# Supervision System 4.0 for a Road Tanker Washing Robot Manipulator
## 1 Introduction  
Fault Detection and Isolation (FDI) systems are critical for ensuring the safe and efficient operation of industrial processes. With the advent of Industry 4.0, there has been a renewed focus on developing advanced FDI systems to handle the increased complexity and variability of modern industrial systems [1; 2]. New insights into FDI systems for Industry 4.0 highlight the importance of data-based approaches and the opportunities presented by increased computing capacity, as Industrial Information Technology (IIT) now has greater processing capacity available near the factory floor--edge computing--and in the transition and analysis of data between the factory floor and the cloud--fog computing [3]. Another important insight is the need for more flexible and adaptable FDI systems. While in the past FDI systems were often designed for specific systems or equipment, today it is becoming increasingly important for FDI systems to be more versatile. This requires the use of general-purpose algorithms and machine learning techniques that can be adapted to different contexts. Finally, there is a growing recognition of the importance of human-machine collaboration in FDI systems for Industry 4.0. While advanced algorithms and techniquescan greatly improve the performance of FDI systems, it is still important for operators and other human personnel to be involved in the process [4]. By providing clear and actionable information to operators, FDI systems can help them make informed decisions and take more effective actions to address faults [5]. Overall, the development of advanced FDI systems for Industry 4.0 is a challenging and ongoing process. Nevertheless, new insights and technologies are leading to FDI systems that are more efficient, reliable and effective than ever before [6]. This work focuses on a proposal for a supervisory system for a road tanker washing robot, designed in the context of industry 4.0, that enables the integration of the following technologies: Digital Twin (DT), quality control based on machine learning (ML) algorithms, FDI and the Industrial Internet of Things (IIoT). The supervision system includes a Human-Machine Interface (HMI) that complies with the ANSI/ISA-101.01-2015 guidelines and an architecture that allows the various systems and software in a factory to be easily and efficiently connected. The system supports machine-to-machine (M2M) communication and therefore can integrate FDI or the ML-based quality control modules in future developments without the need to stop processes. Supervision systems have been introduced in several areas since the 1940s and have become increasingly dominant. The first computer-based Supervision, Control And Data Acquisition (SCADA) systems appeared in the 1960s and, until the 1980s, usually relied on a centralized architecture, in which a single machine was responsible for managing and performing all control and data acquisition functions [7]. From the 1980s, SCADA systems began to use distributed architectures following the introduction of the Local Area Network (LAN), and later (1990s), open system architectures were introduced to replace vendor-controlled architectures [8]. Over time, SCADA systems have become more complex as new functions have been introduced. These were implemented on a networked system consisting of servers, HMI and Programmable Logic Controllers (PLCs) [9]. These systems currently provide the ability to control, supervise and store data in real time. With the Fourth Industrial Revolution, it is now required for SCADA network systems to be integrated with the other communication networks of organizations. This feature underlies the Industry 4.0 paradigm, where all communication networks are integrated through the Industrial Internet of Things (IIoT) and support the creation of a Cyber-Physical System (CPS) [10]. Given the prominent role of SCADA systems in plant operations, it is essential that these systems are fully integrated into this communication infrastructure.