Article
Engineering, Electrical & Electronic
Xinmin Yang, Yu Guo, Jiawei Fan
Summary: The study proposes a demodulation frequency band optimization method based on fast spectral correlation for the detection of compound faults in rolling element bearing. It utilizes fast spectral correlation to obtain the bi-variable map of vibration signal and slices it according to the theoretical fault-related frequency, then optimizes the demodulation frequency band using the crest of envelope spectrum. The method effectively detects REB compound faults with a significantly reduced calculation cost compared to other methods.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Mechanical
Kun Zhang, Yonggang Xu, Zhiqiang Liao, Liuyang Song, Peng Chen
Summary: The Fast Kurtogram is a traditional method for spectrum segmentation analysis, but its use of the binary tree filter bank method to obtain the center frequency and bandwidth is fixed. This paper proposes a new spectrum segmentation method - the Fast Entrogram, which can accurately filter fault information from the frequency domain. By processing the spectrum through Fourier transform and using the frequency slice function to extract different frequency bands, better filtering effects can be achieved than using finite impulse response filters.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Automation & Control Systems
Boyao Zhang, Yonghao Miao, Jing Lin, Hao Li
Summary: The demodulation analysis for bearing diagnosis aims to determine the fault-induced frequency band and detect the potential bearing fault characteristic frequency (FCF) in the demodulated spectrum. A novel FCF-oriented criterion is proposed to determine all informative frequency bands, and a weighted envelope spectrum (WES) is introduced to enhance fault information.
Article
Computer Science, Information Systems
Tian Xue, Huaiguang Wang, Dinghai Wu
Summary: This paper proposes a method for bearing fault diagnosis using the MobileNetV2 network and fast spectral kurtosis. By compressing the feature information layer by layer and adding a cross-local connection structure, the method achieves high-precision and rapid identification and classification. The fast spectral kurtosis algorithm is used to process the signal and improve the efficiency of fault feature extraction. Experimental results show the advantages of the proposed method in terms of accuracy, model size, and training speed, and demonstrate its effectiveness and generality in the field of fault diagnosis.
Article
Engineering, Mechanical
Bingchang Hou, Xiao Feng, Jin-Zhen Kong, Zhike Peng, Kwok-Leung Tsui, Dong Wang
Summary: This paper proposes a new method called optimized weights spectrum autocorrelation (OWSAC) to identify fault characteristic frequencies (FCFs) and their harmonics for rotating machine fault diagnosis. The OWSAC method does not require any fault signature extraction methods for signal preprocessing, and effectively eliminates the influence of interference spectral lines and noise spectral lines by introducing optimized weights spectrum and adaptive threshold method.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Chemistry, Multidisciplinary
Daga Alessandro Paolo, Garibaldi Luigi, Fasana Alessandro, Marchesiello Stefano
Summary: The study focuses on the envelope demodulation method for vibration signal analysis to diagnose potential faults in rolling element bearings. By selecting appropriate demodulation bands and utilizing different utility functions and heuristics, the performance of demodulation can be optimized. Posterior band indicators based on SES defect spectral lines are proposed to evaluate the overall envelope demodulation performance, with Case Western Reserve University bearing dataset used as a test case.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Mechanical
Xinglong Wang, Jinde Zheng, Qing Ni, Haiyang Pan, Jun Zhang
Summary: This paper proposes a novel method for selecting the optimal demodulation frequency band (ODFB) of rolling bearing vibration signals, called traversal index enhanced-gram (TIEgram) method. Through traversal segmentation model and weighted fusion indicator, this method can effectively select the ODFB of rolling bearing vibration signals, leading to a more accurate diagnosis effect.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Engineering, Mechanical
Qing Ni, J. C. Ji, Ke Feng, Benjamin Halkon
Summary: The novel and robust IFB selection method based on the fault energy of correntropy (FECgram) is proposed to address the challenges of demodulation analysis in bearing fault diagnosis, improving the effectiveness of fault feature extraction by overcoming interferences. The superiority of FECgram in combination with squared envelope spectrum is validated on both simulation data and different challenging experimental datasets, showing its potential in practical applications for bearing fault diagnosis.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Engineering, Electrical & Electronic
Bingchang Hou, Yikai Chen, Hong Wang, Zhike Peng, Kwok-Leung Tsui, Dong Wang
Summary: Bearing fault diagnosis is of great significance. This article proposes a data-driven method for selecting informative frequency bands, which is more robust and effective compared to traditional approaches.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2022)
Article
Engineering, Multidisciplinary
Baokun Han, Zujie Yang, Zongzhen Zhang, Huaiqian Bao, Jinrui Wang, Zongling Liu, Shunming Li
Summary: This research proposes a bearing fault diagnosis method based on GNSS, which eliminates the interference of large-amplitude shocks and improves the accuracy of fault extraction by using nonlinear spectral sparsity.
Article
Engineering, Mechanical
Bingyan Chen, Weihua Zhang, James Xi Gu, Dongli Song, Yao Cheng, Zewen Zhou, Fengshou Gu, Andrew Ball
Summary: In this paper, new detection methods of cyclostationarity are developed for rolling bearing fault diagnosis by constructing generalized envelope signals and using product envelope spectrum (PES), which improve the accuracy and robustness of fault diagnosis.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Multidisciplinary
Lingli Cui, Xinyuan Zhao, Dongdong Liu, Huaqing Wang
Summary: This paper proposes a novel bidirectional weighted enhanced envelope spectrum (BWEES) analysis method that can better distinguish fault features and interference components by simultaneously considering spectral and cyclic frequency information.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Chunlei Wang, Ang Gao, Jianping Xuan
Summary: A novel demodulation band extraction method based on weighted geometric cyclic relative entropy (WGCRE) is proposed to identify compound faults in intense noise environments. The method sets local and global thresholds to exclude exogenous noise outliers and avoids harmonic interference to improve the identification of composite faults. Simulation and experimental results verify the effectiveness of this method.
Article
Engineering, Electrical & Electronic
Wenyi Wu, Cai Yi, Jie Bai, Yan Huang, Jianhui Lin
Summary: This paper proposes a parameterless adaptive spectrum analysis technology called EHNR-SSR, which combines an adaptive frequency band segmentation method based on scale-space representation (SSR) with a more robust indicator, envelope harmonic-to-noise ratio (EHNR). The EHNR-SSR method is capable of compound fault detection and has been verified through simulated signals and experimental tests.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
Moise Avoci Ugwiri, Marco Carratu, Vincenzo Paciello, Consolatina Liguori
Summary: This paper presents a methodology based on vibrations for fault detection in rotating machines, focusing on the detection of repetitive transients using time-frequency techniques. By measuring the negentropy of squared envelope and squared envelope spectrum, along with utilizing spectral correlation and kurtosis, the paper aims to capture the signature of repetitive behavior in mechanical signals. Results demonstrate that negentropy combined with spectral correlation significantly extends the applicability of kurtogram in fault detection and localization.
Article
Chemistry, Multidisciplinary
Xiumei Li, Huimin Zhao
Summary: This paper proposes a performance degradation prediction method based on optimized kernel extreme learning machine (KELM), improved particle swarm optimization (PSO), and Ensemble Empirical Mode Decomposition (EEMD). Experimental results confirm the effectiveness of the proposed method.
APPLIED SCIENCES-BASEL
(2022)
