Hierarchical diagnosis network based on sparse deep neural networks and its application in bearing fault diagnosis

Qi, Yumei; You, Wei; Shen, Changqing; Jiang, Xingxing; Huang, Weiguo; Zhu, Zhongkui

doi:10.1109/phm.2017.8079260

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…By using such techniques, the collected detection data could be transformed, and the fault features could be extracted adaptively without relying on a manual design. Qi et al [126] have proposed a hierarchical fault diagnosis network using deep learning, and their results indicated that the expression of diagnosis results in this way is detailed and reliable. Tamilselvan et al [127] have presented a fault diagnosis method based on deep learning.…”

Section: Thing-to-thing: Deep Learningmentioning

confidence: 99%

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Dou

Sun

Wu³

et al. 2021

IJGI

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In the era of big data, mass customization (MC) systems are faced with the complexities associated with information explosion and management control. Thus, it has become necessary to integrate the mass customization system and Social Internet of Things, in order to effectively connecting customers with enterprises. We should not only allow customers to participate in MC production throughout the whole process, but also allow enterprises to control all links throughout the whole information system. To gain a better understanding, this paper first describes the architecture of the proposed system from organizational and technological perspectives. Then, based on the nature of the Social Internet of Things, the main technological application of the mass customization–Social Internet of Things (MC–SIOT) system is introduced in detail. On this basis, the key problems faced by the mass customization–Social Internet of Things system are listed. Our findings are as follows: (1) MC–SIOT can realize convenient information queries and clearly understand the user’s intentions; (2) the system can predict the changing relationships among different technical fields and help enterprise R&D personnel to find technical knowledge; and (3) it can interconnect deep learning technology and digital twin technology to better maintain the operational state of the system. However, there exist some challenges relating to data management, knowledge discovery, and human–computer interaction, such as data quality management, few data samples, a lack of dynamic learning, labor consumption, and task scheduling. Therefore, we put forward possible improvements to be assessed, as well as privacy issues and emotional interactions to be further discussed, in future research. Finally, we illustrate the behavior and evolutionary mechanism of this system, both qualitatively and quantitatively. This provides some idea of how to address the current issues pertaining to mass customization systems.

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Section: Thing-to-thing: Deep Learningmentioning

confidence: 99%

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Dou

Sun

Wu³

et al. 2021

IJGI

View full text Add to dashboard Cite

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Artificial intelligence in prognostics and health management of engineering systems

Ochella

Shafiee

Dinmohammadi

2022

Engineering Applications of Artificial Intelligence

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Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

2020

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A multitude of cyber-physical system (CPS) applications, including design, control, diagnosis, prognostics, and a host of other problems, are predicated on the assumption of model availability. There are mainly two approaches to modeling: Physics/Equation based modeling (Model-Based, MB) and Machine Learning (ML). Recently, there is a growing consensus that ML methodologies relying on data need to be coupled with prior scientific knowledge (or physics, MB) for modeling CPS. We refer to the paradigm that combines MB approaches with ML as hybrid learning methods. Hybrid modeling (HB) methods is a growing field within both the ML and scientific communities, and are recognized as an important emerging but nascent area of research. Recently, several works have attempted to merge MB and ML models for the complete exploitation of their combined potential. However, the research literature is scattered and unorganized. So, we make a meticulous and systematic attempt at organizing and standardizing the methods of combining ML and MB models. In addition to that, we outline five metrics for the comprehensive evaluation of hybrid models. Finally, we conclude by shedding some light on the challenges of hybrid models, which we, as a research community, should focus on for harnessing the full potential of hybrid models. An additional feature of this survey is that the hybrid modeling work has been discussed with a focus on modeling cyber-physical systems. INDEX TERMS Cyber-physical systems, deep learning, deep neural networks, hybrid models, model-based, machine learning, physics guided, physics informed, physics prior, theory guided.

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Hierarchical diagnosis network based on sparse deep neural networks and its application in bearing fault diagnosis

Cited by 6 publications

References 11 publications

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

The Architecture of Mass Customization-Social Internet of Things System: Current Research Profile

Artificial intelligence in prognostics and health management of engineering systems

Driven by Data or Derived Through Physics? A Review of Hybrid Physics Guided Machine Learning Techniques With Cyber-Physical System (CPS) Focus

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