A framework for predicting the remaining useful life of a single unit under time-varying operating conditions

Liao, Haitao; Tian, Zhigang

doi:10.1080/0740817x.2012.705451

Cited by 120 publications

(44 citation statements)

References 21 publications

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“…Because of the linear model assumption, analytical expressions for the posterior distribution as well as those for the residual life distribution were available. The authors in [21] extended the prognostics in a time-varying condition to non-linear models, in which the degradation process was assumed to be governed by a Brownian motion with linear drift. Stress changes were accounted for in the instantaneous drift parameter.…”

Section: Mcmcmentioning

confidence: 99%

“…Because the type of random vector is assumed to follow a bivariate normal distribution, a Hermite polynomial is selected as the basis function in polynomial space. To reduce computational work, a sparse grid containing pairs of integration points and associated weights is generated in a collocation method for computing (19) numerically as (21) As in Section IV, define…”

Section: B Polynomial Chaos Based Stochastic Collocation In Bayesianmentioning

confidence: 99%

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An Integrated Prognostics Method Under Time-Varying Operating Conditions

et al. 2015

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In this paper, we develop an integrated prognostics method considering a time-varying operating condition, which integrates physical gear models and sensor data. By taking advantage of stress analysis in finite element modeling (FEM), the degradation process governed by Paris' law can adjust itself immediately to respond to the changes of the operating condition. The capability to directly relate the load to the damage propagation is a key advantage of the proposed integrated prognostics approach over the existing data-driven methods for dealing with time-varying operating conditions. In the proposed method, uncertainties in material parameters are considered as sources responsible for randomness in the predicted failure life. The joint distribution of material parameters is updated as sensor data become available. The updated distribution better characterizes the material parameters, and reduces the uncertainty in life prediction for the specific individual unit under condition monitoring. The update process is realized via Bayesian inference. To reduce the computational effort, a polynomial chaos expansion (PCE) collocation method is applied in computing the likelihood function in the Bayesian inference and the predicted failure time distribution. Examples based on crack propagation in a spur gear tooth are given to demonstrate the effectiveness of the proposed method. In addition, the example also shows that the proposed approach is effective even when the current loading profile is different from the loading profile under which historical data were collected.Index Terms-Bayesian update, integrated prognostics, polynomial chaos expansion, time-varying operating condition, uncertainty quantification. ACRONYMS AND ABBREVIATIONS: FEM finite element modeling PCE polynomial chaos expansion RUL remaining useful life PHM proportional hazard model FE finite element SIF stress intensity factor Manuscript

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Section: Mcmcmentioning

confidence: 99%

Section: B Polynomial Chaos Based Stochastic Collocation In Bayesianmentioning

confidence: 99%

An Integrated Prognostics Method Under Time-Varying Operating Conditions

et al. 2015

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“…In the worst case, this degradation phenomenon can anticipate the spindle wear or can cause a spindle break. The evolution of the degradation leads to different vibration frequencies that can be measured directly on the motor shaft [25] with a dedicated and non-intrusive equipment [33]. A condition monitoring indicator is used for health monitoring and is related to the actual degradation level D(t).…”

Section: Degradation Phenomenon Of the Case Studymentioning

confidence: 99%

A modeling framework for deteriorating control system and predictive maintenance of actuators

Langeron

Grall

Barros

2015

Reliability Engineering & System Safety

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“…Due to the random nature of D(t), the residual useful lifetime of an actuator at time r noted RU L r is then a stochastic variable Lorton et al (2013); Liao and Tian (2013) so Given the knowledge of the degradation level d i measured at time t i , the probability law of RU L i is the conditional probability such that…”

Section: Actuator Degradation Modelmentioning

confidence: 99%

Joint maintenance and controller reconfiguration policy for a gradually deteriorating control system

Langeron

Grall

Barros

2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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This paper tackles with the maintenance decision-making of gradually deteriorating control systems. The main feature of these systems is their ability within a closedloop to drive actuators in order to set the controlled process in a given state.Usually, the literature deals with the diagnosis of faults of a control system and the means for recovering system performances after their appearance. The controller reconfiguration is one of these means. The root cause of a fault is rarely argued nor its occurrence time. Before designing maintenance policies, this paper proposes a stochastic modeling framework of a degrading control system focusing on the actuator deterioration. It is assumed a close relationship between the controller setting, the actuator deterioration and finally its faulty situations. These latter are related to given degradation thresholds. Due to the stochastic nature of the deterioration, the corresponding hitting times are thus random. The obtained models allow to assess the conditional reliability of the actuator and then the prognosis of its residual useful life namely the RUL. This RUL is finally used to achieve two maintenance policies based upon the reconfiguration of the controller considered as a new maintenance action.Prepared using sagej.cls

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A framework for predicting the remaining useful life of a single unit under time-varying operating conditions

Cited by 120 publications

References 21 publications

An Integrated Prognostics Method Under Time-Varying Operating Conditions

An Integrated Prognostics Method Under Time-Varying Operating Conditions

A modeling framework for deteriorating control system and predictive maintenance of actuators

Joint maintenance and controller reconfiguration policy for a gradually deteriorating control system

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