6 results about "Intermittent fault" patented technology

The application discloses a strategy for extracting fault features of electronic circuit systems, named as SSEST strategy, which is used for perceiving global information and paying attention to notable local information, and mining important local information means realizing expression of intermittent fault features of electronic circuit systems, specifically, first, S transformation is performed on a circuit output time sequence signal to acquire time-frequency domain features, then a squeeze and excitation network attention module is used to distribute channel weights, subsequently, input into a Swin Transformer framework, and pay attention to local information related to faults from global signals, and deep mining is performed on fault features, and two electronic circuits are taken as experimental circuits, the proposed diagnostic strategy realizes rapid and high-precision diagnosis, and shows that the proposed multiple attention mechanism is efficient for feature mining of intermittent faults of electronic circuit systems.

This application belongs to the field of interconnection network reliability and fault diagnosis technology, and discloses a method for intermittent fault diagnosis of low-bandwidth long interconnection networks based on graph attention mechanism. Targeting the hierarchical recursive structure and high connectivity of the network, under the PMC fault diagnosis model, a multi-round testing strategy is used to obtain test symptoms within the node's neighborhood. A feature vector is constructed for each node using local statistical feature extraction methods, and preprocessed using zero-padding and noise enhancement techniques. Finally, a graph attention network model is constructed, and the importance weights of neighboring node test results are dynamically learned using the attention mechanism to achieve accurate diagnosis of node fault states in the network. This application leverages the powerful local information aggregation capability of graph attention networks to overcome the diagnostic limitations of traditional algorithms, maintaining high diagnostic accuracy and robustness even with high fault rates, incomplete test symptoms, and large network sizes.

This invention relates to the field of equipment testing solutions, and discloses an intelligent generation system and method for equipment testing solutions based on feature data recognition. By extracting fault features that characterize immediate equipment anomalies and health status features that depict long-term performance baselines and aging trends, as well as performance degradation and deviation indicators based on health status features, the remaining service life of hardware components can be quantitatively predicted. This predicted value is then used as prior information to input a causal association model, and probabilistic reasoning is performed in conjunction with fault features. This significantly improves the sensitivity of progressive fault perception and the accuracy of root cause localization for intermittent faults, thereby generating more accurate equipment testing solutions. This effectively simplifies the testing process, reduces the average fault repair time, and decreases the reliance on the experience of senior experts, providing intelligent operation and maintenance support with predictive and adaptive capabilities for the stable operation of critical information infrastructure equipment.

The application belongs to the technical field of interconnection network reliability and fault diagnosis, and discloses a low-wrapping-length interconnection network intermittent fault diagnosis method based on a graph attention mechanism. Aiming at the hierarchical recursive structure characteristics and high connectivity characteristics of the network, under the PMC fault diagnosis model, the test signs in the node neighborhood are obtained through a multi-round test strategy. A local statistical feature extraction method is used to construct a feature vector for each node, and a zero padding and noise enhancement technology is used for pretreatment. Finally, a graph attention network model is constructed, the importance weight of the neighbor node test result is dynamically learned by using the attention mechanism, and the accurate diagnosis of the node fault state in the network is realized. The application uses the powerful local information aggregation capability of the graph attention network, breaks through the diagnosis degree limitation of the traditional algorithm, and can still maintain high diagnosis accuracy and robustness under the condition of high fault rate, incomplete test signs and large network scale.

The application discloses a kind of airborne integrated mission processor I / O interface module fault diagnosis method and system, it is related to avionics fault diagnosis technical field.First, part of analog quantity and digital quantity information is obtained from the I / O interface module of airborne integrated mission processor, and corresponding temperature and vibration information is obtained through temperature sensor and vibration sensor;After pre-processing and labeling, a combined model based on attention network and deep separable convolution strategy is constructed;The combined model is trained and used for real-time fault diagnosis of I / O interface module.The application triggers hardware protection for permanent fault, prompts to replace the fault module;For intermittent fault, record fault type and occurrence time, generate degradation trend report, facilitate subsequent key monitoring;The application reduces additional acquisition equipment, improves the fault diagnosis accuracy of I / O interface module of IMP, realizes accurate positioning and targeted processing of fault, and improves reliability and maintainability.