Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence

Shi, Hui; Zeng, Jianchao

doi:10.1016/j.cie.2015.12.016

Cited by 96 publications

(32 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many authors consider the cost associated with maintenance as a key factor in their work and choose a cost objective function (Peng et al, 2009;Tian and Liao, 2011;Shi and Zeng, 2016). On the other hand, for certain complex systems, such as control systems in nuclear power station and satellite systems, system availability is much more important than upkeep cost.…”

Section: Introductionmentioning

confidence: 99%

Availability and maintenance modelling for systems subject to multiple failure modes

Qiu

Cui

Gao

2017

Computers & Industrial Engineering

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Availability and maintenance modelling for systems subject to multiple failure modes

Qiu

Cui

Gao

2017

Computers & Industrial Engineering

View full text Add to dashboard Cite

“…To continuously maintain and improve the quality of products, real-time data must be collected and analyzed, and diagnosis must be performed through AI [3][4][5][6]. Therefore, the use of big data analytics, AI, and platform construction is necessary.…”

Section: Discussion and Evaluation Of Case Examplesmentioning

confidence: 99%

“…The speed and precision of decision-making for bridge repair and maintenance are facilitated by real-time monitoring of bridge conditions. Bansal et al [3] proposed a real-time predictive maintenance system using neural network methods, while Shi and Zeng [4] suggested a condition-based maintenance strategy that considers economic factors for predictive maintenance in real time. Predictive maintenance, also known as condition-based maintenance, is possible today due to the advanced digital technologies [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

The quality management ecosystem for predictive maintenance in the Industry 4.0 era

2019

View full text Add to dashboard Cite

The Industry 4.0 era requires new quality management systems due to the ever increasing complexity of the global business environment and the advent of advanced digital technologies. This study presents new ideas for predictive quality management based on an extensive review of the literature on quality management and five realworld cases of predictive quality management based on new technologies. The results of the study indicate that advanced technology enabled predictive maintenance can be applied in various industries by leveraging big data analytics, smart sensors, artificial intelligence (AI), and platform construction. Such predictive quality management systems can become living ecosystems that can perform cause-effect analysis, big data monitoring and analytics, and effective decision-making in real time. This study proposes several practical implications for actual design and implementation of effective predictive quality management systems in the Industry 4.0 era. However, the living predictive quality management ecosystem should be the product of the organizational culture that nurtures collaborative efforts of all stakeholders, sharing of information, and co-creation of shared goals.

show abstract

“…A real case is presented: an industrial cold box in a petrochemical plant; data collected on the fouling of its tubes show that the extent of fouling of one tube affects the rate of fouling of other tubes due to overloading. Shi and Zeng (2016) describe an opportunistic PM strategy and analyze multi-component systems with stochastic dependence (similar to that by Rasmekomen and Parlikad 2016). Assuming that measures are available of the RULs of components as well as of the impact of a component's degradation on the RULs of other components, filtering theory is used for predictions.…”

Section: Literature Surveymentioning

confidence: 99%

The stochastic opportunistic replacement problem, part III: improved bounding procedures

2019

View full text Add to dashboard Cite

We consider the problem to find a schedule for component replacement in a multi-component system, whose components possess stochastic lives and economic dependencies, such that the expected costs for maintenance during a pre-defined time period are minimized. The problem was considered in Patriksson et al. (Ann Oper Res 224:51-75, 2015b), in which a two-stage approximation of the problem was optimized through decomposition (denoted the optimization policy). The current paper improves the effectiveness of the decomposition approach by establishing a tighter bound on the value of the recourse function (i.e., the second stage in the approximation). A general lower bound on the expected maintenance cost is also established. Numerical experiments with 100 simulation scenarios for each of four test instances show that the tighter bound yields a decomposition generating fewer optimality cuts. They also illustrate the quality of the lower bound. Contrary to results presented earlier, an age-based policy performs on par with the optimization policy, although most simple policies perform worse than the optimization policy.

show abstract

Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence

Cited by 96 publications

References 39 publications

Availability and maintenance modelling for systems subject to multiple failure modes

Availability and maintenance modelling for systems subject to multiple failure modes

The quality management ecosystem for predictive maintenance in the Industry 4.0 era

The stochastic opportunistic replacement problem, part III: improved bounding procedures

Contact Info

Product

Resources

About