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应用于工业机械的模糊故障诊断推理器的模糊分类器的设计与实现

Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery.

作者信息

Zanoli Silvia Maria, Pepe Crescenzo

机构信息

Dipartimento di Ingegneria dell'Informazione, Università Politecnica delle Marche, 60131 Ancona, Italy.

出版信息

Sensors (Basel). 2023 Aug 4;23(15):6954. doi: 10.3390/s23156954.

DOI:10.3390/s23156954
PMID:37571738
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10422568/
Abstract

In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.

摘要

在本工作中，提出了一种用于工业机械的故障检测与隔离（FDI）系统的设计与实现。案例研究以用于合成气制造的多轴离心压缩机为例。该系统旨在监测可能损坏多轴离心压缩机的故障：既考虑了仪器的单个和多个故障，也考虑了诸如压缩机级结垢和推力轴承损坏等过程故障。开发了一种结合主成分分析（PCA）、聚类分析和模式识别的新方法。应用基于统计检验方差分析（ANOVA）的新程序来确定最合适的主成分数量。所提出的故障隔离方案的一个关键设计问题是进行数据聚类分析，以解决在分析通常涉及多个变量的过程故障时所经历的复杂性增长这一实际问题。此外，还提出了一种用于找出最可能故障的自动在线模式识别程序。聚类程序和模式识别在模糊故障分类器模块中实现。对实际工厂数据的实验结果说明了该方法的有效性。FDI系统产生的主要益处包括维护操作的改进、压缩机可靠性和可用性的提高、工厂安全性的增强以及工厂运行成本的降低。

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