Anomaly detection in complex system based on epsilon machine

• Published in: International journal of systems science Vol. 39; no. 10; pp. 1007 - 1016
• Main Authors: Xiang, K., Zhou, X., Jiang, J.P.
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis Group 01.10.2008; Taylor & Francis
• Subjects: Algorithmics. Computability. Computer arithmetics; anomaly detection; Applied sciences; Computer science; control theory; systems; Drives; epsilon machine; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Mechanical engineering. Machine design; Physics; Shafts, couplings, clutches, brakes; Solid mechanics; Structural and continuum mechanics; structure vector; Theoretical computing; Vibration, mechanical wave, dynamic stability (aeroelasticity, vibration control...); Network structure; Complex system; Crankshaft; Fatigue; anomaly detection; Symbolic computation; Computational complexity; Modeling; epsilon machine; Anomaly; Hierarchical system; Causality; Time complexity; structure vector
• ISSN: 0020-7721; 1464-5319
• DOI: 10.1080/00207720802011266

Epsilon machine is a computational mechanics theory and its most effective reconstruction algorithm is causal state splitting reconstruction (CSSR). As CSSR can only be applied to symbol series, symbolising real series to symbol series is necessary in practice. Epsilon machine discovers the hidden pattern of a system. In reconstructed results, the hidden pattern is expressed as the set of causal states. Based on the variation of causal states, a novel anomaly detection algorithm, structure vector model, is presented. The vector is composed of the causal states, and the anomaly measure is defined with the distance of different vectors. An example of the crankshaft fatigue demonstrates the effectiveness of the model. The mechanism of the model is discussed in detail from three aspects, computational mechanics, symbolic dynamics and complex networks. The new idea defining anomaly measure based on the variation of hidden patterns can be interpreted reasonably with the hierarchical structure of complex networks. The jump in anomaly curves is a nature candidate for the threshold, which confirms the positive meaning of the model. Finally, the parameter choice and time complexity are briefly analysed.