An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique

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%.

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.

Introduction

The radiator is a key component of an engine's cooling system, playing an important role in maintaining the operating temperature of the engine. The temperature of an internal combustion engine can reach 2700 °C (combustion gases) when it is operating at full throttle. Most engine component materials are not be able to endure this temperature and would rapidly fail if they are not properly cooled. Overheating of the engine can cause oil to thin, engine parts to expand, lubrication to break down, and engine moving parts to be damaged. Therefore removing heat from an engine is indispensable for the appropriate operation of engine. Most of the heat is removed by convection [1], [2] to environmental air. The radiator is a kind of heat exchanger and important element in the cooling system of vehicles. Its main purpose is moving the excessive heat from the engine block to the surrounding air, which ensures reliable operation of the engine [3], [4], [5].

The importance of thermal studies of radiators arises principally from the acknowledged difficulty of detecting the root cause of crack-induced leakage and other types of failures in radiators [6]. Condition monitoring aims to prevent unplanned breakdowns, make the most of the plant availability and decrease associated hazards. There are some non-destructive techniques that are often used for condition monitoring such as vibration analysis, eddy-current testing, radiography, ultrasonic testing, and acoustic emission [7]. Temperature is one of the most useful parameters that indicates the structural health of a machine. Hence, temperature monitoring of equipment or processes has been identified as one of the best predictive maintenance methodologies [8].

Infrared radiation is emitted from the surface of any physical object with temperature above absolute zero. The infrared (IR) energy is not visual since IR radiation is not in the visible range of the electromagnetic spectrum for human and regular cameras. Infrared thermography is a technique used for converting invisible heat energy into a visual thermal image that shows the thermal energy emitting from the object surface. Based on this trait, thermography is currently applied to machine condition monitoring and diagnosis fields where the temperature represents a key parameter [9].

IR thermography has been used for the nondestructive evaluation of joints [10]. Kim et al. [11] used IR thermography for fault diagnosis of ball bearings when rotational machinery had foreign material inside the bearing sunder a dynamic loading condition. Lee and Kim [12] employed thermal imaging for the early detection and condition monitoring of the leakage from the closure plug of heavy water reactors during on-site inspections. They reported that the location of the leakages could be identified and the leak status could be monitored in real-time with IR thermography. Ge et al. [13] inspected the temperature distributions of air-cooled condensers and calculated the influence of ambient air temperature, natural air flow, and surface defects on the performance of the units by IR thermography. The thermography technique has evolved as a useful method for real-time temperature monitoring of machines or processes in a non-contact and non-intrusive way for various condition monitoring applications, which can decrease breakdowns or emergency shutdowns, maintenance costs and risk of accidents, augment the performance and increase productivity. By applying modern image processing methods to the acquired IR thermal images with artificial intelligence (AI) based approaches, better decisions may be made rapidly without human intervention [8]. Younus and Yang [14] presented an intelligent fault diagnosis system for classification of different rotary machine conditions that utilized the processing of IR thermal images. They used a two-dimensional discrete wavelet transformation (2D-DWT) to decompose the thermal image. In order to assist in diagnosing the different machine conditions, they utilized support vector machines (SVMs) and linear discriminant analysis (LDA) methods as classifiers.

Artificial neural networks (ANNs) are robust, adaptive and strong numerical models for pattern recognition and classification [15]. ANNs are very powerful tools that can be trained to solve complex non-linear classification problems. Huda and Taib [9] applied IR thermography for predictive and preventive maintenance of thermal defects in electrical equipment. They utilized statistical features, a multilayer perceptron (MLP) neural network, and a discriminant analysis classifier to allocate the hotspot thermal status into ‘defect’ and ‘no defect’ categories. Abu-Mahfouz [16] used ANNs for the detection and classification of malfunction, wear and damage of a gearbox operating under steady state conditions. ANNs were applied for the damage indices classification of aerospace structures with the use of Lamb waves [17].

Fault diagnosis of machinery can be handled as a task of pattern recognition and classification that includes data acquisition, feature extraction, feature selection and final condition classification steps which are the demandable tasks of fault diagnosis. To obtain correct data (normal or abnormal), it is important to complete all steps of signal processing whatever the signal type such as vibration, thermal image, current, ultrasonic, or acoustic. Moreover, fault pattern classification from images typically consists of these steps: image acquisition, pre-processing, segmentation, feature extraction or dimension reduction, feature selection, classification and decision [18], [19]. In order to use condition monitoring and fault detection of machines, signal processing techniques are initially required to process the data acquired from the machinery. Wavelet transform is an early technique that has been employed for one-dimensional signal processing. Recently, 2D-DWT is frequently considered as a decomposition algorithm in the image processing field. 2D-DWT is a tool that is applied for analysis of 2D signals such as X-ray images, magnetic resonance images (MRI), synthetic aperture radar (SAR), and RGB images [20]. However, the data obtained from the decomposition process of a wavelet transform are seldom practical because of the huge dimensionality which causes difficulties of data storage and data mining for the next procedures. Representing data as features or dimensionality reductions is a process of extracting the functional information from the dataset to remove artifacts and reduce the dimensionality. However, it must protect the characteristic features which show faults and conditions of the machinery as far as possible. Dimensionality reduction is an important data preprocessing procedure for classification tasks and commonly falls into two categories: feature compression and feature selection [14].

The engine cooling system and the radiator, in particular, as its main component to maintain the temperature of engine are vitally important to the operation of an engine. A radiator that is defective will cause the engine to be stopped or it will reduce engine performance. Thus fault diagnosis and condition monitoring of a radiator is very important. In this paper, a thermography-based technique is considered for fault detection and condition monitoring of radiators because temperature is a key parameter in defining a radiator's condition. Accordingly, the aim of this work is the development and implementation of a new intelligent fault diagnosis and condition monitoring system for classification of common faults occurring in a cooling radiator using IR thermal images. In the present study, the intelligent condition monitoring system has a number of procedures that must be applied sequentially, including: IR thermal image acquisition, preprocessing, image processing via 2D-DWT, feature extraction, feature selection, and classification. The 2D-DWT is applied to thermal image decomposition. Consequently, statistical texture features are extracted from the original and decomposed thermal images. In the next step, the significant features are selected based on a genetic algorithm (GA) to enhance the performance of the ANN classifier in the final stage. Details of the methodology adopted for collecting the infrared images, and for analyzing these are provided in the next section.