集合型故障检测与诊断技术研究
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摘要
故障检测与诊断(FDD, Fault Detection and Diagnosis)是保证现代工业安全性、可靠性、产品质量和效益的重要手段。由于传统的单一型故障检测与诊断技术仅适合处理某些简单系统,难以直接扩展至时变、非线性、非高斯、多模、间歇等复杂的多变量动态随机工业过程,因此,近年来将几种单一型故障检测与诊断技术相结合的集合型故障检测与诊断技术,特别是,集合型实时故障检测与诊断技术成为国内外学术界和工业界研究的热点与难点。
为了提高集合型故障检测与诊断的准确性、实时性和快速性,本文在总结和分析国内外研究现状基础上,重点围绕集合型实时故障检测与诊断的三个关键技术：提升小波实时降噪技术、故障特征增量提取技术、基于在线机器学习的故障分类与聚类技术,展开了深入研究。主要工作包括：1)集合型实时故障检测与诊断一般结构研究；2)针对实时数据,集合型实时故障检测与诊断改进方法研究；3)针对历史数据,批量式集合型快速故障检测与诊断改进方法研究。
本文将提升小波、增量式特征提取和在线分类与聚类技术相结合,提出了一种集合型实时故障检测与诊断典型结构,即“提升小波实时去噪+增量式特征提取+在线故障分类与聚类”结构。
为了降低噪声对集合型实时故障检测与诊断性能的影响,提出了基于提升小波双变量阈值实时降噪方法。
为了提高集合型故障检测的准确性、实时性和快速性,针对高斯过程,提出了3种集合型故障检测改进方法。具体为：针对时变过程,提出了基于提升小波和移动窗PCA (LW-MWPCA)实时故障检测方法；针对动态非线性过程,提出了基于提升小波和动态核PCA (LW-DKPCA)故障检测方法；针对间歇过程,提出了基于提升小波和多项PCA(LW-MPCA)故障检测方法。
为了提高集合型故障诊断的准确性、实时性和快速性,针对高斯过程,提出了基于提升小波和自适应递推最小二乘支持向量机(LW-ARLSSVM)、基于提升小波和增量概率神经网络(LW-IPNN)、基于提升小波和增量聚类(LW-ICLUSTER)3种集合型实时故障诊断改进方法；针对非高斯过程,提出了在提升小波实时降噪基础上,基于快速独立成分分析和在线机器学习两种集合型快速故障诊断改进方法,即LW-FICA-ARLSSVM、LW-FICA-IPNN方法。
理论分析和实验研究表明,本文提出的集合型故障检测与诊断系列方法的性能优于单一类型的故障检测与诊断方法的性能。本论文的研究,为集合型实时故障检测与诊断技术的应用提供了理论依据。
Fault detection and diagnosis (FDD) is an important means to ensure industrial safety, reliability, product quality and effectiveness. Traditional single fault detection and diagnosis techniques are only suitable for handling some simple systems, and are difficult to be directly extended to complex multi-variable, dynamic, stochastic industrial processes with time-varying, nonlinear, non-Gaussian, multi-models, fed batch characteristics. For these reasons, ensemble fault detection and diagnosis techniques, which combine several single methods, in particular, ensemble real-time fault detection and diagnosis techniques have become research hotspots and are full of challenge in recent years.
To improve the accuracy, real-time and rapidity of ensemble fault detection and diagnosis, this thesis based on summary and analysis of existing research work at home and abroad, pays much attention on three key technologies of the ensemble real-time fault detection and diagnosis:data real-time de-noising technique based on lifting wavelet transform, incremental fault feature extraction technique, fault classification and clustering technique based on online machine learning. The main tasks of this thesis are:1) to do research on general structure of ensemble real-time fault detection and diagnosis;2) to present some improved real-time ensemble fault detection and diagnosis methods based on real-time data;3) to put forward some improved fast ensemble approaches of fault detection and diagnosis based on historical data.
A typical structure of ensemble real-time fault detection and diagnosis, which is composed of "lift wavelet de-noising+incremental feature extraction+online fault classification and clustering" is presented in this thesis.
To reduce the impact of noise on the performance of ensemble real-time fault detection and diagnosis, a real-time de-noising method which based on lifting wavelet bivariate threshold is proposed.
To improve the accuracy, real-time and rapidity of ensemble fault detection, three ensemble fault detection approaches are proposed. Specifically, they are the improved real-time fault detection methods based on lifting wavelet and moving window PCA (LW-MWPCA) for stochastic time-varying systems, lifting wavelet and dynamic kernel PCA (LW-DKPCA) for dynamic nonlinear systems, and lifting wavelet and multiway PCA (LW-MPCA) for batch processes.
To improve the accuracy, real-time and rapidity of ensemble fault diagnosis, three improved ensemble real-time fault diagnosis methods that are based on lifting wavelet and adaptive recursive least squares support vector machine (LW-ARLSSVM), or based on lifting wavelet and incremental probabilistic neural network (LW-IPNN), or based on lifting wavelet and incremental clustering (LW-ICLUSTER) are proposed for real-time data. In addition, based on real-time noise reduction by lifting wavelet, two improved ensemble fast fault diagnosis approaches including fast independent component analysis and adaptive recursive support vector machine (LW-FICA-ARLSSVM), fast independent component analysis and incremental probabilistic neural network (LW-FICA-IPNN) are presented for non-Gaussian processes.
Theoretical analysis and experimental studies have shown that the performance of ensemble fault detection and diagnosis approaches proposed in this thesis are superior to that of single type methods of fault detection and diagnosis. This thesis provides a theoretical basis for the application of ensemble real-time fault detection and diagnosis techniques.
为了提高集合型故障检测与诊断的准确性、实时性和快速性,本文在总结和分析国内外研究现状基础上,重点围绕集合型实时故障检测与诊断的三个关键技术：提升小波实时降噪技术、故障特征增量提取技术、基于在线机器学习的故障分类与聚类技术,展开了深入研究。主要工作包括：1)集合型实时故障检测与诊断一般结构研究；2)针对实时数据,集合型实时故障检测与诊断改进方法研究；3)针对历史数据,批量式集合型快速故障检测与诊断改进方法研究。
本文将提升小波、增量式特征提取和在线分类与聚类技术相结合,提出了一种集合型实时故障检测与诊断典型结构,即“提升小波实时去噪+增量式特征提取+在线故障分类与聚类”结构。
为了降低噪声对集合型实时故障检测与诊断性能的影响,提出了基于提升小波双变量阈值实时降噪方法。
为了提高集合型故障检测的准确性、实时性和快速性,针对高斯过程,提出了3种集合型故障检测改进方法。具体为：针对时变过程,提出了基于提升小波和移动窗PCA (LW-MWPCA)实时故障检测方法；针对动态非线性过程,提出了基于提升小波和动态核PCA (LW-DKPCA)故障检测方法；针对间歇过程,提出了基于提升小波和多项PCA(LW-MPCA)故障检测方法。
为了提高集合型故障诊断的准确性、实时性和快速性,针对高斯过程,提出了基于提升小波和自适应递推最小二乘支持向量机(LW-ARLSSVM)、基于提升小波和增量概率神经网络(LW-IPNN)、基于提升小波和增量聚类(LW-ICLUSTER)3种集合型实时故障诊断改进方法；针对非高斯过程,提出了在提升小波实时降噪基础上,基于快速独立成分分析和在线机器学习两种集合型快速故障诊断改进方法,即LW-FICA-ARLSSVM、LW-FICA-IPNN方法。
理论分析和实验研究表明,本文提出的集合型故障检测与诊断系列方法的性能优于单一类型的故障检测与诊断方法的性能。本论文的研究,为集合型实时故障检测与诊断技术的应用提供了理论依据。
Fault detection and diagnosis (FDD) is an important means to ensure industrial safety, reliability, product quality and effectiveness. Traditional single fault detection and diagnosis techniques are only suitable for handling some simple systems, and are difficult to be directly extended to complex multi-variable, dynamic, stochastic industrial processes with time-varying, nonlinear, non-Gaussian, multi-models, fed batch characteristics. For these reasons, ensemble fault detection and diagnosis techniques, which combine several single methods, in particular, ensemble real-time fault detection and diagnosis techniques have become research hotspots and are full of challenge in recent years.
To improve the accuracy, real-time and rapidity of ensemble fault detection and diagnosis, this thesis based on summary and analysis of existing research work at home and abroad, pays much attention on three key technologies of the ensemble real-time fault detection and diagnosis:data real-time de-noising technique based on lifting wavelet transform, incremental fault feature extraction technique, fault classification and clustering technique based on online machine learning. The main tasks of this thesis are:1) to do research on general structure of ensemble real-time fault detection and diagnosis;2) to present some improved real-time ensemble fault detection and diagnosis methods based on real-time data;3) to put forward some improved fast ensemble approaches of fault detection and diagnosis based on historical data.
A typical structure of ensemble real-time fault detection and diagnosis, which is composed of "lift wavelet de-noising+incremental feature extraction+online fault classification and clustering" is presented in this thesis.
To reduce the impact of noise on the performance of ensemble real-time fault detection and diagnosis, a real-time de-noising method which based on lifting wavelet bivariate threshold is proposed.
To improve the accuracy, real-time and rapidity of ensemble fault detection, three ensemble fault detection approaches are proposed. Specifically, they are the improved real-time fault detection methods based on lifting wavelet and moving window PCA (LW-MWPCA) for stochastic time-varying systems, lifting wavelet and dynamic kernel PCA (LW-DKPCA) for dynamic nonlinear systems, and lifting wavelet and multiway PCA (LW-MPCA) for batch processes.
To improve the accuracy, real-time and rapidity of ensemble fault diagnosis, three improved ensemble real-time fault diagnosis methods that are based on lifting wavelet and adaptive recursive least squares support vector machine (LW-ARLSSVM), or based on lifting wavelet and incremental probabilistic neural network (LW-IPNN), or based on lifting wavelet and incremental clustering (LW-ICLUSTER) are proposed for real-time data. In addition, based on real-time noise reduction by lifting wavelet, two improved ensemble fast fault diagnosis approaches including fast independent component analysis and adaptive recursive support vector machine (LW-FICA-ARLSSVM), fast independent component analysis and incremental probabilistic neural network (LW-FICA-IPNN) are presented for non-Gaussian processes.
Theoretical analysis and experimental studies have shown that the performance of ensemble fault detection and diagnosis approaches proposed in this thesis are superior to that of single type methods of fault detection and diagnosis. This thesis provides a theoretical basis for the application of ensemble real-time fault detection and diagnosis techniques.
引文
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