Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying Speed Conditions

TL;DR

This paper introduces a real-time CNN-based method for diagnosing multiple bearing faults under variable speeds and noise, demonstrating superior accuracy, robustness, and efficiency compared to existing approaches.

Contribution

It presents a novel CNN model combined with Fisher-based spectral analysis for effective fault diagnosis in dynamic conditions, advancing practical bearing health monitoring.

Findings

Achieves up to 15.8% accuracy improvement over state-of-the-art methods.

Maintains high performance across various noise levels.

Operates in real-time with processing times five times faster than data acquisition.

Abstract

Detection of rolling-element bearing faults is crucial for implementing proactive maintenance strategies and for minimizing the economic and operational consequences of unexpected failures. However, many existing techniques are developed and tested under strictly controlled conditions, limiting their adaptability to the diverse and dynamic settings encountered in practical applications. This paper presents an efficient real-time convolutional neural network (CNN) for diagnosing multiple bearing faults under various noise levels and time-varying rotational speeds. Additionally, we propose a novel Fisher-based spectral separability analysis (SSA) method to elucidate the effectiveness of the designed CNN model. We conducted experiments on both healthy bearings and bearings afflicted with inner race, outer race, and roller ball faults. The experimental results show the superiority of our model over the current state-of-the-art approach in three folds: it achieves substantial accuracy gains of up to 15.8%, it is robust to noise with high performance across various signal-to-noise ratios, and it runs in real-time with processing durations five times less than acquisition. Additionally, by using the proposed SSA technique, we offer insights into the model's performance and underscore its effectiveness in tackling real-world challenges.