Article
Engineering, Mechanical
Jianhua Yang, Chen Yang, Xuzhu Zhuang, Houguang Liu, Zhile Wang
Summary: The bearing fault diagnosis is an important problem due to the non-stationary nature and background noise interference of bearing vibration signals. This study proposes a method for extracting unknown fault characteristics from non-stationary vibration signals using stochastic resonance technology. The method successfully determines the bearing fault pattern through order tracking and coherence resonance theory.
NONLINEAR DYNAMICS
(2022)
Article
Acoustics
Baojia Chen, Zhichao Hai, Xueliang Chen, Fafa Chen, Wenrong Xiao, Nengqi Xiao, Wenlong Fu, Qiang Liu, Zhuxin Tian, Gongfa Li
Summary: This paper proposes a time-varying instantaneous frequency fault feature extraction method for rolling bearings under variable speed, which combines the improved multisynchrosqueezing transform, empirical Fourier decomposition, and generalized demodulation. The method can accurately extract fault features of rolling bearings and identify fault types.
JOURNAL OF SOUND AND VIBRATION
(2023)
Article
Automation & Control Systems
Yifan Li, Yaocheng Yang, Yuejian Chen, Zaigang Chen
Summary: This paper introduces an improved Cost Function Ridge Detection (ICFRD) method and an Iterative Characteristic Ridge Extraction (ICRE) strategy to estimate bearing fault characteristic orders by calculating the average frequency ratios between characteristic ridges, achieving the detection of bearing faults. The performance of the proposed method outperforms the conventional CFRD in detecting bearing faults under variable speed conditions, with much smaller average relative errors between extracted instantaneous frequencies and theoretical ones.
Article
Engineering, Multidisciplinary
Shiqian Chen, Bo Xie, Yi Wang, Kaiyun Wang, Wanming Zhai
Summary: Fault diagnosis of rolling bearings under variable speed conditions is challenging. Most current research uses adaptive filtering or signal decomposition methods for feature extraction, but these methods cannot remove in-band noise. To address this, a novel method called non-stationary harmonic summation is proposed, based on the fact that the repetitive impulses caused by bearing faults consist of equally-spaced harmonics. The method efficiently extracts non-stationary harmonics and reconstructs the repetitive impulses to remove noise and interference caused by amplitude modulation.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Multidisciplinary
Bingyan Chen, Dongli Song, Weihua Zhang, Yao Cheng, Zhiwei Wang
Summary: This study proposed an adaptive time-varying morphological filtering method and applied diagonal slice spectrum to enhance weak bearing fault diagnosis. Experimental results showed that this method effectively extracts fault features and has high computational efficiency compared to existing methods.
Article
Acoustics
Tingting Wu, Yufen Zhuang, Bi Fan, Hainan Guo, Wei Fan, Cai Yi, Kangkang Xu
Summary: The study proposes a method for multi-speed bearing fault diagnosis using multidomain feature fusion and broad learning system. Extracting time-domain and frequency-domain features from vibration signals at different speeds, and utilizing fused features for classification, significantly improves the diagnosis performance.
SHOCK AND VIBRATION
(2021)
Article
Engineering, Electrical & Electronic
Gang Tang, Yatao Wang, Yujing Huang, Han Wang
Summary: The paper presents a novel method for compound bearing fault detection without the need for a tachometer and resampling, utilizing time-frequency analysis to extract multiple time-frequency curves for classification and determining fault types. Case studies demonstrated the effectiveness and superiority of the proposed method.
IEEE SENSORS JOURNAL
(2021)
Article
Computer Science, Artificial Intelligence
Dongfang Zhao, Shulin Liu, Hongyi Du, Lu Wang, Zhonghua Miao
Summary: In this paper, a single vibration signal-driven variable speed intelligent fault diagnosis scheme is proposed, which uses extreme multi-scale entropy as the alternative characterization parameter for speed information and develops a deep branch attention network to integrate vibration and speed information more flexibly. Experimental results show that the proposed method can effectively integrate vibration and speed information and achieve outstanding results even without a speed signal.
ADVANCED ENGINEERING INFORMATICS
(2023)
Article
Engineering, Mechanical
Zengqiang Ma, Feiyu Lu, Suyan Liu, Xin Li
Summary: The novel method proposed in this study, which utilizes PSO and ACMD to decompose vibration signals, achieves fault diagnosis for rolling bearings under variable speed conditions, demonstrating high application value.
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
(2021)
Article
Engineering, Electrical & Electronic
Yi Qin, Rui Yang, Haiyang Shi, Biao He, Yongfang Mao
Summary: This article proposes an adaptive fast chirplet transform (AFCT) method for the fault diagnosis of bearings with time-varying speed. The method optimizes the search band of frequency modulation (FM) parameters using the modulation operator of synchrosqueezed transform, overcoming the low computational efficiency problem of chirplet transform (CT) and its variants. Simulation results show that the proposed method has good time-frequency concentration performance and low computational complexity. It has been successfully applied to estimate rotation frequencies from fault vibration signals and outperforms existing classical TFA methods in ridge extraction accuracy and computational efficiency.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Aerospace
Xingxing Jiang, Qiang Huang, Changqing Shen, Qian Wang, Kun Xu, Jie Liu, Juanjuan Shi, Zhongkui Zhu
Summary: This study proposes a synchronous chirp mode extraction (SCME) - based method for fault diagnosis of rolling element bearings (REBs) under varying speed conditions. The method estimates the shaft rotational frequency (SRF) from the low-frequency band of the vibration signal and extracts synchronous modes from the envelope waveform of the signal using a cycle-one-step estimation frame. The health conditions of the REBs are evaluated by detecting the exhibited features in the synchronous mode spectrum (SMS). Simulations and experiments demonstrate the effectiveness of the proposed method for fault diagnosis of REBs.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Article
Engineering, Multidisciplinary
Weipeng Ma, Yaoxiang Yu, Liang Guo, Mengui Qian, Hongli Gao
Summary: This paper proposes a new health indicator (RFIS-HI) for condition monitoring of rolling bearings. It utilizes frequency division and order tracking to obtain sub-signals and resampled signals, measures the impact and periodicity of the signal in the time and frequency domains, and quantifies the degradation degree of the bearing through weighting and optimization. Experimental results show that the proposed health indicator achieves better trendability, scale similarity, and stability.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Zuhua Jiang, Kun Zhang, Xiangfeng Zhang, Yonggang Xu
Summary: Rolling bearings often work under nonstationary conditions, which will lead to spectrum smearing phenomenon and the failure of traditional diagnostic methods. Order tracking is a common technique to alleviate this problem by resampling the time-domain signal using the instantaneous rotation frequency. In this article, a tacholess order tracking method based on spectral amplitude modulation (SAM) is proposed, demonstrating effective extraction of instantaneous rotation frequency and detection of nonstationary bearing fault characteristics.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
Article
Engineering, Multidisciplinary
Haoyang Qu, Jianhua Yang, Mengen Shen, Huatao Chen, Dengji Zhou
Summary: This paper focuses on the fault diagnosis of rolling bearings under time-varying speed conditions. By converting vibration signals into image signals and using image recognition networks for fault diagnosis, this method achieves high accuracy even in the case of weak fault characteristics, without the need for professional technicians to supervise.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Zhen Shan, Zhongqiu Wang, Jianhua Yang, Qiang Ma, Tao Gong
Summary: In this article, a method for diagnosing rolling bearing faults under varying speed is proposed, which can effectively extract weak fault features and diagnose them. The proposed method decomposes the signal into modal components using a novel time-frequency mode decomposition (TFMD) method and enhances the features of each modal component through feature fusion. Simulation and experimental results demonstrate the high accuracy and robustness of the TFMD in bearing fault diagnosis.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
