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复杂系统测试性设计与故障诊断策略研究进展

系统工程与电子技术 2024年第7期46卷 2359-2373页

作者：陆宁云李洋姜斌黄守金马坤南京航空航天大学自动化学院江苏南京211106 航空航天结构力学及控制全国重点实验室江苏南京211106 上海大学机电工程与自动化学院上海200444 中国计量科学研究院北京100029

测试性设计是提高系统可靠性、安全性、维修性、保障性的重要前沿技术,决定了系统故障检测率和隔离率,直接影响系统的维护(测试)成本。系统测试性设计包含结构化设计、模型化设计、数据驱动设计等多种设计策略。其中,数据驱动设计于近... 详细信息

测试性设计是提高系统可靠性、安全性、维修性、保障性的重要前沿技术,决定了系统故障检测率和隔离率,直接影响系统的维护(测试)成本。系统测试性设计包含结构化设计、模型化设计、数据驱动设计等多种设计策略。其中,数据驱动设计于近年逐渐兴起并成为重要发展方向之一,该类方法通过对系统测试与故障之间的关系进行建模,依据测试结果进行故障推理,形成故障诊断方案。首先,简要回顾了系统测试性设计的发展历程;其次,重点介绍了测试性设计的研究进展,分析总结了结构化、模型化、数据驱动3类测试方案;然后,介绍了测试性诊断策略构建,根据测试方案中的建模方法确定诊断策略的构建技术,并总结归纳了每类技术的研究特点和适用性;最后,探讨了当前复杂系统测试性设计面临的挑战性问题和可能的未来研究方向。

关键词：测试性设计模型化设计数据驱动测试性诊断策略

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基于数据驱动的无人机集群故障检测与诊断

北京航空航天大学学报 2024年第5期50卷 1586-1592页

作者：李润泽姜斌余自权陆宁云南京航空航天大学自动化学院南京211100

无人机集群系统对安全性和稳定性要求极高,实时的故障检测与诊断技术是保证安全可靠运行的有力手段。提出一种基于统计模型和改进宽度学习(BLS)模型的故障诊断方法。所提方法通过多元数据统计分析表征无人机集群系统在正常与不同故障模... 详细信息

无人机集群系统对安全性和稳定性要求极高,实时的故障检测与诊断技术是保证安全可靠运行的有力手段。提出一种基于统计模型和改进宽度学习(BLS)模型的故障诊断方法。所提方法通过多元数据统计分析表征无人机集群系统在正常与不同故障模式下的行为特征,采用改进的BLS模型实现准确、快速的故障诊断。在此基础上,开发无人机集群系统的高逼真可视化仿真验证平台对所提方法的合理性与有效性进行验证。实验结果表明,所提方法与目前主流方法相比具有明显的诊断优势。

关键词：无人机集群故障诊断统计模型宽度学习仿真验证

A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 412-422页

作者： Chuang Chen ningyun lu Bin Jiang Cunsong Wang College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Remaining useful life(RUL)prediction is an advanced technique for system maintenance *** of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of *** this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation *** proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation ***,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,*** enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection *** designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization *** addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance *** is done using an aero-engine data set from *** results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

关键词： Long short-term memory(LSTM)network predictive maintenance remaining useful life(RUL)estimation risk-averse adaptation support vector regression(SVR)

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.

基于多源域适应JYPLS迁移的间歇过程质量预测

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控制工程 2024年第1期31卷 32-39页

作者：王润褚菲马小平贾润达陆宁云中国矿业大学信息与控制工程学院江苏徐州221116 中国矿业大学地下空间智能控制教育部工程研究中心江苏徐州221116 中国矿业大学人工智能研究院江苏徐州221116 东北大学信息科学与工程学院辽宁沈阳110819 南京航空航天大学自动化学院江苏南京210016

针对间歇过程数据不足,单源域迁移存在模型偏移,跨域信息损失导致建模效果不佳、负迁移等问题,结合域适应学习和多源域学习方法的优势,提出一种基于多源域适应联合Y偏最小二乘(joint-Y partial least squares,JYPLS)迁移的间歇过程质量... 详细信息

针对间歇过程数据不足,单源域迁移存在模型偏移,跨域信息损失导致建模效果不佳、负迁移等问题,结合域适应学习和多源域学习方法的优势,提出一种基于多源域适应联合Y偏最小二乘(joint-Y partial least squares,JYPLS)迁移的间歇过程质量预测方法。该方法通过迁移学习使用相似旧过程的数据辅助新过程建模,提高建模效率和模型预测精度;采用多源域适应的方式,通过引入多个源域,有效避免了负迁移;基于域适应思想减少源域和目标域之间的边缘概率分布差异,使得源域知识在目标域更好地泛化。最后,通过青霉素发酵过程的仿真案例验证了所提方法的有效性。

关键词：间歇过程质量预测多源域迁移学习域适应

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)

A General Data-Driven Algorithm for Lifetime Prediction 25th

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A General Data-Driven Algorithm for Lifetime Prediction

Deep Forest based Multivariate Classification for Diagnostic Health Monitoring

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

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Deep Forest based Multivariate Classification for Diagnostic...