An Automated Edge Computing-Based Condition Health Monitoring System: With an Application on Rolling Element Bearings

Abstract

Abstract A failure of rolling element bearings is a frequent cause of machine breakdowns and results in a production loss due to the sudden failure. A regular condition health monitoring and an associated detection of bearing defects in the early stages can be used to predict such sudden failures. To monitor the bearing's condition, the generated vibration signature can be analyzed, since rotating machines have, in most instances, a unique vibration signature that relates to their health status. Presently, bearing analysis of many machines results in significant cost and complexity due to a large amount of vibration data that must be analyzed. A condition health monitoring system (CMS) was developed to automate and simplify the whole process from the vibration measurement to the analysis results. Additionally, the CMS is embedded into an Internet of Things (IoT) architecture. Thereby, a location-independent control of the CMS, the vibration data, and the analysis results is possible. The embedding of sensors can cause communication problems from the sensor to the cloud due to the low bandwidth of sensors and the amount of data that must be transmitted. To overcome this issue, an edge device that acts as a gateway between the vibration sensor and the cloud is the core of the CMS. It measures the vibration signal locally, analyzes it automatically, and publishes a feedback as to the bearing condition to the cloud.