In fault diagnosis, machine learning theories are gaining popularity as they proved to be an
efficient tool that not only reduces human effort but also identifies the health conditions of the …

Sensors, vital elements in data acquisition systems, play a crucial role in various industries.
However, their exposure to harsh operating conditions makes them vulnerable to faults that …

Gearboxes, fundamental components in the domains of manufacturing, transportation, and
aerospace apparatus, are highly susceptible to impairments. The emerging technique of non …

This research focuses on develo** a denoising filter that effectively enhances subtle non-
stationarities within signals. Initially, the spectral kurtosis has been calculated from each …

The informative frequency band (IFB) plays a vital role in detecting defects in complex
machinery through visible informative features. In the present work, a denoising filter has …

Featured Application The fault diagnosis method proposed in this paper can be applied to
the diagnosis of bearings in machine tool spindle systems. Abstract An enhanced fault …

The fault diagnosis problems of rolling bearings commonly found in modern industrial
equipment are becoming increasingly complex and challenging, and traditional fault …

Bearing fault diagnosis is critical for maintaining the reliability of health monitoring in
electromechanical systems. However, traditional feature extraction methods often struggle to …

Industrial machinery relies heavily on accurate fault diagnosis to maintain its reliability and
operational efficiency. However, analyzing vibration signals for early fault detection is …

The inefficiency of model inference and the limited sample data are significant issues in
electroencephalogram (EEG) emotion recognition models based on neural networks …