Resampling Technique-Based Demodulation Analysis for Planet Bearing Cage Fault Diagnosis Under Nonstationary Conditions

Yang, Yang; Wei, Sha Xin; Li, Tianqi; Liu, Hui; He, Jun

doi:10.1109/jsen.2023.3274795

Cited by 5 publications

(2 citation statements)

References 33 publications

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“…Zhang et al [58] proposed a time-varying sinusoidal mode extraction (TVSME) method for the current signal analysis of induction motor by using generalized demodulation for accurate IF extraction, and then the current signals were resampled by the IF. Yang et al [78] utilized general parameterized time-frequency transform (GPTFT) and peak search method to detect the IF, and then the vibration data was resampled by IF. Then, autogram was utilized to select the resonance frequency band based on angular-domain data, and the square envelope spectrum was obtained to identify the fault locations of bearings.…”

Section: ) Tacholess Order Trackingmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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Rotating machinery often works under time-varying speeds, and nonstationary conditions as well as harsh environments make its key parts, such as rolling bearings and gears, prone to faults. Therefore, a number of fault diagnosis methods including nonstationary signal processing methods and data-driven methods have been developed. This paper presents a comprehensive review on the fault diagnosis of rotating machinery under time-varying speeds proposed mainly during the last five years. First, spectrum analysis-based methods, including order tracking, cyclic spectrum correlation and generalized demodulation, are reviewed. Second, the time-frequency analysis (TFA) methods in machinery fault diagnosis are divided into postprocessing methods and chirplet transform-based methods and are reviewed. Then, the artificial feature extraction-and deep learning-enabled intelligent diagnosis methods proposed specifically for time-varying speed conditions are reviewed. Finally, the research prospects are discussed.We not only review the relevant state-of-art methods and analyze how they overcome the problems caused by speed fluctuations but also discuss their advantages and disadvantages as well as the challenges that will be encountered when applying them to industrial applications. This paper is expected to provide new graduate students, institutions and companies with a preliminary understanding of the methods on this topic.

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Section: ) Tacholess Order Trackingmentioning

confidence: 99%

Rotating Machinery Fault Diagnosis Under Time-Varying Speeds: A Review

Liu,

Cui,

Wang

2023

IEEE Sensors J.

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“…In rotating machinery, the maintenance of bearings is crucial for ensuring the safe operation of the entire mechanical system [2,3]. As pressure-bearing components, it is essential that they remain in a good condition.…”

Section: Introductionmentioning

confidence: 99%

Extraction of time-frequency ridge line based on automatic peak search and curve fitting

Hu,

Wang,

Wang

et al. 2024

Eng. Res. Express

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Instantaneous rotational frequency (IRF) plays a crucial role in diagnosing bearing faults under variable speed conditions. However, accurately extracting the rotational frequency becomes challenging due to significant noise and strong harmonic interference. To address this issue, this paper proposes an approach for extracting Time-frequency ridge line through automatic peak search and curve fitting with a minimal number of parameters. The approach eliminates disturbance from harmonics and noise by locating peak coordinates within specified areas. Moreover, these peak coordinates were fitted as interpolated nodes to prevent sudden changes in the curve due to noise interference. It also enhances the accuracy of the extracted ridge. The efficacy of the proposed technique is confirmed through simulations and specially designed test bench data. Results show that IRF-estimation errors of our method fall by about 20% compared with those of any traditional method, approaching TKEO and Phase Demodulation without using too many parameters.

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