A Concentrated Time–Frequency Analysis Tool for Bearing Fault Diagnosis

2019/03/29 by Gang Yu · 9 citations
Engineering · Mathematics · #A priori and a posteriori #Advanced machining processes and optimization #Algorithm #Artificial intelligence #Bearing (navigation) #Computer science #Computer vision #Digital signal processing #Electronic engineering #Engineering #Entropy (arrow of time) #Fourier transform #Gear and Bearing Dynamics Analysis #Kurtosis #Machine Fault Diagnosis Techniques #Mathematics #Pattern recognition (psychology) #SIGNAL (programming language) #Signal processing #Statistics #Time–frequency analysis #Transient (computer programming)

paper · doi:10.1109/tim.2019.2901514

openalex publication_date 2019/03/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

In industrial rotating machinery, the transient signal usually corresponds to the failure of a primary element, such as a bearing or gear. However, faced with the complexity and diversity of practical engineering, extracting the transient signal is a highly challenging task. In this paper, we propose a novel time-frequency analysis method termed the transient-extracting transform, which can effectively characterize and extract the transient components in the fault signals. This method is based on the short-time Fourier transform and does not require extended parameters or a priori information. Quantized indicators, such as Rényi entropy and kurtosis, are employed to compare the performance of the proposed method with other classical and advanced methods. The comparisons show that the proposed method can provide a much more energy-concentrated time-frequency representation, and the transient components can be extracted with a significantly larger kurtosis. The numerical and experimental signals are used to show the effectiveness of our method.

Cited by

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• Novel Method for Vibration Sensor-Based Instantaneous Defect Frequency Estimation for Rolling Bearings Under Non-Stationary Conditions. [europepmc]
• A combined generalized Warblet transform and second order synchroextracting transform for analyzing nonstationary signals of rotating machinery. [europepmc]
• A Visual Fault Detection Method for Induction Motors Based on a Zero-Sequence Current and an Improved Symmetrized Dot Pattern. [europepmc]
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