IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset
Abstract One of the main focuses of smart industry is machinery failure predictive solutions. To achieve this, IoT-based solutions have been widely deployed. However, data processing and decision making remain challenging. The absence of enough knowledge has been the primarily limitation of statistical methods and supervised learning methods. Therefore, unsupervised learning methods are gaining more popularity but still have limits to cover effectively the pre-signs of failures due to the complexity of training process and results visualization. Previously, we proposed a novel Big Data Analysis method on audio/vibration data to cover effectively the pre-signs of failures through data visualization without complex learning or processing. We validated our proposal on a demo system. In the present work, we are using part of the MIMII dataset to test our proposed analysis method on a real-world-like data and verify the validity of our proposal on a more complex system. We are showing that we can detect abnormal machine behaviors and predict failures without prior training or knowledge of the target monitored machine.
