Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.

Download Full-text

Fault Diagnosis of Flywheel Bearing Based on Parameter Optimization Variational Mode Decomposition Energy Entropy and Deep Learning

Energy ◽

10.1016/j.energy.2021.122108 ◽

2021 ◽

pp. 122108

Author(s):

Deqiang He ◽

Chenyu Liu ◽

Zhenzhen Jin ◽

Rui Ma ◽

Yanjun Chen ◽

...

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

Parameter Optimization ◽

Variational Mode Decomposition ◽

Energy Entropy ◽

Mode Decomposition

Download Full-text

Fault Diagnosis Technique for Hydroelectric Generators using Variational Mode Decomposition and Power Line Communications

Journal of Physics Conference Series ◽

10.1088/1742-6596/1176/6/062058 ◽

2019 ◽

Vol 1176 ◽

pp. 062058

Author(s):

Dong Liang ◽

HuaShan Guo ◽

ZeWei Guo ◽

Tao Zheng

Keyword(s):

Fault Diagnosis ◽

Power Line ◽

Variational Mode Decomposition ◽

Power Line Communications ◽

Mode Decomposition ◽

Diagnosis Technique

Download Full-text

Open-Circuit Fault Diagnosis of Wind Power Converter Using Variational Mode Decomposition, Trend Feature Analysis and Deep Belief Network

Applied Sciences ◽

10.3390/app10062146 ◽

2020 ◽

Vol 10 (6) ◽

pp. 2146 ◽

Cited By ~ 1

Author(s):

Jingxuan Zhang ◽

Hexu Sun ◽

Zexian Sun ◽

Yan Dong ◽

Weichao Dong

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Power Converter ◽

Open Circuit ◽

Original Signal ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Igbt Modules ◽

Igbt Module ◽

Circuit Fault Diagnosis

The power converter is a significant device in a wind power system. The wind turbine will be shut down and off grid immediately with the occurrence of the insulated gate bipolar transistor (IGBT) module open-circuit fault of the power converter, which will seriously impact the stability of grid and even threaten personal safety. However, in the existing diagnosis strategies for the power converter there are few single and double IGBT module open-circuit fault diagnosis methods producing negative results, including erroneous judgment, omissive judgment and low accuracy. In this paper, a novel method to diagnose the single and double IGBT modules open-circuit faults of the permanent magnet synchronous generator (PMSG) wind turbine grid-side converter (GSC) is proposed: Primarily, by collecting the three-phase current varying with a wind speed of 22 states, including a normal state and 21 failure states of PMSG wind turbine GSC as the original signal data. Afterward, the original signal data are decomposed by using variational mode decomposition (VMD) to obtain the mode coefficient series, which are analyzed by the proposed method base on fault trend feature for extracting the trend feature vectors. Finally, the trend feature vectors are utilized as the input of the deep belief network (DBN) for decision-making and obtaining the classification results. The simulation and experimental results show that the proposed method can diagnose the single and double IGBT modules open-circuit faults of GSC, and the accuracy is higher than the benchmark models.

Download Full-text

Fault Diagnosis of On-Load Tap-Changer Based on Variational Mode Decomposition and Relevance Vector Machine

Energies ◽

10.3390/en10070946 ◽

2017 ◽

Vol 10 (7) ◽

pp. 946 ◽

Cited By ~ 8

Author(s):

Jinxin Liu ◽

Guan Wang ◽

Tong Zhao ◽

Li Zhang

Keyword(s):

Fault Diagnosis ◽

Relevance Vector Machine ◽

Variational Mode Decomposition ◽

Mode Decomposition ◽

Tap Changer ◽

Load Tap Changer

Download Full-text

Research on Multi-Domain Fault Diagnosis of Gearbox of Wind Turbine Based on Adaptive Variational Mode Decomposition and Extreme Learning Machine Algorithms

Energies ◽

10.3390/en13061375 ◽

2020 ◽

Vol 13 (6) ◽

pp. 1375 ◽

Cited By ~ 1

Author(s):

Hui Li ◽

Bangji Fan ◽

Rong Jia ◽

Fang Zhai ◽

Liang Bai ◽

...

Keyword(s):

Fault Diagnosis ◽

Extreme Learning Machine ◽

Grey Wolf Optimizer ◽

Mechanical Equipment ◽

Variational Mode Decomposition ◽

Time Frequency ◽

Mode Decomposition ◽

Fault Features ◽

Frequency Domains ◽

Learning Machine

Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.

Download Full-text