Low-Rank Characteristic and Temporal Correlation Analytics for Incipient Industrial Fault Detection With Missing Data

In real industrial applications, process data may get corrupted due to failure of the measurement devices or errors in data management. Besides, incipient faults, which may evolve into serious accidents, are generally more difficult to be detected because of its small magnitudes. In this article, a robust canonical variate dissimilarity analysis method is proposed to detect incipient faults for industrial processes with missing value. According to the low-rank characteristic, the low-rank matrix decomposition (LRMD) method is applied to recover the missing elements and reduce the ambient noise for process data. The output results of LRMD model consist of a low-rank component and a sparse component, which indicate main variance and residual information of the inputted data, respectively. For each component, a canonical variate analysis) model is developed to extract the temporal correlation in the process data. Based on the obtained features, a total of three monitoring statistics are established to reflect the operation status of the online sample. Among them, a statistic is used to measure the static deviation of this sample, and other two indices are applied to evaluate the dissimilarity between the past and future canonical variates. A simulated process and a real industrial process are adopted to illustrate the performance of the proposed method. Experimental results show that the proposed model can be well developed with incomplete training data and robustly detects the incipient faults for industrial applications.