WearSense is a real time wear monitoring system used to monitor the remaining thickness of plate liners in chutes. Thickness data is collected from multiple sensors installed in a chute (the asset) and sent through a cloud database where it is filtered and can be displayed in various formats on a web-based app.
In addition to displaying the current thickness at each sensor location, the web app can display a chart showing the change in thickness over time. While information on the current liner thickness and change in thickness over time is useful, of primary interest to the client is the predicted end of life for the liner. The system currently uses time-based linear regression to provide a prediction of the future liner thickness, and hence an estimate of the end of liner life.
The aim of this project is to develop a better wear prediction algorithm which may include (for example) knowledge of the liner material to provide a more accurate estimate of the end of liner
life. The algorithm could also consider the material throughput (if this data is provided by the client) and/or the operating hours which can be estimated using other information from the sensors (such as vibration).
The web app currently only provides thickness information at the discrete sensor locations. It would also be useful to be able to interpolate the wear data between the sensor locations to provide a heat map across the liner surface.