Fault diagnosis of dynamic processes with reconstruction and magnitude profile estimation for an industrial application

Fault diagnosis is essential for troubleshooting and maintenance of industrial processes that operate dynamically. Traditional reconstruction-based fault diagnosis methods, however, are mostly developed for static processes and are ineffective for faults with similar directions. In this paper, a new hierarchical fault diagnosis strategy that incorporates reconstruction and dynamic time warping is proposed for the feeding anomaly diagnosis of an industrial cone crusher. A novel fault-magnitude-estimation method for dynamic processes is proposed based on the dynamic relations captured by dynamic latent variable (DLV) predictions. A combined index is developed based on the prediction residuals which exclude normal and predictable variations to improve sensitivity to faults. Fault-magnitude-estimation-based dynamic time warping is proposed to evaluate the shape similarity of faults in order to further isolate the fault candidates with similar directions. The reconstructed magnitude is utilized to extract shape features of the faults. The advantages are demonstrated using a Monte-Carlo simulation example of a dynamic process. Finally, the proposed method is applied successfully to diagnose feeding anomalies of an industrial cone crusher.