A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions

Amelie Bender

doi:10.3390/machines9100210

Abstract

While increasing digitalization enables multiple advantages for a reliable operation of technical systems, a remaining challenge in the context of condition monitoring is seen in suitable consideration of uncertainties affecting the monitored system. Therefore, a suitable prognostic approach to predict the remaining useful lifetime of complex technical systems is required. To handle different kinds of uncertainties, a novel Multi-Model-Particle Filtering-based prognostic approach is developed and evaluated by the use case of rubber-metal-elements. These elements are maintained preventively due to the strong influence of uncertainties on their behavior. In this paper, two measurement quantities are compared concerning their ability to establish a prediction of the remaining useful lifetime of the monitored elements and the influence of present uncertainties. Based on three performance indices, the results are evaluated. A comparison with predictions of a classical Particle Filter underlines the superiority of the developed Multi-Model-Particle Filter. Finally, the value of the developed method for enabling condition monitoring of technical systems related to uncertainties is given exemplary by a comparison between the preventive and the predictive maintenance strategy for the use case.

Highlights

The major aim of condition monitoring systems (CMS) is to enable condition-based or predictive maintenance
For both measurement concepts, developed in Section 2.5.4, remaining useful lifetime (RUL) predictions are evaluated based on run-to-failure data measured during lifetime tests
The predictions are analyzed based on the three performance indices: mean absolute percentage error, rate of negative errors and prognostic horizon

Summary

Introduction

The major aim of condition monitoring systems (CMS) is to enable condition-based or predictive maintenance. Both maintenance strategies are characterized by reduced risks and costs compared with reactive or preventive maintenance strategies [1]. The procedure strives for predicting the remaining useful life of the monitored system to support maintenance planning. The procedure’s six steps are: monitoring of the system, data pre-processing, feature extraction, model development, diagnostics or prognostics and decision making. These steps are coupled with different tasks: Before monitoring, suitable measurement quantities need to be identified. A suitable physics-based, empirical, or learned model to predict the remaining useful life of the system needs to be found

Methods

Results

Discussion

Conclusion