Bearing degradation is the primary cause of electrical machine failures, making reliable
condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid …

Rolling element bearings are one of the most critical components of rotating machinery, with
bearing faults amounting up to 50% of the faults in electrical machines. Therefore, the …

Deep transfer learning has evolved into a powerful method for defect identification,
particularly in mechanical systems that lack sufficient training data. Nonetheless, domain …

At present, the complex and varying operating conditions of bearings make the feature
extraction become difficult and lack adaptability. An end‐to‐end fault diagnosis is proposed …

The vibration signal characteristics of rolling bearings are closely related to the performance
decay process, predicting the remaining useful life (RUL) of rolling bearings by vibration …

It has been demonstrated that fast convolutional sparse dictionary learning (FCSDL) is a
useful instrument for diagnosing rolling bearing faults and can recover rolling bearing fault …

Bearings are essential components in the most electrical equipment. Procedures for
monitoring the condition of bearings must be developed to prevent unexpected failure of …

Non-stationary fault detection under bearing fault operation of induction motor is
investigated in this paper. For this aim, the vibration signal is analyzed by wavelet method …

Vibration signal of rolling element bearing is usually much complicated due to the presence
of random slip** of rolling element and a lot of noise. Therefore, it is often difficult to extract …

Defect recognition plays an important part in the health monitoring of in-service equipment.
Surface defects and sub-surface defects of key components have different effects on the …