Fault Diagnosis of Rotating Machinery Based on Two-Stage Compressed Sensing

You, Xianglong; Li, Jiacheng; Deng, Zhongwei; Zhang, Kai; Yuan, Hang

doi:10.3390/machines11020242

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…Therefore, the methods that depend only on the prior knowledge of a specific field have a reduced generalization ability. In recent years, an enormous number of data-driven deep learning approaches have been developed for fault diagnosis [10,11]. Unlike existing model-driven methods, these approaches employ an end-to-end black-box manner and leverage the data mining capabilities of deep learning to solve fault diagnosis tasks.…”

Section: Introductionmentioning

confidence: 99%

A Sparse Learning Method with Regularization Parameter as a Self-Adaptation Strategy for Rolling Bearing Fault Diagnosis

Niu,

Deng,

Zhang

et al. 2023

Electronics

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Sparsity-based fault diagnosis methods have achieved great success. However, fault classification is still challenging because of neglected potential knowledge. This paper proposes a combined sparse representation deep learning (SR-DEEP) method for rolling bearing fault diagnosis. Firstly, the SR-DEEP method utilizes prior domain knowledge to establish a sparsity-based fault model. Then, based on this model, the corresponding regularization parameter regression networks are trained for different running states, whose core is to explore the latent relationship between the regularization parameters and running states. Subsequently, the performance of the fault classification is improved by embedding the trained regularization parameter regression networks into the sparse representation classification method. This strategy improves the adaptability of the sparse regularization parameter, further improving the performance of the fault classification method. Finally, the applicability of the SR-DEEP method for rolling bearing fault diagnosis is validated with the CWRU platform and QPZZ-II platform, demonstrating that SR-DEEP yields superior accuracies of 100% and 99.20% for diagnosing four and five running states, respectively. Comparative studies show that the SR-DEEP method outperforms four sparse representation methods and seven classical deep learning classification methods in terms of the classification performance.

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Section: Introductionmentioning

confidence: 99%

A Sparse Learning Method with Regularization Parameter as a Self-Adaptation Strategy for Rolling Bearing Fault Diagnosis

Niu,

Deng,

Zhang

et al. 2023

Electronics

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“…Intelligent diagnostics based on the analysis of triaxial vibration data was proposed in [3]. The authors proposed a technique that implements the diagnostics of malfunctions of rotating mechanisms based on the reconstruction of compressed vibration analysis data.…”

Section: Introductionmentioning

confidence: 99%

Experimental studies of noise and vibration during milling of cast iron parts

et al. 2023

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Machining processes of cast iron parts are accompanied by significant noise and vibration. Exceeding the standard values of vibration and noise adversely affects the health of the operator and reduces the quality of the final product. The article deals with the scientific problem associated with the study of the causes of increased vibrations and noise during the milling of cast iron products. It has been experimentally shown that the existing ones create increased sound pressure in the range from 250 to 8000 Hz, while the sound pressure level reaches 80-92 dBa, which is 15-20 dBa higher than the maximum allowable. In the higher frequency part of the spectrum, the decrease in the intensity of sound radiation is from 4 to 7 dB per octave. The data obtained are the initial information for the calculation of noise reduction systems, based on compliance with sanitary standards. To simplify engineering calculations, vibrational energy loss coefficients for cast iron solid and hollow products are given.

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