Fault Prognosis from Telemetry Data Using Multivariate Regression

Abstract

Predictive maintenance has changed the traditional principles of old maintenance systems and its technologies include vibration measuring devices and thermal imaging cameras. They have a significant impact on achieving high quality levels, improving the working performance of equipment in industrial facilities, increasing production efficiency and reducing the costs incurred by these facilities for the maintenance of their equipment, especially with the beginning of the Fourth Industrial revolution and the emergence of the Internet of Things concepts, have contributed to the development of the technology of this new approach of maintenance. Failure prediction can be achieved with the application of artificial intelligence tools to examine data and discover patterns that indicate that a problem or failure will occur in the future. These predictions rely on the examination of past and current data to make reliable predictions about when and where failures might occur. In our research, we used telemetry systems to collect information quickly and reliably in order to predict machine failures. This study addresses the importance of artificial intelligence for predictive maintenance by providing a comprehensive overview of modern maintenance techniques and methods