that initial vibration signals are obtained; secondly, an improved wavelet packet threshold de-noising algorithm is used to process the collected vibration signals; thirdly, a complementary ensemble-average empirical mode decomposition algorithm is used to extract intrinsic mode function components from the de-noising vibration signals; fourthly, the quantity Z of the intrinsic mode function components is determined; fifthly, the intrinsic mode function components of the first Z orders are selected and extracted as sample entropies of a characteristic quantity; sixthly, binary tree multi-classifiers based on a relevance vector machine are established; and seventhly, the binary tree multi-classifiers based on the relevance vector machine obtained at the sixth step are used to establish a fault recognition model of the air circuit breaker.

The invention discloses a stacked noise reduction self-coding motor fault diagnosis method based on vibration and current signals, and the method comprises the following steps: 1, obtaining the time domain signals of the vibration and current of the motor during different faults, carrying out the preprocessing, and taking the processed signals as network input; 2, determining network parameters; 3, carrying out the layer by layer training, taking a hiding layer of an AE (Auto encoder) at an upper level as the input layer of an AE at a lower level, thereby obtaining a final feature code which is used for training a Softmax network; 4, carrying out the fine tuning of the whole network, judging whether the expected precision is met or not: finishing the training of the network if the expectedprecision is met, or else adjusting the network parameters, and repeatedly carrying out the step 3; 5, finishing the network construction. According to the invention, the multilayer SDAE network is constructed, and the vibration frequency domain signal and the current time domain signal are combined as the input. The SDAE network and a classifier are sequentially trained, and the supervised finetuning of the whole network is carried, thereby achieving the precise diagnosis of the fault of the motor.

The invention discloses a fault diagnosis method of a wind power transmission system based on a depth generation adversarial network. The fault diagnosis method of the wind power transmission system based on the depth generation adversarial network comprises the following steps: existing wind turbine generator transmission system vibration data of different kinds of working conditions under load are collected, and large wind turbine generator transmission system cross-domain sample training set is established; depth generation adversarial network modules are constructed; the depth generation adversarial network is pre-trained; and an on-line diagnosis to the large wind turbine generator transmission system is carried out by adopting trained modules. According to the fault diagnosis methodof the wind power transmission system based on the depth generation adversarial network, according to similarity and difference of data between a source domain and a target domain, the depth generation adversarial network is used to melt and rectify data between the two domains, similarity features can be extracted from cross domain data by a multi-layer stack self-coding network structure, and differences between the source domain and the target domain can be further rectified by a domain discriminator based on a softmax sorter, and a malfunction diagnosis of the target domain can be realizedby adopting rich knowledge of the source domain.

The invention relates to a network message monitoring and fault recording integrated device for an intelligent transformer substation. The device comprises a signal access unit, a signal processing and analyzing unit and an information output unit, wherein the signal access unit is used for receiving comprehensive signals, which contain a primary system signal and a secondary system signal of the intelligent transformer substation and transmitting the comprehensive signals to the signal processing and analyzing unit; the signal processing and analyzing unit is used for analyzing the comprehensive signals of the intelligent transformer substation; and the information output unit is used for outputting the alarm signal-containing comprehensive signal analyzing result after the analysis of the comprehensive signals. The device comprehensively analyzes the comprehensive signals, which contain the primary system signal and the secondary system signal, brings great convenience to operation, maintenance, modulation and breakdown maintenance, shortens the troubleshooting time, improves the power supply quality of the intelligent transformer substation, saves the space occupied by the traditional equipment and saves investment.

The invention relates to a remote real-time monitoring and intelligent video remote viewing system for a wind farm based on a 3G network. It includes an on-site intelligent measurement and control system (100), a local control center (110) and a central remote control center (120). The on-site intelligent measurement and control system (100) is composed of a data measurement and control terminal (101), an intelligent video terminal (102), a data concentration, analysis and processing module (103), and a 3G wireless data transmission module (104). The fault diagnosis units (111) and (121) are respectively located in the local control center (110) and the central remote control center (120). The invention can realize real-time wind farm status monitoring and fault diagnosis, actively transmit wind farm status information to the remote monitoring center through the 3G network, and the remote monitoring center analyzes the information, and performs fault diagnosis if there is a fault; adopts 3G wireless network transmission mode, It has the characteristics of low construction cost, convenient maintenance and easy expansion.

The invention discloses a battery system fault diagnosis method and a system. According to the method, on the basis of acquired temperature signals, humidity signals, battery current signals and battery voltage signals of the battery system, a BP neural network in a multi-input and multi-output structure is used to complete battery system fault diagnosis. The system comprises a data acquisition module, and a neural network fault diagnosis module, wherein the data acquisition module is used for acquiring temperature signals, humidity signals, battery current signals and battery voltage signals of the battery system; and the neural network fault diagnosis module is used for using the BP neural network in the multi-input and multi-output structure to complete battery system fault diagnosis on the basis of the acquired temperature signals, the humidity signals, the battery current signals and the battery voltage signals of the battery system. Signals of multiple elements of the battery system are fused, fault diagnosis on a complicated battery system is realized, the result has a high accuracy rate, the diagnosis efficiency is high, an adaptive correction function is provided, and the method and the system of the invention can be used for fault diagnosis of a power battery system and an energy storage battery system of an electric vehicle.

The invention relates to a multifunctional intelligent vehicle networking terminal and a method for implementing the multifunctional intelligent vehicle networking terminal, aiming to solve the problem that the existing vehicle-mounted satellite communication products do not have the function of automatically diagnosing the vehicles. The multifunctional intelligent vehicle networking terminal comprises hardware and software; the hardware comprises an upper cover, a lower cover, a satellite positioning system module, a communication antenna, a satellite positioning antenna and the like; the software comprises an operation system layer, a driving layer and an application program layer; the operating system is an embedded real-time operating system and takes charge of the scheduling, management and communication of all tasks; the driving layer is mainly used for completing the configuration, reading, writing and starting of all modules; and the application program layer is used for filling different functions by calling the interface function of the driving layer. Combined with an e-line vehicle networking service system and an intelligent mobile phone, the multifunctional intelligent vehicle networking terminal can realize monitoring, dispatching, fuel consumption management, mileage management, fault diagnosis, a real-time road condition acquisition, incident detection, driving behavior optimization, automatic hibernation, remote upgrade and the like. The multifunctional intelligent vehicle networking terminal is convenient to install and easy to use and has small size and low cost.

The invention relates to a hydroelectric generating set fault diagnosis method based on deep learning. The method comprises the following steps of firstly, acquiring a vibration signal when a hydroelectric generating set runs as a sample, and establishing a database; secondly, preprocessing data, namely reconstructing the original vibration signal; thirdly, segmenting the reconstructed data set into a training set, a verification set and a test set; fourthly, training a network combining a one-dimensional convolutional neural network (1-D CNN) and a gated recurrent unit (GRU), and optimizing network parameters to avoid network overfitting; and finally, establishing a hydroelectric generating set fault diagnosis model by utilizing the trained network parameters, and inputting test set samples into the model to realize the hydroelectric generating set fault diagnosis. According to the method, the accuracy of the hydroelectric generating set fault diagnosis can be improved.

The invention discloses a machine vision detecting system for detecting defects of a rolling roll grinding surface, belonging to the field of grinding processing surface defect detection. The system comprises a cleaning device, an image acquisition device, an image acquisition control unit and an image processing and analysis unit, wherein the cleaning device can be used for forming a uniform laminar flow liquid layer on the surface of a rolling roll to be detected; the image acquisition device is arranged around the rolling roll to be detected; the image acquisition control unit is respectively connected with the rolling roll to be detected and the image acquisition device; the image processing and analysis unit is electrically connected with the image acquisition device and is used for realizing defect classification identification, defect evaluation and over-limit alarm as well as fault diagnosis. The machine vision detecting system can be used for carrying out qualitative and quantitative detection on the defects such as short scratches, micro-vibration, spots, spiral patterns and reticulate patterns on the rolling roll grinding surface, overcomes the defects that the existingartificial naked eye detection method is high in subjectivity and lower in accuracy, easily causes inspection missing, can not realize quantitative detection, and the like, and improves detection efficiency, accuracy and reliability.

An intelligent motorcar body network system comprises a semaphore input module, a front motorcar lamp module, a rear motorcar lamp module, a CAN / LIN gateway module, an electric window module, a wiper module, a central lock module and a rearview mirror module. The present invention applies both CAN and LIN buses for networking, realizing the distributed layout of a network, wherein the front motorcar lamp module, the rear motorcar lamp module, the semaphore input module and the CAN / LIN gateway module are connected together via the CAN bus to construct a network, along with which a motorcar power CAN network forms a large network, and the electric window module, the wiper module, the central lock module, the rearview mirror module and the CAN / LIN gateway module are connected together via the LIN bus to form a LIN network. The present invention is connected with the upper-layer CAN network by means of the CAN / LIN gateway module to perform information interaction. The present invention realizes the functions of information interaction with the motorcar power CAN bus, power supply management, etc. and reduces cost and cabling difficulties.

The invention relates to a logic control unit and a logic control method for trains. The logic control unit (LCU) adopting a hot standby redundant design comprises an input module, an output module and at least two redundant designed master control modules, wherein the output end of the input module is respectively connected with each master control module, and the output end of each master control module is connected with the output module; the input module is used for collecting input direct current voltage information and transmitting to the master control module; the master control modulesare used for acquiring direct current voltage signals, calculating to generate a load-driving control signal and transmitting to load through the output module; and the input module, the output module and the master control modules can be configured into a double 2-vote-2 redundant structures. Each module of the system brings own self-diagnosis circuit, failure diagnosis and redundant switching of each redundant circuit can be realized, and the system reliability can be improved; and the system is configured into to double 2-vote-2 structure, the system safety can be further improved.

The invention relates to a random sampling analog circuit compressed sensing measurement and signal reconstruction method, which belongs to the field of electronic system test and fault diagnosis. Aiming at a fault signal having a sparsity distribution characteristic per se or in an orthogonal space in an output response of an analog circuit, a test node is selected according to a circuit topology structure, circuit output responses are randomly sampled under a distributed sensor test network, response signals are expressed in a sparse way on a transform domain by utilizing discrete orthonormal basis, compressed sensing measurement of the sparse signals is completed under observability matrix projection, and when the recovery rate of signal reconstruction by randomly compressed sampling points reaches more than 80 percent, the compressed measurement values of the circuit output responses are effective, can form a characteristic set and can be used for analog circuit fault diagnosis. The method solves the problems that the traditional analog signal sampling occupies a large number of hardware resources, large signal reconstruction calculated amount and the like; and the random sampling compressed sensing measurement method is utilized to improve the efficiency of electronic system testing.

The invention discloses a wind turbine generator bearing fault diagnosing method based on a deep joint adaptation network. The wind turbine generator bearing fault diagnosing method based on the deepjoint adaptation network comprises the following steps: 1), establishing a multi-element fusion database; 2), establishing a deep joint adaptation model; 3), establishing a wind turbine generator bearing fault diagnosing model of the deep joint adaptation network; 4), establishing a multi-GPU cluster computing system. With the wind turbine generator bearing fault diagnosing method, according to distribution difference characteristics between training data and target data during monitoring under different actual working conditions, an inter-domain invariant characteristic representing and probability distribution difference correcting mechanism is explored, a fault target recognition strategy based on an inter-domain joint distribution adaptation and common characteristic deep learning fusion mechanism is provided, advantages of a deep self-encoding network can be utilized, the characteristics are not required to be artificially selected, better, abstract and advanced characteristics can be automatically extracted, and the computational complexity of a classification algorithm is reduced; the wind turbine generator bearing fault diagnosing method is especially suitable for a multi-noise, large-data, complex-structure and dynamic system.