Sensor data analysis and anomaly detection using predictive analytics for process industries

Pervasive sensing is one of the most prominent technologies being adapted by current process industry. Every process industry is highly equipped with wireless sensors for process monitoring in which location, human intervention is to be limited. Thus, major challenge with these numerous sensors is to store and analyze large volume of sensor data stream. This paper focuses on sensor data analysis along with anomaly detection specific to process sector because the placement and nature of the data generated from these sensors follows a specific pattern during process flow. This data is more structured than other type of big data, in which data is more unstructured. No assurance that any single algorithm can produce optimized results. So this paper presents a generic frame work with ensemble of methods such as probability and statistics, Neural Networks and Clustering. Here Neural Net is supervised learning model to predict new data based on trained data. But unseen data is wrongly predictable by Neural nets. For that reason clustering is used as Unsupervised learning model to efficiently handle concept drifts in sensor data stream. These solutions are implemented to various data scenarios with practical means to improve prediction and anomaly detection accuracy of equipment as well as process flows. To the best of our knowledge no single framework is available to fully analyse sensor data stream related to independent, correlation based, group wise with respect to process flow segmentation and process and sub process hierarchy analysis.