## 1 Introduction  
Revolution of Industry 4.0 (I4.0) introduces new tools and technologies that can be integrated with the ones that are already exploited by factories. Several of them have already been deployed in different manufacturing sectors to improve productivity and to satisfy the expectations of consumers expectations for customisation. One such I4.0 enabling technology is the collaborative robot (cobot) which is widely deployed in industry [1, 2]. A cobot allows the skills of a robot, such as precision and strength, to be combined with human dexterity and problem solving abilities [3] on a human-robot collaborative (HRC) workstation. Cobots are designed to interact with humans directly and physically within a shared workspace [4]. HRC applications that are designed on the basis of reliability and safety standards increase human trust in collaboration and improve the quality and working conditions of employees. In HRC, humans and robots share the same workspace. Cobots are specifically made to halt before any involuntary contact may harm the human coworker can cause harm. However, frequent halts induce accelerated wear and tear of the robot and increase the probability of mechanical failures. Furthermore, cobots should be light weight in order to minimise their inertia and allow them to stop suddenly. Therefore,joints have lower strength compared to standard industrial robots. Accordingly, scheduled maintenance is no longer an appropriate strategy as it cannot account for the number and severity of the forced halts of a robot. Continuous monitoring of the state of health of such robots would be preferable. During monitoring, the system generates useful knowledge and information, such as data about the robot's sensors and event logs, which are stored in historic logging databases and can be recalled to perform analytics. The smart data analytics of collected data, using machine learning techniques, offers an opportunity to monitor the health condition of industrial robots, to predict the mobile autonomy and to perform predictive maintenance, if necessary. The Internet of Things (IoT) is another emerging tool that is used for I4.0: connected devices with embedded systems that are able to interact and communicate with each other or with centralised devices. The integration of the IoT in a decision making system could improve the performance of the human-robot interaction. This is the reason why the industrial internet of things (IIoT) extension refers to its industrial application, that is to interconnect industrial machines and devices, robots, sensors and instruments that are centralised to collect, exchange and analyze data. The IIoT offers the possibility of achieving the complete design of physical cyber-systems through the integration of data processing technologies, intelligent software and sensors. On-line monitoring systems, and predictive maintenance models can be built on the basis of a large set of historical data. Several steps are involved in such a process: preprocessing of the collected data; extracting features from sensor data or feeding sensor data directly to machine learning models; training the predictive models; generating decision support models that will be able to evaluate a new data sent to the system; deploying developed models and integrating them with the system. The on-line visualisation of the health status of a robot and alerts about predicted failures will improve the human robot interaction. However, the applications of such models can go far beyond HRC. The concepts of machine learning (ML) tools used for predictive maintenance applications utilizing data available data on the internet have been discussed in recent studies, such as in [5], and a condition-based monitoring system, using ML tools, has been successfully deployed for a smart railway applications [6]. Hence, this paper has focused on developing a framework using I4.0 enabling technologies to improve reliability and safety in HRC applications.