Data-Driven Fault Prognosis Based on Incomplete Time Slice Dynamic Bayesian Network 1

摘要

Based on a dynamic Bayesian network with an incomplete time slice and a mixture of the Gaussian outputs, a data-driven fault prognosis method for model-unknown processes is proposed in this article. First, according to the requirement of fault prognosis, an incomplete time slice Bayesian network with unknown future observed node is constructed. Moreover, the future states are described by the current measurements and his historic data in the form of conditional probability. Second, according to the completed part of historical data, a parameter-learning algorithm is used to obtain network parameters and the weight coefficients of distribution components. After that, using such weight coefficients as input-output data, the subspace identification method is employed to build a forecasting model which can predict weight coefficients at next sampling time. To achieve fault prognosis, an inference algorithm is developed to predict hidden faults based on the distribution of the measurements directly. Furthermore, the remaining useful life of process is estimated via iterative one-step ahead prognosis. As an example, the proposed method is applied to a continuous stirred tank reactor system. The results demonstrate that the proposed method can efficiently predict and identify the fault, and estimate the remaining useful life of process, even though the measurements are partly missing.