Rotor–Stator Rub-Impact Fault and Position Identification of Aero-Engine Based on VMD-MF-Cepstrum-KNN

Chen, Wangying; Yu, Mingyue; Wu, Peng

doi:10.1080/10402004.2022.2131665

Cited by 5 publications

(2 citation statements)

References 17 publications

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“…Zimroz et al (2011) studied the correlation between rolling device vibration signals and physical quantities such as speed signals and current signals, proposed a method to measure instantaneous shaft speed based on advanced vibration signal processing technology, and applied it to wind turbine gearboxes. Wangying, Mingyue, and Peng (2023) proposed a fault diagnosis method that combines VMD, margin factor (MF) and cepstrum (VMD-MF-cepstrum) to identify the rub-impact fault between the stator and rotor of an aeroengine, which can accurately identify the collision location.…”

Section: Research On Motor Rotation Anomaly Detectionmentioning

confidence: 99%

Research on motor rotation anomaly detection based on improved VMD algorithm

Chen,

Chen

et al. 2024

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PurposeThe electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approachThe VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.FindingsThis paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.Originality/valueBy using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

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Section: Research On Motor Rotation Anomaly Detectionmentioning

confidence: 99%

Research on motor rotation anomaly detection based on improved VMD algorithm

Chen,

Chen

et al. 2024

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“…A greater margin factor in a component signal indicates more sufficient information about the wear degree of the equipment, thereby facilitating the extraction of fault feature information contained in it [27]. Chen et al [28] employed the margin factor to select the optimal sensitive component signal post-VMD from the original signal, took the transmission path characteristics of the optimal component as the feature vector for cluster analysis and finally realized the accurate assessment of the rub-impact faults and their locations by K-nearest neighbor (KNN) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

Ma,

Yu,

et al. 2024

Meas. Sci. Technol.

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The combined failure from rolling bearings is featured by weakness and complexity and hard to be recognized precisely. One-dimensional local binary pattern (1D-LBP) manifests failure information of rolling bearings from textural analysis. However, when signals are quantized with 1D-LBP, the periodic impact feature of fault signal itself will be excluded from consideration and consequently, the fault features will be hard to be dug sufficiently. Feature mode decomposition (FMD) is sensitive to impulse and periodicity of fault signals, but the number of decomposition modes and the length of filter determines accuracy of signal decomposition. To solve these problems, an adaptive local binarization feature mode decomposition (ALBFMD) method was proposed. ALBFMD method represents compound failure information of rolling bearings from textural feature extraction and inherent features of fault signal. Furthermore, with minimum permutation entropy as criterion, the number of decomposition modes and the length of filter of ALBFMD was adaptively determined. According to power spectrums of reconstructed signals, the type of combined faults could be precisely identified. The proposed method was compared with FMD and VMD method and analyzed in different situations. Its superiority in terms of feature extraction and combined failure identification of bearings has been verified.

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Investigation of logarithmic signatures for feature extraction and application to marine engine fault diagnosis

Patil,

Theotokatos,

et al. 2024

Engineering Applications of Artificial Intelligence

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Rotor–Stator Rub-Impact Fault and Position Identification of Aero-Engine Based on VMD-MF-Cepstrum-KNN

Cited by 5 publications

References 17 publications

Research on motor rotation anomaly detection based on improved VMD algorithm

Research on motor rotation anomaly detection based on improved VMD algorithm

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

Investigation of logarithmic signatures for feature extraction and application to marine engine fault diagnosis

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