scholarly journals Fault Detection and Diagnosis for Plasticizing Process of Single-Base Gun Propellant Using Mutual Information Weighted MPCA under Limited Batch Samples Modelling

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 166
Author(s):  
Mingyi Yang ◽  
Junyi Wang ◽  
Yinlong Zhang ◽  
Xinlin Bai ◽  
Zhigang Xu ◽  
...  
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Principal Component ◽  
Fault Detection And Diagnosis ◽  
Multiple Faults ◽  
Single Base ◽  
Normalized Mutual Information ◽  
Detection Model ◽  
Detection And Diagnosis ◽  
Coupling Characteristics

Aiming at the lack of reliable gradual fault detection and abnormal condition alarm and evaluation ability in the plasticizing process of single-base gun propellant, a fault detection and diagnosis method based on normalized mutual information weighted multiway principal component analysis (NMI-WMPCA) under limited batch samples modelling was proposed. In this method, the differences of coupling correlation among multi-dimensional process variables and the coupling characteristics of linear and nonlinear relationships in the process are considered. NMI-WMPCA utilizes the generalization ability of a multi-model to establish an accurate fault detection model in limited batch samples, and adopts fault diagnosis methods based on a multi-model SPE statistic contribution plot to identify the fault source. The experimental results demonstrate that the proposed method is effective, which can realize the rapid detection and diagnosis of multiple faults in the plasticizing process.

Multiple Fault Detection and Diagnosis in a Gas Turbine Using Nonlinear Principal Component Analysis and Structured Residuals

2013 ◽  
Author(s):  
Amilcar Rincon-Charris ◽  
Joseba Quevedo-Casin
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Multiple Fault ◽  
Nonlinear Principal Component Analysis ◽  
Multiple Faults ◽  
Detection And Diagnosis

Multiple fault detection and diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. In addition, multiple faults in dynamic systems may be hard to detect, because they can mask or compensate each other’s effects. This paper presents the study of the detection and diagnosis of multiple faults in a SR-30 Gas Turbine using nonlinear principal component analysis as the detection method and structured residuals as the diagnosis method. The study includes developing a mathematical model, software simulation with Matlab Simulink and implementation of algorithms for detection and diagnosis of multiple faults in the system using nonlinear principal component analysis and structured residuals. A real SR-30 gas turbine was used for our studies. The equipment is at the moment installed in the Inter American University of Puerto Rico, Bayamon Campus, and Department of Mechanical Engineering.

scholarly journals Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Yimin Chen ◽  
Jin Wen
Keyword(s):  
Fault Detection ◽  
Principal Component ◽  
Field Evaluation ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Machine Learning Techniques ◽  
Component Level ◽  
Automation Systems ◽  
Tightly Coupled ◽  
Detection And Diagnosis

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.

Fault Detection and Diagnosis for Sensor in Complex Control System Based on KPCA

2014 ◽  
Vol 623 ◽  
pp. 202-210
Author(s):  
Ping Xu ◽  
You Cai Wang ◽  
Kai Wang ◽  
Qiu Yan Wang
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Principal Component ◽  
Kernel Functions ◽  
Fault Detection And Diagnosis ◽  
Kernel Principal Component Analysis ◽  
Practical Result ◽  
Complex Control ◽  
Complex Control System ◽  
Detection And Diagnosis

The Fault detection and diagnosis for sensors are important for the performance of the complex control system seriously. The kernel principal component analysis (KPCA) effectively captures the nonlinear relationship of the process variables, which computes principal component in high-dimensional feature space by means of integral operators and nonlinear kernel functions. The KPCA method is used in diagnosing for four common sensor faults. At first its fault is detected by Q statistic; secondly its fault is identified by T2 contribution percent change. The simulation and the practical result show the KPCA method has good performance on complex control system in sensor fault detection and diagnosis.

Sign in / Sign up

Export Citation Format

Share Document