Anomaly Detection in Air Handling Units using Motor Current Signal Imaging for Belt Looseness Detection

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Published Sep 4, 2023
Sung Hyun Yun Wonho Jung Daeguen Lim Yong-Hwa Park

Abstract

An air handling unit (AHU) is a critical component of heating, ventilation, and air conditioning (HVAC) systems. Slip of AHU is an intuitive key feature for monitoring a belt looseness fault of an AHU. However, fluctuating rotation speed of the motor and fan makes slip hard to monitor. Since the role of the belt is to deliver torque between the motor and fan, this leads to change of the motor current signal. This paper suggests a normal data-based anomaly detection that utilizes motor current signal imaging to identify belt looseness in AHUs. The overall process proceeds as followings: (1) converting 1-dimensional motor current signal into 2-dimentional image in the amplitude domain, (2) extracting features of normal data by applying convolutional neural networks, (3) calculating health index to detect the belt looseness fault. The technique to transform time-series current data to an image is based on its histogram. The image is obtained by the inner product of the histogram obtained from a current signal and its transpose. The effect of torque load on a motor induces an amplitude modulation of the current signal. Current signal imaging based on histogram provides the fault features in a robust way. To validate the proposed method, a case study using an AHU testbed is conducted. The results demonstrate that the proposed method can detect belt looseness faults in AHU using only normal data, providing an approach for early fault detection in HVAC systems.

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Keywords

air handling unit, belt looseness, anomaly detection, motor current signal imaging, histogram

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Section
Special Session Papers