Assessing machine condition using MLP and vae-based classifiers using acceleration sensor data

Abstract

It is necessary to maintain factory production equipment so it remains in a safe or stable state. Since post-maintenance involves unplanned device shutdowns and greatly affects a wide range of production areas, both behind and ahead of the device, it is often better to prevent such breakdowns by using data, about the time the device has been in service or the number of times it has been used, to replace parts via preventive maintenance. That said, recent advances in IoT-related technology, sensors, and data-acquisition computers, the low cost of cloud databases, and simpler technology, have led to a surge of interest in so-called predictive maintenance, based on monitoring the status of the equipment. Due to recent advances in deep learning, it has become possible to accurately estimate machine states using multidimensional features. Here, we evaluate two methods of estimating a machines state based on acceleration data using deep learning, and compare their accuracy and utility for equipment maintenance.