Real-time self adaptable Prediction system for Mine Equipment

Identifying failure signatures of machines and modeling them to predict problems well before failure occur has been of great interest to reliability and maintenance engineers, primarily because of the unparalleled advantages like improved equipment up-time, lower maintenance cost, and reduced safety risk. Production critical machinery often requires intelligent real time monitoring and an unplanned interruption can have high cost implications. To address this, we utilize the on-board sensor data and develop a near-real time prediction system to identify anomalies and failure patterns of assets. Development of such data driven system will help improve reliability engineering strategies by modeling system dynamics and predicting equipment health problems.