An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network

Peng, Binsen; Xia, Hong; Lv, Xinzhi; Annor-Nyarko, M.; Zhu, Shiping; Liu, Yong-kuo; Zhang, Jiyu

doi:10.1007/s10489-021-02555-4

Cited by 50 publications

(27 citation statements)

References 28 publications

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“…The ResNet is good at two-dimensional image recognition tasks. 29,42–44 To use our proposed WGAN-ResNet analysis with the THz spectrum, we firstly translated a one-dimensional absorption coefficient to a two-dimensional image as follows, where x is the sample absorption coefficient, which is a n -dimensional column vector, and x T is a transpose of x . Thus, A is a n × n size image.…”

Section: Experimental and Theoretical Methodsmentioning

confidence: 99%

“…The residual neural network (ResNet), proposed by He, 25 is a deep learning network, which solves the degradation issues of traditional deep convolutional networks. It has been applied in the real-time quality assessment of pediatric MRI images, 26 the clinical diagnosis of COVID-19 patients, 27 the identification of cashmere and sheep wool fibers, 28 rotating machinery fault diagnosis 29 and so on. However, there are few reports about the application of deep learning in THz spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

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Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Yang

Qin

et al. 2022

RSC Adv.

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Section: Experimental and Theoretical Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Yang

Qin

et al. 2022

RSC Adv.

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“…Thirdly, Bayesian optimization can be leveraged for achieving automatic learning of main significant hyperparameters for constructing an adaptive CNN. In [16], a new FD technique for rotating machinery on the basis of deep residual neural network (DRNN) and data fusion was suggested. Initially, the frequency domain and time domain features of the original signal were derived via the Short-time Fourier transform (STFT) layer, after which the DRNN and the fusion embedding layers were utilized for fusing the frequency domain, spatial domain, and time domain features for obtaining high quality low-dimension fusion features.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Symbiotic Organism Search with Deep Learning for Industrial Fault燚iagnosis

Alnfiai¹

2023

Computers, Materials &Amp; Continua

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Developments in data storage and sensor technologies have allowed the cumulation of a large volume of data from industrial systems. Both structural and non-structural data of industrial systems are collected, which covers data formats of time-series, text, images, sound, etc. Several researchers discussed above were mostly qualitative, and ceratin techniques need expert guidance to conclude on the condition of gearboxes. But, in this study, an improved symbiotic organism search with deep learning enabled fault diagnosis (ISOSDL-FD) model for gearbox fault detection in industrial systems. The proposed ISOSDL-FD technique majorly concentrates on the identification and classification of faults in the gearbox data. In addition, a Fast kurtogram based time-frequency analysis can be used for revealing the energy present in the machinery signals in the time-frequency representation. Moreover, the deep bidirectional recurrent neural network (DBiRNN) is applied for fault detection and classification. At last, the ISOS approach was derived for optimal hyperparameter tuning of the DL method so that the classification performance will be improvised. To illustrate the improvised performance of the ISOSDL-FD algorithm, a comprehensive experimental analysis can be performed. The experimental results stated the betterment of the ISOSDL-FD algorithm over current techniques.

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“…However, machine failures reveal a reaction chain between cause and defect. The rotating machinery works involve rotational motion such as gears, rotors and shafts, rolling element bearings, flexible couplings, and electrical machines (Gu et al 2018;Guan 2017;Guo et al 2021;Gupta and Pradhan 2017;Peng et al 2022;Wang et al 2019b;Zhang et al 2021a;Zhu et al 2021). Due to the complex structure and interaction of multiple components in rotating machinery, there are additional faults that will need to be diagnosed in the future in order to improve results: (i) Coupling faults.…”

Section: Future Research Directionsmentioning

confidence: 99%

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

et al. 2022

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Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method.

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An intelligent fault diagnosis method for rotating machinery based on data fusion and deep residual neural network

Cited by 50 publications

References 28 publications

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Improved Symbiotic Organism Search with Deep Learning for Industrial Fault燚iagnosis

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

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