A non-intrusive approach for fault detection and diagnosis of water distribution systems based on image sensors, audio sensors and an inspection robot

Fault diagnosis is important to maintain the normal operation of air-conditioning systems, reduce the energy consumption in buildings, and increase the service life of air-conditioning system equipment. We present a novel approach for fault detection and diagnosis system that relies on image and audio sensors and relevant algorithms. This paper proposes a fault diagnosis algorithm based on a robot that can automatically capture audio and image signals from microphone arrays and cameras during inspection in a chiller room. It includes audio-and image-based fault diagnosis algorithms. The validity of the algorithm combined with sensors is verified using data from actual equipment in a chiller room. The audio-based algorithm, which can monitor the abnormal sound of pumps to detect faults, utilizes Fourier transform, a finite impulse response digital filter, and an autoregressive integrated moving average model. We analyze the frequency domain of the pump signal and set the appropriate threshold to monitor abnormal signals based on the fitted model. Meanwhile, the image-based algorithms are divided into three sections to achieve three functions: 1) an AlexNet convolutional neural network is modified to classify the images of the chiller room equipment obtained by the visible light camera; 2) image morphology methods and trigonometric functions are used to read the dials' indicators acquired by the visible light camera; and 3) optical character recognition is used to obtain the highest temperature value in the infrared image of the pump captured by the infrared camera, which helps maintenance staff verify the operation of the pump and detect faults as soon as possible. These diagnostic algorithms are non-intrusive, low cost, and easy to deploy. Combined with real-time data collection from the sensors on the robot, the algorithms can effectively improve the intelligence of the equipment room and allocate human resources more reasonably. (c) 2021 Elsevier B.V. All rights reserved.