Fault Detection and Diagnosis with Imbalanced and Noisy Data: A Hybrid Framework for Rotating Machinery

TL;DR

This paper introduces a hybrid fault detection and diagnosis framework for rotating machinery that effectively handles imbalanced and noisy data by combining feature extraction, synthetic data generation, and advanced classification techniques.

Contribution

It proposes a novel hybrid approach integrating Fourier and wavelet features, WGAN-based data augmentation, and a CLSTM-WELM classifier for improved fault diagnosis under challenging conditions.

Findings

GAN-CLSTM-ELM outperforms existing frameworks in various scenarios.

The hybrid framework effectively handles imbalanced and noisy datasets.

Synthetic data generation improves fault class representation.

Abstract

Fault diagnosis plays an essential role in reducing the maintenance costs of rotating machinery manufacturing systems. In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples. At the same time, in an industrial condition, accelerometers encounter high levels of disruptive signals and the collected samples turn out to be heavily noisy. As a consequence, many traditional Fault Detection and Diagnosis (FDD) frameworks get poor classification performances when dealing with real-world circumstances. Three main solutions have been proposed in the literature to cope with this problem: (1) the implementation of generative algorithms to increase the amount of under-represented input samples, (2) the employment of a classifier being powerful to learn from imbalanced and noisy data, (3) the development of an efficient data pre-processing including feature extraction and data augmentation. This paper proposes a hybrid framework which uses the three aforementioned components to achieve an effective signal-based FDD system for imbalanced conditions. Specifically, it first extracts the fault features, using Fourier and wavelet transforms to make full use of the signals. Then, it employs Wasserstein Generative Adversarial Networks (WGAN) to generate synthetic samples to populate the rare fault class and enhance the training set. Moreover, to achieve a higher performance a novel combination of Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning Machine (WELM) is proposed. To verify the effectiveness of the developed framework, different datasets settings on different imbalance severities and noise degrees were used. The comparative results demonstrate that in different scenarios GAN-CLSTM-ELM outperforms the other state-of-the-art FDD frameworks.