Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals

He, Xiuzhi; Zhou, Xiaoqin; Yu, Wennian; Hou, Yixuan J.; Mechefske, Chris K.

doi:10.1016/j.isatra.2020.10.060

Cited by 86 publications

(40 citation statements)

References 31 publications

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“…VMD has been widely studied and applied in the denoising of vibration signals and the fault feature extraction of rotating machinery [24][25][26]. In this paper, VMD is introduced into the research of the NPW signal denoising and inflection point extraction, and the effectiveness of the proposed method is verified by simulation and actual experiments.…”

Section: Introductionmentioning

confidence: 99%

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

2021

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Leakage problems are common in the water supply pipeline system, which will threaten the health of residents and cause economic losses. Negative pressure wave (NPW) technology calculates the time difference through the inflection point to locate the leak. However, due to the nonlinear and nonstationary characteristics of the pressure signal, it is difficult to obtain an accurate inflection point of the NPW by the traditional method. Therefore, the advantages of applying variational mode decomposition (VMD) in NPW technology are explored. Firstly, the correlation coefficient and permutation entropy (PE) are used for effective intrinsic mode function (IMF) component selection and parameter optimization. Thus, an adaptive denoising method based on VMD (AD-VMD) is presented. Then, to effectively separate the detail features of the NPW, a novel inflection point extraction method based on VMD (IPE-VMD) is proposed. Simulation and experimental results show that AD-VMD can effectively suppress noise interference and conserve the mutation characteristic of the leakage. IPE-VMD can obtain a distinct maximum peak at the inflection point and has good robustness to noise interference. This method can calculate the time difference precisely and stably. In addition, the accuracy of the leak location is verified. The average relative positioning error is 5.13%.

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Section: Introductionmentioning

confidence: 99%

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

2021

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“…However, fault detection is difficult because of the diversity and complexity of the industrial process (Liu et al , 2016; Chen et al , 2020; Liu and Ye, 2015). To date, fault detection has been used in a variety of industrial processes, such as the chemical field (Yu et al , 2021), machinery field (He et al , 2021) and biological field (Suryawan et al , 2016).…”

Section: Introductionmentioning

confidence: 99%

Integrated fault detection for industrial process monitoring based on multi-dimensional Taylor network

Chen

et al. 2022

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Purpose This paper aims to provide a novel integrated fault detection method for industrial process monitoring. Design/methodology/approach A novel integrated fault detection method based on the combination of Mallat (MA) algorithm, weight-elimination (WE) algorithm, conjugate gradient (CG) algorithm and multi-dimensional Taylor network (MTN) dynamic model, namely, MA-WE-CG-MTN, is proposed in this paper. First, MA algorithm is taken as data pre-processing. Second, in virtue of approximation ability and low computation complexity owing to the simple structure of MTN, MTN dynamic models are constructed for each frequency band. Furthermore, the CG algorithm is used to discipline the model parameters and the outputs of MTN model of each frequency band are gained. Third, the authors introduce the WE algorithm to cut down the number of middle layer nodes of MTN, reducing the complexity of the network. Finally, the outputs of MTN model for each frequency band are superimposed to achieve outputs of MTN model, and fault detection is proceeded by the residual error generator based on the difference between the output of MTN model and the actual output. Findings The novel proposed method is used to perform fault detection for industrial process monitoring effectively, such as the Benchmark Simulation Model 1 wastewater treatment process. Originality/value The novel proposed method has generality and provides considerably improved performance and effectiveness, which is used to perform fault detection for industrial process monitoring. The proposed method has good robustness, low complexity and easy implementation.

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“…Li et al [15] determined the optimal mode number K adaptively by peak search and similarity principle. Guided by a series of indexes such as permutation entropy and kurtosis, Lian et al [16] used iterative search to determine the mode number K. In the application of VMD, the abovementioned two references only considered the influence of mode number K and ignored the influence of penalty factor α on modal bandwidth [17][18][19]. In [20], K and α were determined by the central frequency observation method, which depended on experience and had poor adaptability.…”

Section: Introductionmentioning

confidence: 99%

An Improved Parameter‐Adaptive Variational Mode Decomposition Method and Its Application in Fault Diagnosis of Rolling Bearings

Liu

Liao

2021

Shock and Vibration

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Variational mode decomposition (VMD) has been applied in the field of rolling bearing fault diagnosis because of its good ability of frequency segmentation. Mode number K and quadratic penalty term α have a significant influence on the decomposition result of VMD. At present, the commonly used method is to determine these two parameters adaptively through intelligent optimization algorithm, namely, the parameter-adaptive VMD (PAVMD) method. The key of the PAVMD method is the setting of an objective function, and the traditional PAVMD method is prone to overdecomposition or underdecomposition. To solve these problems, an improved parameter-adaptive VMD (IPAVMD) method is proposed. A new objective function, the maximum average envelope kurtosis (MAEK), is proposed in this paper. The new objective function fully considers the equivalent filtering characteristics of VMD, and squared envelope kurtosis has good antinoise performance. In the optimization method, this paper uses an improved particle swarm optimization (PSO) algorithm. The MAEK and PSO can make sure the IPAVMD method reaches the best complete decomposition of the signal without an underdecomposition or overdecomposition problem. Through the analysis of simulation data and experimental data, the performance of the IPAVMD and the traditional PAVMD is compared. The comparison results show that the proposed IPAVMD has better performance and stronger robustness than the traditional method and is suitable for both single-fault and multiple-fault cases of rolling bearings. The research results have certain theoretical significance and application value for improving the fault diagnosis effect of rolling bearings.

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Adaptive variational mode decomposition and its application to multi-fault detection using mechanical vibration signals

Cited by 86 publications

References 31 publications

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

Application of VMD in Pipeline Leak Detection Based on Negative Pressure Wave

Integrated fault detection for industrial process monitoring based on multi-dimensional Taylor network

An Improved Parameter‐Adaptive Variational Mode Decomposition Method and Its Application in Fault Diagnosis of Rolling Bearings

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