Statistical Batch-Based Bearing Fault Detection

TL;DR

This paper introduces a multivariate statistical process control method using Fourier transform features for early fault detection in rotating machinery bearings, demonstrating improved detection across various conditions.

Contribution

It presents a novel multivariate fault detection approach based on Fourier features, addressing the limitations of univariate methods in condition monitoring of rotary machines.

Findings

Effective early fault detection across different fault types

Enhanced detection accuracy with multivariate Fourier features

Potential for broader industrial maintenance applications

Abstract

In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep learning methods. Based on the complex working conditions of rotary machines, multivariate statistical process control charts such as Hotelling's $T^2$ and Squared Prediction Error are useful for providing early warnings. However, these methods are rarely applied to condition monitoring of rotating machinery due to the univariate nature of the datasets. In the present paper, we propose a multivariate statistical process control-based fault detection method that utilizes multivariate data composed of Fourier transform features extracted for fixed-time batches. Our approach makes use of the multidimensional nature of Fourier transform characteristics, which record more detailed information about the machine's status, in an effort to enhance early defect detection and diagnosis. Experiments with varying vibration measurement locations (Fan End, Drive End), fault types (ball, inner, and outer race faults), and motor loads (0-3 horsepower) are used to validate the suggested approach. The outcomes illustrate our method's effectiveness in fault detection and point to possible broader uses in industrial maintenance.