Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

Reza Sharifi; Reza Langari

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Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

机译：混合概率PCA模型的非线性传感器故障诊断

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摘要

This paper presents a methodology for sensor fault diagnosis in nonlinear systems using a Mixture of Probabilistic Principal Component Analysis (MPPCA) models. This methodology separates the measurement space into several locally linear regions, each of which is associated with a Probabilistic PCA (PPCA) model. Using the transformation associated with each PPCA model, a parity relation scheme is used to construct a residual vector. Bayesian analysis of the residuals forms the basis for detection and isolation of sensor faults across the entire range of operation of the system. The resulting method is demonstrated in its application to sensor fault diagnosis of a fully instrumented HVAC system. The results show accurate detection of sensor faults under the assumption that a single sensor is faulty.

机译：本文提出了一种使用概率主成分分析（MPPCA）模型的非线性系统中传感器故障诊断的方法。这种方法将测量空间分为几个局部线性区域，每个区域都与概率PCA（PPCA）模型相关联。使用与每个PPCA模型相关的转换，使用奇偶校验关系方案构造残差矢量。残差的贝叶斯分析构成了在系统整个操作范围内检测和隔离传感器故障的基础。所产生的方法在将其应用到功能齐全的HVAC系统的传感器故障诊断中得到了证明。结果表明在单个传感器有故障的假设下，传感器故障的准确检测。

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来源
《Mechanical systems and signal processing》 |2017年第2期|638-650|共13页
作者
Reza Sharifi; Reza Langari;
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作者单位

Philips Research North America, Briarcliff Manor, NY 1051, USA;

Department of Mechanical Engineering, Texas A&M University, College Station, TX 77843-3135, USA;

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原文格式 PDF
正文语种 eng
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关键词
Fault diagnosis; Sensor fault detection; Principal Component Analysis; Mixture of Probabilistic PCA; Nonlinear fault detection;

机译：故障诊断;传感器故障检测;主成分分析;概率PCA的混合物;非线性故障检测;

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1. Dynamic mixture probabilistic PCA classifier modeling and application for fault classification [J] . Zhu Jinlin, Ge Zhiqiang, Song Zhihuan Journal of Chemometrics . 2015,第6期

机译：动态混合概率PCA分类器建模及其在故障分类中的应用
2. Nonlinear feature extraction for soft sensor modeling based on weighted probabilistic PCA [J] . Yuan Xiaofeng, Ye Lingjian, Bao Liang, Chemometrics and Intelligent Laboratory Systems . 2015,第Null期

机译：基于加权概率PCA的软传感器建模非线性特征提取
3. Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach [J] . Guo Yabin, Chen Huanxin International Journal of Refrigeration . 2020,第1期

机译：基于改进的高斯混合模型与PCA方法的VRF空调系统故障诊断
4. A batch-incremental process fault detection and diagnosis using mixtures of probablistic PCA [C] . Nakamura Thiago, Lemos Andre Institute of Electrical and Electronics Engineers Conference on Evolving and Adaptive Intelligent Systems . 2014

机译：使用概率PCA的混合物进行批次增量式过程故障检测和诊断
5. Statistical modeling and sensor fault diagnosis using PCA. [D] . Bai, Edward Y. 2005

机译：使用PCA进行统计建模和传感器故障诊断。
6. Robust Model-Based Sensor Fault Monitoring System for Nonlinear Systems in Sensor Networks [O] . Dejun Wang, Shiyao Song 2014

机译：传感器网络中基于非线性模型的鲁棒传感器故障监测系统
7. An improved mixture of probabilistic PCA for nonlinear data-driven process monitoring [O] . Zhang Jingxin, Chen Hao, Chen Songhang, 2017

机译：用于非线性数据驱动过程监控的概率PCA的改进混合
8. On Models for Diagnosable Systems and Probabilistic Fault Diagnosis. [R] . Maheshwari, S. N., Hakimi, S. L. 1974

机译：可诊断系统模型与概率故障诊断。

Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models

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