Incipient fault detection and isolation for dynamic processes with slow feature statistics analysis

Abstract

Incipient fault detection and isolation is crucial to maintain a high-efficiency operational state for modern complicated manufacturing processes. Nonetheless, many conventional data-driven methods are insensitive to incipient faults with tiny magnitudes. To address this issue, a novel process monitoring method called slow feature statistics analysis (SFSA) is proposed. Slowly changing features containing significant information are first extracted, and the sliding window technique is applied to acquire their statistical information. Afterward, two detection indices are constructed for fault detection. After successful detection, the reconstruction-based grouped contribution for the statistics of slowly changing features is calculated, which is used to locate abnormal slow features. Then, the abnormal variable is identified by implementing a linear mapping strategy. The evaluation for SFSA is conducted by using a numerical example and a benchmark process. The results indicate that SFSA performs better than several other methods in terms of detection sensitivity and isolation accuracy.