Fault detection and diagnosis for multiple faults of VAV terminals using self-adaptive model and layered random forest

2021 ◽  
Vol 193 ◽  
pp. 107667
Author(s):  
Haitao Wang ◽  
Daoguang Feng ◽  
Kai Liu
Keyword(s):  
Random Forest ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Adaptive Model ◽  
Multiple Faults ◽  
Detection And Diagnosis ◽  
Self Adaptive

scholarly journals Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8163
Author(s):  
Wunna Tun ◽  
Johnny Kwok-Wai Wong ◽  
Sai-Ho Ling
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Fault Detection ◽  
Early Stage ◽  
Fault Detection And Diagnosis ◽  
Training Data ◽  
Support Vector ◽  
Hvac System ◽  
Machine Modeling ◽  
Detection And Diagnosis

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

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.

Online Adaptive Model Based Fault Detection, Isolation and Estimation Method

2011 ◽  
Author(s):  
Aniket Gupta ◽  
Karolos Grigoriadis ◽  
Matthew Franchek ◽  
Daniel J. Smith
Keyword(s):  
Fault Detection ◽  
Estimation Method ◽  
Fault Detection And Diagnosis ◽  
Exhaust Gas ◽  
Adaptive Model ◽  
Process Variability ◽  
Model Based ◽  
Cycle Simulation ◽  
Modeling Errors ◽  
Detection And Diagnosis

In the present study a methodology to perform fault detection, isolation and estimation is proposed that is based on adaptive model based techniques. Fault detection and diagnostics is performed by comparing the coefficients of healthy system model with that of adapted online coefficients. This approach is shown to be robust to modeling errors, sensor noise and process variability. The proposed approach is applied to FTP-75 cycle simulation data of exhaust gas recircultaion (EGR) faults and is shown to effectively perform fault detection and diagnosis.

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.

scholarly journals Neural Network Based Fault Detection and Diagnosis System for Three-Phase Inverter in Variable Speed Drive with Induction Motor

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Furqan Asghar ◽  
Muhammad Talha ◽  
Sung Ho Kim
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Fault Detection And Diagnosis ◽  
Diagnosis System ◽  
Multiple Faults ◽  
Phase Inverter ◽  
Three Phase ◽  
Detection And Diagnosis ◽  
Three Phase Inverter

Recently, electrical drives generally associate inverter and induction machine. Therefore, inverter must be taken into consideration along with induction motor in order to provide a relevant and efficient diagnosis of these systems. Various faults in inverter may influence the system operation by unexpected maintenance, which increases the cost factor and reduces overall efficiency. In this paper, fault detection and diagnosis based on features extraction and neural network technique for three-phase inverter is presented. Basic purpose of this fault detection and diagnosis system is to detect single or multiple faults efficiently. Several features are extracted from the Clarke transformed output current and used in neural network as input for fault detection and diagnosis. Hence, some simulation study as well as hardware implementation and experimentation is carried out to verify the feasibility of the proposed scheme. Results show that the designed system not only detects faults easily, but also can effectively differentiate between multiple faults. These results prove the credibility and show the satisfactory performance of designed system. Results prove the supremacy of designed system over previous feature extraction fault systems as it can detect and diagnose faults in a single cycle as compared to previous multicycles detection with high accuracy.

Sign in / Sign up

Export Citation Format

Share Document