A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective

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

“…Furthermore, IoT enables the real-time interconnection of different objects, for example, sensors, actuators, machines and robots among others, in a safe and reliable manner. At the heart of IoT lie heterogeneous communication networks, including fifth generation (5G) networks, Wi-Fi, machine-to-machine (M2M) deployments and cloud technologies (cloud, fog, edge) [10][11][12]. At the same time, humans should maintain an important role in the Industry 4.0 environments, empowered with smart devices, virtual and augmented reality (VR/AR), being in the loop of the manufacturing process and taking advantage of the AI/ML-based decision-making [13].…”

“…With the contribution of ML algorithms, early and accurate fault detection can lead to minimum downtime, owing to the recognition of damaged and defected products or parts, in real-time. The wealth of data contributes to accurate prediction of machine condition, remaining useful life (RUL), and faults, leading to an appropriate and cost-effective maintenance schedule, thus minimizing the downtime, due to a fault occurrence [10,15]. The human factor has also an important role in the production procedure of the Industry 4.0 ecosystem, whereas collaboration with robots and machines is a critical challenge within factory halls.…”

“…On the other hand, fast changing dynamic environments and different machine working states impose significant challenges to ML-based fault detection, prediction and prevention. Overall, the need for reliable and accurate real-time transmission and computation arises, while security issues are becoming increasingly serious, due to the increased level of interconnection among the different subsystems [10].…”

“…The research area of Industry 4.0 has received several contributions in recent years. The survey in Reference [10] presented an overview of CPS in IIoT settings, giving in detail a relevant architecture, enabling the control of systems and processes, while ML was presented as a promising solution for CPS. Then, the authors of Reference [16] provided a thorough analysis of the role of BDA in IoT and a taxonomy of ML algorithms in BDA-enabled applications, such as self-maintenance, self-prediction and self-configuration.…”

An Agent-Based Industrial Cyber-Physical System Deployed in an Automobile Multi-stage Production System

“…Driven by the needs to attend the ever-changing market trends, such digital transformation is mainly based on the use of Internet of Things (IoT), Cloud Computing and Artificial Intelligence (AI) technologies [12]. While the first enables the interconnection of equipment and consequently the digitization of the industrial environment [22], the second provides on demand high processing and storage resources [15]. On the other hand, AI provides advanced data analysis algorithms, such those based on Machine-Learning (ML), that can take advantage of the huge amounts of IoT data and the power of Cloud Computing, in order to provide actionable information and support data-driven decision-making [20,8].Although Cloud manufacturing [15] has been seen as a new paradigm in the realization of the 4th industrial revolution (4IR) [12], the traditional Cloud-based approaches, where IoT devices send all the data to be processed by centralized applications, present some drawbacks.…”