Application of robust statistical models to measure data quality for improved use of sensors and diagnostics in an active mine setting

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We propose to investigate novel approaches of statistical analysis to improve upon predictions of normal and**abnormal behavior in industrial sensors. The ability to detect subtle changes within a wide range of operational**conditions and gauge their potential individual and compounding influence on measurements has significant**potential value for companies deploying sensing devices in challenging conditions. It can be very difficult to**perform reactive sensor system maintenance in many industrial settings because it may require operational shut**down or suspension. Accordingly, the opportunity to still work with compromised sensor data is significant, as**is the opportunity to predict when preventative maintenance should be undertaken if an opportunity presents**itself within regular operational routines. Working confidently with compromised data, however, requires deep**understanding of an entire system, including variables perhaps not normally considered as relevant for the**sensing mode(s).**MineSense Technologies Ltd. is a B.C.-based mining technology company specializing in multivariate**telemetry of ore at all stages of mining. Their clients are mining companies that want to optimize their**operations through pre-concentration and sorting of mined ore as well as the rejection of waste rock. Algorithm**development for real-time sensing of mined ore is a complex task that requires intimate discussion and sharing**of data and knowledge with mine staff. Deployment of machine learning approaches with foundations in**statistical patterns and assessment would be another approach that the company could leverage. Harnessing the**experience of statistical academic experts would accelerate the process of (1) integrating the various types of**data being collected and (2) exploring opportunities that advanced statistical research could bring.**The main objectives of this project are to (1) characterize and monitor system data quality, (2) develop the**scientific and technical basis for data quality models, and (3) develop approaches to integrate these models**within broader system health monitoring. Results of the research have the potential to provide significant