The occurrence of unexpected malfunctions of mechanical systems such as bearings, gearboxes, turbofan engines, and other rotating machinery is still the main cause of downtime, safety hazards, and maintenance cost throughout the manufacturing, energy, and transportation sectors. Recent technological advances in artif...
Bearing faults are one of the most failure mechanisms in induction motors, leading to increased vibration, reduced efficiency, and unexpected downtime. Existing diagnostic approaches are often limited by reliance on single-sensor measurements or computationally intensive learning-based models, which reduces robustness...
Results show that using statistical vibration features with ensemble classifiers is a good way to diagnose multi-class bearing faults and establishes a comprehensive benchmark for ML- and DL-based rolling bearing FDD.
An end-to-end predictive maintenance system is proposed for high stress mechanical drivetrain and rotating machinery and the architecture proposed combines an industrial Internet of Things edge sensory network and hybrid machine learning and deep learning pipelines.
A fault diagnosis technique based on vibration analysis and explainable AI models is presented, revealing that features such as the second harmonic of the rotational speed and skewness play a critical role in distinguishing between similar fault patterns.
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