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基于Box-Cox变换和随机系数回归的非线性退化数据建模方法

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中国测试 2024年第1期50卷 9-17页

作者：杨保奎李天梅张建勋司小胜火箭军工程大学导弹工程学院陕西西安710025

变换方法是处理设备非线性退化建模与剩余寿命预测的一种重要方式和可行途径,目前常见的变换方法主要为对数变换和时间尺度变换,其适用范围有限。鉴于此,文章提出一种基于Box-Cox变换(Box-Cox transformation,BCT)的非线性退化数据建模... 详细信息

变换方法是处理设备非线性退化建模与剩余寿命预测的一种重要方式和可行途径,目前常见的变换方法主要为对数变换和时间尺度变换,其适用范围有限。鉴于此,文章提出一种基于Box-Cox变换(Box-Cox transformation,BCT)的非线性退化数据建模方法。首先,采用BCT对非线性退化数据进行变换,将变换后的退化数据通过线性随机系数回归模型进行建模。然后,通过构建观测数据的概率密度函数,利用极大似然估计对于BCT中的模型参数进行辨识,并运用Bayesian理论对参数进行在线更新实现退化模型的动态校准。最后,分析经过BCT后的锂电池和轴承实际退化数据,其线性度与相关系数最高分别提升69.63%、9.19%,证明文章方法可行,具有潜在的工程应用价值。

关键词：非线性退化 Box-Cox变换随机系数回归在线更新

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An Age-Dependent and State-Dependent Adaptive Prognostic Approach for Hidden Nonlinear Degrading System

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IEEE/CAA Journal of Automatica sinica 2022年第5期9卷 907-921页

作者： Zhenan Pang xiaosheng si Changhua Hu Zhengxin Zhang Department of Automation Rocket Force University of EngineeringXi’an 710025China

Remaining useful life(RUL)estimation approaches on the basis of the degradation data have been greatly developed,and significant advances have been *** an applicable degradation model of the system is the foundation and key to accurately estimating its *** current researches focus on age-dependent degradation models,but it has been found that some degradation processes in engineering are also related to the degradation states *** addition,due to different working conditions and complex environments in engineering,the problems of the unit-to-unit variability in the degradation process of the same batch of systems and actual degradation states cannot be directly observed will affect the estimation accuracy of the *** order to solve the above issues jointly,we develop an age-dependent and state-dependent nonlinear degradation model taking into consideration the unit-to-unit variability and hidden degradation ***,the Kalman filter(KF)is utilized to update the hidden degradation states in real time,and the expectation-maximization(EM)algorithm is applied to adaptively estimate the unknown model ***,the approximate analytical RUL distribution can be obtained from the concept of the first hitting *** the new observation is available,the RUL distribution can be updated adaptively on the basis of the updated degradation states and model *** effectiveness and accuracy of the proposed approach are shown by a numerical simulation and case studies for Li-ion batteries and rolling element bearings.

关键词： Expectation-maximization(EM) hidden degradation state Kalman filter(KF) remaining useful life(RUL) unit-to-unit variability.

数据驱动的闭环控制系统剩余寿命预测方法综述

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中北大学学报（自然科学版） 2024年第1期45卷 1-11页

作者：冯磊张正新李天梅张建勋司小胜火箭军工程大学智剑实验室陕西西安710025

随着先进传感与监测技术的快速发展,数据驱动的设备剩余寿命(RUL)预测技术已成为可靠性和自动化领域的研究前沿,并在设备运维决策中得到广泛应用,提高了设备运行的安全性、可靠性与经济性。关于数据驱动的设备剩余寿命预测方法,多数文... 详细信息

随着先进传感与监测技术的快速发展,数据驱动的设备剩余寿命(RUL)预测技术已成为可靠性和自动化领域的研究前沿,并在设备运维决策中得到广泛应用,提高了设备运行的安全性、可靠性与经济性。关于数据驱动的设备剩余寿命预测方法,多数文献主要聚焦于部件级剩余寿命预测技术,并没有关注闭环控制系统剩余寿命预测技术。不同于部件级的预测,利用数据驱动的方法预测闭环控制系统剩余寿命,需要考虑控制器及反馈控制机制对系统性能退化及剩余寿命预测结果的影响。本文系统综述了数据驱动的闭环控制系统剩余寿命预测及基于预测信息的延寿控制方法的发展动态,剖析了基于Poisson过程的方法、基于Gamma过程的方法、基于Wiener过程的方法和基于混合模型方法的原理、特点与局限性。同时,对于未来数据驱动的闭环控制系统剩余寿命预测及延寿控制,在健康状态表征与综合健康指标构建、多退化部件系统剩余寿命预测及基于预测信息的延寿控制理论和验证应用等方向进行了展望。

关键词：剩余寿命预测闭环控制系统数据驱动退化过程延寿控制

Remaining useful life prediction based on nonlinear random coefficient regression model with fusing failure time data

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Journal of Systems Engineering and Electronics 2023年第1期34卷 247-258页

作者： WANG Fengfei TANG Shengjin SUN Xiaoyan LI Liang YU Chuanqiang si xiaosheng Department of Mechanical Engineering Rocket Force University of EngineeringXi’an 710025China

Remaining useful life(RUL) prediction is one of the most crucial elements in prognostics and health management(PHM). Aiming at the imperfect prior information, this paper proposes an RUL prediction method based on a nonlinear random coefficient regression(RCR) model with fusing failure time ***, some interesting natures of parameters estimation based on the nonlinear RCR model are given. Based on these natures,the failure time data can be fused as the prior information reasonably. Specifically, the fixed parameters are calculated by the field degradation data of the evaluated equipment and the prior information of random coefficient is estimated with fusing the failure time data of congeneric equipment. Then, the prior information of the random coefficient is updated online under the Bayesian framework, the probability density function(PDF) of the RUL with considering the limitation of the failure threshold is performed. Finally, two case studies are used for experimental verification. Compared with the traditional Bayesian method, the proposed method can effectively reduce the influence of imperfect prior information and improve the accuracy of RUL prediction.

关键词： remaining useful life(RUL)prediction imperfect prior information failure time data nonlinear random coefficient regression(RCR)model

Remaining useful life prediction of aero-engines based on random-coefficient regression model considering random failure threshold

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Journal of Systems Engineering and Electronics 2023年第2期34卷 530-542页

作者： WANG Fengfei TANG Shengjin LI Liang SUN Xiaoyan YU Chuanqiang si xiaosheng College of Missile Engineering Rocket Force University of EngineeringXi’an 710025China

Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of *** paper proposes an RUL prediction method of aero-engines considering the randomness of failure ***,a random-coefficient regression(RCR)model is used to model the degradation process of ***,the RUL distribution based on fixed failure threshold is *** prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian *** failure threshold in this paper is defined by the actual degradation process of *** that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of *** addition,the conditional probability is used to satisfy the limitation of failure ***,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a ***,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.

关键词： aero-engine remaining useful life(RUL) random failure threshold(RFT) random-coefficient regression(RCR) parameters estimation

Prognostics based on the generalized diffusion process with parameters updated by a sequential Bayesian method

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Science China(Information Sciences) 2022年第6期65卷 172-188页

作者： Hong PEI xiaosheng si Changhua HU Jianxun ZHANG Dangbo DU Zhenan PANG Shengfei ZHANG School of Missile Engineering Rocket Force University of Engineering School of Mechanical Engineering Xi'an Jiaotong University

The realistic degradation process for the engineering equipment is generally stochastic and complicated owing to the uncertain operational condition and multiple functional loading, exhibiting the absolute nonlinear distinction. Such a nonlinear degradation process is widely modeled as a generalized diffusion process. When utilizing the generalized diffusion process-based model, certain model parameters are considered as the random variables to characterize the unit-to-unit discrepancies. Hence, the estimation of these kinds of parameters usually resorts to the Bayesian method. However, owing to the complex pattern of the model parameters in the generalized diffusion process, computing the Bayesian updated parameters requires plenty of repeated calculation and integration operations once the new degradation monitoring information is available. This will inevitably lower the computing efficiency and real-time performance. Toward this end, this paper presents an adaptive prognostic method based on the generalized diffusion process to determine the remaining useful life(RUL) of degraded equipment. First, a generalized diffusion process-based degradation modeling framework is constructed to describe the health performance of stochastic degraded equipment under complex conditions. Then, we utilize the maximum likelihood estimation(MLE) method to estimate the initial model parameters by analyzing the historical degradation information. Furthermore, a sequential Bayesian method is proposed to recursively update the stochastic model parameters in the generalized diffusion process for particular equipment in service. Unlike the existing studies utilizing the Bayesian method,the primary contrast in the presented method lies in that there is no need to implement the calculation process with complicated integration repeatedly utilizing the whole degradation information obtained before the current time. Particularly, the current measured information is incorporated into the esti

关键词： generalized diffusion process stochastic model parameters remaining useful life maximum likelihood estimation sequential Bayesian methods

Optimal replacement of degrading components: a control-limit policy

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Science China(Information Sciences) 2021年第10期64卷 260-262页

作者： xiaosheng si Tianmei LI Qi ZHANG Department of Automation Rocket Force University of Engineering School of Mechanical Engineering Xi'an Jiaotong University

Dear editor,Components such as gyros, turbines, and bearings are vital in aircraft and spacecraft, and must run safely during their entire life cycle. However, gradual damage inevitably occurs resulting in component failure. Therefore, necessary maintenance such as component replacement should be scheduled. Traditional maintenance models are generally timebased, and in such models, maintenance decisions are made to minimize the expected cost rate,

关键词： SYSTEMS

A survey on life prediction of equipment

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Chinese Journal of Aeronautics 2015年第1期28卷 25-33页

作者： Hu Changhua Zhou Zhijie Zhang Jianxun si xiaosheng Department of Automation High-Tech Institute of Xi'an

Once in the hands of end users, such durable equipment as spacecraft, aircraft, ships,automobiles, computers, etc. are in a state of debugging, working or storage. In either state, availability, reliability and super-efficiency are the ultimate goals, which have been achieved through constant monitoring as well as regular, preventive, routine and corrective maintenance. Although some advanced instruments can visualize certain invisible malfunctioning phenomena into visible ones, deeply hidden troubles cannot be found unless monitoring and testing data are addressed using tools that process the data statistically, analytically and mathematically. Some state-of-theart trouble-shooting and life-predicting techniques and approaches are introduced in this paper.

关键词： hidden uncertainty accelerated instruments shooting ultimate maintenance visible addressed trouble

Online remaining-useful-life estimation with a Bayesian-updated expectation-conditional-maximization algorithm and a modified Bayesian-model-averaging method

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Science China(Information Sciences) 2021年第1期64卷 146-161页

作者： Yong YU xiaosheng si Changhua HU Jianfei ZHENG Jianxun ZHANG School of Missile Engineering Rocket Force University of Engineering School of Mechanical Engineering Xi'an Jiaotong University

Online remaining-useful-life(RUL) estimation is an effective method with respect to ensuring the safety of complex-huge systems. Generally, current methods assume a specific degradation model when degradation values are observed in the initial degradation phase. However, this assumption may not always be robust enough owing to the often-ambiguous inherent incipient-degradation characteristic. Therefore,besides model-parameter uncertainty, the uncertainty of the degradation model is worth examining in online RUL estimations. In this paper, a Bayesian-updated expectation-conditional-maximization(ECM) algorithm is adopted to address the uncertainty of prior parameters, and a modified Bayesian-model-averaging method is used to deal with the uncertainty of the degradation model. Then, simulation studies are conducted to analyze the effectiveness of the proposed fusion algorithm. Results suggest that the Bayesian-updated ECM algorithm and modified Bayesian-model-averaging method effectively address the associated uncertainties of model parameters and the degradation model itself. Finally, we apply the proposed fusion algorithm to predict the RUL of a gyroscope.

关键词： online RUL estimation parameter uncertainty model uncertainty Bayesian method ECM algorithm Bayesian model averaging