scholarly journals A nonlinear support vector machine‐based feature selection approach for fault detection and diagnosis: Application to the Tennessee Eastman process

AIChE Journal ◽  
2019 ◽  
Vol 65 (3) ◽  
pp. 992-1005 ◽  
Author(s):  
Melis Onel ◽  
Chris A. Kieslich ◽  
Efstratios N. Pistikopoulos
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Support Vector ◽  
Tennessee Eastman Process ◽  
Selection Approach ◽  
Detection And Diagnosis ◽  
Feature Selection Approach ◽  
Nonlinear Support

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.

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