RESEARCH ARTICLE Industrial Automation with Safety Aspects using Machine Learning Techniques Jayasri Kotti 1 Received: 7 April 2020 /Revised: 14 May 2020 /Accepted: 20 May 2020 # Springer Nature Switzerland AG 2020 Abstract Human Machine Interactive(HMI) system is an interactive system which is used for the communication between user and machine. Based on Training data, the system is made to understand the simple commands even in peculiar timings and server will be able to predict user’s command acting on the basis of the previous history to make an intelligent workspace. The history is saved in datasets, whenever hardware senses the parameters pushes all values to dataset. With Training data machines can work as intelligent, just make them feed on relevant data. Industries with electronic appliances are controlled automatically. Machine Learning(ML) is one of the growing platforms for automation, through which new advancements are made through which easily monitor as well control the system using datasets. ML algorithms can discover associations, perceive patterns, understand composite tasks and make decisions. This paper uses past datasets of sensors, and then design the hardware using ML and connecting to embed system with training datasets. Based on the information, system is made to understand the simple com- mands and the server will be able to predict user’s command acting on the basis of the previous history to make an intelligent workspace. So we can provide safety aspects in industrial automation. This paper extending the use of random forest algorithm to fulfill the purpose of reduce false deduction, take important decisions and to get accurate results in very short period of time. It will raise alarms besides it will send notification to the mobile with danger message before accidents occur. Keywords Human machine interactive (HMI) . Machine learning (ML) . Sensors . Training data . Intelligent workspace Introduction Maximum number of industries and organizations depends on the usage of machines, electronic widgets, and various re- courses for completing different tasks. All are operated with the help of machines. Even though there are so many benefits to us, running with them without any safety aspects will cause injury and even leads to loss of life (Joseph Zulick 2019; Fairoze et al., 2018). Machines can works on relevant data or inputs (Bradley et al., 1991). But preparing relevant data to solve a particular problem is a challenging task to engineers. And make sure that the relevant data is in a useful scale and correct format with meaningful features should include. Novelty and adaptation are tremendously significant to the industrial automation (Raffaele Cioffi et al., 2020). As technology advances while the development of standards, de- signers are often left with future predictions. This causes them to overestimate or underestimate the necessary safety func- tions (Tina Hull 2019). Safety system helps us take some crucial decisions in Industries. Usage of Internet of Things the system becomes secured and live data monitoring is also possible (Vijayalakshmi and Muruganand, 2017; Deshpande and Sangitasanap, 2016). Large variety of industrial Internet of Things and its applications have been prepared and used in recent years (Da Xu et al., 2014; Khan and Bhat, 2014). Machine Learning (ML) is one of the emergent platforms for automation, through which new advancements are made and easily monitor as well control the system using datasets. ML is a part of computer science that often uses arithmetical techniques to give computers the ability to be trained with datasets, without programming (S. Kavitha et al., 2019). Machine Learning, Artificial Intelligence and Internet of Things with software engineering practices are very useful practices in providing safety in industries. Machine Learning is a notion which allows the system to learn from experiments. So that it can recognize patterns make * Jayasri Kotti jayasrikotti@gmail.com 1 LENDI Institute of Engineering and Technology, Computer Science and Engineering, Vizianagaram, AP 535005, India Safety in Extreme Environments https://doi.org/10.1007/s42797-020-00020-y