Research on feature extraction and separation of mechanical multiple faults based on adaptive variational mode decomposition and comprehensive impact coefficient

Zhang, Wei; Li, Junxia; Li, Tengyu; Ge, Shuangchao; Wu, Lei

doi:10.1088/1361-6501/aca117

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…To solve this problem, an idler test rig for fault diagnosis was developed. The test rig consisted of a belt conveyor, vibration sensor, data acquisition instrument, power supply, computer, and fault idlers [30], as shown in Figure 5. Four common fault types of the idler were simulated: the inner and outer race faults were formed by machining 2 mm cracks on the inner and outer races of the bearing with electro-discharge machining; the idler rotated intermittently by invading the bearing with foreign matter (gravel and dust); and the idler could not rotate because it was improperly installed.…”

Section: Experimental Casesmentioning

confidence: 99%

Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis

Zhang

Huang

et al. 2023

Machines

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Extracting fault features in mechanical fault diagnosis is challenging and leads to low diagnosis accuracy. A novel fault diagnosis method using multi-scale convolutional neural networks (MSCNN) and extreme learning machines is presented in this research, which was conducted in three stages: First, the collected vibration signals were transformed into images using the continuous wavelet transform. Subsequently, an MSCNN was designed to extract all detailed features of the original images. The final feature maps were obtained by fusing multiple feature layers. The parameters in the network were randomly generated and remained unchanged, which could effectively accelerate the calculation. Finally, an extreme learning machine was used to classify faults based on the fused feature maps, and the potential relationship between the fault and labels was established. The effectiveness of the proposed method was confirmed. This method performs better in mechanical fault diagnosis and classification than existing methods.

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Section: Experimental Casesmentioning

confidence: 99%

Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis

Zhang

Huang

et al. 2023

Machines

Self Cite

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“…Time-frequency analysis methods that combine time and frequency domains are usually short-time Fourier analysis, wavelet analysis [25,26], empirical mode decomposition combined with order statistics filter (OSF), and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) [27][28][29], variational modal decomposition combined with cyclic spectrum slice energy (CSSE) or comprehensive impact coefficient (CIC) based fitness function of the sparrow search algorithm (the fitness function of the sparrow search algorithm) [30,31] and other methods. Therefore, the advantage of the time-frequency analysis method in bearing fault diagnosis is reflected in its ability to provide information in both the time and frequency domains, enabling a more comprehensive understanding of the signal changes, which is particularly important for capturing and analyzing non-stationary signals (e.g., transient vibrations caused by bearing faults).…”

Section: Introductionmentioning

confidence: 99%

ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

Qin,

Fu,

et al. 2024

Processes

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Aiming at the problem of difficult fault diagnosis work caused by the difficulty of data acquisition of the bearing in the traction part of a coal mining machine, a method of ADAMS simulation and HHT feature extraction of the bearing fault of a coal mining machine is proposed. First of all, take the traction section bearing as the research object, use the virtual prototype in the establishment of the healthy state of coal mining machine traction section model based on the establishment of the bearing inner ring fault, rolling body fault, outer ring fault of the coal mining machine traction section dynamics model, and then after the EMD decomposition, each IMF component of the Hilbert transform, to obtain the signal in the time-frequency plane of the time-frequency joint characteristics, to get the HHT marginal spectra and to different Under different working conditions, the bearing vibration signal features are mined by quantitative feature extraction. Finally, a variety of mainstream machine learning algorithms are introduced to classify the features, and the results show that the feature extraction method in this paper is universal and provides valuable theoretical support and technical guidance for the field application of coal mining machine-bearing fault diagnosis.

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“…At present, some optimization algorithms are mainly used in literatures to obtain the modes number. These advanced intelligent optimization algorithms, such as Archimedes optimization algorithm (AOA) [18], sparrow search algorithm (SSA) [19], particle swarm optimization (PSO) [20], grasshopper algorithm (GA) [21], and differential search (DS) [22] can search for an appropriate modes number, but these methods require iterative experiments, resulting in low computational efficiency and too much time consumption in engineering diagnosis. Moreover, these methods lack robustness and may lead to excessive decomposition in cases of multi-source aliasing and severe noise pollution.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing weak fault detection using transient structure‐optimal VMD and adaptive group sparse coding

Yuan,

Liu,

Zhang

2023

IET Science Measure & Tech

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Rolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background noise, discrete harmonic frequency interference and non‐stationary service conditions. This investigation proposes a hybrid fault diagnosis approach utilizing transient structure‐optimal variational mode decomposition (TS‐OVMD) and adaptive group sparse coding (AGSC) for addressing the formidable problem. According to the singular value structure between transient signal and the interference signal, this work investigates the singular value shrinkage (SVS) technique to adaptively obtain the independent components number. Then, we present a transient structure measure (TSM) to adaptively optimize the balance factor. This measure index systematically quantifies the typical characteristics of the bearing fault signal, which can maximize the fault information representation and effectively reduces the useful information loss caused by improper selection of VMD parameters. Finally, a sparse coding model called AGSC is furthermore designed to enhance the fault impulses readability and suppress residual noise based on the sparsity within group property and the TSM. The proposed approach is verified using experimental data and is found to be superiority comparison with the state‐of‐the‐art method.

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Research on feature extraction and separation of mechanical multiple faults based on adaptive variational mode decomposition and comprehensive impact coefficient

Cited by 4 publications

References 38 publications

Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis

Application of Multi-Scale Convolutional Neural Networks and Extreme Learning Machines in Mechanical Fault Diagnosis

ADAMS Simulation and HHT Feature Extraction Method for Bearing Faults of Coal Shearer

Rolling bearing weak fault detection using transient structure‐optimal VMD and adaptive group sparse coding

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