Post Prognostic Decision for Predictive Maintenance Planning with Remaining Useful Life Uncertainty

This paper investigates the use of the Particle Swarm Optimization (PSO) algorithm to quantify the effect of RUL uncertainty on predictive maintenance planning. The prediction of RUL is influenced by many sources of uncertainty, and it is required to quantify their combined impact by incorporating the RUL uncertainty in the optimization process to minimize the total maintenance cost. In this work, predictive maintenance of a multi-functional single machine problem is adopted to study the impact of RUL uncertainty on maintenance planning. Therefore, the PSO algorithm is integrated with a random sampling-based strategy to select a sequence that performs better for different values of RUL associated with different jobs. Through a numerical example, results show the importance of optimizing maintenance actions under the consideration of RUL randomness.

[1]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[2]  Noureddine Zerhouni,et al.  Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction , 2016, J. Intell. Manuf..

[3]  B. Saha,et al.  Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques , 2008, 2008 IEEE Aerospace Conference.

[4]  Raphael T. Haftka,et al.  Uncertainty Reduction of Damage Growth Properties Using Structural Health Monitoring , 2010 .

[6]  Noureddine Zerhouni,et al.  A Decison-Making Approach for Job Shop Scheduling with Job Depending Degradation and Predictive Maintenance , 2016 .

[7]  K. Umamaheswari,et al.  Task Scheduling in Distributed Systems Using Heap Intelligent Discrete Particle Swarm Optimization , 2017, Comput. Intell..

[8]  Mustapha Nourelfath,et al.  Integrating production, inventory and maintenance planning for a parallel system with dependent components , 2012, Reliab. Eng. Syst. Saf..

[9]  K. Goebel,et al.  Analytical algorithms to quantify the uncertainty in remaining useful life prediction , 2013, 2013 IEEE Aerospace Conference.

[10]  Fatih Camci,et al.  European Journal of Operational Research Maintenance Scheduling of Geographically Distributed Assets with Prognostics Information , 2022 .

[11]  Matthew Daigle,et al.  An Efficient Deterministic Approach to Model-based Prediction Uncertainty Estimation , 2012 .

[12]  Noureddine Zerhouni,et al.  Post-prognostics decision in cyber-physical systems , 2018, 2018 International Conference on Advanced Systems and Electric Technologies (IC_ASET).

[13]  Sankaran Mahadevan,et al.  Uncertainty Quantification in Fatigue Crack Growth Prognosis , 2011 .

[14]  Stephen J. Engel,et al.  Prognostics, the real issues involved with predicting life remaining , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[15]  Chunming Ye,et al.  Single-machine-based joint optimization of predictive maintenance planning and production scheduling , 2018, Robotics and Computer-Integrated Manufacturing.

[16]  N. E. Toklu.  Matheuristics for robust optimization , 2014 .

[17]  Alan G. Merten,et al.  Variance Minimization in Single Machine Sequencing Problems , 1972 .

[18]  Huairui Guo,et al.  Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model , 2006, RAMS '06. Annual Reliability and Maintainability Symposium, 2006..

[19]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[20]  N. Zerhouni,et al.  Scheduling predictive maintenance in flow-shop , 2012, Proceedings of the IEEE 2012 Prognostics and System Health Management Conference (PHM-2012 Beijing).

[21]  Shih-Wei Lin,et al.  Particle swarm optimization for parameter determination and feature selection of support vector machines , 2008, Expert Syst. Appl..

[22]  Noureddine Zerhouni,et al.  Integrated Production and Predictive Maintenance Planning based on Prognostic Information , 2019, 2019 International Conference on Advanced Systems and Emergent Technologies (IC_ASET).

[23]  Yisha Xiang,et al.  A review on condition-based maintenance optimization models for stochastically deteriorating system , 2017, Reliab. Eng. Syst. Saf..

[24]  Robert Pellerin,et al.  Simulation‐based approach to joint production and preventive maintenance scheduling on a failure‐prone machine , 2011 .

[25]  Guangzhao Cui,et al.  Modified PSO algorithm for solving planar graph coloring problem , 2008 .

[26]  Matthew Daigle,et al.  Model-based prognostics under limited sensing , 2010, 2010 IEEE Aerospace Conference.

[27]  Jie Gu,et al.  Uncertainty Assessment of Prognostics of Electronics Subject to Random Vibration , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[28]  Rasit Köker,et al.  Solving Single Machine Total Weighted Tardiness Problem with Unequal Release Date Using Neurohybrid Particle Swarm Optimization Approach , 2015, Comput. Intell. Neurosci..

[29]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[30]  Asma Ladj,et al.  IPro-GA: an integrated prognostic based GA for scheduling jobs and predictive maintenance in a single multifunctional machine , 2016 .