Roller bearing fault diagnosis based on LMD and multi-scale symbolic dynamic information entropy

Han, Minghong; Wu, Yaman; Wang, Yumin; Liu, Wei

doi:10.1007/s12206-021-0417-3

Cited by 30 publications

(9 citation statements)

References 18 publications

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“…(1) Signal processing methods: including empirical wavelet transform (EWT) [13][14][15][16], local mean decomposition (LMD) [17,18], and variational mode decomposition (VMD) [19][20][21], etc. These methods can handle nonlinear and nonsmooth vibration signals and extract richer fault features.…”

Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Zhang,

Xu,

Chen

et al. 2024

Meas. Sci. Technol.

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Bearings are key components in mechanical equipment, which are widely used in various fields such as automobiles and airplanes. Aiming at the analysis of vibration signal processing under the variable speed condition of bearings, this paper proposes a new bearing fault diagnosis method, which firstly resamples the vibration signals in the angular domain, and then converts the resampled signals into images by the relative position matrix method, and finally uses the transfer learning to automatically extract the features and classify them. To verify the effectiveness of the method, it is tested on the Case Western Reserve University bearing fault dataset and University of Ottawa bearing fault dataset respectively. Compared with other time series to image methods (Recurrence Plot, etc) and other pre-trained models (GoogLeNet, etc), the proposed method has some advantages in terms of accuracy, image generation time, training time, and testing time. The accuracy of the proposed method in this paper reaches more than 90%, which suggests its potential effectiveness in the classification of bearing faults under variable speed working conditions.

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Section: Introductionmentioning

confidence: 99%

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Zhang,

Xu,

Chen

et al. 2024

Meas. Sci. Technol.

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show abstract

“…Once a rolling bearing fails, the corresponding mechanical equipment will also produce a series of adverse efects. Terefore, it is of great signifcance to diagnose faults in rolling bearings [3]. Te fault signals of rolling bearing usually exhibit nonlinear and nonstationary characteristics and are also easily afected by the environment, resulting in a large amount of noise mixed in the fault signals.…”

Section: Introductionmentioning

confidence: 99%

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

Ji,

Huang,

2024

Shock and Vibration

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The rolling bearing is one of the commonly used mechanical components in rotating machinery, and its health directly affects the normal operation of equipment. However, the fault signal of rolling bearing is susceptible to noise interference, which makes it difficult to extract the fault characteristics of the rolling bearing and thus affects the accuracy of the diagnosis results. To address this problem, this paper proposes a method by using a snake optimization algorithm to optimize variational mode decomposition (SOA-VMD) and applies it to the extraction of the fault feature of rolling bearing. First, the minimum Shannon entropy to kurtosis ratio (EKR) is used as the fitness function of SOA to search for the best parameter combination of VMD. Second, the optimized VMD is used to decompose the vibration signal of rolling bearing to obtain K intrinsic mode functions (IMFs). Then, the IMF with the most fault information is selected for reconstruction through EKR. The Teager–Kaiser energy operator (TKEO) spectrum analysis is performed on the reconstructed signal. Finally, this method is used to analyze the simulation signal and rolling bearing vibration signal and compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithms to verify the feasibility and effectiveness of the SOA-VMD method.

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“…It discretizes the continuous and complex motion states and maps them to several intervals marked with simple symbols, and uses symbols to describe dynamic character It is beneficial to discover the implicit mode of the system and improve the calculation speed. As an effective nonlinear signal processing method, it has been successfully applied in bio-medicine (Yao et al 2019;Gu et al 2021), mechanical engineering (Zhang et al 2019Dou 2019;Yang and Jia 2020;Han et al 2021;Wang and Li 2022) and other fields. The vibration signal of the quay crane gearbox contains a lot of impact and strong noise, and the symbolic dynamics method can be introduced in a targeted manner, thereby effectively reducing the noise and improving analysis effect and calculation efficiency.…”

Section: Introductionmentioning

confidence: 99%

Improved KW entropy: a complexity measurement technique for time-series and its application in feature extraction of quay crane gearbox

Wang

2023

Preprint

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The working environment of the quay crane is harsh and special. As an important transmission equipment of hoisting mechanism, health condition of gearbox is very important for reliable operation. In order to extract degradation features from complex vibration monitoring signals, an improved KW symbol entropy feature extraction technique is proposed. Considering the unity of symbol standard, the method takes the root mean square of the normal condition signal as the symbol standard and combines the symbol coefficient to construct a unified symbol scale. At the same time, symbols number variable is introduced to expand symbols set and improve the information expressing ability. On this basis, combining with information entropy theory, complexity of symbol sequence in symbol sequence and symbol distribution is calculated respectively, and two features named improved symbol sequence entropy (IKSE) and improved symbol distribution entropy (IKDE) are obtained. The Logistic chaotic sequence and the lifetime signal of the hoisting gearbox are used for analysis respectively. The result show that the proposed features are able to characterize the complexity of nonlinear time series, so as to describe the whole process of the performance degradation of the hoisting gearbox sensitively. The parameters have little influence and the technique have a good stability.

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Roller bearing fault diagnosis based on LMD and multi-scale symbolic dynamic information entropy

Cited by 30 publications

References 18 publications

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Bearing fault diagnosis method based on angular domain resampling, relative position matrix and transfer learning

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

Improved KW entropy: a complexity measurement technique for time-series and its application in feature extraction of quay crane gearbox

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