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Condition-based maintenance optimization for continuously monitored degrading systems under imperfect maintenance actions

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Journal of Systems Engineering and Electronics 2020年第4期31卷 841-851页

作者： CHEN Chuang lu ningyun JIANG Bin XING Yin College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Jiangsu Key Laboratory of Internet of Things and Control Technologies Nanjing University of Aeronautics and AstronauticsNanjing 211106China School of Earth Sciences and Engineering Hohai UniversityNanjing 211100China

Condition-based maintenance(CBM) is receiving increasing attention in various engineering systems because of its effectiveness. This paper formulates a new CBM optimization problem for continuously monitored degrading systems considering imperfect maintenance actions. In terms of maintenance actions,in practice, they scarcely restore the system to an as-good-as new state due to residual damage. According to up-to-data researches, imperfect maintenance actions are likely to speed up the degradation process. Regarding the developed CBM optimization strategy, it can balance the maintenance cost and the availability by the searching the optimal preventive maintenance *** maximum number of maintenance is also considered, which is regarded as an availability constraint in the CBM optimization problem. A numerical example is introduced, and experimental results can demonstrate the novelty, feasibility and flexibility of the proposed CBM optimization strategy.

关键词： condition-based maintenance(CBM) imperfect maintenance maintenance cost availability constraint optimization

Useful life prediction using a stochastic hybrid automata model for an ACS multi-gyro subsystem

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Journal of Systems Engineering and Electronics 2019年第1期30卷 154-166页

作者： CHENG Yuehua JIANG Liang JIANG Bin lu ningyun College of Astronautics Engineering Nanjing University of Aeronautics and Astronautics College of Automation Engineering Nanjing University of Aeronautics and Astronautics

A useful life prediction method based on the integration of the stochastic hybrid automata(SHA) model and the frame of the dynamic fault tree(DFT) is proposed. The SHA model can incorporate the orbit environment, work modes, system configuration, dynamic probabilities and degeneration of components,as well as spacecraft dynamics and kinematics. By introducing the frame of DFT, the system is classified into several layers, and the problem of state combination explosion is artfully *** improved dynamic reliability model(DRM) based on the Nelson hypothesis is investigated to improve the defect of cumulative failure probability(CFP), which is used to address the failure probability of components in the SHA model. The simulation using the Monte-Carlo method is finally conducted on two satellites, which are deployed with the same multi-gyro subsystem but run on different orbits. The results show that the predicted useful life of the attitude control system(ACS) with consideration of abrupt failure,degradation, and running environment is quite different between the two satellites.

关键词： useful life prediction stochastic hybrid automata (SHA) multi-gyro subsystem dynamic fault tree (DFT) dynamic reliability.

Attention mechanism based multi-scale feature extraction of bearing fault diagnosis

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Journal of Systems Engineering and Electronics 2023年第5期34卷 1359-1367页

作者： LEI Xue lu ningyun CHEN Chuang HU Tianzhen JIANG Bin College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China State Key Laboratory of Mechanics and Control of Mechanical Structures Nanjing University of Aeronautics and AstronauticsNanjing 211106China College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816China

Effective bearing fault diagnosis is vital for the safe and reliable operation of rotating *** practical applications,bearings often work at various rotational speeds as well as load ***,the bearing fault diagnosis under multiple conditions is a new subject,which needs to be further ***,a multi-scale deep belief network(DBN)method integrated with attention mechanism is proposed for the purpose of extracting the multi-scale core features from vibration signals,containing four primary steps:preprocessing of multi-scale data,feature extraction,feature fusion,and fault *** key novelties include multi-scale feature extraction using multi-scale DBN algorithm,and feature fusion using attention *** benchmark dataset from University of Ottawa is applied to validate the effectiveness as well as advantages of this ***,the aforementioned method is compared with four classical fault diagnosis methods reported in the literature,and the comparison results show that our pro-posed method has higher diagnostic accuracy and better robustness.

关键词： bearing fault diagnosis multiple conditions atten-tion mechanism multi-scale data deep belief network(DBN)

Review on uncertainty analysis and information fusion diagnosis of aircraft control system

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Journal of Systems Engineering and Electronics 2024年第5期35卷 1245-1263页

作者： ZHOU Keyi lu ningyun JIANG Bin MENG Xianfeng College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China Avic Xi’an Flight Automatic Control Research Institute Xi’an 710076China

In the aircraft control system,sensor networks are used to sample the attitude and environmental *** a result of the external and internal factors(e.g.,environmental and task complexity,inaccurate sensing and complex structure),the aircraft control system contains several uncertainties,such as imprecision,incompleteness,redundancy and *** information fusion technology is usually used to solve the uncertainty issue,thus improving the sampled data reliability,which can further effectively increase the performance of the fault diagnosis decision-making in the aircraft control *** this work,we first analyze the uncertainties in the aircraft control system,and also compare different uncertainty quantitative *** the information fusion can eliminate the effects of the uncertainties,it is widely used in the fault ***,this paper summarizes the recent work in this ***,we analyze the application of information fusion methods in the fault diagnosis of the aircraft control ***,this work identifies existing problems in the use of information fusion for diagnosis and outlines future trends.

关键词： aircraft control system sensor networks information fusion fault diagnosis uncertainty

A General Data-Driven Algorithm for Lifetime Prediction

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A General Data-Driven Algorithm for Lifetime Prediction

第25届中国控制与决策会议

作者： Qiao Li ningyun lu Bin jiang College of Automation Engineering Nanjing University of Aeronautics and Astronautics

ISBN: (纸本)9781467355322

This paper introduces a general data-driven method to predict the lifetime of systems or components. Different from other methods, this algorithm doesn’t require the knowledge about the mechanism of object, thus applicable for various kinds of objects. The algorithm first analyzes the working curves to get a matrix which contains the historical information, and the degradation factors of all states can be calculated by the inverse or pseudo-inverse of this matrix. Then, with the degradation factors, the lifetime of objects can be predicted. The prediction methods are different for objects with or without future working plan. If there is a plan, the prediction algorithm can calculate the precise lifetime. But if there is no plan, the algorithm can still provide a range of the possible lifetime. The algorithm had been verified on a battery model, and the results meet the expected goals. In addition, this paper mainly considers the objects with discrete working states, and the continuous states will be studied in future papers.

关键词： Lifetime prediction Prognostic Working curve battery

Deep Forest based Multivariate Classification for Diagnostic Health Monitoring

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Deep Forest based Multivariate Classification for Diagnostic...

第30届中国控制与决策会议

作者： Cunsong Wang ningyun lu Yuehua Cheng Bin Jiang IEEE College of Automation Engineering Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing University of Aeronautics and Astronautics

Diagnostic health monitoring without prior knowledge is still a hard problem in the Prognostic and Health Management(PHM) field. A traditional approach is to construct one-dimensional Synthesized Health Index(SHI) and set the thresholds of different system health stages referred to empirical values. However, there is still a difficult problem to be solved on choosing appropriate SHI and thresholds for a complicated system, which are the key factors seriously influencing the result of diagnostic health monitoring. Compared with this approach, a new method of deep forest is proposed to achieve multivariate classification for diagnostic health monitoring. Firstly, an unsupervised feature selection method based on spearman’s correlation is improved to remove redundant features automatically. Then, the k-means algorithm is introduced to acquire stage knowledge of degradation process, therefore achieving supervised ***, deep forest method is adopted to train the data samples to obtain the classification model of system health for online diagnostic health monitoring. The verification results on NASA data sets demonstrate that the proposed diagnostic health monitoring scheme is effective and feasible.

关键词： PHM Diagnostic health monitoring Deep forest K-means Feature selection

Bond Graph Modeling and Fault Injection of CRH5 Traction System

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Bond Graph Modeling and Fault Injection of CRH5 Traction Sys...

第30届中国控制与决策会议

作者： Chuanyu Zhang Xiuli Wang ningyun lu Bin Jiang College of Automation Engineering Nanjing University of Aeronautics and Astronautics IEEE

CRH5 electric multiple unit（EMU） traction system is a multi-domain mechatronical system with complex structure. Mechanisms on various faults or failures are not clear yet. To develop accurate system model for fault prognosis, diagnosis and propagation analysis, bond graph（BG） theory is adopted due to its unique ability in multi-domain modeling and clear cause-effect representation. With the aid of a commercial software（20-SIM）, a BG topological structure model which contains inverter, motor and gearbox, and direct torque control（DTC） unit have been developed, where DTC control the stator flux and electromagnetic torque. Common faults in EMU traction system such as motor and sensor faults are injected into the BG model. Broken rotor bar fault in driving motors is simulated by increasing gradually the number of broken bars. Three kinds of speed sensor faults are also injected to analyze their influences to the whole traction system. The simulation results are well in accordance with real CRH5 traction systems. The developed model can provide a useful platform for the study and verification of fault prognosis, diagnosis and propagation methods.

关键词： CRH5 Traction system Bond Graph Modeling Fault Injection

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基于工况识别的注塑过程产品质量预测方法

化工学报 2013年第7期64卷 2526-2534页

作者：赵斐陆宁云杨毅南京航空航天大学自动化学院江苏南京210016 浙江大学控制工程与科学系浙江杭州310007

针对多工况注塑过程的在线质量预测问题,考虑了过程数据高维、耦合、非线性等特点,采用拉普拉斯特征映射(LE)方法实现过程数据的非线性降维;在低维特征空间中采用Mean Shift聚类算法完成样本的工况聚类,以便注塑过程的工况分析和知识挖... 详细信息

针对多工况注塑过程的在线质量预测问题,考虑了过程数据高维、耦合、非线性等特点,采用拉普拉斯特征映射(LE)方法实现过程数据的非线性降维;在低维特征空间中采用Mean Shift聚类算法完成样本的工况聚类,以便注塑过程的工况分析和知识挖掘;同时运用Mean Shift原理,提出一种新样本的在线工况识别方法;最后应用基于混合粒子群(PSO)参数寻优的偏最小二乘支持向量机(PLS-LSSVM)方法,建立了多工况注塑过程的产品质量软测量模型。实验结果表明,相较于PLS-LSSVM方法,本文方法的预测精度和泛化性能均有明显提高,可为实际注塑企业提供一种效果良好的多工况产品质量在线预测方法。

关键词：注塑过程产品质量预测拉普拉斯特征映射 MeanShift聚类偏最小二乘支持向量机

Operating Performance Assessment of Complex Industrial Process Based on Kernel Locally Linear Embedding PLS

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Operating Performance Assessment of Complex Industrial Proce...

第32届中国过程控制会议(CPCC2021)

作者： Chu Fei Mo Shuangshuang Wang Fuli lu ningyun Ma Xiaoping Research Center of Underground Space Intelligent Control Engineering of the Ministry of Education China University of Mining and Technology School of Information and Control Engineering China University of Mining and Technology State Key Laboratory of Automatic Control Technology for Mining and Metallurgy Process Beijing General Research Institute of Mining & Metallurgy State Key Laboratory of Integrated Automation for Process Industries Northeastern University College of Automation Engineering Nanjing University of Aeronautics and Astronautics

Due to the process disturbances and uncertainties,the operating performance of industrial process may deviate from the optimal operating point along with ***,non-optimal operation may lead to decline production quality and economic *** it is significant to implement online assessment strategy and further analyze the actual process operating performance,so as to make the production process in the best operating performance as far as *** process data is strongly nonlinear due to the change of system state caused by the change of machine performance or operation *** this time,the relationship between the local nearest neighbor samples is ***,the existing methods of operating performance assessment for solving nonlinear problems only focus on the global structure of data,i.e.,the variance,while ignoring the local neighborhood structure in each performance ***,a new method called kernel locally linear embedding PLS(KLLEPLS) is developed and applied for operating performance *** includes offline modeling,online evaluation and non-optimal cause *** algorithm not only has concise expression and similar analytical solutions for PLS,but also retains the original geometric structure of strongly nonlinear data when extracting the maximum correlation information between process ***,the practice and effectiveness of the proposed method are demonstrated by the dense medium coal preparation process and Tennessee Eastman(TE) process.

关键词： Complex industrial process operating performance assessment kernel locally linear embedding PLS non-optimal cause identification