Deep learning-based fault identification in condition monitoring

TL;DR

This paper introduces a CNN-based method for real-time fault detection in rolling element bearings, emphasizing both accuracy and inference speed using vibration signal encoding and analysis of processing time.

Contribution

It presents a novel approach combining vibration signal encoding with CNNs for fast, accurate fault classification in condition monitoring.

Findings

Achieved high fault classification accuracy.

Demonstrated real-time inference capability.

Analyzed trade-off between accuracy and processing time.

Abstract

Vibration-based condition monitoring techniques are commonly used to identify faults in rolling element bearings. Accuracy and speed of fault detection procedures are critical performance measures in condition monitoring. Delay is especially important in remote condition monitoring and time-sensitive industrial applications. While most existing methods focus on accuracy, little attention has been given to the inference time in the fault identification process. In this paper, we address this gap by presenting a Convolutional Neural Network (CNN) based approach for real-time fault identification in rolling element bearings. We encode raw vibration signals into two-dimensional images using various encoding methods and use these with a CNN to classify several categories of bearing fault types and sizes. We analyse the interplay between fault identification accuracy and processing time. For training and evaluation we use a bearing failure CWRU dataset.