Intelligent Fault Diagnosis for Industrial Big Data

Si, Jia; Li, Yibin; Ma, Sile

doi:10.1007/s11265-017-1316-9

Cited by 17 publications

(6 citation statements)

References 25 publications

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“…Therefore, DL-based models can be applied to address machine health monitoring in a very general manner. The data and feature fusion methods provided by machine learning greatly reduce the dependence on experience and have enabled more researchers to enter the field of drive fault diagnosis [30]. Deep learning is a novel machine learning method based on multiple nonlinear transformations that can be used to extract deep features from raw data automatically.…”

Section: Related Workmentioning

confidence: 99%

“…In general, the deep learning model is mainly divided into auto-encoders [31,32], deep belief networks [33,34], deep Boltzmann machines [28], convolutional neural networks [29,35], Deep Neural Networks (DNN) [10], recurrent neural networks [30], and various variants and optimized versions derived therefrom. Deep learning generally refers to a network with a multi-layer structure, more like an artificial neural network.…”

Section: Related Workmentioning

confidence: 99%

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Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Zhang

Deng

et al. 2019

Sensors

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Given its importance, fault diagnosis has attracted considerable attention in the literature, and several machine learning methods have been proposed to discover the characteristics of different aspects in fault diagnosis. In this paper, we propose a Hybrid Deep Belief Network (HDBN) learning model that integrates data in different ways for intelligent fault diagnosis in motor drive systems, such as a vehicle drive system. In particular, we propose three data fusion methods: data union, data join, and data hybrid, based on detailed data fusion research. Additionally, the significance of the fusion is explained from the energy perspective of the signal. In particular, the appropriate fusion methods and data structures suitable for model training requirements can help improve the accuracy of fault diagnosis. Moreover, mixed-precision training is used as a special fusion method to further improve the performance of the model. Experiments with the datasets obtained from the simulation platform demonstrate the superiority of our proposed model over the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Hybrid Data Fusion DBN for Intelligent Fault Diagnosis of Vehicle Reducers

Zhang

Deng

et al. 2019

Sensors

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“…Li et al [7] proposed a new monitoring index based on the analysis of typical variables and a Pearson correlation analysis method for early fault diagnosis and identification and improved the metabolic grey prediction model to predict the remaining life and solve the key safety problems often encountered in the prediction of sex and complex assets. Han and Si et al [8,9] proposed an industrial big data intelligent fault prediction method and implemented and compared a support vector machine (SVM) and neural network. The results show that SVM has good performance in intelligent fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

“…Han and Si et al. [8, 9] proposed an industrial big data intelligent fault prediction method and implemented and compared a support vector machine (SVM) and neural network. The results show that SVM has good performance in intelligent fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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Research on condition monitoring and fault diagnosis of intelligent copper ball production lines based on big data

Zhang

Zhao

Zhao³

2021

IET Collab Intel Manufact

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With the continuous upgrading and transformation of the intelligentisation of China's manufacturing industry, and in response to the requirements for further intelligentisation of the phosphor copper ball production line proposed by a new electronic material company, this study proposes a fault prediction and diagnosis method based on big data. A high-efficiency distributed big data platform is constructed, and a workshop-level monitoring centre with the Windows control centre (WinCC) as the core is formed. The WinCC configuration software is used to monitor the key parameters of the equipment during the operation phase, and the login interface is configured according to the requirements of workshop information integration, for example, display interface, alarm interface, debugging interface, trend graph and other common functions. Cloud platforms and virtual private network (VPN) communication are used to realise remote maintenance. Aiming at the common fault problems in the production process, an expert diagnosis system based on fault tree analysis is constructed by fusing the fault tree theory and expert systems. The fault tree model of the unqualified phosphor copper ball production quality and the failure of the hydraulic system is highlighted. Therefore, ensuring the safety of the phosphor copper ball production line is of great significance to the entire production system.

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