The efficient diagnosis of bearing faults requires the extraction of informative features. This
paper presents a novel approach that combines Weighted Principal Component Analysis …

Rotating machines are extensively utilized in diverse industries, and their malfunctions can
result in significant financial consequences and safety risks. Consequently, there has been …

Rolling bearings are critical for maintaining the stability, reliability, and safety of mechanical
systems. However, diagnosing faults in rolling bearings objectively can be challenging due …

Considering the importance of complex systems fault detection, much efforts have been
dedicated to fault feature extraction with monitoring data. The graph-based approach has …

Efficient health monitoring for identifying and quantifying damages can substantially improve
the performance and structural integrity of engineered systems. Specifically, new advances …

Fault detection in mechatronic systems is crucial for their maintainability and safety.
However, the systems monitoring variables are often abundant, with intricate connections. It …

Spectrum correlation (SC), as a typical demodulation algorithm, has been investigated for
fault extraction by restraining interference components. However, SC ignores the uneven …

Traditional maintenance strategies risk unforeseen failure, sophisticated physics-based
modeling, and manual feature extraction. Early detection and accurate predictions of …

With increasing customer demand, industry 4.0 gained a lot of interest, which is based on
smart factories. In smart factories, robotic components are vulnerable to failure due to …

Speed fluctuations are a key issue that directly affects the motor fault diagnosis performance.
Deep convolutional neural networks (DCNNet) can automatically perform fault feature …