In space probes,anomaly detection of sequence data collected by various sensors is essential to help detect potential faults promptly,improve the reliability of equipment operation,and ensure the smooth operation of t...In space probes,anomaly detection of sequence data collected by various sensors is essential to help detect potential faults promptly,improve the reliability of equipment operation,and ensure the smooth operation of the mission.However,sensors'signals often contain a superposition of various frequencies,changing fluctuations,and correlations between features.This complexity of data attributes makes building effective models challenging.This paper proposes a TimeEvolving Multi-Period Observational(TEMPO)anomaly detection method for space probes.First,fusing wavelet analysis and natural periods improves the ability to capture multi-period features in data.Then,the feature extraction framework proposed enhances the effectiveness of anomaly detection by comprehensively extracting the complex features of data through the multi-module synergy of temporal and channel.The results demonstrate that the proposed method enhances anomaly detection accuracy and its effectiveness is confirmed.Additionally,the ablation experiment results further validate the efficacy of each module.An evaluation of the algorithm's computational complexity confirms its suitability for real-time processing.展开更多
In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic op...In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings,highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime.However,varying conditions induce feature distribution shifts,and scarce fault samples limitmodel generalization.Therefore,this paper proposes a causal-Transformer-based meta-learning(CTML)method for bearing fault diagnosis in CNC machine tools,comprising three core modules:(1)the original bearing signal is transformed into a multi-scale time-frequency feature space using continuous wavelet transform;(2)a causal-Transformer architecture is designed to achieve feature extraction and fault classification based on the physical causal law of fault propagation;(3)the above mechanisms are integrated into a model-agnostic meta-learning(MAML)framework to achieve rapid cross-condition adaptation through an adaptive gradient pruning strategy.Experimental results using the multiple bearing dataset show that under few-shot cross-condition scenarios(3-way 1-shot and 3-way 5-shot),the proposed CTML outperforms benchmark models(e.g.,Transformer,domain adversarial neural networks(DANN),and MAML)in terms of classification accuracy and sensitivity to operating conditions,while maintaining a moderate level of model complexity.展开更多
Identifying and segmenting spacecraft components is vital in many on-orbit space missions,such as on-orbit maintenance and component recovery.Integrating depth maps with visual images has been proven effective in impr...Identifying and segmenting spacecraft components is vital in many on-orbit space missions,such as on-orbit maintenance and component recovery.Integrating depth maps with visual images has been proven effective in improving segmentation accuracy.However,existing methods ignore the noise and fallacy in collected depth maps,which interfere with the network to extract representative features,decreasing the final segmentation accuracy.To this end,this paper proposes a Filtering and Regret Network(FRNet)for spacecraft component segmentation.The FRNet incorporates filtering and regret mechanisms to suppress the abnormal depth response in shallow layers and selectively reuses the filtered cues in deep layers,avoiding the detrimental effects of low-quality depth information while preserving the semantic context inherent in depth maps.Furthermore,a two-stage feature fusion module is proposed,which involves information interaction and aggregation.This module effectively explores the feature correlation and unifies the multimodal features into a comprehensive representation.Finally,a large-scale spacecraft component recognition dataset is constructed for training and evaluating spacecraft component segmentation algorithms.Experimental results demonstrate that the FRNet achieves a state-of-the-art performance with a mean Intersection Over Union(mIOU)of 84.13%and an average inference time of 133.2 ms when tested on an NVIDIA RTX 2080 SUPER GPU.展开更多
基金supported by the National Natural Science Foundation of China(Nos.92467108,62141604,62032016,92467206)Beijing Nova Program,China No.(20220484106,20230484451)。
文摘In space probes,anomaly detection of sequence data collected by various sensors is essential to help detect potential faults promptly,improve the reliability of equipment operation,and ensure the smooth operation of the mission.However,sensors'signals often contain a superposition of various frequencies,changing fluctuations,and correlations between features.This complexity of data attributes makes building effective models challenging.This paper proposes a TimeEvolving Multi-Period Observational(TEMPO)anomaly detection method for space probes.First,fusing wavelet analysis and natural periods improves the ability to capture multi-period features in data.Then,the feature extraction framework proposed enhances the effectiveness of anomaly detection by comprehensively extracting the complex features of data through the multi-module synergy of temporal and channel.The results demonstrate that the proposed method enhances anomaly detection accuracy and its effectiveness is confirmed.Additionally,the ablation experiment results further validate the efficacy of each module.An evaluation of the algorithm's computational complexity confirms its suitability for real-time processing.
基金the National Key Research and Development Program of China(Grant No.2022YFB3302700)the National Natural Science Foundation of China(Grant No.52375486)the Shanghai Rising-Star Program(Grant No.22QB1404200).
文摘In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings,highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime.However,varying conditions induce feature distribution shifts,and scarce fault samples limitmodel generalization.Therefore,this paper proposes a causal-Transformer-based meta-learning(CTML)method for bearing fault diagnosis in CNC machine tools,comprising three core modules:(1)the original bearing signal is transformed into a multi-scale time-frequency feature space using continuous wavelet transform;(2)a causal-Transformer architecture is designed to achieve feature extraction and fault classification based on the physical causal law of fault propagation;(3)the above mechanisms are integrated into a model-agnostic meta-learning(MAML)framework to achieve rapid cross-condition adaptation through an adaptive gradient pruning strategy.Experimental results using the multiple bearing dataset show that under few-shot cross-condition scenarios(3-way 1-shot and 3-way 5-shot),the proposed CTML outperforms benchmark models(e.g.,Transformer,domain adversarial neural networks(DANN),and MAML)in terms of classification accuracy and sensitivity to operating conditions,while maintaining a moderate level of model complexity.
文摘Identifying and segmenting spacecraft components is vital in many on-orbit space missions,such as on-orbit maintenance and component recovery.Integrating depth maps with visual images has been proven effective in improving segmentation accuracy.However,existing methods ignore the noise and fallacy in collected depth maps,which interfere with the network to extract representative features,decreasing the final segmentation accuracy.To this end,this paper proposes a Filtering and Regret Network(FRNet)for spacecraft component segmentation.The FRNet incorporates filtering and regret mechanisms to suppress the abnormal depth response in shallow layers and selectively reuses the filtered cues in deep layers,avoiding the detrimental effects of low-quality depth information while preserving the semantic context inherent in depth maps.Furthermore,a two-stage feature fusion module is proposed,which involves information interaction and aggregation.This module effectively explores the feature correlation and unifies the multimodal features into a comprehensive representation.Finally,a large-scale spacecraft component recognition dataset is constructed for training and evaluating spacecraft component segmentation algorithms.Experimental results demonstrate that the FRNet achieves a state-of-the-art performance with a mean Intersection Over Union(mIOU)of 84.13%and an average inference time of 133.2 ms when tested on an NVIDIA RTX 2080 SUPER GPU.
