Real-time monitoring of fan operation in livestock houses based on the image processing. (1st March 2023) - Ding, Luyu; Lv, Yang; Yu, Ligen; Ma, Weihong; Li, Qifeng; Gao, Ronghua; Yu, Qinyang
Highlights: A machine vision method was developed to monitor fan operations in animal houses. Aggregability and discriminability were found for the displacement matrix of pixels. Proper resolution and threshold were recommended in using the proposed method. Fan running state was 100% identified with the error of 1.84% for airflow rate. Abstract: Real-time monitoring of fan operation is essential for supervising and regulating the airflow rate in a mechanical ventilated animal house. This study proposed a real-time method based on image processing and mathematical modelling to monitor the running state and airflow rate of exhaust fans to ensure the adequate ventilation in a livestock house. Videos and actual airflow rates of a running fan were collected at different operating levels (15, 25, 35, and 45 Hz). The Hough transformation was used to locate the fan in the image, and the dense optical flow was used to calculate the displacement of pixels during fan operation. It was found that the vector sum of the displacements for all pixels in the image of a running fan tends to be zero and the variances of the displacements were discriminable at different operation levels, which can be potentially used to monitor the running state and airflow rate of an exhaust fan. Running state (yes or no) of the fan was identified according to the proportion of moving pixels in the target area and its airflow rate can be estimated by curve fitting between the variance of pixel displacement and measured airflow rates at different operating levels. Buffer time window was used for smoothing to increase the accuracy and stability of fan operation monitoring. Using the proposed method, running state of the fan could be 100 % identified and all fans in sight of the camera could be monitored at the same time. In estimating airflow rate, average absolute percentage error of the propose method was 1.84 % (115.30 m 3 /h) and it reaches the detection limitation when the operating levels was above 72 % of the maximum airflow rate (50 Hz, 18, 000 m 3 /h) due to the time gap between fan running and the frame of imaging. Furthermore, results showed that a minimum resolution of 1280 × 720 was required when using this method to estimate airflow rate of a fan and the higher resolution of images could improve the estimation accuracy.