Abstract
Cyber-physical systems (CPS) have emerged with development of most great applications in the modern world due to their ability to integrate computation, networking, and physical process. CPS and ML applications are widely used in Industry 4.0, military, robotics, and physical security. Development of ML techniques in CPS is strongly linked according to the definition of CPS that states CPS is the mechanism of monitoring and controlling processes using computer-based algorithms. Optimizations adopted with ML in CPS include domain adaptation and fine tuning of current systems, boosting, introducing more safety and robustness by detection and reduction of vulnerabilities, and reducing computation time in time-critical systems. Generally, ML helps CPS to learn and adapt using intelligent models that are generated from training of large-scale data after processing and analysis.
TopBackground
Cyber-physical Systems and industry 4.0 are bonded concepts in the industrial revolution. CPS is the infrastructure of industry 4.0 standard, which describes the new industrial environment proposed in industry 4.0. It involved intelligent senses and controls to approximately all manufacturing processes. This development doesn’t consider an ordered pattern of innovations, but a package of functionalities and features are introduced in a parallel way (Jiang, 2017). Where while the development of some technology another one is been developed too. Researchers and developers had found the way along to achieve objectives of industry 4.0 by adopting the ML techniques with the industrial process which adds the intelligence feature. The approach of intelligent manufacturing is been developed from the 70th and 80th of the last century, while the official initiation of ML in industrial manufacturing can be tracked lately to the 90th. Instead of industrial manufacturing, ML has various types of techniques for different types of applications. The most common ML methods include statistical methods, rule induction, genetic algorithm, nearest neighbor clustering, decision trees, and neural networks (Lee, 2018).
The impact of ML was obvious in the development of industry 4.0, as can be also obtained by the revolution of CPS architecture that is standardized by ANSI with the introduction of IEC/ISO 62264, ISA-95 Architecture, then 3C, 5C, and 8C architectures are developed. While this rabid development, revolutionary systems are raised such as intelligent manufacturing systems (IMS), holonic- and agent-based systems. From these developments, the usage of ML in different processes is obtained, as resulted in various developing technologies such as self-aware machines, real-time and low latency embedded applications, fog computing, cooperative manufacturing systems, and context-adaptive autonomous systems. Is also obtained the major role of ML is security and privacy application in industry as in intrusion detection and threats mitigation. A highlighted use cases for ML that led into an industrial artificial intelligence are predictive maintenance, quality assurance, and prediction, optimization of manufacturing process and supply chain, automated management and security, smart assistant, intelligent resource exploration, intelligent research & development tools (Maliszewska and Schlueter, 2019).
Key Terms in this Chapter
ISA-95 Architecture: Is the international standard for the integration of enterprise and control systems. It was developed to be applied in all industries, and all sorts of processes.it provides consistent terminology for supplier and manufacturer communications, consistent information models, and consistent operations models which is a foundation for clarifying application functionality and how information is to be used.
Digital Twin (DT): Meant as the virtual and computerized counterpart of a physical system that can be used to simulate it for various purposes, exploiting a real-time synchronization of the sensed data coming from the field; such synchronization is possible thanks to the enabling technologies of Industry 4.0.
Intelligent Manufacturing Systems: Name of an international organization devoted to developing the next generation of manufacturing and processing technologies. The organization provides support for projects consistent with the protection of intellectual property rights.
IEC/ISO 62264: Describes the manufacturing operations management domain (Level 3) and its activities, and the interface content and associated transactions within Level 3. It defines transactions in terms of information exchanges between applications performing business and manufacturing activities.
Source of Knowledge (SoK): Is the leading educational content capture and distribution company, for the IT industry, focusing on software, hardware, and firmware user groups, in addition to computer security groups.
Holonic-Based Systems: Is a manufacturing system (MS) that is distributive controlled according to the holonic system paradigm. Holon’s means, manufacturing system components are modeled as autonomous, collaborative entities (agents).
ANSI: Stands for, American National Standards Institute. It is a primary organization for fostering the development of technology standards in the United States works with industry groups and is the U.S. member of the International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC).