Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning

Yan, Hehua; Wan, Jiafu; Zhang, Chunhua; Tang, Shenglong; Hua, Qingsong; Wang, Zhongren

doi:10.1109/access.2018.2809681

Cited by 96 publications

(51 citation statements)

References 29 publications

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“…DL has emerged as a significant advancement in data-driven analysis and optimization for industrial applications, especially for those focusing on the accurate prediction of RUL. The authors of Reference [73] presented a device electrocardiogram (DECG) framework, employing a deep denoising autoencoder (DDA) and regression operation to enhance the accuracy of RUL prediction of industrial machines. DECG avoids sensor installation and acquires temporal data for each operation at the device or operator level, from the programmable logic controller (PLC).…”

Section: Deep Learning-based Solutionsmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

191

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Deep Learning-based Solutionsmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

191

View full text Add to dashboard Cite

show abstract

“…AEs are typically used in combination with other regression techniques for the purpose of fault prognosis. The literature contains examples of AE-based techniques applied to RUL estimation of bearings (Ren et al, 2018;Xia et al, 2019), machining centers (Yan et al, 2018), aircraft engines (Ma et al, 2018) and lithium-ion batteries (Ren et al, 2018b). The role of AEs in all the above references is to perform automatic feature extraction to facilitate the work of regression or classification methods used for health state assessment or RUL estimation.…”

Section: Prognosis 3231 Autoencodermentioning

confidence: 99%

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

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“…Other solutions that have been proposed to solve the challenges that emanates from big data include the use of game theory [222][223][224], and machine learning algorithms, such as deep learning [225][226][227][228], selective encryption [229][230][231], and defense-in-depth (DiD) [232,233].…”

Section: Cloud Computing Frameworkmentioning

confidence: 99%

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

et al. 2018

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Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid's security, reliability and resiliency.

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Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning

Cited by 96 publications

References 29 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

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