338 results about "Fault recognition" patented technology

This invention relates to a method, system, device, and medium for synchronous control of dual motors in an electric bus door enclosure, belonging to the fields of smart home and automatic control technology. The method includes: collecting local motor speed data and sending it in real-time to other motor control systems within the same linkage group; receiving motor position data from other motor control systems; calculating speed and position deviations in real-time based on the local motor speed data and the received motor position data; and then, based on the deviations and preset full-stroke friction characteristic data, compensating for the speed of the local motor to ensure synchronous operation of all motors within the same linkage group. This application can reduce the false alarm rate of stall, reduce mechanical impact noise, extend the life of transmission components, and achieve accurate fault identification.

This application relates to the field of three-phase distribution network fault detection and location technology, and discloses a method, device, system, and storage medium for fault location in distribution systems. It addresses the problem of low accuracy in fault identification and location in existing solutions due to poor traceability of PMU measurement data and high computational costs. The method includes: acquiring power grid data collected by acquisition units located on the distribution network of the distribution system; fusing the three-phase π model and state estimation equations characterizing the distribution network branches to obtain equations corresponding to the distribution branches, and solving them to obtain theoretical values; using a threshold filtering method to remove data with numerical fluctuations exceeding preset conditions from the power grid data, and compensating the removed power grid data based on the data acquisition accuracy to obtain target power grid data; using the LNR algorithm to perform fault detection based on the target power grid data and theoretical values; and employing a multi-stage candidate branch location strategy to locate the fault when it is detected.

The application discloses a TN-C-S low-voltage power distribution system single-phase ground fault identification method, and belongs to the technical field of power system fault characteristics and relay protection. The single-phase ground fault identification method effectively solves the inadaptability problem of traditional single residual current protection by collecting residual current and zero sequence current and the fault current approximately separated by the algorithm of the application. The application collects residual current, zero sequence current data and three-phase power supply voltage data respectively, relies on the short-time invariability of the neutral point grounding resistance and the repeated grounding resistance, proposes to separate the fault current by using the residual current and the zero sequence current, then compares the fault current with the setting threshold to identify the single-phase high-resistance ground fault, and obtains the fault phase according to the matching of the phase of the separated fault current and the phase of the power supply voltage, so that the single-phase high-resistance fault identification and phase selection identification are realized.

The application provides a data center infrastructure equipment state intelligent diagnosis method and system, comprising the following steps: transmitting encrypted equipment data set to a diagnosis center to obtain qualified equipment data set; performing model construction on the infrastructure equipment set to obtain a fault identification model group; identifying a current identification model in the fault identification model group based on the qualified equipment data; performing fault identification on the qualified equipment data by using the current identification model to obtain equipment fault value and fault type probability group; obtaining a preliminary diagnosis result; if the preliminary diagnosis result is a suspected fault, performing multi-equipment joint diagnosis on the equipment fault value to obtain a joint fault value; and if the joint fault value is greater than a maximum fault value, confirming the equipment fault data. The application can reduce the false negative rate of data center infrastructure equipment state diagnosis and improve the accuracy of infrastructure implicit coupling fault diagnosis.

This invention relates to the field of wind power generation technology and discloses a method, device, equipment, and medium for fault assessment of wind turbine transmission systems. The method utilizes a data dimensionality reduction algorithm to reduce the dimensionality of multidimensional raw data, retaining core distinguishing features and simplifying calculations. Then, a data clustering algorithm is used to intelligently classify operating states, and a preliminary fault mode mapping is constructed by combining historical faults. Subsequently, the rationality of clustering is verified using signal source correlation coefficients, and long-term historical operating data is filtered through historical data matching rates to reduce the risk of misjudgment. Next, based on time series analysis and degradation path analysis, the coupling relationship between vibration trend slope, temperature accumulation offset, and torque decay cycle is obtained. Finally, core features are extracted through convolutional neural networks to accurately output the probability distribution and specific location of fault occurrence, thereby improving the accuracy of fault identification in the transmission system of offshore wind turbines and the ability to predict component performance degradation.

This invention discloses a data-driven intelligent operation and maintenance method for vehicle-to-grid (V2G-Vehicle-Storage) systems. The method collects real-time operating status data of heterogeneous power electronic converters and performs data preprocessing. Based on the preprocessed data, standardized input is generated and fed into a pre-built and trained fault identification model based on a convolutional neural network, outputting fault type identification results. Based on the fault type identification results, fault classification, fault item confirmation, fault cause analysis, and automatic generation of fault handling solutions are performed. This invention's technical solution optimizes the problems of lagging operation and maintenance diagnosis and high false alarm rates under current conditions through deep learning algorithms, realizing a data-driven end-to-end process of system fault diagnosis, location, analysis, and operation and maintenance, ensuring the safe operation of the system.

This invention discloses a method and system for monitoring and resetting the software heartbeat of a spaceborne receiver. The system includes a dual-channel GNSS positioning module, a dual-channel orbit determination module, and an antifuse FPGA chip. Both the GNSS positioning module and the orbit determination module have corresponding primary and backup FLASH memory. The antifuse FPGA acts as the heartbeat monitoring and control center, connecting to the GNSS positioning module and the orbit determination module via GPIO interfaces. It receives the software heartbeat signals from each module and sends reset commands to faulty modules via reset pins. This solution solves the problems of poor radiation resistance, low fault identification accuracy, and low recovery efficiency in existing spaceborne receiver software fault monitoring.

The application relates to the technical field of fault diagnosis, and provides a high-frequency transformer fault diagnosis method and system, which comprises the following steps: collecting a target signal with a time stamp and extracting corresponding features, simultaneously relying on a transformer structure, material parameters and physical rules to build a digital twin model, simulating insulation and structure degradation equivalent working conditions, solving multi-physical field data and generating multi-physical field mechanism samples; then, the mechanism samples and field measured data are fused through a generative adversarial network to expand the fault sample data set and solve the sample scarcity problem; in the running stage, the digital twin model is updated in real time through parameter online inversion, a feature dynamic graph representing multi-physical coupling is constructed by combining the parameter deviation of internal mechanism degradation and the multi-source features of external working conditions, finally, the feature aggregation and time sequence reasoning are completed through the graph neural network and the time sequence neural network trained offline, the fault probability is output, and the optimal diagnosis result is determined; thereby, the fault recognition accuracy and the robustness in the running stage are improved.

This invention discloses an AI-based predictive maintenance and closed-loop control system for industrial motor health, belonging to the field of motor monitoring and predictive maintenance. The system includes: a multi-dimensional high-frequency synchronous sensing unit for collecting motor operating parameters; a dynamic operating condition feature decoupling unit to eliminate the influence of speed fluctuations on signals under variable frequency drive through order analysis; an electromagnetic-mechanical coupling feature fusion unit to achieve deep fusion of current and vibration signals and construct a composite fault feature map; an artificial intelligence health prediction engine to output quantitative health assessment results based on a spatiotemporal sequence model; and an adaptive closed-loop control execution unit to automatically adjust motor operating parameters according to the assessment results, forming a complete link from sensing and prediction to closed-loop control. This invention solves the problems of high false alarm rate and difficulty in early fault identification under variable frequency operating conditions, achieving accurate assessment and automated closed-loop control of motor health status, significantly improving equipment reliability and intelligent operation and maintenance levels.

This invention discloses a motion risk assessment system and method for autonomous vehicles based on multi-mode trajectory prediction, comprising four sensors, a time synchronization module, a verification module, a local anomaly voting and fault detection counter, a trajectory correction and feature enhancement module, and an LSTM network. The four sensors include an INS (Inertial Navigation System) combined inertial navigation sensor, an inertial navigation IMU (Inertial Measurement Unit) sensor, a drive-by-wire chassis sensor, and a visual recognition sensor. The INS combined inertial navigation sensor includes GPS and an IMU. The verification module includes a χ² (X-ray) sensor. 2 The system includes checks for consistency in motion patterns and residual consistency; the LSTM network comprises an LSTM encoder, a classification decoder, and an LSTM decoder. This invention proposes a complete "detection-evaluation-decision" closed-loop solution. By constructing a multi-mode prediction network incorporating fault characteristics, not only is fault identification achieved, but the impact of faults on driving safety can also be accurately assessed, providing comprehensive safety decision-making support for autonomous driving systems.

This invention discloses a simulation testing system for integrated circuits, belonging to the field of integrated circuit testing technology. It includes a simulation execution module, a data acquisition module, a fault diagnosis module, a parameter correction module, a control module, a visualization module, and an alarm module. The simulation execution module runs a digital twin model of the integrated circuit and outputs simulation parameters, such as temperature, voltage, and timing margin, providing simulation benchmark data for subsequent deviation quantification. This invention overcomes the limitations of traditional single-parameter monitoring by combining multi-parameter comprehensive deviation quantification and dynamic weight allocation with a conditional probability model to accurately diagnose faults and generate a priority list. The hierarchical digital twin model interacts with real-time data streams to achieve accurate simulation of physical behavior, providing a high-precision benchmark for deviation quantification. The model adaptively optimizes, and with iterative control closed-loop, it improves fault identification accuracy and enhances system robustness and practicality.

This invention provides a fault identification method for test equipment based on diagnostic semantic adaptation, belonging to the field of test equipment operation status monitoring technology. It includes: S1, collecting multi-measurement channel sensor signals and image information generated during the test and performing preprocessing; S2, performing measurement channel mapping, unit conversion, range matching, and normalization processing on multi-source asynchronous data; S3, determining the fault probability of the test equipment, obtaining the fused fault confidence level, and generating a structured test equipment fault diagnosis record package; S4, analyzing the fault types of the test equipment and executing the test equipment fault identification alarm program. This invention uses an edge-side temporal fault discrimination network to perform real-time fault screening on a unified state vector, and employs a multimodal transfer diagnostic network for fault verification and incremental learning, forming a parameter optimization closed loop to improve the accuracy and robustness of fault identification.

The application provides a capacitor fault identification method based on parameter fusion. By collecting multi-dimensional characteristic quantity data such as capacitance value, equivalent series resistance value and dielectric loss tangent value, and inputting the characteristic fusion into a target network model, the accurate classification of the capacitor fault type is realized. Through the complementarity of multi-dimensional characteristic quantity and the extraction of time domain statistical characteristics combined with the feature fusion technology, the correlation between the characteristics is fully mined, and the representation ability of the model to the fault mode is enhanced. The target network model can specifically fuse a deep feedforward neural network, a convolutional neural network and a long short-term memory network to extract static characteristics, dynamic characteristics and time sequence dependency, respectively, and form a spatio-temporal joint feature expression. Therefore, the effectiveness, reliability and practicability of the capacitor fault diagnosis in engineering application are improved.

This invention relates to the field of power distribution fault location technology, specifically to an AI-driven power distribution fault location system and method. The system includes: a state projection construction unit that constructs multiple fault state projection surfaces; a state collision elimination unit that uses a topology response collaborative constraint model to identify structural logic conflicts and uses a structural collision-driven multi-path filtering mechanism to eliminate fault state projection surfaces with structural logic conflicts; a differential comparison scoring unit that uses an AI response scoring model to generate confidence scores for each candidate fault state projection surface and uses a working condition semantic embedding model to correct the confidence scores; a structural feedback location unit that uses the AI ​​response scoring model to update and sort the confidence score sequence to determine the fault point; and a fault terminal processing and display unit that displays the fault points of each power distribution station and generates inspection tasks and maintenance work orders. This invention combines AI-driven intelligent judgment and location of power distribution faults and constructs a fault identification method for multi-site maintenance scenarios.

This invention discloses a transformer fault detection method, system, processor, and medium based on an improved chimpanzee optimization algorithm. The method includes acquiring multi-source heterogeneous monitoring data of the transformer; performing source-specific preprocessing and feature extraction on the multi-source heterogeneous monitoring data to form a standardized feature set; performing cross-modal feature extraction and fusion on the standardized feature set based on a pre-constructed multi-branch deep neural network fusion model; employing a chimpanzee optimization algorithm based on chaotic mapping, with the diagnostic performance of the model as the optimization objective, to globally adaptively optimize the parameters of the model to obtain an optimized fault diagnosis model; inputting real-time monitoring data of the transformer to be diagnosed into the optimized fault diagnosis model, and outputting the fault type diagnosis result. This invention can fully identify the complementarity and deep correlation between multi-physics field signals, improving diagnostic accuracy and early fault identification capabilities.

This application relates to the field of vehicle intelligent control technology, and in particular to a dual-redundancy fault identification method and device for a seat control system. The method includes: acquiring first operating data of the seat control system and extracting principal component feature vectors; determining key feature vectors; determining the fault type when the key feature vectors meet preset fault conditions; generating a switching request signal from the main controller to the corresponding backup controller based on the fault type, second operating data, and third operating data; switching the main controller to the corresponding backup controller according to the switching request signal, and generating fault description information. This solves the problems in related technologies, such as insufficient consideration of signal synchronization, quality assessment, and switching decision optimization between the main and backup controllers, resulting in poor fault tolerance; low signal processing fault tolerance and high fault misjudgment rate when facing environmental interference; difficulty in balancing real-time performance and accuracy; and inability to quickly locate faults and generate effective decisions in dynamic scenarios.

The present application relates to a kind of motor early demagnetization fault identification method, device, equipment and storage medium, wherein motor early demagnetization fault identification method includes the following steps: obtaining the vibration signal of several fault motor samples, demagnetization type and demagnetization degree;Extract the time domain feature and frequency domain feature of the vibration signal of the fault motor sample;Based on the time domain feature, frequency domain feature, demagnetization type and demagnetization degree of several vibration signals of the fault motor sample, construct training data set;Train complete fault identification model;The vibration signal of the motor to be diagnosed is acquired, the time domain feature and frequency domain feature of the vibration signal of the motor to be diagnosed are extracted and input into the fault identification model, to output the demagnetization type and demagnetization degree of the motor to be diagnosed;Can identify motor permanent magnet deterioration signs in the early stage of motor sub-health state, effectively solve the problem that early fault is not sensitive to motor health management, life prediction.

Embodiments of the present application disclose a power transformation equipment fault detection method and device and electronic equipment, comprising: obtaining running audio data and running vibration data of a to-be-tested power transformation equipment in a target time period; dividing the running audio data into a plurality of audio sub-data according to a preset time interval, to determine a stationary attribute of the running audio data based on the similarity between the plurality of audio sub-data; in the case that the stationary attribute of the running audio data meets a preset condition, inputting the running audio data and the running vibration data into a pre-trained fault detection model to determine a fault category of the to-be-tested power transformation equipment, most signals of the power transformation equipment in a stable running state can be discarded, and more dimensional fault features can be captured through joint analysis of audio and vibration signals, comprehensiveness, detection efficiency and accuracy of power transformation equipment fault recognition are improved.

The invention relates to a wind power monitoring technology, in particular to a floating fan structure damage detection method and system based on transfer learning, and the method comprises the steps: constructing a motion scaling model of a floating fan, designing a blade vibration data collection unit, collecting and processing structural damage monitoring data, and obtaining a scaling model test data set; constructing a source domain structure damage fault identification model based on deep learning, and training the identification model according to the scale model test data set; establishing a floating fan full-coupling simulation model, and constructing a damage simulation method; performing multi-working-condition analogue simulation on the floating fan to generate damage simulation data; and fusing actually-measured structural damage fault data and damage simulation data to obtain a target domain data set, carrying out transfer learning on an identification model to obtain a high-precision identification model, and carrying out real-time monitoring and fault early warning on a fan state. According to the method, the limitation caused by data scarcity is overcome, and floating fan structure damage identification modeling of effective knowledge migration from the model to a real machine is realized.

This invention discloses a method for detecting the health status of a new energy vehicle power supply, belonging to the field of power supply technology. The invention first extracts abnormal current signals from the phase line current signals of the three-phase inverter of the new energy vehicle power supply at different scale windows. It then extracts amplitude dispersion values ​​from the frequency domain, calculates the three-phase frequency domain cumulative values, generates frequency domain amplitude dispersion weights, and constructs a multi-scale frequency domain feature matrix and weight matrix. Next, it extracts effective abrupt change values ​​from the time domain, calculates the three-phase time domain cumulative values, generates time domain abrupt change weights, and constructs a multi-scale time domain feature matrix and weight matrix. Finally, it fuses the feature matrix and weight matrix of the same dimension, inputs them into a fault detection neural network for processing, and outputs the power supply health status. This invention improves the sensitivity and accuracy of early power supply fault identification, effectively reducing the probability of false positives and false negatives.

The application belongs to the technical field of teaching practical training scene simulation control, and specifically discloses a teaching practical training scene simulation control method based on PLC technology, which comprises the following steps: constructing a fault mode library containing low, medium and high levels, randomly distributing faults from the low fault level, loading a simulation scene, collecting fault identification response time and adjustment operation sequence, verifying diagnosis accuracy and evaluating fault recovery degree after executing the sequence, generating a practical operation qualification degree according to different conditions, then judging promotion and dynamically adjusting the fault level according to the qualification degree, and generating a student comprehensive ability evaluation report based on the qualification degrees in the previous iterations; the application constructs a fault mode library containing low, medium and high fault levels based on historical power operation data, and dynamically adjusts the fault level to be distributed subsequently according to the practical operation qualification degree, thereby overcoming the defects of static preset fault situations, and realizing a personalized and gradual skill upgrading path.

This invention discloses a rolling bearing fault detection method based on Kolmogorov-Arnold network + STFT, belonging to the field of mechanical fault diagnosis technology. This method collects rolling bearing operating signals through vibration sensors, performs time-frequency analysis on non-stationary vibration signals using Short-Time Fourier Transform (STFT), generates a time-frequency energy distribution map, and extracts multi-scale time-frequency features as fault characterization. Subsequently, a Kolmogorov-Arnold network (KAN) model is constructed, and its unique nonlinear superposition structure performs high-order correlation analysis on the time-frequency features, automatically learning the complex mapping relationship between fault modes and features. Compared with traditional neural networks, KAN uses a learnable activation function and achieves high-precision feature fitting through spline interpolation, significantly improving the sensitivity to weak fault features. This method integrates the advantages of time-frequency analysis and adaptive networks, solving the problems of insufficient feature extraction for non-stationary signals and weak model generalization ability in traditional methods. Experiments show that its fault identification accuracy reaches 98.7%, the detection response time is reduced by 40%, and it maintains over 90% detection stability even in strong noise environments, making it suitable for online monitoring systems of industrial equipment.

This invention relates to a method and system for intelligent processing and health assessment of multi-parameter acoustic-vibration data in GIS, belonging to the field of power equipment monitoring technology. The method includes: collecting acoustic signature data, vibration data, and geographic location information from GIS equipment to form a raw acoustic-vibration data stream with geographic tags; writing the raw acoustic-vibration data stream into a storage-computing architecture, sequentially extracting and fusing features from the acoustic signature data and vibration data to obtain a multi-parameter feature matrix with geographic tags; using a lightweight convolutional neural network to infer and label suspected faults on the multi-parameter feature matrix; using a support vector machine (SVM) model to identify faults in the multi-parameter feature matrix with suspected fault labels, obtaining the final fault type and confidence level; and combining equipment runtime, maintenance records, fault frequency, and GIS location information to perform a health assessment of the GIS equipment and classify fault levels. This invention significantly improves the accuracy of GIS fault identification and reduces the operation and maintenance costs of large-scale GIS applications.

The application discloses a kind of ring network cabinet fault diagnosis method, equipment and storage medium, method includes: through the synchronous acquisition operation data of multiple source sensor group deployed on ring network cabinet;After the pre-processing of the collected multi-source data, respectively extract improved mel frequency cepstrum coefficient, transient voltage feature, ultrasonic feature and electrical feature based on high-order cumulant;The extracted features are spliced to form high-dimensional fusion feature vector, and the compact features are obtained by dimension reduction using sparse autoencoder;The improved aurora optimization algorithm is used to optimize the support vector machine, and the IPLO-SVM model is constructed to identify faults.The application can fully integrate multi-source information, efficiently extract fault features, achieve high-precision classification, and is suitable for intelligent operation and maintenance of distribution network ring network cabinet.

The application provides a bearing temperature and vibration signal fault evolution path intelligent identification method and system, relates to the technical field of fault identification, and comprises the following steps: collecting and time-aligning bearing temperature, vibration signals and working condition parameters to construct a multi-source heterogeneous data matrix; extracting multi-dimensional features after adaptive noise reduction processing; constructing a fault evolution mechanism causal correlation network by using a causal inference model; performing time series clustering to divide health state clusters based on the network and establishing a state transition model by using a hidden Markov model; and finally applying a Viterbi algorithm to decoding to obtain an optimal fault evolution path. The method can effectively identify the bearing fault evolution law and improve the prediction accuracy.

The present application relates to the field of power distribution network automation and relay protection technology, and more particularly to a distributed power distribution network traveling wave distance measurement and fault identification method based on 5G communication. The method introduces a dual physical criterion of box dimension rough estimation and Lipschitz index accurate calculation in the distributed traveling wave distance measurement system, combined with power frequency quantity change, to realize reliable identification of high resistance grounding fault; wave coherence verification based on cross wavelet transform confirms the homology of traveling waves at different sites from the physical nature, solving the problem that the traditional request-response verification is easily disturbed and cheated by external interference; dynamic weight master device election based on wave head energy, coherence coefficient and signal-to-noise ratio ensures that the device with the optimal signal quality undertakes the calculation task; the field device only reports the final positioning result and a small amount of characteristic parameters, and the bandwidth occupation is reduced by more than 80%, creating conditions for large-scale access and fast self-healing control of the power distribution network.

This invention relates to the field of equipment data processing, and discloses an interactive data processing method, system, device, storage medium, and program product. The interactive data processing method provided in this invention includes: collecting multi-source data information from steel equipment, including a three-dimensional spatial visual image and sensor information of the steel equipment; uniformly semantically encoding the data information of different formats from the multi-source data information and inputting it into a pre-acquired semantic reasoning model, so that the semantic reasoning model generates structured instructions based on the multi-source data information; responding to the structured instructions, generating three-dimensional spatial rendering information and interactive feedback information of the steel equipment, and superimposing the three-dimensional spatial rendering information and interactive feedback information on the three-dimensional spatial visual image of the steel equipment. The method provided in this invention improves data processing efficiency and the accuracy of fault identification in steel equipment, realizing precise and immersive guidance for the AR operation and maintenance system of steel equipment.

This invention discloses a method, equipment, and medium for safety protection and emergency response of a liquid nitrogen fire extinguishing system. The method includes deploying an environmental monitoring network, collecting temperature distribution data and smoke particle concentration, establishing a graded early warning threshold model, and when the temperature distribution data or smoke particle concentration data triggers a risk level, the central controller outputs an alarm signal of the corresponding level. Based on the alarm signal level, the central controller automatically activates the linked ventilation protection device and simultaneously triggers an emergency isolation procedure, recording environmental monitoring network data, alarm logs, and the operational status of rescue sub-equipment, generating a standardized handling record. This invention's system self-check response time is reduced by 70.6% compared to traditional systems, and the fault identification accuracy is improved by 23.1%. It solves the problems of low self-check efficiency, inaccurate fault identification, incomplete records, and inconvenient querying in traditional systems, providing complete data support for subsequent system maintenance, fault diagnosis, and technical optimization, and improving the system's practicality and maintainability.

The application discloses a small sample motor fault identification method, a model training method, a device and equipment, relates to the technical field of motor fault identification, and comprises the following steps: a motor fault identification model comprises a gram angle field module, a two-dimensional convolution module and a compressed excitation module, the gram angle field module is used for converting a plurality of training samples in a training sample data set into two-dimensional images through polar coordinate mapping and gram matrix operation respectively, so as to retain the time dependence characteristics and nonlinear characteristics of the signals in the training samples; the two-dimensional convolution module is used for extracting features of different scales in the two-dimensional images based on a multi-scale convolution kernel group to obtain a feature extraction result; and the compressed excitation module is used for dynamically adjusting the weights of feature channels to enhance key features and suppress redundant features; the application can meet the fault diagnosis requirements of the small sample motor fault identification in the application scene with complex and changeable working conditions, improve the noise interference robustness of the signals, and ensure the reliability of the diagnosis results.

The present disclosure provides a device fault processing method, device, management system and electronic device, the processing method comprising constructing a digital twin of a target device and visualizing the digital twin in a mixed reality device; collecting a plurality of modal actual correlation parameters; based on the actual correlation parameters and a pre-trained preset fault recognition model, obtaining a target fault recognition result; displaying the target fault recognition result in the digital twin; in response to an external interactive operation, obtaining target fault analysis data based on a pre-constructed preset fault physical knowledge graph. In the present disclosure, a real-time interaction and mutual enhancement closed-loop processing scheme of fault recognition between the "physical world-digital twin-fault recognition model-MR interaction-human expert" is constructed, ensuring the accuracy and efficiency of fault recognition in any target device, and based on the display and interaction in the MR environment, the interactive experience of the fault processing scene is effectively improved.