An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique. (5th August 2015) - Taheri-Garavand, Amin; Ahmadi, Hojjat; Omid, Mahmoud; Mohtasebi, Seyed Saeid; Mollazade, Kaveh; Russell Smith, Alan John; Carlomagno, Giovanni Maria
Abstract: This research presents a new intelligent fault diagnosis and condition monitoring system for classification of different conditions of cooling radiator using infrared thermal images. The system was adopted to classify six types of cooling radiator faults; radiator tubes blockage, radiator fins blockage, loose connection between fins and tubes, radiator door failure, coolant leakage, and normal conditions. The proposed system consists of several distinct procedures including thermal image acquisition, image pre-processing, image processing, two-dimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection using a genetic algorithm (GA), and finally classification by artificial neural networks (ANNs). The 2D-DWT is implemented to decompose the thermal images. Subsequently, statistical texture features are extracted from the original images and are decomposed into thermal images. The significant selected features are used to enhance the performance of the designed ANN classifier for the 6 types of cooling radiator conditions (output layer) in the next stage. For the tested system, the input layer consisted of 16 neurons based on the feature selection operation. The best performance of ANN was obtained with a 16-6-6 topology. The classification results demonstrated that this system can be employed satisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator. Highlights: Intelligent fault diagnosis of cooling radiator using thermal image processing. Thermal image processing in a multiscale representation structure by 2D-DWT. Selection features based on a hybrid system that uses both GA and ANN. Application of ANN as classifier. Classification accuracy of fault detection up to 93.83%.