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

Majdani, Farzan; Petrovski, Andrei; Doolan, Daniel

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.

Citation

MAJDANI, F., PETROVSKI, A. and DOOLAN, D. 2017. Evolving ANN-based sensors for a context-aware cyber physical system of an offshore gas turbine. Evolving systems [online], 9(2), pages 119-133. Available from: https://doi.org/10.1007/s12530-017-9206-8

• Journal Article Type: Article
• Acceptance Date: Oct 17, 2017
• Online Publication Date: Oct 27, 2017
• Publication Date: Jun 30, 2018
• Deposit Date: Oct 31, 2017
• Publicly Available Date: Oct 31, 2017
• Journal: Evolving systems
• Print ISSN: 1868-6478
• Electronic ISSN: 1868-6486
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 2
• Pages: 119-133
• DOI: https://doi.org/10.1007/s12530-017-9206-8
• Keywords: Smart condition monitoring; Context awareness; Cyber physical system; Asset integrity; Artificial neural network
• Public URL: http://hdl.handle.net/10059/2562
• Contract Date: Oct 31, 2017