Online fault detection and diagnosis in VAV air handling unit by RARX modeling

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.

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Sensor Fault Detection and Diagnosis Method for AHU Using 1-D CNN and Clustering Analysis

Computational Intelligence and Neuroscience ◽

10.1155/2019/5367217 ◽

2019 ◽

Vol 2019 ◽

pp. 1-20 ◽

Cited By ~ 2

Author(s):

Jingjing Liu ◽

Min Zhang ◽

Hai Wang ◽

Wei Zhao ◽

Yan Liu

Keyword(s):

Fault Detection ◽

Clustering Analysis ◽

Fault Detection And Diagnosis ◽

Sensor Faults ◽

Control Loop ◽

One Dimensional ◽

Air Handling Unit ◽

Sensor Fault Detection ◽

Diagnosis Method ◽

Detection And Diagnosis

This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature (Tsup) control loop of the air handling unit. In this approach, 1-D CNN is employed to extract man-guided features from raw data, and the extracted features are analyzed by WaveCluster clustering. The suspicious sensor faults are indicated and categorized by denoting clusters. Moreover, the Tc acquittal procedure is introduced to further improve the accuracy of FDD. In validation, false alarm ratio and missing diagnosis ratio are mainly used to demonstrate the efficiency of the proposed FDD method. Results show that the abrupt sensor faults in Tsup control loop can be efficiently detected and diagnosed, and the proposed method is equipped with good robustness within the noise range of 6 dBm∼13 dBm.

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