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Novel Pattern-Matching Integrated KCVA with Adaptive Rank-Order Morphological Filter and Its Application to Fault Diagnosis
Industrial & Engineering Chemistry Research ( IF 4.2 ) Pub Date : 2020-01-14 , DOI: 10.1021/acs.iecr.9b05403
Yuan Xu 1, 2, 3 , Cuihuan Fan 1, 2 , Qun-Xiong Zhu 1, 2 , Abbas Rajabifard 3 , Nengcheng Chen 4 , Yiqun Chen 3 , Yan-Lin He 1, 2
Affiliation  

With the scale expansion of industrial processes, the relationship between process variables has become complex and highly nonlinear. As a result, the requirements for fault diagnosis and safety monitoring has become demanding. To address this problem, a novel and effective pattern-matching method using kernel canonical variate analysis (KCVA) integrated with an adaptive rank-order morphological filter (ARMF) is proposed for fault diagnosis. In the proposed method, KCVA is first used to extract the nonlinear correlation information with dynamic characteristics from the original process data and achieve feature dimension reduction; the features extracted by KCVA are then subjected to ARMF transformation for output trend features and pattern matching. To accurately evaluate the morphological similarity between the test trends and template trends of ARMF, the dynamic time warping distance is adopted for pattern classification. Finally, the proposed KCVA–ARMF pattern-matching method is developed as an effective fault diagnosis model for complex industrial processes. To validate the performance of the proposed method, case studies using the Tennessee Eastman process are performed. Compared with some other multivariate statistical process monitoring methods, the simulation results indicate that the proposed KCVA–ARMF method can obtain higher accuracy in fault diagnosis, especially for difficult-to-diagnose faults.

中文翻译:

自适应秩序形态滤波器的新型模式匹配集成KCVA及其在故障诊断中的应用

随着工业过程规模的扩大,过程变量之间的关系变得复杂且高度非线性。结果,对故障诊断和安全监视的要求变得苛刻。为了解决这个问题,提出了一种新的有效的模式匹配方法,该方法利用核规范变异分析(KCVA)与自适应秩序形态学滤波器(ARMF)相集成来进行故障诊断。在该方法中,首先使用KCVA从原始过程数据中提取具有动态特性的非线性相关信息,并实现特征量的缩减。然后,对KCVA提取的特征进行ARMF转换,以输出趋势特征和模式匹配。为了准确评估ARMF的测试趋势和模板趋势之间的形态相似性,采用动态时间规整距离进行模式分类。最后,提出的KCVA–ARMF模式匹配方法被开发为复杂工业过程的有效故障诊断模型。为了验证所提出方法的性能,使用田纳西州伊士曼过程进行了案例研究。与其他多元统计过程监测方法相比,仿真结果表明,所提出的KCVA-ARMF方法在故障诊断中,尤其是对于难以诊断的故障,具有较高的诊断精度。提出的KCVA–ARMF模式匹配方法被开发为复杂工业过程的有效故障诊断模型。为了验证所提出方法的性能,使用田纳西州伊士曼过程进行了案例研究。与其他多元统计过程监测方法相比,仿真结果表明,所提出的KCVA-ARMF方法在故障诊断中,尤其是对于难以诊断的故障,具有较高的诊断精度。提出的KCVA–ARMF模式匹配方法被开发为复杂工业过程的有效故障诊断模型。为了验证所提出方法的性能,使用田纳西州伊士曼过程进行了案例研究。与其他多元统计过程监测方法相比,仿真结果表明,所提出的KCVA-ARMF方法在故障诊断中,尤其是对于难以诊断的故障,具有较高的诊断精度。
更新日期:2020-01-15
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