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Label Recovery and Trajectory Designable Network for Transfer Fault Diagnosis of Machines With Incorrect Annotation

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 932-945页

作者： Bin Yang Yaguo Lei Xiang li naipeng li Asoke K.Nandi the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System the School of Mechanical EngineeringXi’an Jiaotong UniveristyXi’an 710049 the Hunan Provincial Key Labortory of Health Maintenance for Mechanical Equipment Hunan Univeristy of Science and TechnologyXiangtan 411201China the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniveristyXi’an 710049China the Department of Electronic and Electrical Engineering Brunel University LondonUxbridge UB83PHUK

The success of deep transfer learning in fault diagnosis is attributed to the collection of high-quality labeled data from the source ***,in engineering scenarios,achieving such high-quality label annotation is difficult and *** incorrect label annotation produces two negative effects:1)the complex decision boundary of diagnosis models lowers the generalization performance on the target domain,and2)the distribution of target domain samples becomes misaligned with the false-labeled *** overcome these negative effects,this article proposes a solution called the label recovery and trajectory designable network(LRTDN).LRTDN consists of three ***,a residual network with dual classifiers is to learn features from cross-domain ***,an annotation check module is constructed to generate a label anomaly indicator that could modify the abnormal labels of false-labeled samples in the source *** the training of relabeled samples,the complexity of diagnosis model is reduced via semi-supervised ***,the adaptation trajectories are designed for sample distributions across *** ensures that the target domain samples are only adapted with the pure-labeled *** LRTDN is verified by two case studies,in which the diagnosis knowledge of bearings is transferred across different working conditions as well as different yet related *** results show that LRTDN offers a high diagnosis accuracy even in the presence of incorrect annotation.

关键词： Deep transfer learning domain adaptation incorrect label annotation intelligent fault diagnosis rotating machines

Dynamic Vision Enabled Contactless Cross-Domain Machine Fault Diagnosis With Neuromorphic Computing

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 788-790页

作者： Xinrui Chen Xiang li Shupeng Yu Yaguo Lei naipeng li Bin Yang the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China

Dear Editor,This letter presents a novel dynamic vision enabled contactless cross-domain fault diagnosis method with neuromorphic *** event-based camera is adopted to capture the machine vibration states in the perspe... 详细信息

关键词： fault less diagnosis

Remaining Useful life Prediction With Partial Sensor Malfunctions Using Deep Adversarial Networks

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 121-134页

作者： Xiang li Yixiao Xu naipeng li Bin Yang Yaguo Lei IEEE the Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’an 710049China Student Member Senior Member

In recent years,intelligent data-driven prognostic methods have been successfully developed,and good machinery health assessment performance has been achieved through explorations of data from multiple ***,existing datafusion prognostic approaches generally rely on the data availability of all sensors,and are vulnerable to potential sensor malfunctions,which are likely to occur in real industries especially for machines in harsh operating *** this paper,a deep learning-based remaining useful life(RUL)prediction method is proposed to address the sensor malfunction problem.A global feature extraction scheme is adopted to fully exploit information of different *** learning is further introduced to extract generalized sensor-invariant *** explorations of both global and shared features,promising and robust RUL prediction performance can be achieved by the proposed method in the testing scenarios with sensor *** experimental results suggest the proposed approach is well suited for real industrial applications.

关键词： Adversarial training data fusion deep learning remaining useful life(RUL)prediction sensor malfunction

功能性动作筛查在高原官兵军事训练伤发生中的风险评估

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海军医学杂志 2024年第8期45卷 784-788页

作者：李丹丹李乃鹏闫双银联勤保障部队大连康复疗养中心小平岛疗养区辽宁大连116023

目的探讨功能性动作筛查(FMS)在高原官兵军事训练伤风险评估中的应用价值。方法回顾性分析2021年4月1日至9月30日在大连康复疗养中心疗养的高原官兵FMS数据,通过问卷调查了解官兵基本信息、身体状况及军事训练伤情况,综合高原官兵FMS情... 详细信息

目的探讨功能性动作筛查(FMS)在高原官兵军事训练伤风险评估中的应用价值。方法回顾性分析2021年4月1日至9月30日在大连康复疗养中心疗养的高原官兵FMS数据,通过问卷调查了解官兵基本信息、身体状况及军事训练伤情况,综合高原官兵FMS情况与军事训练伤情况绘制受试者操作特征(ROC)曲线,找出相应的最佳截断点,并计算出损伤风险的比值比(OR),评估高原官兵FMS在军事训练伤中的应用价值,并将FMS结果作为参考因素运用到军事体能训练中。结果按照高原官兵FMS结果绘制的ROC曲线,其曲线下面积(AUC)为0.788(P<0.01);最大约登指数为0.518,最佳截断点对应的FMS得分是13.50分。根据FMS得分截断点进行分组,发现FMS阴性组损伤率低于阳性组,OR值为3.828。结论FMS总分与军事训练伤具有一定的关联性,高原官兵FMS结果可以应用于军事训练伤风险评估。

关键词：功能性动作筛查高原官兵军事训练伤风险

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基于运动模式划分的工业机器人健康监测方法

中北大学学报（自然科学版） 2024年第1期45卷 12-21页

作者：张中伟王传刚韩炎序张超李乃鹏张帅华电莱州发电有限公司山东莱州261441 西安交通大学现代设计及转子轴承系统教育部重点实验室陕西西安710064 华电电力科学研究院有限公司浙江杭州310030

由于工业机器人存在多运动模式耦合的问题,传统的健康监测方案需要在每个关节处单独安装传感器,难以满足实际工业现场需求。本文以6关节工业机器人为研究对象,基于振动信号研究了多运动模式切换场景下工业机器人的健康监测方法。首先,... 详细信息

由于工业机器人存在多运动模式耦合的问题,传统的健康监测方案需要在每个关节处单独安装传感器,难以满足实际工业现场需求。本文以6关节工业机器人为研究对象,基于振动信号研究了多运动模式切换场景下工业机器人的健康监测方法。首先,通过跳变点算法实现机器人运动模式划分,获取不同运动模式对应的信号区间。其次,对不同运动模式的信号分别提取监测指标。最后,基于控制图法实现工业机器人不同关节的健康监测。在工业机器人退化实验数据中验证了本文所提方法,表明本文所提方法能够在仅使用两个振动传感器的条件下实现机器人6个关节的健康状态监测。本文所提运动模式划分算法在对大量历史退化数据进行分析时,所需运行时间更短、单次精度更高且重复性更好。本文提出的方法能够在使用少量传感器的条件下,有效避免运动模式耦合和采样信号的差异可能导致的监测结果误判,使得监测结果更加精准可靠,适用于实际工业现场的工业机器人健康状态监测。

关键词：工业机器人健康监测运动模式划分控制图法

Residual Convolution Long Short-Term Memory Network for Machines Remaining Useful life Prediction and Uncertainty Quantification

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Journal of Dynamics, Monitoring and Diagnostics 2022年第1期1卷 2-8页

作者： Wenting Wang Yaguo Lei Tao Yan naipeng li Asoke KNandi Key Laboratory of Education Ministry for Modern Design&Rotor-Bearing System Xi’an Jiaotong UniversityXi’anChina Department of Electronic and Electrical Engineering Brunel University LondonUxbridgeUK School of Mechanical Engineering Xi’an Jiaotong UniversityXi’anChina

Recently,deep learning(DL)has been widely used in the field of remaining useful life(RUL)*** various DL technologies,recurrent neural network(RNN)and its variant,e.g.,long short-term memory(LSTM)network,have gained extensive attention for their ability to capture temporal *** existing RNN-based methods have demonstrated their RUL prediction effectiveness,they still suffer from the following two limitations:1)it is difficult for the RNN to directly extract degradation features from original monitoring data and 2)most RNN-based prognostics methods are unable to quantify RUL *** address the aforementioned limitations,this paper proposes a new prognostics method named residual convolution LSTM(RC-LSTM)*** the RC-LSTM,a new ResNet-based convolution LSTM(Res-ConvLSTM)layer is stacked with a convolution LSTM(ConvLSTM)layer to extract degradation representations from monitoring ***,under the assumption that the RUL follows a normal distribution,an appropriate output layer is constructed to quantify the uncertainty of prediction ***,the effectiveness and superiority of the RC-LSTM are verified using monitoring data from accelerated bearing degradation tests.

关键词： Deep learning residual convolution LSTM network remaining useful life prediction uncertainty quantification

Prognostics and Remaining Useful life Prediction of Machinery:Advances,Opportunities,and Challenges

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Journal of Dynamics, Monitoring and Diagnostics 2023年第1期2卷 1-12页

作者： JDMD Editorial Office Nagi Gebraeel Yaguo Lei naipeng li Xiaosheng Si Enrico Zio Editorial Office of JDMD Chongqing University of TechnologyChongqingPeople’s Republic of China H.Milton Stewart School of Industrial and Systems Engineering Georgia Institute of TechnologyAtlantaGAUSA Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an Jiaotong UniversityXi’anPeople’s Republic of China Zhijian Laboratory Rocket Force University of EngineeringXi’anPeople’s Republic of China MINES Paris PSL Research UniversitySophia AntipolisFrance Energy Department Politecnico di MilanoMilanItaly

As the fundamental and key technique to ensure the safe and reliable operation of vital systems,prognostics with an emphasis on the remaining useful life(RUL)prediction has attracted great attention in the last *** this paper,we briefly discuss the general idea and advances of various prognostics and RUL prediction methods for machinery,mainly including data-driven methods,physics-based methods,hybrid methods,*** on the observations fromthe state of the art,we provide comprehensive discussions on the possible opportunities and challenges of prognostics and RUL prediction of machinery so as to steer the future development.

关键词： prognostics remaining useful life data-driven machine learning degradation modeling

融合图标签传播和判别特征增强的工业机器人关键部件半监督故障诊断方法

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机械工程学报 2022年第17期58卷 116-124页

作者：韩特李彦夫雷亚国李乃鹏李响清华大学工业工程系北京100084 西安交通大学现代设计及转子轴承系统教育部重点实验室西安710049

RV减速器作为工业机器人关键部件之一,其机械故障将造成整机定位精度下降。围绕RV减速器开展健康状态监测与智能故障诊断具有重要意义。基于故障标记数据充足的假设,数据驱动的智能诊断方法可以有效建立监测信号与健康状态的非线性映射... 详细信息

RV减速器作为工业机器人关键部件之一,其机械故障将造成整机定位精度下降。围绕RV减速器开展健康状态监测与智能故障诊断具有重要意义。基于故障标记数据充足的假设,数据驱动的智能诊断方法可以有效建立监测信号与健康状态的非线性映射关系。然而,在工程实际中收集大量故障标记样本需要昂贵的标记代价和人力成本。针对上述问题,提出了一种融合图标签传播和判别特征增强的RV减速器半监督故障诊断方法。首先,利用标签传播算法赋予无标记样本伪标签。然后,通过信息熵定量评估伪标签置信度,降低误标记对模型半监督学习的干扰。同时,在深度特征嵌入空间下优化少量标记样本度量损失,构造更具判别力的特征图,提升伪标签质量。最后,采用实际工业机器人RV减速器故障数据进行方法验证。结果表明,所提半监督故障诊断方法可以对无标记样本精准地传播标签,仅利用少量标记样本获取更优的故障识别精度。

关键词： RV减速器工业机器人半监督故障诊断

自适应多核组合相关向量机预测方法及其在机械设备剩余寿命预测中的应用

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机械工程学报 2016年第1期52卷 87-93页

作者：雷亚国陈吴李乃鹏林京西安交通大学机械制造系统工程国家重点实验室西安710049

针对支持向量机(Support vector machine,SVM)的惩罚系数难以确定、核函数必须满足Mercer定理等问题,相关向量机(Relevance vector machine,RVM)应运而生以解决上述问题,并在趋势预测等领域得到一定的应用。核函数是决定RVM预测精度的... 详细信息

针对支持向量机(Support vector machine,SVM)的惩罚系数难以确定、核函数必须满足Mercer定理等问题,相关向量机(Relevance vector machine,RVM)应运而生以解决上述问题,并在趋势预测等领域得到一定的应用。核函数是决定RVM预测精度的关键因素之一,目前的研究通常是人为选择单一核函数,因此增加了对参数的依赖性并降低了RVM预测的鲁棒性。为了解决以上问题,提出一种新的自适应多核组合RVM预测方法。该方法首先选择多个核函数,利用粒子滤波产生核函数权重,建立多核组合RVM集,然后经过不断地迭代预测、权值更新和重采样,自适应获取最优多核组合RVM,从而自适应融合多个核函数的特性,克服基于单一核函数RVM的局限,提高预测精度和鲁棒性。利用仿真对提出方法进行了验证,并将其应用于机械设备的剩余寿命预测,取得了比基于单一核函数RVM更好的预测效果。

关键词：多核相关向量机机械设备剩余寿命预测