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首页> 外文期刊>IFAC PapersOnLine >Sparse Contribution Plot for Fault Diagnosis of Multimodal Chemical Processes ★ ★ This work was partially supported by the NSFC under grants 61210012, 61290324, 61490700, 61473164
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Sparse Contribution Plot for Fault Diagnosis of Multimodal Chemical Processes ★ ★ This work was partially supported by the NSFC under grants 61210012, 61290324, 61490700, 61473164

机译：多峰化学过程故障诊断的稀疏贡献图 ★ ★ 根据61210012、61290324， 61490700，61473164

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摘要

Chemical processes usually work under different operating modes to meet the market demand and to achieve higher profits. This necessitates investigating related algorithms and alarm systems for multimodal chemical processes. Although some research effort has been made to monitor multimodal processes, little attention was paid to fault diagnosis issue when addressing multiple modes. In this paper, we present both a label consistent dictionary learning (LCDL) based multimode process monitoring approach and sparse contribution plot (SpCP) for fault diagnosis. Firstly, a discriminative and reconstructive dictionary is obtained from normal historical process data via label consistent K-SVD algorithm. In addition, we augment the learned dictionary to get another dictionary, which consists of two blocks, one for multiple normal operating modes and another for faults. Then, during online monitoring period, a new sample is coded sparsely using the aforementioned augmented dictionary. After that, its dictionary reconstruction residual (DRR) is calculated for fault detection purpose. At last, a novel sparse contribution plot is proposed to figure out the root cause of the detected fault. The SpCP is better able to highlight the real cause with no ambiguity in that only a small fraction of variables’ sparse contributions are nonzeros. The effectiveness of the proposed methodology is demonstrated by both a numerical simulation and a continuous stirred tank heater (CSTH) process.
机译：化学工艺通常在不同的操作模式下工作，以满足市场需求并获得更高的利润。这需要研究用于多峰化学过程的相关算法和警报系统。尽管已经进行了一些研究来监视多模式过程，但是在解决多模式问题时很少关注故障诊断问题。在本文中，我们同时提出了基于标签一致性字典学习（LCDL）的多模式过程监视方法和用于故障诊断的稀疏贡献图（SpCP）。首先，通过标签一致性K-SVD算法从正常的历史过程数据中获得判别式和重构式字典。另外，我们扩充学习的字典以获得另一个字典，该字典由两个块组成，一个块用于多种正常操作模式，另一个块用于故障。然后，在在线监视期间，使用上述增强字典稀疏地编码新样本。之后，计算其字典重构残差（DRR）以进行故障检测。最后，提出了一种新的稀疏贡献图来找出故障的根本原因。 SpCP能够更好地突出真正的原因，因为变量的稀疏贡献中只有一小部分为非零。数值模拟和连续搅拌釜式加热器（CSTH）工艺均证明了所提出方法的有效性。

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