Real-Time Remaining Useful Life Prediction for a Nonlinear Degrading System in Service: Application to Bearing Data

Wang, Zhao-Qiang; Hu, Changhua; Fan, Hongdong

doi:10.1109/tmech.2017.2666199

Cited by 65 publications

(43 citation statements)

References 45 publications

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“…For simplicity, the method proposed in this paper is referred to as M1, the method based on traditional Bayesian method is referred to as M2. The traditional Bayesian method for the nonlinear method can be referred to [10], [26], where the linear degradation is a special case. In order to better illustrate the estimated accuracy, the relative error (RE) and the mean square error (MSE) are defined as follows:…”

Section: B the Practical Case Studymentioning

confidence: 99%

“…linear Wiener process, nonlinear Wiener process [22], and the validity of these methods has also been verified by many simulation examples and case studies. This Bayesian mechanism has also been applied to the generalized Wiener process [23], Wiener process with skew-normal random effects [24], bivariate Wiener degradation process [25], nonlinear Wiener process [26], [27], additive Wiener process [28], Adaptive Wiener process [29], Wiener process with imperfect inspections [30], Wiener process with measurement error [10], etc. Under the Bayesian framework, the RUL estimation results include the degradation information of congeneric units and the field assessed unit.…”

Section: Introductionmentioning

confidence: 99%

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Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

et al. 2020

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Accurate remaining useful life (RUL) prediction has a great significance to improve the reliability and safety for key equipment. However, it often occur imperfect or even no prior degradation information in practical application for the existing RUL prediction methods, which could produce prediction error. To solve this issue, this paper proposes a two-step RUL prediction method based on Wiener processes with reasonably fusing the failure time data and field degradation data. First, we obtain some interesting natures of parameters estimation based on the basic linear Wiener process. These natures explain the relationship between the parameters estimation results and the feature of degradation data, i.e. item sample numbers, detection time and detect frequency, and give the basis regarding how to reasonably fuse the failure time data and field degradation data. Second, under the Bayesian framework, we further propose a two-step method by fusing the failure time data and field degradation data with considering the random effects based on the proposed natures of parameters estimation. In this method, we propose an EM algorithm to estimate the mean and variance drift parameter of Wiener processes by the failure time data. Next, we generalize this two-step RUL prediction method to the nonlinear Wiener process. Last, we use two case studies to demonstrate the usefulness and superiority of the proposed method. INDEX TERMS Remaining useful life prediction, wiener processes, fusing, failure time data, field degradation data, random effects, Bayesian framework.

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Section: B the Practical Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

et al. 2020

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“…The simplified manufacturing system model is established by two types of machines in which station 3 has a rework process. So, the sum of input parameters r si1 (i = 1, 2, 3, 4 and 5) and r s32 is 1, and the qualification rates of these machines are calculated on the basis of equations (7) and (8) as r s11 = 0:95, r s21 = 0:93, r s31 = 0:94, r s41 = 0:97 and r s51 = 0:94.…”

Section: Numerical Examplementioning

confidence: 99%

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

Zhao

Liu

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Predictive health analysis and diagnostics are essential functional elements of “Smart Manufacturing,” but existing studies are mostly limited to discrete manufacturing equipment without a holistic analysis on the entire system health state. In consideration of the enhancement of risk control in production quality management advocated in ISO 9001:2015, modeling and monitoring the risk during operation are the core aspects and bottlenecks for achieving prognostics. Therefore, a novel holistic operational risk modeling approach for discrete manufacturing systems is proposed by fusing the operational data to quantify the risk-oriented operational performance systematically. First, the connotation of operational risk is expounded and associated with system operational mechanism and mission reliability connotation. Second, a modeling foundation is proposed on the basis of quantifying machine, task execution and work-in-process quality from mission reliability framework in accordance with the operational data characteristics and properties. Third, operational risk evaluation is conducted using the above indicators, where reasonable aggregation of three indicators is performed by fuzzy evidence theory and the risk levels are determined. Finally, a case study is conducted to illustrate the effectiveness and advantages of the proposed approach.

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“…Degradation modeling approaches, based on Wiener processes, Gamma processes and Markov chain processes, are systematically reviewed in [6]. Nowadays, the Wiener processes have been successfully applied in many fields, including batteries [3], gyroscopes [7], aviation piston pumps [8], blast furnace walls [9], bearings [22], and liquid coupling devices [11], etc., due to its excellent mathematical properties. Thus, this paper focuses mainly on the Wiener-process-based stochastic degradation modeling.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is necessary to incorporate these three sources of variability into stochastic degradation modeling to reduce the uncertainty of the RUL estimation. Nevertheless, most previous studies on the use of stochastic degradation models to estimate the RUL of degraded systems focused primarily on considering one or two sources of variability [4], [8], [9], [11], [12], [17], [18], [20]- [22]. For example, in [9], the measurement variability and temporal variability were considered to estimate the RUL of a multi-component degradation system, and the expectation maximization (EM) algorithm was used to estimate model parameters online.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

Wang

et al. 2019

IEEE Access

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As the central component of prognostic and health management (PHM) field, remaining useful life (RUL) estimation approaches based on degradation modeling have played an extremely significant role in recent years. For the newly developed systems working in complex environments, the associated degradation processes not only lack historical data and prior information but also have strong nonlinearity and three-source variability. Therefore, this paper proposes an adaptive RUL estimation approach for the newly developed system based on a nonlinear model. Specifically, a general nonlinear Wiener-process-based degradation model is established to simultaneously characterize three-source variability and nonlinearity, and the associated RUL distribution is derived with an explicit form. In order to utilize the condition monitoring (CM) data of the service system up to date, we present a parameter estimation method based on the expectation maximization algorithm to adaptively estimate and update the model parameters online. As such, the RUL distribution can be updated once the new CM data are available. Finally, the effectiveness and superiority of the proposed method are demonstrated by the numerical example an empirical study for battery data. The results show that the proposed method can provide accurate and robust RUL prediction for the newly developed system. INDEX TERMS Remaining useful life, nonlinear, three-source variability, battery, prognostic and health management.

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Real-Time Remaining Useful Life Prediction for a Nonlinear Degrading System in Service: Application to Bearing Data

Cited by 65 publications

References 45 publications

Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

Remaining Useful Life Prediction With Fusing Failure Time Data and Field Degradation Data With Random Effects

Operational risk modeling based on operational data fusion for multi-state manufacturing systems

An Adaptive Remaining Useful Life Estimation Approach for Newly Developed System Based on Nonlinear Degradation Model

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