Rolling Bearing Incipient Degradation Monitoring and Performance Assessment Based on Signal Component Tracking

Dong, Shuzhi; Wen, Guangrui; Zhang, Zhifen; Yuan, Yujiao; Luo, Jianqing

doi:10.1109/access.2019.2907050

Cited by 11 publications

(4 citation statements)

References 30 publications

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“…However, the process might not work well for transient or stationary signals whose frequency components vary in time, usually the case in the real world. For non-stationary signals, it is common to transform the raw signals into the time-frequency domain using Short Time Fourier Transform (STFT) [92]- [94], Wavelet Transform (WT) [95]- [98] or Empirical Mode Decomposition (EMD) [99], [100]. STFT adopts a window function of fixed length, thus suffering from the time-frequency resolution trade-off problem.…”

Section: ) Vibration Datamentioning

confidence: 99%

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A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

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Section: ) Vibration Datamentioning

confidence: 99%

“…Note that signals in the time-frequency domain naturally have a two-dimensional form, making them suitable for the input of a traditional CNN. After a proper normalization step, those time-frequency domain signals were treated as images and various CNN-based variants were built by [92], [94]- [98], [101], [106], [107].…”

Section: ) Vibration Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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“…This interference often leads to a decrease in the diagnostic accuracy of intelligent diagnostic models. Dong et al [64] proposed a method for bearing performance evaluation based on signal component tracking to detect bearing degradation. This approach incorporates abnormal information in degradation monitoring to facilitate initial bearing fault diagnosis, demonstrating high efficiency in early fault detection and diagnosis.…”

Section: Analysis Of Co-cited Literaturementioning

confidence: 99%

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Chen,

Zhao,

Zhang

et al. 2024

Eng. Res. Express

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As a key component of mechanical equipment, real-time monitoring and diagnosis of rolling bearings play a critical role in ensuring the stable operation of equipment and the safety of operators. In order to present the current status and trends of fault diagnosis research on rolling bearings more intuitively, the scientific knowledge mapping was used to visualize and analyze the relevant literature in the article. The results show that the number of publications in this area of research has grown significantly in recent years, with China, India, the United States, and England having contributed significantly. The journals such as MECHANICAL SYSTEMS AND SIGNAL PROCESSING, MEASUREMENT, and JOURNAL OF SOUND AND VIBRATION have played an important role in disseminating cutting-edge technologies in this field. In addition, the exploration of modern methods based on data-driven and artificial intelligence, as well as their application to real-world problems, are gradually becoming the focus of research. Through summarising and analysing, the application of modern data processing techniques, the development of more efficient and practical intelligent fault diagnosis techniques, and the close integration of laboratory research and practical applications will become future research trends.

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“…The collected vibration signals of the bearing are easily disturbed by heavy background noise, which causes the fault characteristic frequency to be buried, and the fault cannot be diagnosed effectively. To solve the difficulty in feature information extraction of rolling bearings with early weak faults, various signal processing methods have been proposed and applied successively, such as the short-time Fourier transform [5], Wigner-Ville distribution [6], and wavelet transform (WT) [7] Huang et al Although the above time-frequency analysis methods are shown to have certain improvements over the traditional signal analysis methods, they lack decomposition adaptivity. In recent years, some scholars have proposed adaptive signal decomposition methods for the fault diagnosis of rolling bearings, such as empirical mode decomposition (EMD) [8] and its variants , local mean decomposition [9], local characteristicscale decomposition [10], singular value decomposition [11], and empirical wavelet transform [12] etc.…”

Section: Introductionmentioning

confidence: 99%

Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background

Luo¹,

Wen²,

Lei³

et al. 2023

Meas. Sci. Technol.

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Owing to the nonlinearity and nonstationarity of the bearing fault signal, it is difficult to identify fault characteristics under the influence of a strong noise environment. The extraction of early weak fault features is critical for the reliability of bearing operations. Therefore, an urgent problem is reasonable noise reduction and feature enhancement in weak-signal processing. Traditional variational modal decomposition (VMD) and stochastic resonance (SR) are commonly applied to detect weak signals in fault diagnosis. The VMD method can decompose the signal into several intrinsic mode functions (IMFs) to effectively reduce the modal aliasing problem. However, uniform standards for the key parameters of decomposition and the selection of the optimal IMF after decomposition are lacking. Meanwhile, some disadvantages of SR still exist; for example, the interference of multiscale noise may lead to false detection by incorrect selection of high-pass filter parameters, and the system parameters are not adaptive to different signals to achieve the best response output. To better address the weak signal feature enhancement, a novel rolling bearing fault diagnosis method combining adaptive VMD and SR by improved differential search (IDS) optimization is proposed. First, the bearing fault signal is decomposed into several IMFs using the IDS-VMD. Second, the feature information of the fault signal is retained and reconstructed using the correlation kurtosis for sensitive modal extraction. Furthermore, the fault features of the reconstructed signal are effectively enhanced by the variable-step IDS-SR, which can reasonably transfer the noise energy of the input components to the fault characteristic frequency. Finally, the periodic pulse can be observed in the corresponding envelope spectrum. The simulated and experimental data show that the proposed method can not only effectively extract the signal feature information in the actual fault but also realize early weak fault diagnosis of rolling bearings more accurately.

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Rolling Bearing Incipient Degradation Monitoring and Performance Assessment Based on Signal Component Tracking

Cited by 11 publications

References 30 publications

A Review on Deep Learning Applications in Prognostics and Health Management

A Review on Deep Learning Applications in Prognostics and Health Management

Analysis of hot spots and trends in rolling bearing fault diagnosis research based on scientific knowledge mapping

Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background

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