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

Abstract

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.

A smart process controller framework for Industry 4.0 settings

“…Consequently, it does not and should not compete with any ML specific technique. For the various ML techniques used with fault detection and predictive maintenance see Carvalho et al (2019), Lo et al (2019) and Angelopoulos et al (2020). However, the framework advocates and supports the use of recurrent machine learning capabilities suggested here; these capabilities include dynamically changing the ML model itself (not just weights) and incorporating new factors in the model itself.…”

Industry 4.0‐Driven Development of Optimization Algorithms: A Systematic Overview

“…e purpose of this table is to show which concepts should be used to achieve the different requirements and how it is possible to find cross-conceptions based on these keywords. For example, if autonomy should be achieved, one of the key concepts is machine 6 Complexity Table 2: Effect of the requirements and characteristics of Industry 4.0 on optimization.…”

“…Although an enormous number of information sources can be used, no review papers have been written about the optimization solutions of Industry 4.0. e goal, inspired by this shortcoming, is to publish a review paper targeting the topic of optimization in Industry 4.0, which is suitable as a starting point for researchers and developers in the field. e need for this kind of focused analysis has been proven by the publication of many targeted review articles over recent years, e.g., on topics such as the influence of Industry 4.0 on energy consumption [5], the enhancement of fault diagnosis by machine learning methods [6], the combination of opportunities of Industry 4.0 and lean philosophy [7], and the handling of cybersecurity risks [8]. e concept of this work is based on the requirement to develop Industry 4.0 solutions to systematize the publications related to optimization tasks.…”

A Review of Cybersecurity Guidelines for Manufacturing Factories in Industry 4.0

“…The authors particularly analyse the shift of an Industrial Control System (ICS) from a stand-alone plant to a cloud-based environment, while focusing on machine learning solutions. Among the most recent surveys, [8] also investigated machine learning solutions for tackling faults in the Industry 4.0 era. However, their study does not specifically address cybersecurity.…”