Fault detection and diagnosis (FDD) is essential for ensuring the safe and economic operation of chemical plants, as well as minimizing downtime. This is why research in this area has been active in recent years. Typically, faults are detected and diagnosed only after they have occurred. Because of this the damage caused cannot be prevented. Predictive detection and diagnosis methods could prevent this by detecting and resolving faults before they occur. In this context, drifts and their detection and diagnosis are considered. Another complicating factor is that multiple faults may occur simultaneously and require diagnosis. How many methods address the predictive detection and diagnosis of faults? How effective are FDD methods at detecting and diagnosing simultaneous faults? To answer these questions, a systematic literature review was conducted using the SPAR-4-SLR protocol. Several hybrid approaches were compared based on criteria regarding their hybridity, requirements, diagnostic capabilities, goals, and applications. The methods were evaluated based on their ability to perform predictive FDD and diagnose simultaneous faults. Then, a qualitative decision-making logic was developed to support the selection of methods for different purposes. However, the evaluation results showed that none of the methods could perform early FDD and multiple-fault diagnosis. Conclusions were therefore drawn regarding promising strategies for developing new methods. Future research should focus on overcoming structural limitations and other obstacles to enable early fault detection and multiple-fault diagnosis.
Eydam et al. (Thu,) studied this question.