Real-Time Vibration-Based Bearing Fault Diagnosis Under Time-Varying
  Speed Conditions
Tuomas Jalonen Mohammad Al-Sa'd Serkan Kiranyaz Moncef Gabbouj
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.