An Enhanced TK Technology for Bearing Fault Detection Using Vibration Measurement

TL;DR

This paper introduces an improved Teager–Kaiser technique for detecting bearing faults in rotating machines using vibration signals to enhance early fault detection and reduce maintenance costs.

Contribution

The novel eTK technique combines empirical mode decomposition and a denoising filter to improve bearing fault detection accuracy.

Findings

The eTK technique effectively identifies bearing faults through vibration signal analysis.

Empirical mode decomposition helps isolate relevant frequency components for fault detection.

The proposed denoising filter improves signal-to-noise ratio, enhancing fault diagnosis reliability.

Abstract

Rolling element bearings are commonly used in rotating machines. Bearing fault detection and diagnosis play a critical role in machine operations to recognize bearing faults at their early stage and prevent machine performance degradation, improve operation quality, and reduce maintenance costs. Although many fault detection techniques are proposed in the literature for bearing condition monitoring, reliable bearing fault detection remains a challenging task in this research and development field. This study proposes an enhanced Teager–Kaiser (eTK) technique for bearing fault detection and diagnosis. Vibration signals are used for analysis. The eTK technique is novel in two aspects: Firstly, an empirical mode decomposition analysis is suggested to recognize representative intrinsic mode functions (IMFs) with different frequency components. Secondly, an eTK denoising filter is proposed to improve the signal-to-noise ratio of the selected IMF features. The analytical signal spectrum analysis is conducted to identify representative features for bearing fault detection. The effectiveness of the proposed eTK technique is verified by experimental tests corresponding to different bearing conditions.

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