126results about How to "Implement fault diagnosis" patented technology

The invention provides a full-process monitoring and fault diagnosis method and system for a pneumatic conveying system. The method comprises the following steps: collecting multi-source monitoring data of the pneumatic conveying system; processing the multi-source monitoring data to obtain standardized monitoring data; constructing a training set based on the historical operation data, and training a pre-constructed deep learning model to obtain a system state evaluation model; based on the system state evaluation model and the multi-source monitoring data, adopting a system state vector algorithm to generate a differential flatness index; according to the differential flatness index, designing a k induction control algorithm, and generating control parameters for the rotating speed of an induced draft fan and the opening degree of a material distributing valve of the pneumatic conveying system; and constructing a disturbance observer with a self-adaptive safety boundary, and controlling the pneumatic conveying system based on the control parameters. According to the method, intelligent monitoring and safety control of the pneumatic conveying system are achieved in a mode of combining deep learning and intelligent control, and the operation efficiency and reliability of the system are improved.

The invention belongs to the technical field of fault diagnosis of wind power generation equipment, and relates to a fan gearbox fault image recognition method and system. According to the method disclosed by the invention, the multi-modal data of the fan gear box is acquired, and the multi-modal data complement each other, so that the accuracy and reliability of the data on fault characterization are improved. And carrying out deep learning analysis on the preprocessed multi-modal data to obtain a deep learning result, and carrying out efficient and accurate classification on fault types of the fan gearbox by training a deep learning model. Fault reasoning is carried out based on expert knowledge and historical data to obtain a knowledge graph reasoning result, the expert knowledge and the historical data are combined, a knowledge graph is constructed, knowledge fusion and sharing are realized, and logical reasoning is carried out by using the knowledge graph. Fault diagnosis is carried out according to a deep learning result and an identification map reasoning result, the advantages of various methods are integrated, the fault type and part can be identified more quickly, and timely guidance is provided for subsequent maintenance work.

The invention discloses a bearing fault diagnosis method and device, and relates to the technical field of bearing fault diagnosis, and the method comprises a model training step, a model integration step and a fault diagnosis step, and the model training step provides a trained diagnosis model for the model integration step. The anomaly detection model output in the model integration step is used as the model algorithm input in the fault diagnosis step, and the model integration step is used for integrating a plurality of models to improve the diagnosis accuracy and the system robustness; the model integration step comprises the steps of multi-model integration, dynamic model selection and anomaly detection. According to the bearing fault diagnosis method, the model integration step is designed, the advantage integration function of multiple models is achieved, the limitation problem when a single model processes complex and changeable fault modes is solved, fault diagnosis is difficult to comprehensively and accurately conduct, the accuracy and robustness of a bearing fault diagnosis system are improved, and the fault diagnosis efficiency is improved. Fault detection under complex working conditions can be better handled, and the probability of false alarm and missing alarm is reduced.

The invention provides an intelligent power grid safety early warning system and a monitoring method in the technical field of intelligent power grids, which are used for solving the problem of safety early warning and monitoring function defects of the existing imperfect intelligent power grids, and comprise an information acquisition unit, a data management unit, an abnormity analysis unit and a safety early warning unit, the method comprises the steps of collecting power grid real-time data and line equipment data; constructing an adjacent matrix and a target matrix, calculating and analyzing to obtain a node weight and a target correlation degree, and analyzing by using a discrete model to obtain a dispersion index; a data evaluation value is obtained, and a data abnormal value is obtained through analysis; and generating intelligent power grid safety early warning information and a disaster recovery strategy according to the data abnormal value and the line equipment data. By constructing an intelligent power grid monitoring and early warning system, power grid equipment with potential safety problems is predicted in real time, the operation and maintenance efficiency is improved, the method can be suitable for power grid systems of different scales, the influence of faults on power grid operation is reduced, and large-area power failure events are effectively prevented.

The invention belongs to the technical field of mechanical fault diagnosis, and relates to a bolt fault diagnosis model construction method, a bolt fault diagnosis method, a bolt fault diagnosis system and a bolt fault diagnosis device. According to the method, a vibration signal of a bolt operation state is obtained, and a data set is constructed according to the vibration signal of the operation state; preprocessing the vibration signal in the operation state to obtain a preprocessed vibration signal; extracting fault feature data of the preprocessed vibration signal; performing hierarchical processing on each data segment of the fault feature data to obtain a plurality of low-frequency and high-frequency components, constructing a graph model for each low-frequency and high-frequency component, and calculating a graph similarity score of the graph model and the data set, combining the graph similarity scores of all the low-frequency and high-frequency components into an integral similarity index through an adaptive input weighted fusion technology; a convolutional neural network algorithm is combined with a similarity index to carry out model training on an existing data set, a bolt fault diagnosis model with relatively high precision is obtained, and accurate detection and diagnosis of a bolt fault are realized.

The invention provides a rolling bearing fault diagnosis method and system oriented to complex working conditions, and the method comprises the steps: firstly carrying out the preprocessing of a collected rolling bearing vibration signal through employing a rolling bearing fault signal noise reduction method oriented to optimization SSA-SGMD, and carrying out the preprocessing of the vibration signal; then, screening the preprocessed bearing vibration signals, performing synchronous extrusion wavelet transform on the screened vibration signals, and converting one-dimensional vibration signals into two-dimensional images; and finally, performing image expansion by using a novel integrated generative adversarial network, extracting and classifying two-dimensional features by using an improved VGG network model, extracting feature differences among different fault types, and finally identifying and classifying the two-dimensional features to finally achieve the purpose of fault diagnosis.

The invention discloses a nuclear power device fault diagnosis method based on feature vectors and Bayesian reasoning, belongs to the technical field of Bayesian reasoning, and particularly relates to nuclear power device fault diagnosis. The method solves the problem that a traditional Bayesian model only uses parameter values as a fault reasoning basis and ignores a large amount of parameter time-frequency feature information, so that the model is difficult to distinguish feature similar faults and realize accurate positioning of the faults, and the fault diagnosis accuracy and the model robustness are affected. The method comprises the following steps: a model learning step: performing structure learning and parameter learning on a Bayesian model according to fault historical operation data to obtain a Bayesian vector reasoning model; the nuclear power device fault diagnosis method based on the feature vector and Bayesian reasoning is suitable for fault diagnosis of a nuclear power device.

The invention provides a wind turbine generator transmission chain fault diagnosis method based on time-frequency plane expectation maximization, and belongs to the technical field of fault diagnosis. The method comprises the following steps that a cylindrical MEMS acceleration sensor is arranged on a transmission chain of a wind turbine generator, and vibration signals of the transmission chain of the wind turbine generator are collected through the cylindrical MEMS acceleration sensor; an unsupervised classification method based on a spectrogram zero point is adopted, a vibration signal and a rotating speed signal during operation of a wind turbine generator are collected, short-time Fourier transform is used for generating time-frequency representation for the vibration signal, the spectrogram zero point is extracted, unsupervised classification is carried out, and noise reduction processing of the vibration signal is achieved; based on a multi-component signal estimation method under time-frequency plane expectation maximization, the instantaneous frequency and the instantaneous amplitude of a multi-component signal are accurately estimated on a time domain plane of the denoised signal; and order spectrum analysis is carried out, vibration signal characteristics of the wind turbine generator transmission chain are identified, and fault diagnosis of the wind turbine generator transmission chain under the variable rotation speed is realized.

The invention relates to the technical field of unmanned aerial vehicle fault diagnosis, in particular to an unmanned aerial vehicle sensor fault diagnosis method based on weighted migration domain adaptive learning. Comprising the following steps: constructing a multi-scale neural network model which comprises three multi-scale neural network modules including a feature extractor, a domain discriminator and a classifier; the feature extractor extracts high-dimensional feature information of the data; the domain discriminator carries out confrontation training with the feature extractor in a model training process, so that the domain discriminator cannot distinguish whether data is from a source domain or a target domain; the classifier identifies and classifies the sample features output by the feature extractor; obtaining a source domain sample data set and a target domain sample data set; the constructed multi-scale neural network model is trained; and performing fault identification and diagnosis on the target domain data through the trained multi-scale neural network model. The method has the advantages that a better learning model is established for unmanned aerial vehicle fault diagnosis under the condition of lacking a target domain data set label, and fault diagnosis of a target domain data set is realized.

The invention discloses a power grid full-cycle operation safety supervision system based on artificial intelligence, and belongs to the field of power grid safety supervision, and the system comprises the steps: collecting historical charge data and corresponding timestamp records, carrying out the preprocessing, and dividing the obtained time series data into a training sample set and a verification sample set; through constructing a prediction model and carrying out training and optimization evaluation, an optimal prediction model is obtained; real-time charge data are collected and input into the optimal prediction model, predicted charge data of the next time step are obtained, the charge change rate is calculated and compared with a preset change threshold value, and whether the charge data of the next time step are abnormal data or normal data is judged; performing fault type prediction on the abnormal data by using a preset random forest model; and periodically traversing the fault record to obtain all fault types, setting a power grid operation safety level, and performing risk assessment on power grid operation by calculating a risk index to obtain the power grid operation safety level.

The invention relates to the technical field of fault diagnosis, and discloses a fault diagnosis method and system applied to an automobile air conditioner compressor. The method comprises the following steps: firstly, introducing auxiliary noise to decompose a vibration signal, calculating a decomposition result by using a sample entropy method to obtain a sample entropy, and identifying and removing noise; secondly, variational mode decomposition is carried out on the vibration signals after noise elimination, an improved crayfish optimization algorithm is used for optimizing a decomposition scale and a penalty factor in the variational mode decomposition process, and a final IMF component signal set is obtained; mapping the final IMF component signal set to polar coordinates through a symmetric polar coordinate transformation method to form a fault diagnosis image set; and finally, training to obtain a convolutional neural network fault diagnosis model, identifying a fault diagnosis image set, and outputting a fault diagnosis result of the automobile air conditioner compressor. The vibration signal of the automobile air conditioner compressor is processed and analyzed, the purpose of fault diagnosis is achieved, and the method is objective and accurate.

The invention relates to an electric energy meter data reporting method and system, and the method comprises the steps: activating a WIFI hotspot function of a WIFI communication and maintenance device, enabling a concentrator, an electric energy meter and the WIFI communication and maintenance device to establish communication connection through a WIFI hotspot after the WIFI hotspot function is activated, carrying out the networking of the concentrator and the electric energy meter in a WIFI hotspot coverage range through an MESH network, and carrying out the data reporting of the electric energy meter. Internal networking is carried out among the electric energy meters through the meter control gateway, the electric energy meters are connected to the electric energy meter control gateway through the meter control gateway, and the electric energy meter control gateway is connected with a plurality of concentrators through an ad hoc network. Based on communication networking of the MESH network, an interface provided by a meter control gateway is connected to an electric energy meter in the networking, and read electric energy meter data is transferred to the electric energy meter control gateway; and the concentrator receives the electric energy meter data transferred in the electric energy meter control gateway through a link control frame, is connected to the equipment side master station through a built-in 485 interface, and reports the electric energy meter data to the equipment side master station through a 485 bus.

The invention relates to a dust remover fault self-diagnosis system, and belongs to the technical field of industrial automation and environmental protection. The system comprises a dust concentration monitoring device, a vibration sensor, a spray water quantity monitoring device, an operation resistance monitoring device and a central processing unit. And the central processing unit receives the data acquired by each monitoring device, performs fault diagnosis according to preset fault judgment logic, and outputs a diagnosis result. The system can also adjust the operation parameters of the dust remover according to the dust concentration data, such as wind speed, spray water quantity and the like, so as to improve the dust removal efficiency. In addition, the system can be connected with a remote monitoring system to realize remote monitoring and fault diagnosis. The method can automatically diagnose the fault of the dust remover, improves the operation efficiency and reliability of the dust remover, and guarantees the production safety and environment quality.

The invention relates to the technical field of thermal power plant boilers, and discloses a maintenance alarm method and system for a thermal power plant boiler, and the method comprises the steps: obtaining the real-time feature data of the thermal power plant boiler, and dividing the real-time feature data into normal feature data and abnormal feature data; analyzing the normal feature data and the abnormal feature data, and determining an actual deviation coefficient between the normal feature data and the abnormal feature data; historical maintenance records of the thermal power plant boiler are collected and analyzed, and historical maintenance coefficients of the thermal power plant boiler are calculated based on analysis results; the maintenance alarm factor of the thermal power plant boiler is calculated according to the actual deviation coefficient and the historical maintenance coefficient, whether maintenance alarm reminding is sent out or not is judged according to the maintenance alarm factor, the boiler state can be monitored and early warned in real time, early fault detection, fault diagnosis and predictive maintenance are achieved, an alarm is given out in time, and the maintenance efficiency is improved. Preventive measures can be taken or maintenance can be arranged, so that accidental shutdown and potential safety hazards are avoided.

The invention relates to a power equipment motor operation fault diagnosis method and system, and the method comprises the steps: obtaining the current power generation demand and the power equipment basic data of the current power generation execution power equipment, generating the current power generation demand raw material total amount, and generating the estimated motor working time; generating a motor power sampling detection time period, and generating an estimated working power curve; calculating a motor power difference value with the estimated working power curve according to real-time motor working data; and judging whether the motor power difference value is greater than or equal to a pre-stored standard power difference value, if so, generating a current motor fault warning, and sending the current motor fault warning to a motor maintenance management main body, and the prompting module is used for prompting the motor maintenance management main body to overhaul the currently used motor in the current power generation execution power equipment. According to the invention, fault monitoring and fault diagnosis of equipment using a motor in power equipment are realized.

The invention discloses a camera fault intelligent prediction method and system, and belongs to the technical field of electronic equipment fault prediction, and the method comprises the steps: collecting the operation data of a camera in real time through an intelligent collector disposed on the camera; the method comprises the following steps: preprocessing operation data to obtain operation sequence data; constructing a fault prediction model based on a long short-term memory network; the method comprises the following steps: processing collected real-time operation data to obtain a real-time feature vector, inputting the real-time feature vector into a fault prediction model to obtain a corresponding prediction result, and realizing intelligent prediction and intelligent early warning of a camera fault; and according to a prediction result, constructing a knowledge graph to perform further fault diagnosis on the camera in a fault state, and obtaining a fault reason and a corresponding maintenance measure.

The invention relates to the technical field of energy storage cell fault diagnosis, in particular to an energy storage cell fault diagnosis method and system, electronic equipment and a storage medium. The invention discloses an energy storage cell fault diagnosis method. The method comprises the following steps: acquiring charging and discharging original data samples of each cell in a set historical period; generating ordered clusters based on the charging and discharging original data samples, and constructing a clustering model; taking the charging and discharging original data samples of the clusters which are ranked later in the ordered clusters as original abnormal data samples, and constructing an anomaly detection model based on the original abnormal data samples; inputting the real-time operation data of the plurality of cycles of the battery cell into the clustering model to obtain real-time abnormal data; inputting the real-time abnormal data into an abnormal detection model to obtain the number of potential faults of each battery cell; and determining the battery cell of which the potential fault frequency is greater than a set threshold frequency as a fault battery cell, and performing early warning. According to the method, the real-time operation data can be detected in time through the preset clustering model and the anomaly detection model, so that the subtle change of the battery cell data can be found early, and the early fault diagnosis of the battery cell is realized.

The invention discloses a control system for a new energy automobile charging pile, and the system comprises an intelligent charging control module which is used for monitoring a charging pile battery and is electrically connected with a plurality of monitoring sensors. The intelligent charging control module monitors the voltage, current and temperature of a charging pile battery in real time through multiple monitoring sensors. The intelligent charging control module is provided with a charging power adaptive adjusting system, and the intelligent charging control module is electrically connected with a remote monitoring and fault diagnosis module of an integrated Internet of Things (IoT) technology. The invention relates to the technical field of new energy charging piles, in particular to a control system for a new energy automobile charging pile, and the system can achieve the automatic management of the charging process of a new energy automobile through intelligent control, optimizes the charging power scheduling, guarantees the safety and high efficiency of battery charging, and improves the user experience.

The invention discloses a fault diagnosis method based on acoustic signals of a carrier roller bearing of a belt conveyor, and the method comprises the steps: carrying out the self-adaptive global optimization of key parameters in a variational mode decomposition algorithm through employing a raccoon optimization algorithm, obtaining a global optimal solution, feeding back the global optimal solution to the variational mode decomposition algorithm, and obtaining the acoustic signals of the carrier roller bearing of the belt conveyor. Carrying out adaptive signal decomposition on the acquired carrier roller bearing acoustic signal by using the algorithm to obtain an IMF; screening components in the IMF by using the comprehensive index to obtain an optimal IMF component, and calculating a time domain feature value of the optimal IMF component to form a denoised signal feature vector; and optimizing hyper-parameters of the convolutional neural network by using a raccoon optimization algorithm to obtain an optimized convolutional neural network fault diagnosis model, and inputting the denoised signal feature vector into the model for fault classification diagnosis to obtain a diagnosis result. According to the method, the acoustic signal of the carrier roller bearing can be decomposed in a self-adaptive manner, the fault features are extracted, and the problems of mode aliasing and the like are effectively solved.

A method for fault diagnosis of rotating machinery based on multi-sensor fusion and ECA-CNN, 1) Data acquisition: Collect multi-sensor vibration signals of key transmission components of rotating machinery in various health states; 2) Data preprocessing: Sampling, converting and channel fusion of multi-sensor vibration signals to obtain multi-channel images with rich and comprehensive features; 3) Model building: Introducing efficient channel attention into convolutional neural networks to construct ECA-CNN models; 4) Model training: Training the model using multi-channel image sample sets; 5) Fault diagnosis: Using the trained model to perform fault diagnosis on rotating machinery. The present invention realizes channel fusion of multi-sensor vibration signals; introduces efficient channel attention into convolutional neural networks to improve feature learning capabilities and focus attention on more sensitive features; constructs an ECA-CNN model with high computational efficiency to learn representative fault features from multi-sensor fusion data, thereby realizing fault diagnosis.

The invention belongs to the technical field of time-frequency analysis, and particularly relates to a rolling bearing fault method and system based on Riesz atlas sparse decomposition, which utilizes a new time-frequency analysis method-Riesz four-parameter spectrum time-frequency analysis method to obtain four-parameter information of amplitude, frequency, phase and direction of a vibration signal in a time-frequency domain. The time-frequency aggregation and the anti-noise performance of an analysis result are improved; in order to screen out an optimal parameter spectrum and expand the symbol dynamics entropy to a two-dimensional scale, a new entropy calculation method, namely cosine symbol dynamics entropy, is provided; and reconstructing components of the parameter spectrum obtained by entropy screening, and constructing a sparse filter parameter optimization component amplitude, so that a decomposition result has physical significance. The bearing vibration signal under the variable speed working condition and the bearing vibration signal under the constant speed are analyzed, the RSSD method is provided on the basis, and feature extraction and diagnosis of rolling bearing faults under the constant rotating speed working condition and the variable rotating speed working condition are achieved.

The utility model provides a driving loop of a heat management controller and an electric automobile, and relates to the technical field of new energy automobiles. A driving loop of the thermal management controller comprises a main chip, a plurality of driving chips and a plurality of feedback resistors. The main chip comprises a plurality of output pins and a plurality of feedback voltage pins; each driving chip comprises an input pin, a first output pin and a second output pin; an output pin of the main chip is connected with an input pin of a driving chip to output a PWM (Pulse-Width Modulation) signal; a first output pin of the driving chip is connected with the thermal management actuator to output load current; a second output pin of the driving chip is connected with a first end of the feedback resistor to output feedback current; the second end of the feedback resistor is grounded; a feedback voltage pin of the main chip is connected with a first end of a feedback resistor to collect a voltage signal of the feedback resistor. According to the scheme of the utility model, the working condition of the thermal management actuator can be identified through the voltage of the feedback resistor, and fault diagnosis is realized.

The invention provides a rotating machine fault diagnosis method and system, and the method comprises the steps: collecting monitoring signals of a rotating machine in different fault states in a certain time period through a plurality of different types of sensors, and converting the monitoring signals into time-frequency images through a constant Q conversion method; performing graying, cutting and normalization processing in sequence to obtain a plurality of normalized pixel matrixes, and stacking each pixel matrix along the channel dimension of the tensor to obtain a plurality of third-order tensor samples in each fault state; then obtaining an intuitionistic fuzzy score of each three-order tensor sample by adopting an exponential membership function and an exponential non-membership function, extracting a low-rank feature tensor sample by utilizing a tensor robust principal component analysis method so as to construct a high-dimensional feature matrix, and finally establishing a nonlinear intuitionistic fuzzy twin support tensor machine model to obtain a solution vector; and a decision function is constructed to obtain a prediction label of each test sample in the test set, and fault diagnosis of the rotating machinery is realized.

The invention relates to a map information diagnosis method and device, a controller and a vehicle, and relates to the technical field of data processing. The method comprises the following steps: receiving map information of a vehicle; pre-diagnosing the map information and determining a pre-diagnosis result of the map information; wherein the pre-diagnosis comprises at least one of frame loss pre-diagnosis, continuity pre-diagnosis and invalid value pre-diagnosis, and the pre-diagnosis result comprises at least one of a frame loss pre-diagnosis result, a continuity pre-diagnosis result and an invalid value pre-diagnosis result; and determining a diagnosis result of the map information according to the pre-diagnosis result. By adopting the method, the fault diagnosis of the map information can be realized.

The invention discloses a vehicle fault diagnosis model and a training and using method and device thereof, and a medium, and the method comprises the steps: obtaining a fault signal, a fault description text and a real fault type to obtain the time characteristics and space characteristics of a fault, and the correlation between different types of faults, thereby achieving the diagnosis of various types of faults, and improving the diagnosis efficiency. The fault signal is converted into a fault picture to reduce a noise signal in the fault signal, the fault picture and the fault description text are fused based on the time feature and the space feature to obtain a picture feature of the fault picture and a text feature of the fault description text, and a predicted fault type is obtained based on the picture feature and the text feature; the multi-modal features are fused to improve the diagnosis precision, and the image features and the text features are matched from the two dimensions of time and space, so that the diagnosis precision and accuracy of the model are further improved.

The application discloses a kind of SF6 circuit breaker fault prediction method and system considering the effect of breaking arc, collect breaking arc data and SF6 gas decomposition product content after breaking, in the process of operation and after fault, establish circuit breaker historical breaking data set, build cumulative arc energy-SF6 gas decomposition product content relationship model, obtain the SF6 gas decomposition product content corresponding to different breaking times, breaking current;Establish SF6 gas decomposition product content historical operation data set;Build SF6 gas decomposition product content prediction model based on LSTM, obtain the prediction result of SF6 gas decomposition product content;Establish SF6 gas decomposition product content historical fault data set, build SF6 circuit breaker fault diagnosis model;The prediction result of SF6 gas decomposition product is as the input of SF6 circuit breaker fault diagnosis model, obtains fault diagnosis result, realizes the fault prediction of SF6 circuit breaker.

The invention provides a vehicle fault diagnosis method and device and a vehicle, and the method is applied to the technical field of vehicles, and the method comprises the steps: determining a target space based on a target event under the condition that the target event is detected; wherein the target space is a space, used for storing to-be-diagnosed data, in a controller of the target vehicle; sending a first request message carrying the target address link to the target vehicle; wherein the target address link is an address link corresponding to the target space; the first request message is used for requesting to obtain the to-be-diagnosed data in the target space; receiving the to-be-diagnosed data sent by the target vehicle; wherein the to-be-diagnosed data is obtained by the target vehicle from the target space in real time based on the first request message; and performing diagnosis analysis on the to-be-diagnosed data to obtain a diagnosis result. According to the method, vehicle faults can be diagnosed.

The invention relates to the technical field of vehicle emission control, in particular to a fault diagnosis method and device for a vehicle particle trap, a vehicle and a storage medium, and the method comprises the steps: judging whether the vehicle meets a preset monitoring condition or not; if the vehicle meets the preset monitoring condition, a pressure difference sequence of the particulate filter is calculated, and whether the particulate filter meets a preset diagnosis condition or not is judged according to the pressure difference sequence; and if the particle trap meets the preset diagnosis condition, calculating a Pearson's correlation coefficient of the central point sequence and the pressure difference sequence of the particle trap, and generating a fault diagnosis result of the particle trap according to the Pearson's correlation coefficient. Therefore, the problems that in the related technology, when the differential pressure model is established, the accuracy of the differential pressure model is not high, and a single diagnosis strategy is difficult to meet the in-use monitoring frequency requirements of two vehicle types at the same time, so that the diagnosis efficiency is low, vehicle emission is affected, and the environment is polluted are solved.

The invention discloses a remote fault diagnosis method and system for a stacker-reclaimer based on bus communication, and relates to the technical field of coal conveying systems.The remote fault diagnosis method comprises the steps that a sensor and a data acquisition device are installed on the stacker-reclaimer, and operation parameters of equipment are acquired in real time; data are transmitted to an auxiliary machine central control room in a Profibus bus communication mode, a fault diagnosis platform is built on a server of the auxiliary machine central control room, fault early warning and analysis are achieved, and fault information is provided. According to the remote fault diagnosis method for the stacker-reclaimer based on bus communication, the reliability of equipment is improved, the fault occurrence rate is reduced, and stable operation of a coal conveying system is guaranteed. Remote monitoring and fault diagnosis are realized, the labor cost is effectively reduced, and the working efficiency is improved. The fault elimination time is shortened, the influence of equipment shutdown on production is reduced to the maximum extent, and the overall operation efficiency of the system is improved.

The invention relates to a fault monitoring method and system for a vehicle-mounted optical cable, and the method comprises the steps: segmenting the vehicle-mounted optical cable according to the arrangement position of the vehicle-mounted optical cable in a vehicle body, and forming a plurality of segment intervals and segment nodes; performing temperature detection at each segment node position outside the vehicle-mounted optical cable, and performing fitting by using multi-point temperature detection information to form a fitting temperature curve outside the vehicle-mounted optical cable; detecting the temperature of the optical fiber in the vehicle-mounted optical cable by using an optical fiber temperature measurement technology to form an actual temperature curve in the vehicle-mounted optical cable; comparing the change trends of the fitting temperature curve and the actual temperature curve in real time, and judging whether the vehicle-mounted optical cable breaks down or not; and comparing the fitting temperature curve with the actual temperature curve in an equal proportion, determining a segment interval where the fault occurs, and calculating the distance from the fault point to the nearest segment node. Real-time monitoring and fault diagnosis of the vehicle-mounted optical cable are realized through segmented monitoring, temperature curve fitting, internal and external temperature curve comparison and fault positioning.