… machines. In this regard, this paper reviews the literature on the “multi-Fault diagnosis
using multi-sensor data fusion” of Industrial Rotating Machines employing Machine learning/Deep …

… leads to a strong reliance on the amplitudes at the different harmonics during faults diagnosis.
Hence, faults diagnosis with the earlier data fusion method of the CS could entail the use …

… method proposed can only diagnosis faults in specific rotating machinery. Our subsequent
work … , data fusion method and generalization of fault diagnosis model of rotating machinery. …

… noisy or corrupted sensor readings, resulting in degraded diagnosis performance. This …
for fault diagnosis of rotating machinery based on deep learning and data fusion techniques, …

… Other researchers have also attempted to standardise rotating machines fault diagnosis by
incorporating artificial intelligence (AI) techniques such as artificial neural networks (ANN) [23,…

… machines. Furthermore, this review paper highlights the current challenges encountered
in multi-sensor data fusion for intelligent fault diagnosis of rotating machines. By considering …

… The study of fault diagnosis in rotating machines is well-established and continues to …
between closely related approaches in fault diagnosis of rotating machines and the current study, …

… and identification algorithm for rotating machinery fault detection. Consequently, we utilize
ELM classifier for rotating machinery fault detection in … for rotating machinery fault detection is …

… later experiments on rotating machinery. Since expressions of formulas are the same, here
we address the data fusion as a unified problem regardless of which fusion case will be used. …

… bearing faults as a measurement for vibration fault diagnosis. … These benefits make
feature-level fusion fit various … -level data-fusion technique for rotating imbalance diagnosis …