Fault detection for Nonstationary Process with Decomposition and Analytics of Gaussian and Non-Gaussian Subspaces
2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)(2020)
摘要
Process monitoring is a challenging task for modern industrial processes which are commonly nonstationary in nature, revealing typical non-Gaussian characteristics. Nowadays, data-driven based fault detection methods have drawn increasing attention, most of which work under an assumption that the process is subject to Gaussian distribution. But in practice, the underlying non-Gaussian characteristics may be typical in the complex process, which cannot be properly enclosed by a statistical model with a close confidence region and thus may be insensitive to fault detection. Hence, it is necessary to explore and separate the underlying Gaussian and non-Gaussian distributions in fine-grain. In this work, a Gaussian and non-Gaussian subspace decomposition method is proposed by designing a variant of stationary subspace analysis (VSSA) for nonstationary process monitoring. First, the whole time-wise nonstationary process can be neatly converted to condition-wise slices. Then, a Monte Carlo sampling based VSSA technique is designed to separate Gaussian and non-Gaussian subspaces from each other, which focuses on analyzing sample distribution rather than time series properties. Here the Gaussian subspace, which is readily characterized by a statistical model, is used for revealing similar condition slices and affiliate them into the same condition mode. And two monitoring statistics are developed to explore the Gaussian and non-Gaussian distribution structures, thus providing fine-grained distribution analytics and promoting monitoring performance. The feasibility and performance of the proposed method are demonstrated on a real thermal power plant process.
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关键词
nonGaussian subspaces,modern industrial processes,data-driven based fault detection methods,Gaussian distribution,underlying nonGaussian characteristics,complex process,statistical model,underlying Gaussian,nonGaussian distributions,nonGaussian subspace decomposition method,stationary subspace analysis,nonstationary process monitoring,time-wise nonstationary process,Gaussian subspace,nonGaussian distribution structures,fine-grained distribution analytics,promoting monitoring performance,thermal power plant process
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