Induction motor process monitoring in power plants based on multi‐step reconstruction‐based <scp>PCA</scp>

Abstract Fault diagnosis of induction motors is crucial for enhancing the reliability of industrial processes. Reconstruction‐based principal component analysis (RB‐PCA) is commonly used in fault diagnosis for industrial equipment because it effectively solves the problem of smearing effects. However, RB‐PCA encounters challenges related to temporal inconsistency in the industrial processes. This issue arises in the early stages of a fault, where fault indicators fluctuate around the control threshold. Such oscillations can cause the model to switch intermittently between reconstruction and non‐reconstruction states, which diminishes diagnostic accuracy and model stability. This paper provides a multi‐step reconstruction‐based principal component analysis (MS‐RBPCA) algorithm that integrates a moving time window. Additionally, spatial distance reconstruction and sequence floating forward search are introduced to improve the computational efficiency of fault isolation. The effectiveness of the MS‐RBPCA is demonstrated through one simulation study and one industrial case involving fault samples from induction motors in a power plant. The results show that MS‐RBPCA can significantly reduce computational time, achieving a speed improvement of up to 50% while maintaining the fault detection rate above 97% and the false alarm rate below 1.5%, providing a viable solution for industrial process monitoring.