Proceedings of the

The 33rd European Safety and Reliability Conference (ESREL 2023)
3 – 8 September 2023, Southampton, UK

Predictive Maintenance Planning Using Renewal Reward Processes and Probabilistic RUL Prognostics – Analyzing the Influence of Accuracy and Sharpness of Prognostics

Mihaela Mitic1, Ingeborg de Pater2, Zhiguo Zeng3,a and Anne Barros3,b

1Faculty of Science, Utrecht University, The Netherlands.

2Faculty of Aerospace Engineering, Delft University of Technology, The Netherlands.

3Laboratoire Genie Industriel, CentraleSupélec - Université Paris-Saclay, France.


We pose the maintenance planning for systems using probabilistic Remaining Useful Life (RUL) prognostics as a renewal reward process. Data-driven probabilistic RUL prognostics are obtained using a Convolutional Neural Network with Monte Carlo dropout. The maintenance planning model is illustrated for aircraft turbofan engines. The results show that in the initial monitoring phase, the accuracy and sharpness of the RUL prognostics is relatively small. The maintenance of the engines is therefore scheduled far in the future. As the usage of the engine increases, the accuracy of the prognostics improves, while the sharpness remains relatively small. As soon as the estimated probability of the RUL is skewed towards 0, the maintenance planning model consistently indicates it is optimal to replace the engines immediately, i.e., "now". This shows that probabilistic RUL prognostics support an effective maintenance planning of the engines, despite being imperfect with respect to accuracy and sharpness.

Keywords: Predictive maintenance planning, Probabilistic RUL prognostics, Aircraft engines, Renewal processes, Convolutional neural network, Monte Carlo dropout.

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