A Time-series Rank Ordering Control-system Data-driven Fault Detection Approach for HVAC Systems in Buildings

2021 ◽  
pp. 1-12
Author(s):  
James Shia ◽  
David M. Auslander
Keyword(s):  
Control System ◽  
Fault Detection ◽  
Air Conditioning ◽  
Low Cost ◽  
Hvac Systems ◽  
Fault Detection And Diagnosis ◽  
Machine Learning Algorithms ◽  
Total Energy Consumption ◽  
Model Free ◽  
System Data

Abstract About 41% of total energy consumption in the U.S. of year 2014 is used for heating and air conditioning, that is about 40 quadrillion (40×1015) British thermal units (BTU). Despite the fact that people have been working on on fault detection and diagnosis (FDD) for Heating, Ventilation, and Air Conditioning (HVAC) systems for a long time, very few publications have focused on scalability and low cost. In order to address this challenge, we will propose an approach which focuses on control-system data. Several machine learning algorithms are introduced for data exploration and analysis, a control-system data focused model free approach is presented as well, and finally, FDD is carried out by implementing anomaly algorithms. A simulation model is used to evaluate the performance of the various algorithms.

scholarly journals Fault Detection and Diagnosis of Air Handling Unit: A Review

2019 ◽  
Vol 255 ◽  
pp. 06001 ◽  
Author(s):  
Cheng Yew Leong
Keyword(s):  
Fault Detection ◽  
Air Conditioning ◽  
Early Stage ◽  
Fault Detection And Diagnosis ◽  
Commercial Building ◽  
Air Handling Unit ◽  
Operations And Maintenance ◽  
Detection And Diagnosis ◽  
Air Conditioning Systems ◽  
And Control

Air-conditioning systems consumed the most energy usage nearly 45% of the total energy used in commercial-building. Where AHU is one of the most extensively operated equipment and this device is typical customize and complex which can results in hardwire failure and controller errors. The efficiency of the system is very much depending on the proper functioning of sensors. Faults arising from the sensors and control systems are a major contribution to the energy wastage. As such faults often go unnoticed for extended periods of time until the deterioration in performance becomes great enough to trigger comfort complaints or total equipment failure. Energy could be reduced if those faults can be detected and identified at early stage. This paper aims to review of various existing automated fault detection and diagnosis (AFDD) methods for an Air Handling Unit. The background of AHU system, general fault detection and diagnosis framework and typical faults in AHU is described. Comparison and evaluation of the various methodologies will be reviewed in this paper. This comparative study also reveals the strengths and weaknesses of the different approaches. The important role of fault diagnosis in the broader context of air- conditioning is also outlined. By identifying and diagnosing faults to be repaired, these techniques can benefits building owners by reducing energy consumption, improving indoor air quality and operations and maintenance.

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.

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