Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global Fuzzy Entropy

Zhu, Keheng; Yue, Xiucheng; Sun, Dalin; Xiao, Shichang; Xiong, Hu

doi:10.20855/ijav.2022.27.11827

Cited by 4 publications

(3 citation statements)

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“…where δ( The LMSST improves the degree of energy aggregation in the spectrogram by detecting local maxima in the frequency direction of the spectrogram, and then performing frequency point compression [6]. The specific assignment rules are expressed as arg max ( , ) , [ , ], ( , ) 0 ( , ) 0, ( , ) 0…”

Section: The Local Maximum Synchrosqueezing Transformmentioning

confidence: 99%

A time-frequency method for induction motor fault feature extraction using instantaneous power signals

Sun,

Duan

2023

J. Phys.: Conf. Ser.

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To solve the issue that the characteristic frequencies of the broken rotor bar fault (BRBF) are always submerged by the fundamental component during the stator current spectrum analysis in light loads, this paper proposes an effective approach based on the instantaneous power signal and the local maximum synchrosqueezing transform (LMSST). By multiplying the current and voltage, the instantaneous power signal can provide more fault features and enhance fault component amplitudes. The LMSST method is used to obtain a time-frequency analysis with more concentrated energy, and more fault characteristics are extracted from the reconstructed signal, which helps to accurately identify the motor BRBF.

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Section: The Local Maximum Synchrosqueezing Transformmentioning

confidence: 99%

A time-frequency method for induction motor fault feature extraction using instantaneous power signals

Sun,

Duan

2023

J. Phys.: Conf. Ser.

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“…In particular, in [3], which is devoted to the diagnostics of REBs, it is proven that signalbased techniques, thanks to their analytical definition, which is formulated taking into consideration to some extent the mathematical nature of the signal, allow fault signatures to be clearly identified, and their temporal development to be fully traced, confirming their ability as powerful means to constantly monitor the health state of a system even in real-time applications. Other interesting recent articles on the diagnostics of REBs can be found in [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Signal Analysis Techniques for the Diagnostics of the IMS Rolling Element Bearing Dataset

2023

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In this paper, a comparison of signal analysis techniques for the diagnostics of rolling element bearings is carried out. Specifically, the comparison is performed in terms of fault detection, diagnosis and prognosis techniques with regards to the first rolling element bearing dataset released by NASA IMS Center in 2014. As for fault detection, it is obtained that RMS value, Kurtosis and Detectivity, as statistical parameters, are able to properly detect the arising of the fault on the defective bearings. Then, several signal processing techniques, such as deterministic/random signal separation, time-frequency and cyclostationary analyses are applied to perform fault diagnosis. Among these techniques, it is found that the combination of Cepstrum Pre-Whitening and Squared Envelope Spectrum, and Improved Envelope Spectrum, allow the faults to be correctly identified on specific bearing components. Finally, the Correlation, Monotonicity and Robustness of the previous statistical parameters are computed to identify the most accurate tools for bearing fault prognosis.

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“…Fault detection for rotating machinery have received extensive attention in recent years, with methods such as acoustic emission analysis based, [4][5][6] vibration analysis based, [7][8][9] and oil analysis based. [10][11][12] Compared to others, the vibration-based fault diagnosis is generally more technically matured and cost-effective and thus widely used in wind turbine applications.…”

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confidence: 99%

Fault diagnosis of low‐speed rolling bearing based on weighted reconstruction of vibration envelopes

et al. 2023

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The main bearing supports the rotation of the main shaft of a wind turbine. It bears heavy dead weights as well as variable speed dynamic loading during operations; thus, it is a vulnerable part in a wind turbine drive train. Because of the low speed and time-varying operations of the main bearing, vibrations generated by bearing faults are often weak in response amplitudes, low in frequency range, and smeared in damage feature energy. As a result, the applicability of the conventional acceleration envelope analysis (AEA) technique, a traditionally effective technology for bearing fault diagnosis, is limited in such cases. In order to resolve this, a modified AEA method specially designed for bearings with low and variable speed operation is proposed in this paper. First, the structural response is decomposed by means of variational mode decomposition (VMD) for the low frequency components to form a series of band-limited intrinsic mode functions (BLIMFs). Next, weighting factors are determined for the BLIMFs by defined energy ratios. Finally, a new envelope is reconstructed by weighting the envelopes of each BLIMF for bearing fault diagnosis.The effectiveness and practicality of the proposed method for the diagnosis of main bearing faults in wind turbines is verified through the analysis of measured data from a wind turbine in the field. The proposed method provides an effective way for bearing fault diagnosis at low and variable rotational speeds.

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Rolling Bearing Fault Feature Extraction Using Local Maximum Synchrosqueezing Transform and Global Fuzzy Entropy

Cited by 4 publications

References 0 publications

A time-frequency method for induction motor fault feature extraction using instantaneous power signals

A time-frequency method for induction motor fault feature extraction using instantaneous power signals

A Comparison of Signal Analysis Techniques for the Diagnostics of the IMS Rolling Element Bearing Dataset

Fault diagnosis of low‐speed rolling bearing based on weighted reconstruction of vibration envelopes

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