Self-Supervised Health Index Curve Generation for Condition-Based Predictive Maintenance

Author:

Seitz Steffen1ORCID,Arnold Marvin1,Tetzlaff Ronald1,Holstein Peter2

Affiliation:

1. Institute of Circuits and Systems, Technische Universität Dresden, 01307 Dresden, Germany

2. Technische Universität Ilmenau, 98693 Ilmenau, Germany

Abstract

Modern machine degradation trend evaluation relies on the unsupervised model-based estimation of a health index (HI) from asset measurement data. This minimizes the need for timely human evaluation and avoids assumptions on the degradation shape. However, the comparability of multiple HI curves over time generated by unsupervised methods suffers from a scaling mismatch (non-coherent HIs) caused by the slightly different asset initial conditions and distinct HI model training. In this paper, we propose a novel self-supervised approach to obtain HI curves without suffering from the scale mismatch. Our approach uses an unsupervised autoencoder based on a convolutional neural network (CNN) to detect initial faults and autonomously label measurement samples. The resulting self-labeled data is used to train a 1D-CNN health predictor, effectively eliminating the scaling mismatch problem. On the basis of a bearing test-to-failure experiment, we show that our self-supervised scheme offers a promising solution for the non-coherent HI problem. In addition, we observed that our method indicates the gradual wear affecting the bearing prior to the independent analysis of a human expert.

Funder

German Ministry for Economic Affairs and Climate Action

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering

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