Fault monitoring of high-sulfur gas sweetening process by combined indicator of CW-HCA

GU Xiaohua; ZHANG Kun; WANG Tian; HOU Song; SONG Hongfei; LI Taifu; QIU Kui

doi:10.11949/j.issn.0438-1157.20180492

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Fault monitoring of high-sulfur gas sweetening process by combined indicator of CW-HCA

更新时间：2025-12-10

- Fault monitoring of high-sulfur gas sweetening process by combined indicator of CW-HCA
- CIESC Journal Issue 10, Pages: 4292-4301(2018)
- 作者机构：
  
  1. 重庆科技学院电气与信息工程学院,重庆,401331
  2. 四川理工学院四川省人工智能实验室,四川,自贡,643000
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20180492
  CLC： P277
- Online First：05 October 2018，
  
  Published：05 October 2018
- 稿件说明：
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GU Xiaohua, ZHANG Kun, WANG Tian, et al. Fault monitoring of high-sulfur gas sweetening process by combined indicator of CW-HCA[J]. CIESC Journal, 2018, (10): 4292-4301.
DOI：

GU Xiaohua, ZHANG Kun, WANG Tian, et al. Fault monitoring of high-sulfur gas sweetening process by combined indicator of CW-HCA[J]. CIESC Journal, 2018, (10): 4292-4301. DOI： 10.11949/j.issn.0438-1157.20180492.

摘要

高含硫天然气（HSG）净化过程复杂，导致安全开发风险极高。因此保障净化系统可靠运行、实现过程安全生产具有重要意义。对于类似化工过程，在独立成分分析（ICA）方法基础上，高阶累积量分析（HCA）用样本三阶累积量代替均值方差构造统计指标大大提高了检测率。然而，HCA构造独立分量空间指标时未考虑不同独立分量间重要性差异，这可能致使监测结果经样本高阶累积后出现一定程度的偏差。同时，采用多指标监测策略可能出现指标间监测结果相互冲突问题。为此，提出一种基于贡献度加权高阶累积量分析（CW-HCA）联合指标的故障监测方法。该方法根据独立分量的贡献度对样本的三阶累积量进行加权；再将加权后的指标与残差空间指标联合获得联合指标，实现监测。TE以及HSG净化过程的实验结果表明，所提算法相比ICA算法、HCA算法具有有效性和优越性。

Abstract

The high-sulfur gas (HSG) sweetening process is large-scale and complicated. The acid component may cause great security risk. Applying real-time monitoring of the production process has important significance to ensure the system normal work and safety. Regarding to such chemical processes

the higher-order cumulants analysis (HCA) used the higher-order cumulants and their polyspectras of samples to construct the statistical index

which greatly improves the detection rate. Nevertheless

the different importance of independent components is not concerned in the construction of HS statistical indicators

which may lead to a certain degree of deviation of the monitoring results. Meanwhile

using multiple-indicators may cause the conflicting results among indicators so that the accuracy cannot be guaranteed. Therefore

a fault monitoring method based on contribution weighted higher-order cumulants analysis (CW-HCA) is proposed. The approach weights the sample's third order cumulant according to the contribution of independent components respectively

afterwards

the new weighted index and the residual spatial index are combined to realize the real-time monitoring. The results in TE and HSG sweetening process indicate that

compared with ICA and HCA

the proposed algorithm shows significant efficiency and superiority.

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