Towards Developing a Novel Framework for Practical PHM: a Sequential Decision Problem solved by Reinforcement Learning and Artificial Neural Networks

Luca Bellani, Michele Compare, Piero Baraldi, and Enrico Zio
Publication Target: 
IJPHM
Publication Issue: 
Special Issue on Deep Learning and Emerging Analytics
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_19_031.pdf1.22 MBJanuary 16, 2020 - 5:28am

The heart of prognostics and health management (PHM) is to predict the equipment degradation evolution and, thus, its Remaining Useful Life (RUL). These predictions drive the decisions on the equipment Operation and Maintenance (O&M), and these in turn influence the equipment degradation evolution itself. In this paper, we propose a novel PHM framework based on Sequential Decision Problem (SDP), Artificial Neural Networks (ANNs) and Reinforcement Learning (RL), which allows properly considering this feedback loop for optimal sequential O&M decision making. The framework is applied to a scaled-down case study concerning a real mechanical equipment equipped with PHM capabilities. A comparison of the proposed PHM framework with traditional PHM is performed.

Publication Year: 
2019
Publication Volume: 
10
Publication Control Number: 
031
Page Count: 
15
Submission Keywords: 
PHM
Maintenance planning
Artificial neural network
Sequential Decision Problem
Reinforcement Learning
Submission Topic Areas: 
CBM and informed logistics
Submitted by: 
  
 
 
 

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