Comprehensive Fault Diagnosis of Shaft Furnace Roasting Processes Using Simplified Concurrent Projection to Latent Structures
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摘要: 竖炉焙烧过程因运行条件异常变化或操作不当会造成上火、冒火、过还原和欠还原等运行故障.这些故障直接影响过程运行安全和产品质量（比如，磁选管回收率），但难以采用基于模型和基于知识的方法建模故障与产品质量的关系，以及诊断故障变量.针对上述问题，本文提出数据驱动的基于并发潜结构映射（Concurrent projection to latent structures，CPLS）的竖炉焙烧过程综合故障诊断方法.首先，将并发潜结构映射分解的过程变量共有子空间与残差空间精简合并来建立磁选管回收率相关的过程变化空间，提出基于精简并发潜结构映射模型的竖炉焙烧过程综合监控方法；接下来，定义相应的重构贡献图并与竖炉焙烧过程相结合，提出CPLS精简重构贡献方法用于竖炉焙烧过程故障变量诊断；最后，利用竖炉焙烧过程半实物仿真平台采集的数据进行实验研究，结果表明所提方法不仅可以诊断出质量相关的故障，而且可诊断出回路设定值之外的故障变量.Abstract: 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.1) 本文责任编委 钟麦英
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图 2 竖炉焙烧过程正常工况与上火故障工况数据
Fig. 2 Data collected from shaft furnace roasting process under normal operation and fire-emitting fault
图 5 上火故障重构贡献诊断结果
Fig. 5 Reconstruction contribution based fault diagnosis results for fire-emitting fault
表 1 竖炉焙烧过程统计指标及其控制限[12]
Table 1 The statistics and control limits for shaft furnace roasting processes[12]
统计指标 控制限 $\begin{array}{l} T_c^2 ={{\pmb t}}_c^{\rm T} {{\pmb \Lambda }}_c^{-1} {{\pmb t}}_c= \\ \quad \, \boldsymbol{x}^{\rm T}{{\pmb R}}_c {{\pmb \Lambda }}_c^{-1} {{\pmb R}}_c^{\rm T} \boldsymbol{x} \\ \end{array}$ $\tau _c^2 =\frac{l_c (n^2-1)}{n(n-l_c)}F_{l_c, n-l_c, \alpha } $ $\begin{array}{l} T_x^2 ={{\pmb t}}_x^{\rm T} {{\pmb \Lambda }}_x^{-1} {{\pmb t}}_x =\\ \quad \, \boldsymbol{x}^{\rm T}{{\pmb P}}_x {{\pmb \Lambda }}_x^{-1} {{\pmb P}}_x^{\rm T} \boldsymbol{x} \\ \end{array}$ $\tau _x^2 =\frac{l_x (n^2-1)}{n(n-l_x)}F_{l_x, n-l_x, \alpha } $ $\begin{array}{l} T_y^2 ={{\pmb t}}_y^{\rm T} {{\pmb \Lambda }}_y^{-1} {{\pmb t}}_y =\\ \quad \, {{\tilde {\pmb y}}}_c^{\rm T} {{\pmb P}}_y {{\pmb \Lambda }}_y^{-1} {{\pmb P}}_y^{\rm T} {{\tilde{ \boldsymbol{y}}}}_c \\ \end{array}$ $\tau _y^2 =\frac{l_y (n^2-1)}{n(n-l_y)}F_{l_y, n-l_y, \alpha} $ $\begin{array}{l} Q_x =\left\| {{{\tilde{ \boldsymbol{x}}}}} \right\|^{{2}} =\\ \quad \, \boldsymbol{x}^{\rm T}\left({{{\pmb I-P}}_x {{\pmb P}}_x^{\rm T} } \right)\boldsymbol{x} \\ \end{array}$ $\delta _x^2 =g_x \cdot \chi _{h_x, \alpha }^2 $ $\begin{array}{l} Q_y =\left\| {{{\tilde {\boldsymbol{y}}}}} \right\|^{{2}} =\\ \quad \, {{\tilde {\boldsymbol{y}}}}_c^{{\rm T}} \left({{{\pmb I-P}}_y {{\pmb P}}_y^{{\rm T}} } \right){{\tilde {\boldsymbol{y}}}}_c \\ \end{array}$ $\delta _y^2 =g_y \cdot \chi _{h_y, \alpha }^2 $ 
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