12 results about "Fault detection and identification" patented technology

The invention belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The method comprises the following steps of: dividing candidate fault sections according to a topological structure by synchronously acquiring steady-state and transient-state traveling wave signals of key nodes of a power grid; identifying a fault by using a multi-feature fusion criterion and extracting traveling wave features; executing mode recognition-based steady-state section judgment and multi-terminal ranging and polarity verification-based transient positioning in parallel to generate two types of positioning result sets; and finally, carrying out intelligent fusion judgment on the two types of results under topological constraints. According to the method, the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks space positioning capability and is insufficient in adaptability are solved, section-level accurate positioning of the fault is realized, and the positioning accuracy, robustness and fault processing efficiency are remarkably improved.

The invention relates to the technical field of power distribution. The invention discloses a power distribution cabinet wire fault detection and identification method, which is characterized in that a plurality of wires with different specifications are arranged in a power distribution cabinet, and the method comprises the following steps: obtaining real-time operation parameter data of the plurality of wires, and determining a fault wire based on a reference parameter set of normal work of a to-be-detected wire and a deviation characteristic value relation of real-time parameters; fault parameter data of the faulty electric wire are input into a multi-layer fault identification model, the model comprises a plurality of sub-identification units connected in sequence, each unit detects different types of faults, and finally a fault mode is obtained. According to the method, at least three parameters are acquired through distributed sensing, and data reliability is guaranteed through noise reduction and normalization processing. On the basis of a double-threshold criterion and dynamic weight calculation, faulty electric wires are accurately distinguished, and misjudgment and missed judgment are completely eradicated. The multi-layer model carries out progressive detection, an emergency fault is preferentially recognized, and the sub-recognition units are matched with an exclusive algorithm to improve the accuracy.

The invention discloses a multi-mode on-orbit component irradiation fault diagnosis method and a micro module. The method comprises the following steps: acquiring voltage and current data of a to-be-tested device through an analog end circuit module; performing data preprocessing on in-phase data input and orthogonal data input acquired by the ADC; building a neural network as a fault diagnosis module; the data source of fault diagnosis is the data preprocessing output of the preceding stage; the method comprises the following steps: establishing a reverse neural network, combining a data preprocessing module, establishing a neural network training platform, training and testing the neural network through a fault data set, adaptively adjusting the learning rate of each parameter in combination with an Adam algorithm, extracting weight and bias parameters of the trained neural network, and transmitting the weight and bias parameters to an FPGA-end feedforward neural network; and fault diagnosis: inputting the preprocessed data into an FPGA-end feedforward neural network to complete calculation and output of a fault result. According to the method, more irradiation fault types are detected, the detection period is shorter, the detection precision is higher, and after training is completed, irradiation fault detection and recognition can be independently carried out according to collected information.

The application belongs to the technical field of smart grid fault detection and identification, and relates to a power distribution network intelligent inspection and fault diagnosis method based on multi-source information fusion. The method comprises an unmanned aerial vehicle inspection platform, an airborne edge computing terminal, a cloud operation and management platform, a PMS system and a mobile terminal. The application synchronously collects visible light images, infrared thermal images and independent temperature sensing data through the unmanned aerial vehicle platform, and utilizes deep learning and multi-modal fusion technology to realize data complementation and collaborative analysis at a feature level, so that real-time and accurate identification of power distribution network lines and equipment faults is finally completed.

The present application belongs to the technical field of fault detection, and relates to a high-voltage transmission line fault detection and identification method. The present application synchronously collects steady-state and transient traveling wave signals of key nodes of a power grid, divides candidate fault sections according to a topological structure; further utilizes a multi-feature fusion criterion to identify faults and extract traveling wave features; performs steady-state section discrimination based on pattern recognition and transient positioning based on multi-terminal ranging and polarity verification in parallel, to generate two types of positioning result sets; finally, intelligently fuses and judges the two types of results under topological constraints. The method solves the problems that a traditional single-point signal analysis method cannot determine a fault section, lacks spatial positioning capability and is insufficient in adaptability, realizes accurate positioning of a fault section, and significantly improves positioning accuracy, robustness and fault handling efficiency.

This invention provides a method and system for detecting faults in distribution network lines based on unmanned aerial vehicle (UAV) inspection, belonging to the field of power grid fault detection and identification technology. The method includes: determining a target line based on line anomaly information and obtaining the path information of the target line; dispatching a UAV to inspect along the target line direction, obtaining magnetic field measurements, UAV position data, and the distance between the UAV and the target line, thus obtaining a magnetic field sampling sequence; constructing a magnetic field distribution map along the target line based on the preprocessed magnetic field sampling sequence; determining suspected anomaly locations, fault types, and fault location information based on the magnetic field distribution map, and outputting the magnetic field distribution map, fault type, and fault location information to a display terminal for display. This method enables rapid inspection of abnormal distribution network lines, achieving identification of suspected anomaly locations, fault type discrimination, fault location information determination, and visual display, thereby improving the efficiency of distribution network line fault inspection and maintenance.

The present application relates to quantum magnetic sensing and intelligent detection technical field, specifically a kind of non-contact electronic equipment ultrafast electromagnetic fault detection identification device and identification method based on diamond NV color center.This identification device includes efficient excitation microstrip array antenna module, electromagnetic fault detection identification main control board, laser modulation and coupling interface unit, photoelectric detection control interface unit, laser heat management copper-based heat dissipation assembly, voltage follower circuit control switch unit, pixel-level quantum magnetic sensing unit group, laser control circuit mainboard and photoelectric detection control mainboard;Wherein each pixel-level quantum magnetic sensing unit group includes three narrow linewidth laser excitation source, three diamond NV color center quantum magnetic sensitive element, three narrow-band optical filter and three photoelectric detector.The identification device has the characteristics of fast detection speed, high sensitivity, strong real-time, low power consumption and no need to contact the measured target, etc., can effectively improve the efficiency and accuracy of complex electronic equipment electromagnetic fault diagnosis.

The invention discloses an open-circuit fault diagnosis method and device for a driver of an electric excavator, and the method comprises the following steps: firstly, obtaining a three-phase current signal of an inverter; filtering and smoothing the current signal; performing positive and negative half-wave separation on the current signal to obtain an odd symmetric component and an even symmetric component; calculating an integral mean value, constructing a phase current characteristic vector, and comparing the maximum value of the integral mean value with a detection threshold value to judge whether an open-circuit fault occurs or not; calculating a positioning threshold value in real time, comparing the phase current characteristic vector with the positioning threshold value, and separating an abnormal value; and finally, obtaining a fault positioning result according to the index relation between the abnormal value and the inverter switching tube. According to the method, information containing fault characteristic essence can be extracted only by using three-phase current data, a phase current vector is constructed through odd-even symmetric components, and a distributed adaptive threshold algorithm is constructed, so that open-circuit fault detection and identification of the inverter module under a complex working condition can be realized without complex calculation.

This invention proposes a sensor fault monitoring and failure information reconstruction method and system. First, it uses a robust improved principal component analysis method for sensor fault detection and identification in nuclear power plants. Then, it reconstructs the failure information of nuclear power plant sensors based on maximum mutual information coefficients, convolutional autoencoders, long short-term memory networks, and self-attention mechanisms. Finally, it uses an improved particle swarm optimization algorithm for automatic hyperparameter optimization. This method achieves more robust sensor fault detection and faster, more accurate sensor failure information reconstruction. It can accurately detect abnormal readings from steam generator water level sensors, accurately determine whether the abnormality is caused by a "false water level" or a sensor malfunction, and accurately and quickly reconstruct the abnormal water level information of the steam generator, ensuring the normal operation of the control system and improving the safety and economy of nuclear power plants.

The invention relates to a fault detection and identification method and device for a current collection line of a new energy field station, and the method comprises the steps: determining a fault phase of the current collection line, inputting an external power supply based on the fault phase, and obtaining a parameter value of a sensor in the current collection line; based on the parameter value and a preset error value, judging whether the line to which the sensor belongs has a fault, and if so, obtaining fault data corresponding to the fault; and matching the fault data with fault features stored in a fault information database, and if matching succeeds, judging and identifying the fault data based on a matching result. By executing the above method, fine positioning inspection is performed on the fault section based on the parameter values obtained by the sensor, the fault range is given to find the fault point, and the fault finding time can be shortened, so that power transmission is recovered as soon as possible after tripping, and the lost generating capacity is reduced as much as possible.

The invention discloses a rudder control driving system current sensor fault detection and identification method based on coordinate transformation. The method is suitable for permanent magnet synchronous servo and the situation that only two-phase current sensors are used. According to the method, the phase current of the rudder control driving system and the reference current of the control loop are converted into the first static coordinate system space and the second static coordinate system space which meet fault decoupling by adopting specific coordinate conversion, so that the fault of a certain phase current is only represented in a specific variable and is prevented from being confused with phase current faults of other channels; through designing a reasonable detection statistic and a fault detection threshold, identification of a fault channel current sensor is realized.

This invention discloses a method for fault detection and identification in a navigation system cluster based on common-mode combination of cooperative navigation information. It utilizes the cooperative system to construct common-mode pseudorange residuals to improve the fault detection sensitivity of the GNSS receiver, and leverages the cooperative characteristics of the cluster system to enhance the efficiency of identifying faulty satellites. By combining and modeling the observations of auxiliary sensors in the cooperative navigation system with the pseudorange observations of the GNSS receiver, the method can detect whether auxiliary sensors have malfunctioned after monitoring the integrity of the GNSS receiver. This method can address various factors that induce pseudorange faults in complex environments, employing decentralized fault handling to improve the fault detection and identification rate of the GNSS receiver in the cooperative navigation system. Furthermore, it detects and identifies faults in auxiliary sensors within the cooperative navigation system, improving the overall integrity of the cooperative navigation system and ensuring its accuracy and robustness.