基于精简并发潜结构映射的竖炉焙烧过程综合故障诊断

Operational faults of shaft furnace roasting processes can appear when operational conditions change abnormally or operators do not react properly or timely. Typical operational faults, including fire-emitting, flame-out, under-reduction and over-reduction, are highly related to process safety and product quality, e.g., magnetic tube recovery rate (MTRR). Fault diagnosis of shaft furnace roasting processes deserves more attentions. However, it is difficult to apply model-based or knowledge-based fault diagnosis methods. In particular, it is difficult to model the relations between fault and product quality. In this paper data-driven concurrent projection to latent structures (CPLS) based fault diagnosis is developed for shaft furnace roasting processes. First, a CPLS based comprehensive monitoring method for shaft furnace roasting processes is proposed by combining co-variation and residual of process spaces of concurrent projection to latent structures into a simplified MTRR-relevant process-variation space. Secondly, a corresponding simplified reconstruction-based contribution method is proposed and used to pinpoint the faulty variable. Finally, the proposed methods are verified using the data collected from a hardware-in-loop simulation platform. The results demonstrate that the quality-relevant faults as well as faulty variables are successfully diagnosed.