Sensor data analysis for equipment monitoring

Sensors play a key role in modern industrial plant operations. Nevertheless, the information they provide is still underused. Extracting information from the raw data generated by the sensors is a complicated task, and it is usually used to help the operator react to undesired events, other than preventing them. This paper presents SDAEM (Sensor Data Analysis for Equipment Monitoring), an oil process plant monitoring model that covers three main goals: mining the sensor time series data to understand plant operation status and predict failures, interpreting correlated data from different sensors to verify sensors interdependence, and adjusting equipments working set points that leads to a more stable plant operation and avoids an excessive number of alarms. In addition, as time series data generated by sensors grow at an extremely fast rate, SDAEM uses parallel processing to provide real-time feedback. We have applied our model to monitor a process plant of a Brazilian offshore platform. Initial results were promising since some undesired events were recognized and operators adopted the tool to assist them finding good set points for the oil processing equipments.