Fault Diagnosis of Nuclear Power Equipment Based on HMM-SVM and Database Development
This paper mainly introduced the basic theory of Hidden Markov Model (HMM) and Support Vector Machines (SVM). HMM has strong capability of handling dynamic process of time series and the timing pattern classification, particularly for the analysis of non-stationary, poor reproducibility signals. It has good ability to learn and re-learn and high adaptability. SVM has strong generalization ability of small samples, which is suitable for handling classification problems, to a greater extent, reflecting the differences between categories. Based on the advantages and disadvantages between the two models, this paper presented a hybrid model of HMM-SVM. Experiments showed that the HMM-SVM model was more effective and more accurate than the two single separate models. The paper also explored the application of its database system development, which could help the managers to get and handle the data quickly and effectively.
Liangchi Zhang, Chunliang Zhang and Tielin Shi
H. Y. Zhu et al., "Fault Diagnosis of Nuclear Power Equipment Based on HMM-SVM and Database Development", Advanced Materials Research, Vols. 139-141, pp. 2532-2536, 2010