109 results about "Fault severity" patented technology

The invention discloses a network fault diagnosis method based on deep learning in a network virtualization environment. The network fault diagnosis method comprises the steps of: dividing a network into a physical network and a virtual network, combining the characteristics of occurrence of network faults, considering the time influencing factor, network topological connection characteristics and a mapping relation between the virtual network and the physical network, and comprehensively evaluating the network faults by means of a fault severity grading probability; regarding network characteristic parameters with influence degrees as a model learning resource, paying attention to the correspondence between variation trend of network historical data and fault tags, establishing a network fault diagnosis model with multiple fault grading probabilities in the network virtualization environment based on a viewing angle of deep learning, and training network parameters by using the network fault diagnosis model; and adjusting a fault prediction model in the training process, and utilizing an optimized and adjusted deep learning network to realize fault diagnosis in the network virtualization environment. The network fault diagnosis method can carry out deep analysis on the network parameters in the network virtualization environment, therefore the network fault diagnosis method has higher precision in predicting the network faults.

A method and apparatus is provided for fault notification based on a severity level. The method comprises detecting a fault associated with a processing tool that is adapted to process one or more workpieces, determining a fault severity level of the detected fault and selecting at least one user to notify of the fault based on the severity level of the fault.

The invention discloses a locomotive wheelset bearing fault diagnosis method based on the angular domain-time domain-frequency domain, and belongs to the technical field of bearing testing. The method includes the steps that firstly, collected vibration acceleration signals of locomotive wheelset bearings are analyzed in the time domain and the frequency domain, whether the bearings are in a fault state or not is judged, the specific portions of the faults are judged, angular domain analysis is conducted on vibration acceleration creatively, and quantitative analysis data of the faults are acquired, so that the severity degrees of the faults are grasped, and basic data are provided for further fault monitoring. The analysis method in which the time domain, the frequency domain and the angular domain are combined is adopted, information such as fault occurrence, fault types and fault severity degrees of the bearings can be accurately provided, especially, the angular domain fault diagnosis method is creatively provided, an accurate monitoring mode is provided for the fault development degree, and more abundant reference data are provided for subsequent fault processing.

A system and method for detecting a rotor fault condition in an AC induction machine is disclosed. The system includes a processor programmed to receive voltage and current data from an AC induction machine, generate a current frequency spectrum from the current data, and identify rotor-fault related harmonics in the current frequency spectrum. The processor is also programmed to calculate a fault severity indicator using the voltage and current data, identified rotor-fault related harmonics, and motor specifications, analyze the fault severity indicator to determine a possibility of rotor fault. The processor generates an alert based on the possibility of rotor fault.

A system and method for detecting a rotor fault condition in an AC induction machine is disclosed. The system includes a processor programmed to receive voltage and current data from an AC induction machine, generate a current frequency spectrum from the current data, and identify rotor-fault related harmonics in the current frequency spectrum. The processor is also programmed to calculate a fault severity indicator using the voltage and current data, identified rotor-fault related harmonics, and motor specifications, analyze the fault severity indicator to determine a possibility of rotor fault. The processor generates an alert based on the possibility of rotor fault.

The invention relates to the technical field of satellite fault diagnosis and fault-tolerant control, and proposes a fault diagnosis method, which has very good robustness to external interference, isfree of influence of external interference in a fault diagnosis process, prevents generation of misinformation and missing report, and can effectively estimate fault severity. According to the satellite fault diagnosis and fault-tolerant control method based on a self-adaptive observer, first a satellite kinematics and dynamics model under an actuator fault and external interference influence isestablished to analyze influence of fault effective factors on a system; secondly, an observer is designed for a satellite fault system, a residual error evaluation function is established based on observer output and system actual output, an evaluation threshold value is obtained, and the purpose of fault detection is achieved; then, when occurrence of a fault is detected, a self-adaptive observer is designed to complete a fault diagnosis task; and finally, based on an estimated value of the fault, a finite time self-adaptive fault-tolerant controller is designed. The satellite fault diagnosis and fault-tolerant control method based on a self-adaptive observer is mainly applied to fault diagnosis and fault-tolerant control occasions.

The invention discloses a control method of an electronic automobile battery management system (BMS). The control method comprises a charge control method; the BMS has a function of receiving a charge connection confirmation signal on a charge plug, and is capable of adopting different control strategies according to the situation that whether an overall controller is in an idle mode or a powered or driving mode. By adopting the system, alarms of two grades can be made to a whole automobile according to abnormal states of a battery pack on the basis of fault severity; in the situation of a second-grade alarm, a motor controller can be operated in a power reduction manner according to available charge / discharge current; in the situation of a first-grade alarm, charging / discharging of a battery is stopped, so that safe use of the power battery can be ensured, and the service life of the power battery can be effectively prolonged.

The embodiment of the present invention provides a diagnostic method and device of a train bearing failure. The method mainly comprises a step of using a vibration sensor to collect the vibration data of a train bearing and a framework and obtaining a bearing signal according to the train bearing and the framework, a step of carrying out analysis processing on the bearing signal and obtaining the time-domain characteristic parameter and the frequency domain characteristic parameter of the bearing signal, and a step of using a failure classification algorithm to solve the failure state information of the bearing based on the time domain characteristic parameter and the frequency domain characteristic parameter of the bearing signal and the data of the bearing of a training sample under various states. Through collecting the vibration data of the train bearing and the framework by using the vibration sensor, the time domain characteristic parameter of the bearing signal is obtained, whether the bearing corresponding to the sensor is failed or not is judged according to the time domain and frequency domain characteristic parameters, the position of a failure is positioned at the same time, and the severity degree of the failure is identified. According to the embodiment of the present invention, the fault accurate diagnosis, fault positioning and fault severity degree discrimination of a subway train bearing are realized.

The invention relates to a transformer risk prediction method based on markov and entropy weight fuzzy comprehensive evaluation. The method comprises the following steps of: (1) establishing a transformer full-stage model based on a markov process; (2) acquiring a markov state transition diaphragm of a transformer at a running state according to the transformer full-stage model, and the probability of the transformer at full state as well as the fault frequency of each part of the transformer; (3) calculating the fault severity of each part in faults through an entropy weight fuzzy algorithm; and (4) determining the risk of each part of the transformer according to the fault frequency and the fault severity of each part. Compared with the prior art, the transformer risk prediction method based on markov and entropy weight fuzzy comprehensive evaluation provided by the invention has the advantages of high precision, high accuracy and the like.

The invention provides a rolling bearing fault diagnosis method based on improved multi-scale amplitude perceived permutation entropy, relates to the field of digital signal processing, and aims at solving the problems of low separability of feature extraction, low accuracy of fault recognition and insufficient analysis of fault severity in the existing fault diagnosis method of the rolling bearing vibration signal. The method comprises the following steps: step 1. obtaining vibration signal sample sets of the rolling bearing under different fault types and different fault degrees; step 2. obtaining the optimal PR component for subsequent feature extraction; step 3. obtaining fault feature vectors of different fault types and different fault degrees; step 4. inputting the feature vector into the random forest classifier; and step 5. obtaining the fault type and the fault severity of the rolling bear. The extracted feature vector has good separability and high fault description ability,and the average recognition accuracy rate reaches 99.25%. The method can be widely applied to the field of bearing fault diagnosis.

The invention discloses an industrial equipment intelligent fault diagnosis system and method based on data fusion, wherein in the fault diagnosis system, a factory client is used for acquiring and displaying equipment ledger information of industrial equipment stored in an equipment ledger database, the factory client is used for acquiring patrol inspection task information stored in a patrol inspection task standard database; industrial patrol inspection equipment and online monitoring equipment are both used for acquiring different types of operation parameters of the industrial equipment,the acquired operation parameters are sent to an equipment historical database, and a fuzzy diagnosis logic database is used for carrying out mode recognition according to a fuzzy diagnosis rule basedon a membership degree function in combination with a fuzzy relation so as to obtain the reliability of fault reasons. According to the technical scheme of the invention, the fault severity levels ofall components of the industrial equipment are timely given, and then the health state of the unit is evaluated, so that operation and maintenance suggestions of the industrial equipment are given.

The invention discloses a method and a system for detecting a device fault. The method includes: 1, acquiring model parameters and a rule base, wherein the model parameters include a device type, a device structure, the number of measuring points, the distribution of the measuring points and the attributes of the measuring points, and the rule base includes fault types, corresponding fault characteristic parameters and set thresholds; 2, constructing the fault models of a device according to the model parameters and the rule base, wherein every device type corresponds to multiple fault types, and every fault model comprises the fault characteristic parameters, the set thresholds of the fault characteristic parameters, and the corresponding fault types; 3, acquiring the infrared thermogram of every measuring point of the device; 4, extracting the device characteristic parameters from the infrared thermogram according to the model parameters; and 5, acquiring the fault detection result of the device according to the fault models and the device characteristic parameters, wherein the fault detection result comprises the fault location and the fault severity level. The method and the system have the advantages of realization of the analysis of the running state of the device, realization of online detection, and improvement of the detection accuracy of the device faults.

The invention provides a self-adaptive current differential motion protection method for direct-current lines. The method is suitable for direct-current power transmission lines in two-end and multi-end direct-current power transmission systems. To the two-end system, setting-point currents are respectively calculated through electric quantities at two ends so as to realize the current differential protection; and to the multi-end system, branch-point currents are respectively calculated through the voltage and the current of a converter station line end so as to realize the current differential protection. A self-adaptive fault severity setting method is brought forward, and the self-adaptive adjustment of a differential current threshold is realized through the change of the voltage, so that the rapidity and the sensitivity of the protection are taken into account. The self-adaptive current differential protection method for the direct-current lines can improve the sensitivity and the reliability of the protection of direct-current lines and is suitable for the two-end and multi-end direct-current power transmission systems and a voltage source converter type direct-current power transmission system.

The invention relates to an offshore doubly fed motor stator winding turn-to-turn short circuit early fault identification method, and is used for judging the doubly fed motor stator winding turn-to-turn short circuit and determining the phase of the turn-to-turn short circuit. The method comprises the following steps that 1) the three-phase signals of an offshore doubly fed motor under fault identification are acquired, including the stator three-phase voltage, the rotor three-phase voltage, the stator three-phase current, the rotor three-phase current and the rotor rotating speed; 2) the three-phase signals are converted to a dq axis coordinate system, the motor parameters are acquired, then the three-phase signals are converted to an alphabeta axis coordinate system, the estimated stator current is acquired through an observer, and the estimated current difference is calculated and decomposed so that a fault severity factor xi is obtained; and 3) when the amplitude of the fault severity factor xi caused by the turn-to-turn short circuit fault reaches the fault early warning value, the stator winding turn-to-turn short circuit fault occurs on the offshore doubly fed motor under fault identification. Compared with the methods in the prior art, the offshore doubly fed motor stator winding turn-to-turn short circuit early fault identification method has the advantages that the fault phase can be reliably located and the robustness is high.

A system and method for detecting, localizing and quantifying stator winding faults in AC electrical machines is disclosed. A diagnostic system configured to detect a stator winding fault in an AC electrical machine includes a processor programmed to receive measurements of three-phase voltages and currents provided to the AC electrical machine from voltage and current sensors associated with the electrical distribution circuit, compute positive, negative and zero sequence components of voltage and current from the three-phase voltages and currents, and calculate a fault severity index (FSI) based on at least a portion of the positive, negative and zero sequence components of voltage and current, wherein calculating the FSI further comprises identifying a voltage gain in one or more phases of the AC electrical machine due to a stator winding fault and localizing the stator winding fault to one or more phases in the AC electrical machine.

The invention provides a method for detecting fiber bragg grating acoustic emission of a helicopter bearing fault in order to detect an early-time state of the fault of rolling bearings on a helicopter, enhance the capacity for recognizing the fault of the bearings of the helicopter and prolong the service life of the bearings of the helicopter. The detecting method includes the steps of sensor installation, signal sensing and demodulation, wherein the sensor installation includes pasting-installation of a grating sensor (packaged or non-packaged), the single-fiber-bragg-grating sensing method is adopted for the sensing, and the demodulation includes photoelectric conversion, amplification, acquiring, resonance demodulation processing and the like. The method for detecting fiber bragg grating acoustic emission of the helicopter bearing fault is mainly used for detecting the fault of the bearings of the helicopter and faults of bearings of other rotating machines at the home position and on line. Compared with traditional detecting methods (eye observing, vibration, magnetic powder and the like), the method for detecting fiber bragg grating acoustic emission of the helicopter bearing fault can effectively detect the early-time state of the faults and accurately judge the fault type and the fault severity order, and the method has great significance for on-condition maintenance and service life prolonging of the helicopter.

The invention provides a voltage stabilization fault screening and ranking method based on a grey correlation analysis method, which comprises the steps of determining indexes for the severity of voltage stabilization faults in an electric power system; calculating to acquire values of the indexes; forming an initialization decision-making matrix according to the indexes; standardizing the initialization decision-making matrix to acquire a standardization decision-making matrix; calculating a correlation coefficient and a grey correlation degree according to the grey correlation analysis method; and screening to acquire a fault screening comprehensive index, and acquiring a fault severity ranking result according to the fault screening comprehensive index. The method provided by the invention reflects fault severity conditions more comprehensively and more effectively, thereby being convenient for the operating and scheduling personnel to take correspondingly prevention and correction measures timely, reducing influences imposed on power grid voltage stabilization by accident disturbance, and having important significance for guiding reliable running of an actual power grid.

The invention provides a method for monitoring the state of an underwater vehicle based on fuzzy support vector domain description, and belongs to the technical field of fault diagnosis for the underwater vehicle. The method comprises the steps of respectively extracting feature information from AUV (Autonomous Underwater Vehicle) longitudinal velocity signal wavelet approximate components and propeller control signals when the operating state of a propeller is to be monitored based on a modified Bayesian classification algorithm, constructing a sample to be monitored based on the extracted feature information, calculating a fuzzy membership coefficient, substituting the fuzzy membership coefficient into a fuzzy support vector domain description monitoring model so as to acquire a monitoring coefficient, and judging the operating state of an AUV propeller based on the monitoring coefficient. The method provided by the invention not only can judge whether the AUV propeller has a fault or not, but also can judge the fault severity of the AUV propeller, and is especially suitable for being applied to state monitoring for the autonomous underwater vehicle propeller.

The present invention relates to a multi-dimensional evaluation-based power transmission line risk evaluation system and an evaluation method thereof. The evaluation system comprises a monitoring subsystem for each unit of a power transmission line, a multi-dimensional evaluation subsystem for each unit of the power transmission line, a risk index calculating and sorting subsystem for each unit of the power transmission line, a power transmission line comprehensive risk evaluation system and a computer. According to the technical points of the evaluation method, a multi-dimensional evaluation method for each unit of the power transmission line is constructed in four aspects: namely the fault occurrence possibility of a unit event, the fault severity of the unit event on the line at the same level, the error code unpredictability of the unit monitoring information, and the fault possibility of the unit event on the line at the next level. Furthermore, the operation risk state of the power transmission line at each level can be comprehensively evaluated. Based on the system and the method, the risk state of each unit of the power transmission line can be timely found out and accurately judged. Moreover, the overall operation risk state of the power transmission line at each level can be comprehensively and overally reflected.

The invention provides a fault statistic analysis based motor train unit repair decision method and apparatus. The method comprises the steps of calculating an instantaneous fault rate, fault severity level magnitude and fault probability level magnitude of a fault according to fault information of a fault mode of a part of a motor train unit; calculating fault criticality level magnitude according to the fault severity level magnitude and the fault probability level magnitude; determining a fault repair mode according to the fault criticality level magnitude and the fault severity level magnitude; calculating running mileage or running time, corresponding to a loss critical point of the part, of the motor train unit according to the instantaneous fault rate; and determining a repair cycle of the part with the corresponding fault mode according to the fault repair mode and the running mileage or the running time, corresponding to the loss critical point of the part, of the motor train unit. According to the motor train unit repair decision method and apparatus, the fault can be quantitatively analyzed, the judgment efficiency of a repair policy for important parts of the motor train unit can be improved, and judgment standards of repair decision are unified.

A method of health management of a monitored system includes collecting component condition indicator data used to calculate a plurality of component health indicators. Component fault severity and potential failure modes are determined utilizing the component condition indicator data. The potential failure modes are ranked in order of likelihood to isolate the failure mode. A system of health management for monitored apparatus includes a fault severity module to derive a plurality of component health indicators from collected component condition indicator data, the plurality of component health indicators indicative of fault severity of a plurality of components. A fault isolation module separately derives a ranked listing of potential fault / failure modes utilizing the component condition indicator data. The system further includes a communication portion to which the plurality of component health indicators and the ranked listing of fault / failure modes are transmitted for review and / or action by cognizant personnel.

The invention discloses a train speed limit control method and management system for rail transit. The train speed limit control method for the rail transit comprises the following steps that completevehicle state information and speed limit mode data of trains are collected and analyzed, and whether a triggering condition of train speed limit control are met or not is judged by utilizing a preset fault speed limit list; when the triggering conditions is met, a fault speed limit value corresponding to the fault severity level and a mode speed limit value under the speed limit mode data are determined according to the fault information and the fault speed limit list, and when a plurality of triggering conditions are met, a lowest speed limit value is taken as a train speed limit target value; according to the train speed limit target value, speed limit information is fed back, and / or a train protection mechanism including traction blockade and emergency braking is started to realize the speed limit control of the trains. The speed can be comprehensively and reliably controlled, the running safety of the trains is ensured, the train speed limit control method and management system for the rail transit can be flexibly arranged according to different items, and the multi-level train protection mechanism is achieved, so that the speed limit management is more reliable.

The invention discloses a method and system for controlling active limp home returning mode entering of an automatic gearbox. The method comprises the step of obtaining a fault response value of the active limp home returning mode entering of the automatic gearbox, the step of judging whether the fault response value is equal to different triggering values corresponding to entering of different limp home returning modes or not and the step of controlling the automatic gearbox to enter the limp home returning mode corresponding to the triggering value when the fault response value is equal to the triggering value of one mode in different limp home returning modes. By means of the method and system for controlling active limp home returning mode entering of the automatic gearbox, the automatic gearbox can be automatically controlled to carry out detailed distinguishing on the limp home returning modes according to the fault severity degrees, and therefore the automatic gearbox can be better protected.

The invention provides a virtual network mapping method and device based on regional fault prediction. The method comprises the following steps: constructing a virtual request queue by received virtual network requests; determining regional fault severity according to the statistical information of an initialized regional fault model; selecting a virtual network request having a maximum average profit; executing a node mapping scheme based on a greedy strategy on the virtual network request having the maximum average profit; judging whether the virtual network request having the maximum average profit permits link splitting or not; when link splitting is permitted, allocating a corresponding bandwidth for each branch in a candidate mapping path according to the regional fault severity; and executing virtual link mapping on each branch till the execution of virtual link mapping of all virtual links is completed. The probability of potential regional fault occurrence is predicted and mapping of a virtual network is guided by using the regional fault model and a risk assessment method, so that fault regions are avoided, or the performance loss expectancy of a regional fault is reduced.

Various systems, methods, and apparatuses for operating a wireless charging device in an electric vehicle are disclosed. One method includes detecting a system fault indicative of one or more faults in the wireless charging device in the electric vehicle or in the transmitter. The method further includes determining a fault severity level from a plurality of fault severity levels based on a type of the system fault detected. A total number of types of system faults can be greater than a total number of the plurality of fault severity levels. The method further includes performing one or more system fault response operation based on the determined fault severity level. Each of the plurality of fault severity levels can be associated with a different set of system fault response operations.

A power grid risk comprehensive assessment method based on improved power betweenness is used for objectively evaluating power grid risks. The technical scheme is that the method first calculates improved power betweenness of each node and line; then the improved power bewteenness is introduced into a power grid fault consequence severity index, thereby obtaining a power grid fault consequence severity index based on improved power betweenness; then an intuition fuzzy analytic hierarchy process is adopted to make a comprehensive assessment of a fault severity index, and an evaluation result and a power grid fault probability are combined, thereby obtaining a final power grid risk value; and finally, power grid risks are accurately assessed according to the final power grid risk value. The power grid risk comprehensive assessment method based improved power betweenness introduces the improved power betweenness into the power grid fault consequence severity index, so that each index contains consideration of influence on a load side in a calculation process, and the overall fault severity evaluation result is relatively reasonable. The power grid risk comprehensive assessment method based improved power betweenness can comprehensively, objectively and accurately make an evaluation of power grid risks.

The invention discloses a multi-source automobile security information processing method. According to the method, automobile security information from multiple sources is acquired, including owner complaint information and vehicle security correlation information; according to a preset automobile fault expert knowledge base, an automobile fault tag and a fault severity level of each piece of automobile security information are determined; according to all the owner complaint information and corresponding tags, clustering is performed on all the owner complaint information to form automobile fault cases; and according to all the vehicle security correlation information and corresponding tags, the vehicle security correlation information and the automobile fault cases are correlated to forman automobile fault case knowledge graph, and the automobile fault case knowledge graph can be used for automobile security risk level assessment. Thus it can be known that sources of reference information for automobile security risk assessment are enriched, and the reference value of the reference information for automobile security risk assessment is greatly raised.

The invention provides a motor fault early warning method and system. The method comprises the following steps of respectively calculating a rotor broken bar generalization stage number, a stator winding turn-to-turn short circuit generalization stage number, a bearing outer raceway fault generalization stage number, a bearing inner raceway fault generalization stage number, a bearing rolling bodyfault generalization stage number and a bearing retainer fault generalization stage number according to three-phase voltage and three-phase signal of a motor and a preset rotor broken bar fault diagnosis model, a stator winding fault diagnosis model and a bearing fault diagnosis model; and evaluating the health state of the motor according to the generalization series. By adopting the motor faultearly warning method and system, the motor fault severity can be judged, a shutdown maintenance plan is arranged in advance, the production efficiency is improved, the equipment operation and maintenance cost is reduced, and the economic and safety benefits of enterprises are increased.

The invention discloses a transient stability risk assessment method of an electric power system containing a large-scale wind power station. The method comprises the following steps: establishing a power grid fault uncertainty model; obtaining a predicated fault set according to a power grid fault; calculating the fault occurrence probability of the predicated fault set; simulating a typical wind speed scene, and carrying out interval division on a wind speed sample; calculating wind speed interval probability; carrying out time-domain simulation analysis on each wind speed scene and a fault scene and constructing a non-linear utility function; calculating to obtain a fault severity degree; calculating a risk index under each scene and obtaining a general transient stability risk index according to the risk index of each scene. The assessment method disclosed by the invention considers the uncertainty of wind speeds and the uncertainty of a system fault state, and a scene simulation and interval probability analysis method is adopted and is used for researching system transient stability under each wind speed scene; the defect of a traditional method that the time consumption is great is overcome; the method for measuring the fault severity degree meets the objective reality better.