Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine

Abstract

An adaptive multi-tiered framework, that can be utilised for designing a context-aware cyber physical system to carry out smart data acquisition and processing, while minimising the amount of necessary human intervention is proposed and applied. The proposed framework is applied within the domain of offshore asset integrity assurance. The suggested approach segregates processing of the input stream into three distinct phases of Processing, Prediction and Anomaly detection. The Processing phase minimises the data volume and processing cost by analysing only inputs from easily obtainable sources using context identification techniques for finding anomalies in the acquired data. During the Prediction phase, future values of each of the gas turbine’s sensors are estimated using a linear regression model. The final step of the process— Anomaly Detection—classifies the significant discrepancies between the observed and predicted values to identify potential anomalies in the operation of the cyber physical system under monitoring and control. The evolving component of the framework is based on an Artificial Neural Network with error backpropagation. Adaptability is achieved through the combined use of machine learning and computational intelligence techniques. The proposed framework has the generality to be applied across a wide range of problem domains requiring processing, analysis and interpretation of data obtained from heterogeneous resources.