Robust recursive transformed component statistical analysis for incipient industrial fault detection with missing data

Abstract

In practical industrial processes, data integrity is often compromised by sensor malfunctions or issues in data management. Furthermore, incipient faults, which can escalate into severe accidents, are typically challenging to detect due to their subtle nature. This paper introduces a robust recursive transformed component statistical analysis method for detecting incipient faults in industrial processes with missing data. Within a sliding window, missing data are restored by minimizing the detection index in a recursive way, and the converged statistical model is then used for fault detection. The detectability of the proposed method is analyzed theoretically in scenarios with incomplete data. To validate the effectiveness of the proposed method, experiments are conducted on both a numerical case study and the Tennessee Eastman process. The results demonstrate robust performance under incomplete training and testing data, enabling accurate detection of incipient faults in industrial settings. Furthermore, compared to existing methods, the proposed approach achieves significant improvements in fault detection under missing-data conditions, attaining a detection rate close to 100% for most fault scenarios while maintaining a near-zero false alarm rate.