Abstract
Accurate modelling and simulation of a nuclear power plant are important factors in the strategic planning and maintenance of the plant. Several nonlinearities and multivariable couplings are associated with real-world plants. Therefore, it is quite challenging to model such cyberphysical systems using conventional mathematical equations. A visual analytics approach which addresses these limitations and models both short term as well as long term behaviour of the system is introduced. Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) is used to extract features from the data, k-means clustering is applied to label the data instances. Finite state machine representation formulated from the clustered data is then used to model the behaviour of cyberphysical systems using system states and state transitions. In this paper, the indicated methodology is deployed over time-series data collected from a nuclear power plant for nine years. It is observed that this approach of combining the machine learning principles with the finite state machine capabilities facilitates feature exploration, visual analysis, pattern discovery, and effective modelling of nuclear power plant data. In addition, finite state machine representation supports identification of normal and abnormal operation of the plant, thereby suggesting that the given approach captures the anomalous behaviour of the plant.
Highlights
Cyber Physical Systems (CPS) involve proper monitoring and control of physical processes through computational procedures
Nuclear Power Plants (NPPs) belong to a key domain of CPS whose operation must be kept economically viable to compete with other energy sources and kept safe to avoid any potential hazards to humanity
Feature extraction followed by k-means clustering effectively clusters the data into separate groups each corresponding to a unique state of the plant
Summary
Cyber Physical Systems (CPS) involve proper monitoring and control of physical processes through computational procedures. Nuclear Power Plants (NPPs) belong to a key domain of CPS whose operation must be kept economically viable to compete with other energy sources and kept safe to avoid any potential hazards to humanity. A straightforward way to visualise a large amount of time-series data is to plot them with a visualisation tool. Such an approach has several drawbacks, namely: (i) it is difficult to identify correlations among data blocks far away from one another; (ii) it is difficult to identify the existence of clustering of data blocks in the display of raw data; and (iii) much human efforts are needed to interpret raw data
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