Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Lu, Quanbo; Shen, Xinqi; Wang, Xiujun; Li, Mei; Li, Jia; Zhang, Mengzhou

doi:10.1155/2021/2530315

Cited by 32 publications

(18 citation statements)

References 28 publications

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“…KNN is an instance-based learning algorithm, which searches for the most similar eigenvectors of the instance in the database [ 29 ]. For a training sample

, the KNN algorithm searches for the k nearest instances to x based on a setting distance.…”

Section: Entropy-based Fault Diagnosis Methodsmentioning

confidence: 99%

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Xue

Chen

et al. 2021

Entropy

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The multi-disc wet clutch is widely used in transmission systems as it transfers the torque and power between the gearbox and the driving engine. During service, the buckling of the friction components in the wet clutch is inevitable, which can shorten the lifetime of the wet clutch and decrease the vehicle performance. Therefore, fault diagnosis and online monitoring are required to identify the buckling state of the friction components. However, unlike in other rotating machinery, the time-domain features of the vibration signal lack efficiency in fault diagnosis for the wet clutch. This paper aims to present a new fault diagnosis method based on multi-speed Hilbert spectrum entropy to classify the buckling state of the wet clutch. Firstly, the wet clutch is classified depending on the buckling degree of the disks, and then a bench test is conducted to obtain vibration signals of each class at varying speeds. By comparing the accuracy of different classifiers with and without entropy, Hilbert spectrum entropy shows higher efficiency than time-domain features for the wet clutch diagnosis. Thus, the classification results based on multi-speed entropy achieve even better accuracy.

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“…KNN is an instance-based learning algorithm, which searches for the most similar eigenvectors of the instance in the database [ 29 ]. For a training sample

, the KNN algorithm searches for the k nearest instances to x based on a setting distance.…”

Section: Entropy-based Fault Diagnosis Methodsmentioning

confidence: 99%

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Xue

Chen

et al. 2021

Entropy

View full text Add to dashboard Cite

show abstract

“…It can adaptively decompose a multi-component signal into several sub-signals and is not restricted by the uncertainty principle. VMD has been proven to outperform EMD and many other time-frequency analysis techniques in many applications, such as detection, separation and fault diagnosis [8,[11][12][13][14]. However, both EMD and VMD are designed for real-valued data, which do not satisfy the meet of complex-valued signal processing.…”

Section: Introductionmentioning

confidence: 99%

A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend

Miao

Shu

et al. 2022

Sensors

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Complex variational mode decomposition (CVMD) has been proposed to extend the original variational mode decomposition (VMD) algorithm to analyze complex-valued data. Conventionally, CVMD divides complex-valued data into positive and negative frequency components using bandpass filters, which leads to difficulties in decomposing signals with the low-frequency trend. Moreover, both decomposition number parameters of positive and negative frequency components are required as prior knowledge in CVMD, which is difficult to satisfy in practice. This paper proposes a modified complex variational mode decomposition (MCVMD) method. First, the complex-valued data are upsampled through zero padding in the frequency domain. Second, the negative frequency component of upsampled data are shifted to be positive. Properties of analytical signals are used to get the real-valued data for standard variational mode decomposition and the complex-valued decomposition results after frequency shifting back. Compared with the conventional method, the MCVMD method gives a better decomposition of the low-frequency signal and requires less prior knowledge about the decomposition number. The equivalent filter bank structure is illustrated to analyze the behavior of MCVMD, and the MCVMD bi-directional Hilbert spectrum is provided to give the time–frequency representation. The effectiveness of the proposed algorithm is verified by both synthetic and real-world complex-valued signals.

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“…ese strategies can enhance the algorithm's global and local search capabilities, with the disadvantage that the method requires a lot of memory. erefore, addressing the problems of random selection of initial parameters of wavelet neural networks, low diagnostic accuracy, and poor stability, this paper introduces the improved GWO algorithm into the WNN model and [22,23], vibration signal [24,25], and acoustic signals [26,27].…”

Section: Introductionmentioning

confidence: 99%

Early Detection of Network Fault Using Improved Gray Wolf Optimization and Wavelet Neural Network

Pan

Jin

Yang

et al. 2022

Mathematical Problems in Engineering

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To address the problem of diagnostic accuracy and stability degradation caused by random selection of the initial parameters for the wavelet neural network (WNN) fault diagnosis model, this paper proposes a network troubleshooting model based on the improved gray wolf algorithm (IGWO) and the wavelet neural network. First, the convergence factor and policy for the weight update are redesigned in the IGWO algorithm. This study uses a nonlinear convergence factor to balance the global and local search capabilities of the algorithm and dynamically adjusts the weights according to the adaptability of the head wolf α to strengthen its leadership position. Thereafter, the initial weights and biases of the WNN are optimized using the IGWO algorithm. During the backpropagation of the WNN error, momentum factors are introduced to prevent the model from falling into local optimization. Experimental results show that the IGWO algorithm is far better than GWO in terms of convergence speed and convergence accuracy. Furthermore, the average diagnostic accuracy of the IGWO-WNN model on the KDD-CUP99 dataset reaches 99.22%, which is 1.15% higher than that of the WNN model, and the stability of the diagnostic results is significantly improved.

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Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Cited by 32 publications

References 28 publications

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

Experimental Investigation and Fault Diagnosis for Buckled Wet Clutch Based on Multi-Speed Hilbert Spectrum Entropy

A Modified Complex Variational Mode Decomposition Method for Analyzing Nonstationary Signals with the Low-Frequency Trend

Early Detection of Network Fault Using Improved Gray Wolf Optimization and Wavelet Neural Network

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