The rise of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) has brought about a new era of connectivity, building intelligence into the very structure of our society. There are both challenges and possibilities that come with this change, especially when it comes to maintaining industrial machinery. When it comes to big, important machines like electrical transformers, predictive maintenance is very important because the costs of unplanned downtimes can be too high to bear. This essay looks at how the IoT can be used to actively keep an eye on these tools, with a focus on how sensor data can be collected and used to keep operations running smoothly. We look at how machine learning (ML) and deep learning (DL) can be used to look at this data and guess how much useful life a piece of machinery still has. This lets the machine be shut down for repair and keeps it from breaking down without warning. Even though these technologies have clear benefits, there isn’t much written about how to combine them into a unified business process for predictive analytics. This study fills in the blanks by proposing a business process made just for analyzing sensor data and predicting Remaining Useful Life (RUL) of industrial machinery. To facilitate testing of the proposed business process, we provide a prototype implementation and discuss its details. We also present a way to evaluate this business process by applying the prototype implementation to a real-world dataset. The evaluation results show that the proposed business process is promising.

The Internet of Things (IoT) usually consists of many fully synchronized devices. Since these devices sense data from outside, they are usually outside and sometimes in places that are difficult to access in case of failure. Sometimes, late-recognized errors cause the system not to work and cause significant damage to the system. For this reason, it is crucial to avoid faulty situations with minor damage or even prevent these situations before they occur. Although there are studies on the topic in the literature, there is an emerging need to examine this problem comprehensively and target predictive maintenance and self-healing systems utilizing IoT systems. In this study, we propose a business workflow architecture using streaming-based machine learning algorithms to provide predictive maintenance utilizing IoT systems. In the proposed predictive maintenance workflow, we utilized various machine learning algorithms such as Adaptive Random Forest, Hoeffding Tree, Leveraging Bagging, SPegasos, and Single Drift Classifier classification algorithms. To show the usability of the proposed business workflow, we provide a prototype implementation. We provide an experimental study on the prototype to investigate the prediction success. We also investigate how fast the system can learn from the streaming data. We conduct this evaluation with unbalanced and balanced data. In this manuscript, we report the results of our experimental study.