60 results about "Fault detection and identification" patented technology

The invention discloses a fault detection and identification method for civil aircraft system based on LSTM-AE depth learning framework and relates to the technical field of condition monitoring and fault diagnosis of complex civil aircraft system, and can be used to realize the monitoring and identification of flight faults. The invention comprises the following steps: selecting time series dataof multi-state parameters of an aircraft in flight under a certain stable condition, and according to the characteristics of the monitored object, the time series data of state parameters under suitable conditions are selected for the training of the system reconstruction model, then the fault-free state of civil aircraft system is modeled and reconstructed by making full use of the long-time series-dependent memory ability of LSTM model. The fault monitoring and identification are realized by further analyzing the reconstruction error of its state parameters. The invention solves the problemof insufficient fault monitoring means of civil aircraft system, utilizes the advantage of deep learning in big data analysis to mine massive operation and maintenance data of civil aircraft, and provides important support for fault monitoring of civil aircraft system and route fault isolation.

A system to control a dynamic system, comprising actuators, sensors, a control module and a plurality of observers to monitor the state of the actuators is disclosed. Certain embodiments of the invention comprise a global failure detection and identification module, a baseline controller, and a retrofit controller. Methods to control the dynamic system are disclosed. Some of the methods utilize a second-order mathematical model of the dynamics of the actuators. In some embodiments, this mathematical model is parameterized by a loss-of-effectiveness (LOE) parameter and a lock-in-place (LIP) parameter. The method may further comprise the step of detecting disturbance conditions and structural damage conditions.

The invention discloses a high-voltage line patrol fault detection system based on a visual attention mechanism. The system comprises an image sensor, an ADC analog-to-digital conversion module, an FPGA module, a DDR storage module, a DSP image processing module, an automatic control module and a communication module. The automatic control module is mainly used for receiving an instruction signal of the communication module and controlling operation of the other modules, and when receiving a fault signal from the DSP, notifying the communication module to send the fault information to a control station; the image sensor is used for carrying out video image acquisition, and after ADC analog-to-digital conversion, video images are subjected to video sampling and image preprocessing by the FPGA module; and the DSP image processing module is used for carrying out fault detection and identification on video data in an RAM based on the visual attention mechanism. The invention also discloses a high-voltage line patrol fault detection method based on the visual attention mechanism. Power transmission line characteristics are extracted through an image identification method, and then, fault detection is carried out, and thus the problems of high intensity, high cost and low efficiency and the like of a conventional patrol mode can be solved.

The present invention relates to the field of fault detection and identification of a flexible DC grid, and particularly relates to a fault identification method for a flexible DC grid based on a convolutional neural network. The method comprises: constructing a convolutional neural network model with a branch structure; simulating to obtain fault case data, training the model and adjusting the model parameters; saving the model structure and parameters with high identification accuracy and a small loss function in the training verification; setting a fault detection and identification starting criterion, starting a fault identification program, sampling a data window in 2ms at a sampling signal detection point, and collecting the positive and negative voltages and currents of the line under the actual working condition; and performing normalized processing on the data and identifying an actual fault type by using the model. According to the method provided by the present invention, window information is sampled by fully using the 2ms, the comprehensive utilization of various dimension fault features can be realized by using the branch structure of the model, the accuracy of faultidentification in the flexible DC grid can be improved, the resistance to transition can be improved, and quickness, selectivity, and sensitivity requirements of fault identification can be satisfied.

The invention discloses a flexible spacecraft active fault tolerant control method based on dynamic output feedback control, which belongs to the field of spacecraft attitude control. The method comprises the steps that a dynamic equation of a flexible spacecraft attitude control system is transformed into a general state space equation; a fault model with additive sensor measurement offset is established; a fault detection and identification module composed of an unknown input observer and a filter is established; real-time detection and on-line estimation are carried out on unknown sensor fault; and a fault tolerant controller based on dynamic output feedback is designed based on acquired fault estimation information. According to the invention, when a flexible spacecraft can normally reach an expected posture in the case of an additive sensor measurement offset fault; the influence of disturbance generated by modeling uncertainty and flexible accessory is considered in a design process; and a fault diagnosis and identification module and the fault tolerant controller can be independently designed, and can be easily realized by a project.

The present invention discloses an omnidirectional fault automatic identification method of an isolation switch. The visible light image information and infrared detection image information are collected through each direction of the isolation switch, then the corresponding image information is subjected to preprocessing, the algorithm fusion is carried out, the feature point extraction and description are carried out and a description vector is generated, then the description vector is subjected to feature matching, a wrong matching feature point is eliminated, an identification area and the coordinate of an edge point are determined, the obtained edge point is subjected to affine transformation are is marked in the infrared fusion image of the isolation switch, thus the automatic identification of the isolation switch infrared fusion image is completed, the template image of the isolation switch is subjected to mode identification, and thus whether the isolation switch has a fault or not is judged. According to the method, the defect in current isolation switch fault detection technology is solved, and the method has the advantages of non-contact precise identification, isolation switch component comprehensive identification and fully automatic fault detection and identification.

The present disclosure provides methods, systems, and computer-readable media for the fault detection and identification in an aircraft that may occur in real time during a flight, or any time the aircraft is operating. For example, a controller may receive and calculate various parameter values at various times during an aircraft flight, and compare those values to baseline values in order to determine if a fault has occurred. Additionally, the controller may identify a fault that has occurred by comparing a calculated fault signature value with a fault signature database comprising fault signatures and their associated faults.

The invention provides a gradual fault backtracking fault-tolerant method for an inertial and satellite integrated navigation system, and the method comprises the following steps of performing initialalignment of the inertial navigation system; collecting and sliding the stored sensor data; performing gradual fault detection and identification on the satellite navigation position information by using a state chi-square and residual chi-square hybrid detection method, cutting into an inertia/satellite integrated navigation mode when the satellite navigation has no fault, and fusing the satellite navigation output information; performing the inertial navigation backtracking algorithm and the Kalman backtracking algorithm when the satellite navigation has a gradual fault to obtain the attitude matrix, speed and position of the integrated satellite gradual fault information at the k-1 time; backtracking to the satellite navigation fault point, performing pure inertial navigation trackingand recalculation, recursively stepping to the kth sampling time, and outputting the attitude, velocity and position information after isolating the satellite historical fault information. The methodprovided by the invention can effectively judge satellite navigation gradual faults, and perform the fault tolerance processing on the fault by the inertial navigation and the Kalman backtracking algorithm to avoid historical fault information pollution.

The invention provides an air conditioner fault detection and identification method and device based on a prediction and classification mode. The method comprises the steps that S101, an operation prediction model and a fault classification model are built respectively through normal state operation data and fault operation data according to the normal state operation data and the fault operationdata of an air conditioner; S102, whether the air conditioner breaks down or not is judged through the operation prediction model, if the air conditioner breaks down, the step S103 is executed, and ifthe air conditioner does not break down, the step S101 is executed; and S103, data segment where the air conditioner breaks down is located through the operation prediction model, and a fault type corresponding to the data segment is obtained through the fault classification model. According to the method, the operation prediction model and the fault classification model are built respectively through the normal state operation data and the fault operation data of the air conditioner, the state prediction model and the classification model are creatively fused, high detection and identification accuracy are achieved, meanwhile, the continuously accumulated operation data can be fully utilized, and through continuous machine learning, fault diagnosis accuracy is improved.

The invention, which belongs to the technical field of the industrial process monitoring and diagnosis, discloses an improved-principle-component-tracking-based industrial process monitoring method and application. Decomposition of a low-rank-matrix-expression-based principle component tracking method is carried out on industrial collection data to obtain a low-rank coefficient matrix including all variable relations of the process; and on the basis of the low-rank coefficient matrix as well as correlated coefficient weights of variables in a training matrix, an L2 statistic is constructed to carry out fault detection and identification. According to the invention, according to the principles of the low-rank matrix expression and the principle component tracking method, the low-rank matrix expression algorithm is fused into the principle component tracking to construct a low-rank-matrix-expression-based principle component tracking algorithm model; and the model is used for carrying out on-line monitoring. Therefore, the correlated relations between variables in the training matrix and effective information included by the variables in the training matrix are utilized fully. With the method, the accuracy of industrial fault detection and identification with abnormal values is high.

The invention discloses a multi-dimensional fault detection and recognition method, wherein the method comprises the following steps: S1, constructing a fault detection statistic, and carrying out thedetection of a fault based on chi-square detection; S2, when a fault is detected, constructing a fault set by fusing identification results of a data detection method and a correlation coefficient method; and S3, identifying and eliminating faults according to the fault set. Two typical RAIM algorithms of a data detection method and a correlation analysis method are fused to identify and processa fault. Under the condition of multi-dimensional faults, compared with independently adopted two RAIM algorithms of the data detection method and the correlation analysis method, the method has higher fault recognition accuracy and recognition efficiency, and the integrity and reliability of a satellite navigation system can be further improved.

The invention discloses a rapid self-adaptive fault detection and identification method for a micro-grid line section. The method comprises the following steps: collecting node three-phase current atnodes at two ends of the line section; calculating an instantaneous phase difference value and a phase threshold value of a three-phase current high-frequency component of the line section, and a nodezero-sequence current effective value and a zero-sequence current threshold value at nodes at two ends of the line section; judging a fault based on comparison of an instantaneous phase difference value and a phase threshold value of a three-phase current high-frequency component of a line section, and on this basis, further obtaining a specific fault type based on comparison of node zero-sequence current effective values and zero-sequence current threshold values at nodes at the two ends of the line section. According to the method, various faults occurring in the microgrid line section canbe detected, and the method can be universally applied to microgrids in grid-connected operation and island operation states; according to the method, the fault judgment threshold value can be updatedin real time, and good adaptability is achieved; the method is high in detection speed, and fault detection can be completed within tens of milliseconds and the fault type can be correctly identified.

The invention discloses an electronic checkpoint fault detection and identification method, and the method comprises the steps: detecting a real-time voltage value and a real-time current value of anelectronic checkpoint, and obtaining a first detection result; detecting the working state of an electronic police in the electronic checkpoint, and obtaining a second detection result; detecting theworking state of the traffic signal machine in the electronic checkpoint, and obtaining a third detection result; according to the first detection result, the second detection result and the third detection result, comprehensively judging whether the electronic checkpoint has a fault or not and a specific fault type. By detecting the voltage value and the current value of the electronic checkpoint, the working state of the electronic police and the working state of the traffic signal lamp, the working states of the components can be monitored in real time, and if one component breaks down, thefault type can be accurately determined at the first time; and the fault types can be uploaded to the background, so workers can timely and accurately know the fault types and implement solutions, the workers do not need to manually find out specific faults on site, the working efficiency is improved, and the manpower and the cost are saved.

The invention discloses a photovoltaic panel fault detection and identification method. The method comprises the following steps of preprocessing a photovoltaic panel infrared video, and extracting frame by frame to obtain the photovoltaic panel image data after perspective transformation; determining a photovoltaic array area; extracting to obtain a photovoltaic panel array image; for the extracted photovoltaic panel array image, removing invalid lines line by line by adopting a threshold method, and extracting to obtain the small photovoltaic panel area images; calculating a pixel mean valueand a variance of each small photovoltaic panel area image, and carrying out fault detection and fault category judgment by adopting a threshold method; numbering the faults by calculating a fault photovoltaic panel characteristic function value, obtaining the fault position information through calculation in combination with the unmanned aerial vehicle route data and a photovoltaic panel numbersequence, wherein the fault position information comprises the number and the position of the photovoltaic panel where a fault is located. According to the invention, the photovoltaic panel fault detection can be carried out automatically, the fault images, fault types and fault positions are outputted, and the workers of a photovoltaic power station can determine the fault information timely andconveniently for photovoltaic panel maintenance.

The invention provides an autonomous underwater robot propeller weak fault feature extraction method, which belongs to the technical field of underwater robot fault diagnosis and comprises two parts: fault feature enhancement and feature fusion. Firstly, Gaussian properties of all modes of a multi-source state signal and a control signal are judged through negentropy, so that parameters are optimized, noise reduction is completed, and fault features are extracted and enhanced based on a modified Bayesian algorithm. Afterwards, the characteristic signals are divided into a plurality of time intervals, faults occurring in all the intervals serve as focal elements, first-time characteristic fusion is conducted on all the signals except for the longitudinal speed, second-time fusion is conducted on the result of the first-time fusion and the characteristic signals of the longitudinal speed, and the fault characteristics are further enhanced; and meanwhile, monotonicity is presented between the fault feature and the fault degree. The method can provide a basis for subsequent fault detection and identification, and is especially suitable for state monitoring of an autonomous underwater robot propeller.

The invention relates to a power transmission line vehicle-mounted inspection image classification method. A plurality of potential safety hazards exist in the conventional manual inspection process, and the conventional manual inspection method is low in efficiency. The method comprises the following steps of: performing multi-dimension corner detection on input images; determining two types of images according to the number of corners; determining laplace-of-gaussian (LoG) transformation aiming at undetermined images to obtain wire masks; marking communication regions of the wire masks, and obtaining wire gray images according to the wire masks and the input images I; and finally, calculating the width of each wire in the vertical direction to obtain a histogram, and judging the other two types of images according to the histogram. By the method, a large number of inspection images can be classified and filtered, and the subsequent operation of detecting and recognizing failure manually or through software is simplified.

The invention relates to a ground-based augmentation system integrity fault identification method and system, belongs to the technical field of satellite navigation, and solves problems that a fault identification method in the prior art is low in precision and cannot better identify potential integrity faults. The method comprises the following steps: acquiring historical measured data provided by a ground reference receiver and an airborne receiver; calculating a differential positioning error and a protection level of the airborne receiver based on the historical measured data; extracting afault data set from the historical measured data based on the differential positioning error and the protection level; and performing fault feature extraction on the fault data set and determining afault type. The accuracy of fault detection and identification is improved, and the requirement for higher-level integrity fault identification is met.

The invention discloses a real-time fault detection method for a large-scale grid-connected solar photovoltaic power station. The method comprises the steps of simulating a theoretical photovoltaic power station in MATLAB/Simulink, achieving the theoretical photovoltaic power station, enabling the theoretical photovoltaic power station to generate a direct current similar to an actual photovoltaic power station in real time, obtaining the irradiance and photovoltaic module temperature data in real time from the theoretical photovoltaic power station in the Simulink, simulating the generated photovoltaic output, carrying out abnormal value detection by using a developed statistical tool Z-score, comparing the direct current power of a single photovoltaic string of an actual photovoltaic power station with the average direct current power photovoltaic string of the theoretical photovoltaic power station, and carrying out fault detection and identification through a Z-score value and a comparison result based on a Z-score threshold value.

The invention discloses a satellite fault identification method, a satellite fault identification device and a software receiver, and relates to the technical field of satellite navigation and positioning. The method comprises the following steps: acquiring receiver position data and clock correction, constructing an observation matrix according to the receiver position data and the clock correction, and decomposing the observation matrix by adopting an odd-even space algorithm to generate an odd-even space matrix; performing matrix operation on the odd-even space matrix to obtain the slope ofeach satellite participating in positioning, and extracting the maximum slope value; calculating a similar radial error protection value of the positioning satellite based on the maximum slope valueand the false alarm detection value, and when the similar radial error protection value is not less than a preset value, calculating a check statistic according to satellite deviation data corresponding to the odd-even space matrix; detecting whether a satellite participating in positioning has a fault or not according to the verification statistics, the false alarm detection value and a preset threshold factor. The method has the characteristics of low time complexity and low space complexity, and can adapt to satellite fault detection and identification of normal flight and high dynamic flight.

The invention discloses a photovoltaic power generating assembly fault detection and identification method based on digital twinning. The method comprises the following steps: detecting and outputting a characteristic quantity y (t) in a physical entity of a to-be-detected photovoltaic power generating assembly, wherein the photovoltaic power generating assembly comprises a solar cell assembly and a DC-DC converter; constructing a digital twin body having the same physical entity structure as the to-be-detected photovoltaic power generating assembly, and calculating and outputting the measurement characteristic quantity z (t) of the photovoltaic power generating assembly in the digital twin body; calculating and outputting a residual vector gamma (t) according to the characteristic quantity y (t) and the measured characteristic quantity z (t); outputting a detection result according to the residual vector gamma (t); when a fault exists in the detection result, calculating and outputting an L2 inner product according to the residual vector gamma (t) and a fault characteristic value fi, wherein the fault characteristic value fi is calculated by the residual vector gamma (t) and a 2-norm gamma (t) 2 of the residual vector gamma (t); and outputting a fault type according to the L2 inner product. The method can be used for detecting whether the photovoltaic power generating assembly has a fault or not and identifying the type of the generated fault. The reliability and the practicability are high.