35results about How to "Improve troubleshooting performance" patented technology

The invention discloses an EEMD-based multi-scale fuzzy entropy OLTC fault diagnosis method. The method comprises steps: (1) an accelerating vibration sensor is placed on the top cover of an on-load tap changer, the vibration signals generated during the action process of the on-load tap changer in a normal state, a contact loose state, a contact wear state and a contact burnout state are acquiredrespectively, and multiple groups of vibration signals under each state are collected respectively; (2) the original vibration signals are subjected to EEMD to obtain IMF components; (3) first multiple IMF components are selected, and the fuzzy entropy of the selected IMF components is calculated; and (4) the calculated fuzzy entropy is used as a feature vector and is inputted to an SVM for training, an SVM classifier is obtained, and the SEn value of an IMF component of a test sample is inputted to the SVM classifier for working state recognition. The method can monitor the working state ofthe transformer on-load tap changer in real time, and the requirements of OLTC real-time fault diagnosis are met.

The invention provides a transformer partial discharge fault diagnosis method based on hierarchical threshold synchrosqueezed wavelet. The method is characterized by comprising the following steps of: 1, subjecting a transformer partial discharge signal f(t) to SWT decomposition to obtain an SWT decomposition coefficient S<y><a(i)>(t), wherein the i = 1, 2, L, I; 2, estimating a noise standard deviation [sigma]n by using a formula described in the specification; 3, assigning values to ca(i) step by step at a fixed step length for the ith coefficient of the SWT, calculating the minimum mean square error by a mean square error formula, and determining the optimal value of the ca(i); 4, calculating the hierarchical threshold [gamma]a(i) = ca(i) [sigma]n, and achieving the ith SWT coefficient noise reduction through the ith SWT coefficient formula; 5, using a noise reduction formula to reconstruct de-noised layer coefficients to obtain a de-noised de-noising transformer local discharge signal y(t); and 6, performing feature extraction on the de-noised de-noising transformer local discharge signal y(t), and performing fault diagnosis according to extracted features.

The invention discloses a fault diagnosis method, system and device for a permanent magnet synchronous motor as well as a readable medium. The fault diagnosis method for the permanent magnet synchronous motor comprises the following steps: carrying out calculation based on an analytic relationship model from stator current to stator flux linkage to obtain a first set of stator flux linkage values;carrying out calculation based on an analytic relationship model from stator voltage to the stator flux linkage to obtain a second set of stator flux linkage values; solving the difference between the second set of stator flux linkage values and the first set of stator flux linkage values to serve as an initial flux linkage residual; carrying out negative sequence primary and negative sequence secondary synchronous coordinate transformation on the initial flux linkage residual to generate a primary flux linkage residual and a secondary flux linkage residual; and extracting direct current components of the initial flux linkage residual, the primary flux linkage residual and the secondary flux linkage residual, and comparing the direct current components with preset thresholds respectivelyto determine relevant information about permanent magnet faults or sensor faults. Through the application of the above scheme, multiple faults existing in a phase current sensor, a rotor position sensor, a permanent magnet and the like of the permanent magnet synchronous motor can be detected online.

The invention discloses a transformer fault type diagnosis method based on a semi-supervised DBNC (deep belief network classifier). The method comprises the following steps: selecting a sample data set; dividing sample data into an unlabelled pre-training set, a labelled set, a test set 1 and a test set 2; performing state coding on a fault type; building a DBNC-based transformer fault diagnosis model; initializing parameters of each layer of the model; training each RBM at the bottom layer by layer by utilizing comparison divergence; optimizing the whole network parameter through back propogation, so as to enable network classification performance to be globally optimal; and storing a trained network, and verifying classification performance of a network by utilizing sample data of the test set 1. The method disclosed by the invention solves the problems that only a small amount of complete data samples can be obtained under normal conditions when deep learning network fault data is adopted for performing analysis and processing in transformer fault diagnosis, a large number of labelled complete data samples are very difficult to acquire and a large amount of manpower and resources are required.

A fault determination system of a button-type shifter is provided. The system maximally guarantees the operation of a shift button while determining a failure of the shift button and improves reliability and fault diagnosis performance for the shift button. The system includes a base that is installed within a vehicle and includes shift buttons and contact points for each of the shift buttons. A controller receives contact signals from the contact points to thus determine a failure of the shift button when any one of the contact points is not sensed or is stuck when the shift button is engaged.

The invention discloses a Sobel operator and Wigner-Hough transform based gear fault feature extraction method, which comprises the steps of (1) inputting a gear fault signal S(t); (2) calculating to acquire Wigner-Ville distribution of the gear fault signal; (3) using the Wigner-Ville distribution acquired in the step (2) to act as an image, and performing edge detection by using a Sobel operator firstly; and then (4) extracting fault signal features through Hough transform. According to the invention, a time-frequency spectrum of the gear fault signal is regarded as a two-dimensional image, analysis and state recognition are performed by applying image processing, the fault diagnosis effect is good, and the signal detection result is more reliable.

The invention relates to the technical field of spacecraft fault diagnosis, and provides a spacecraft intelligent fault diagnosis method based on a deep neural network in order to realize spacecraft intelligent fault diagnosis, guarantee safe and stable operation of a spacecraft and reduce the detection cost, and the method comprises the steps: firstly, building a fault diagnosis model based on the deep convolutional neural network; extracting fault features from the telemetry data set with strong noise; secondly, pre-training the network by using empirical data of other spacecrafts to obtain initial network parameters; and finally, on the basis of a domain adaptive method in transfer learning, constructing a cost function based on maximum mean difference, and performing parameter readjustment on the network model, thereby improving the accuracy of fault data diagnosis. The method is mainly applied to spacecraft fault detection and diagnosis occasions.

The invention provides a VMD-SSAE-based rolling bearing fault diagnosis classification method. The method comprises the steps of collecting vibration signals of rolling bearings of different fault types; performing time domain, frequency domain and time-frequency domain feature extraction on the vibration signal based on variational mode decomposition; forming a data set by the features, and dividing the data set into a training set, a verification set and a test set; connecting a stack sparse auto-encoder with a Softmax classifier, constructing a VMD-SSAE classification model, and using the training set to train the VMD-SSAE classification model; adopting a grey wolf optimization algorithm and an error back propagation algorithm to optimize the VMD-SSAE classification model, and obtaining an ideal VMD-SSAE classification model; and inputting data in the test set into the ideal VMD-SSAE classification model to obtain a diagnosis classification result. According to the VMD-SSAE-based rolling bearing fault diagnosis classification method provided by the invention, the fault type of the rolling bearing can be diagnosed more accurately.

The invention discloses an analog circuit fault test diagnosis method, which comprises the following steps of: acquiring fault information at each test node of a circuit to be diagnosed by adopting different types of power supply signals as test excitation signals; Determining an optimal test excitation signal and an optimal test node set by using a fuzzy clustering FCM algorithm according to thecollected fault information; Respectively acquiring fault feature information of the circuit in the determined optimal test node set; Carrying out dimension reduction on the fault feature informationby adopting a sparse random projection method, and carrying out normalization processing to obtain a preprocessed fault sample; And carrying out fault diagnosis on the fault sample by using a naive Bayes classifier. According to the invention, the optimal test excitation signal and the optimal test node set can be automatically selected, and the effect of analog circuit fault diagnosis is greatlyimproved. By adopting the sparse random projection method, redundancy and interference components of fault characteristic attribute types are removed, and the data preprocessing time can be saved.

The present invention provides a system and a method for the fault diagnosis of a blast furnace. The system comprises a historical data acquisition module, an actual data acquisition module, a feature weight matrix construction module, a model building module and a blast furnace fault diagnosis module. The method includes the steps of collecting the actual attribute data, the historical attribute data and the corresponding fault state types of the production condition of a blast furnace; according to the importance degree of each attribute for the fault diagnosis, determining the feature weight of the attribute and constructing a feature weight matrix; establishing a twin hyper-sphere support vector machine model for the feature weighting of the fault diagnosis of the blast furnace; taking the actual attribute data of the production condition of the blast furnace into the above established twin hyper-sphere support vector machine model to obtain the operation fault state type of the blast furnace that corresponds to the actual attribute data of the production condition of the blast furnace; and completing the fault diagnosis of the blast furnace. According to the technical scheme of the invention, the importance degree of each attribute for the fault diagnosis of the blast furnace is quantized. Meanwhile, the importance degree of each attribute is integrated into the constructing process of a learning machine. Therefore, the accuracy of the fault diagnosis is improved.

The invention discloses an OLTC mechanical fault diagnosis method based on sample entropy and SVM. The method comprises the steps that step 1, an acceleration vibration sensor is placed on an OLTC topcover to collect vibration signals in various states; step 2, performing EEMD decomposition on the original vibration signal to obtain components IMF, and further processing the first four IMF components; step 3, calculating and selecting a sample entropy of the IMF component; step 4, for the training data set, using the sample entropy obtained through calculation as a feature vector, inputting the feature vector into an SVM for training to obtain an SVM classifier, inputting the SampEn value of the IMF component of the test sample into the SVM classifier, and outputting the SampEn value of the IMF component of the test sample through the SVM classifier to obtain the operation state of the test sample. The working state of the transformer on-load tap-changer can be monitored in real time,the real-time fault diagnosis requirement of the transformer on-load tap-changer is met, data support and theoretical basis are provided for targeted maintenance, and waste of manpower, material resources and time is avoided.

The invention provides a fault diagnosis method and device based on a knowledge graph, equipment and a medium. The method comprises the steps that when a device breaks down, the feature vector of a current fault device is determined based on a device fault knowledge graph, and the device fault knowledge graph is constructed based on fault diagnosis data of historical fault devices; determining the similarity between the feature vector of the current fault device and the feature vector of each historical fault device in the device fault knowledge graph; and determining the diagnosis result of the historical fault equipment corresponding to the highest similarity as the diagnosis result of the current fault equipment, and pushing a solution corresponding to the diagnosis result to the current fault equipment. The accuracy of equipment fault diagnosis can be improved, and the equipment fault diagnosis effect is improved.

The invention discloses an FFCNN-SVM transfer learning fault diagnosis method based on feature fusion under a small sample. The method comprises the following steps: migrating a mature model in a source domain to a target domain through a model migration method in migration learning to form a preliminary model of the target domain; then, by utilizing a convolutional layer and extracting characteristics of picture features, adding the convolutional layer on the preliminary model, then training the preliminary model by utilizing a small amount of scarce sample data provided by the target domain, and forming a target domain shallow model after fitting; replacing a full connection layer of the CNN with the SVM to achieve a classification effect. The method is advantageous in that: through a bearing fault data set, the new fault diagnosis performance of the method can be well verified; experimental results show that compared with other transfer learning methods, the method provided by the invention has a better fault diagnosis effect.

The invention provides a fault diagnosis method based on multi-head attention and shafting equipment periodicity. The method comprises the following steps: step 1, collecting to-be-diagnosed samples of the shafting equipment in a plurality of periods, adding periodic information of the shafting equipment into the to-be-diagnosed samples, and performing standardization processing; and 2, taking the standardized sample data as the input of a multi-head attention fault diagnosis model to obtain a fault diagnosis result. Aiming at the characteristics of periodicity, nonlinearity and coupling of shafting equipment vibration signals, the method integrates the periodicity characteristics of shafting equipment into time domain fault signal data, distinguishes the directionality of long-distance information by using two position codes, and has relatively strong long-distance information extraction capability and relatively high parallel calculation capability.

The invention provides an information processing method and an information processing device for an optical fiber heading and attitude system, a facility and a storage medium. A first time mark is adopted to mark a first memory, a second time mark is adopted to mark a second memory, if the first time mark is set to be greater than the second time mark, the first memory records current system information; if the first memory is saturated, the first time mark is transmitted to the second time mark, and the second memory records the current system information; the system information of the firstmemory is backed up to a third memory; the system information of the first memory is erased; if the second memory is saturated, the second time mark is transmitted to the first time mark, the first memory records the current system information, and the system information of the second memory is backed up to the third memory; and the system information of the second memory is erased. According to the method, the built-in self-inspection of the optical fiber strapdown heading and attitude system is realized, the information is stored in real time, and the testability, maintainability and fault diagnosis capability of the product are improved.

The invention discloses a rolling bearing fault diagnosis method based on an adaptive termination criterion OMP, and the method comprises the steps of calculating the theoretical fault characteristic frequency of a diagnosed bearing through combining the geometric parameters and rotating speed of the diagnosed bearing; analyzing the frequency spectrum of the fault signal, and observing the approximate interval of the high-frequency natural vibration frequency of the bearing; setting a Laplace wavelet parameter set of the initial dictionary; obtaining wavelet frequency and damping which are most matched with fault impact components in the fault signals through a correlation filtering method, and building a needed complete dictionary; using an adaptive termination criterion OMP algorithm to decompose the fault signal, searching the optimal algorithm iteration frequency, and rebuilding a fault impact component in the signal; and analyzing the envelope spectrum of the reconstructed signal, and extracting the fault characteristic frequency in the spectrum to complete fault mode diagnosis. According to the invention, the fault impact component can be reconstructed from the fault signal more accurately, the fault characteristic frequency is extracted, and fault diagnosis of the rolling bearing is realized.

The invention discloses an elevator fault judgment method and system based on big data feature analysis, and the method comprises the steps: firstly obtaining a vibration signal of an elevator car, carrying out the preprocessing and fast Fourier transform of the vibration signal, converting the vibration signal into a feature map, carrying out the graying and normalization processing of the feature map, and carrying out the recognition of the vibration signal; the method comprises the following steps: firstly, processing a feature map, then taking the processed feature map as a training sample which comprises a normal sample and a fault sample, inputting the training sample into a CNN (Convolutional Neural Network) for learning and training, enabling a model to have a better fault diagnosis effect by optimizing parameters, and realizing real-time judgment on a signal by utilizing the trained CNN so as to quickly and accurately diagnose an elevator fault.

A data collection device is disclosed, which is used to collect data according to received instructions, including: N analog boards, the N analog boards are sequentially connected in series, and N is a natural number greater than or equal to 1 for collecting data; digital boards , connected to the first analog board in the N analog boards, for forwarding the received instructions to the N analog boards, and uploading the data collected by the N analog boards according to the instructions; wherein, The first analog board among the N analog boards receives the instructions forwarded by the digital board, and sequentially forwards them to the next-level analog boards, and the N analog boards collect data according to the received instructions, and forward them sequentially to the upper The primary analog board is sent to the digital board through the first analog board among the N analog boards. The invention also provides a data acquisition system and method thereof, which have strong anti-interference ability, strong real-time data transmission ability, high data transmission bandwidth, convenient fault diagnosis and high reliability.

The invention discloses a novel deep feature learning method for planetary gear fault diagnosis. The method comprises the following steps: a, detecting an original vibration signal generated in the operation process of a planetary gear box of electromechanical equipment by using a vibration sensor; b, introducing a sparsity penalty term and a contractibility limit term on the basis of the loss function of the automatic coding machine; c, optimizing specific positions and key parameters of each sparse automatic coding machine and each contraction automatic coding machine in the deep learning architecture by using a quantum ant colony optimization algorithm; d, determining the initial depth of the deep learning architecture and the initial width of each layer by taking the acquired originalvibration signal of the planetary gear box as the input of the novel deep learning architecture. According to the novel deep feature learning method for planet gear fault diagnosis provided by the invention, the data learning capability and the feature extraction robustness can be exerted to the optimal at the same time, and the positions of a sparse automatic coding machine and a contraction automatic coding machine in a deep learning architecture can be actively adjusted.

The invention provides a blast furnace fault diagnosis system and method. The system includes: a historical data collection module, an actual data collection module, a feature weight matrix construction module, a model building module, and a blast furnace fault diagnosis module. The method includes: collecting actual attribute data of blast furnace production conditions, historical attribute data and corresponding blast furnace operation fault state types; determining the characteristic weight of each attribute according to the importance of each attribute for fault diagnosis, and constructing a characteristic weight matrix; establishing a method for blast furnace Feature-weighted twin hypersphere support vector machine model for fault diagnosis; the actual attribute data of the blast furnace production status is brought into the established feature-weighted twin hypersphere support vector machine model to obtain the type of blast furnace operating fault state to which the actual attribute data of the blast furnace production status belongs. Complete blast furnace fault diagnosis. The present invention quantifies the importance of each feature of blast furnace faults and integrates the importance of each feature into the construction process of the learning machine to improve the accuracy of fault diagnosis.

The invention relates to a fault detection method of a motor. Aiming at the shortcomings of the prior art in detecting motor faults by detecting the motor current, the present invention discloses a motor fault detection method. The technical scheme of the present invention greatly improves the It improves the fault diagnosis performance when the disturbance is too large, and realizes fault diagnosis and online status monitoring through multiple analysis of multiple fault characteristics of the motor current. Multiple analysis of the collected fault frequency greatly improves the diagnostic reliability and almost avoids misstatement and omission. The invention is very suitable for on-line detection of motor faults of electric vehicles.

The present invention provides a real-time intelligent diagnosis method and system for bearing faults based on the Attention CNN model, including using a vibration sensor to collect faulty bearing vibration signals, and then segmenting the faulty bearing vibration signals using a fixed-length random segmentation method to obtain data samples; After the data samples are affixed with labels corresponding to each type according to the state type of the rolling bearing, they are divided into a training set, a verification set and a test set according to a certain ratio; according to the data in the training set and the verification set, a variety of data in the The bearing fault data set in the unbalanced state and all the bearing fault data sets produced constitute the unbalanced data set; construct the above model, train the above model with different bearing fault data sets respectively, and obtain the above training model; use the above training model Real-time fault detection is performed on the rolling bearing. The invention can identify the operating state of the bearing in real time, accurately and automatically, thereby effectively maintaining the normal operation of mechanical equipment.

The purpose of the present invention is to provide a method for extracting sensitive fault features of common rail fuel injectors based on CHDE and PWFP. Firstly, high-precision pressure sensors are used to collect high-pressure oil pipe pressure signals; then the composite level discrete entropy of fuel pressure signals is calculated; The proximity between the levels of discrete entropy is scored according to the proximity, and the scores are arranged in ascending order. The lower the score, the more sensitive the level of discrete entropy is to the fault feature; finally, the test sample is input into the trained binary tree support vector machine multi-classifier for Fault diagnosis and pattern recognition, and output fault diagnosis results. The invention is suitable for extracting sensitive fault features of the common rail fuel injector under complicated working conditions, and has good fault diagnosis effect.

The invention discloses an annealing genetic optimization method for diagnosing excitation of a nonlinear analog circuit. Due to nonlinearity, soft fault and other hardly diagnosed characteristics of the common analog circuit, fault diagnosis theory and method are not perfect and become a bottleneck of restricting a test of an integrated circuit to a certain degree. The method comprises the following steps of: determining various states of a tested nonlinear analog circuit; applying a multi-frequency excitation signal to the tested nonlinear analog circuit in various states, measuring input and output signals to obtain a sampling data sequence, and performing data processing to obtain a previous n-order Volterra frequency-domain kernel corresponding to each fault state of the tested circuit; and taking parameter selection of the tested excitation signal as an optimization problem, taking lumped Euclidean distance responding to various fault states of a certain excitation signal as an evaluation function of the signal, optimizing the tested excitation signal by using the annealing genetic optimization method, and finally obtaining optimized excitation signal parameters. The method is used for fault diagnosis of an electronic circuit.

The invention provides a fault diagnosis method and device for a satellite ground station, and the method comprises the steps: receiving a downlink signal returned by a satellite communication device through a signal receiving link of the satellite ground station, and determining that the downlink signal is abnormal through a signal processing module, under the condition of determining that the signal transmitting link of the satellite ground station does not have a fault, a fault test signal can be simulated and generated through the signal simulation terminal, so that a fault diagnosis result of the signal receiving link is determined based on the fault test signal; besides, under the condition that the signal receiving link is not abnormal, loops can be constructed between different components in the signal transmitting link and the signal receiving link, so that fault diagnosis can be carried out on the different components in the signal transmitting link through the different loops.

The invention relates to a predictive maintenance method for a cooled detection module, comprising: a detector (1) comprising a matrix consisting of pixels that are sensitive to light signals; a cryostat (2) containing the detector (1); and a cooling machine (3), said method being characterized in that it comprises a step according to which a processing board (4) of the module, which is electrically connected to the detector (1), to the cryostat (2), and to the cooling machine (3) measures, stores, and processes at least: one motor current, i.e. a supply current of the machine (3); one motor voltage, i.e. a supply voltage of the machine (3); and a number of defective pixels of the detector (1). The invention further relates to a module for implementing the invention.

The invention provides a transformer partial discharge fault diagnosis method based on hierarchical threshold synchrosqueezed wavelet. The method is characterized by comprising the following steps of: 1, subjecting a transformer partial discharge signal f(t) to SWT decomposition to obtain an SWT decomposition coefficient S<y><a(i)>(t), wherein the i = 1, 2, L, I; 2, estimating a noise standard deviation [sigma]n by using a formula described in the specification; 3, assigning values to ca(i) step by step at a fixed step length for the ith coefficient of the SWT, calculating the minimum mean square error by a mean square error formula, and determining the optimal value of the ca(i); 4, calculating the hierarchical threshold [gamma]a(i) = ca(i) [sigma]n, and achieving the ith SWT coefficient noise reduction through the ith SWT coefficient formula; 5, using a noise reduction formula to reconstruct de-noised layer coefficients to obtain a de-noised de-noising transformer local discharge signal y(t); and 6, performing feature extraction on the de-noised de-noising transformer local discharge signal y(t), and performing fault diagnosis according to extracted features.

The invention discloses a transformer fault type diagnosis method based on semi-supervised DBNC, which includes: selecting a sample data set; dividing the sample data into a pre-training set without labels, a labeled set, a test set 1 and a test set 2; State encoding of fault types; establishment of a transformer fault diagnosis model based on a deep belief network classifier; initialization of the parameters of each layer of the model; use of contrastive divergence to train each RBM at the bottom layer by layer; Optimize the parameters to make the network classification performance reach the global optimum; save the trained network, and use the sample data of the test set 1 to verify the classification performance of the network; solve the problem of using deep learning network fault data for transformer fault diagnosis Analysis and processing, usually only a small number of complete data samples can be obtained, and it is very difficult to obtain a large number of complete data samples with labels, which requires a lot of manpower and material resources.

The invention relates to an engine fault detection method, system, storage medium and equipment, comprising the following steps: obtaining the vibration-acoustic signal data of the engine for preprocessing, obtaining the frequency characteristics, Mel cepstrum coefficient characteristics and difference characteristics of the vibration-acoustic signal, and constructing multiple The characteristic normal sub-model is used to obtain the sample scores for samples of known fault types, and the hierarchical judgment normal sub-model is constructed based on the sample scores; the multi-branch characteristic normal sub-model and the hierarchical normal sub-model are used to construct multi-branch normal sub-models for all fault types. Branch-level normal anomaly detection model, the engine vibration and sound signal samples to be tested are sequentially input into various types of multi-branch-level normal anomaly detection models; when the sample is not within the set range of any known type, the corresponding The engine state is judged to be an unknown abnormal state. It can detect the working state and fault type of the engine under the detection link after the engine is manufactured, and reflect the abnormal situation without false detection.

The invention provides a fault diagnosis method and device for a sensor, and the method comprises the steps: constructing a fault diagnosis model based on a coding and decoding network, adding a data attention mechanism into a coder of the fault diagnosis model, and adding a time attention mechanism and a fusion module into a decoder; and training the fault diagnosis model, determining a fault threshold value of the sensor, and performing fault diagnosis of the sensor based on the fault threshold value. On the basis of a traditional encoder-decoder network (EDN), a novel data-time attention network (DAN) for air conditioning unit sensor fault diagnosis is provided, and the problem that reading of a sensor shows dynamic data-time dependence due to an operation mechanism of an air conditioning unit system, and is prone to being influenced by external factors and control parameters is solved When fault diagnosis is carried out based on the model, the method is more sensitive to deviation faults, and the fault diagnosis performance of the sensor is greatly improved.