Abstract
According to massive dynamic data of a chemical engineering system, in order to effectively utilize fault case data and select kernel parameter and principal component number for improving fault detection effect, an improved KPCA method based on dual-parameter optimization, which combines kernel parameter and principal component number, is proposed in this paper. Firstly, the input data are mapped to the characteristic space through a nonlinear mapping function, and PCA is adopted to extract nonlinear characteristics in the characteristic space, and meanwhile the fault detection capability judgment criteria is also prepared. Then, the influence of the kernel parameter and the principal component number on the fault detection rate is analyzed, and T 2 detection rate and SPE detection rate are introduced therein to calculate kernel parameter \( \sigma \) and principal component number p under maximum detection rate, thus realizing the process monitoring method based on dual-parameter optimization. Finally, TE process data and the compressor set monitoring data of a certain enterprise are adopted for the simulation experiment. The experiment result shows that the improved KPCA method has better nonlinear data processing capability and can obtain higher process monitoring efficiency.
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Acknowledgements
This work was financial supported by the National Natural Science Foundation of China (grant no. 151175402, 51375375), also supported by the open research fund of state key laboratory for manufacturing systems engineering Xi’an Jiaotong University (grant no. sklms2015009).
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Jiarula, Y., Sun, W., Fan, J., Tao, Q., Musha, R. (2018). A Process Monitoring Method Based on Dual-Parameter Optimization. In: Tan, J., Gao, F., Xiang, C. (eds) Advances in Mechanical Design. ICMD 2017. Mechanisms and Machine Science, vol 55. Springer, Singapore. https://doi.org/10.1007/978-981-10-6553-8_42
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DOI: https://doi.org/10.1007/978-981-10-6553-8_42
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