# Novel Methodology for Condition Monitoring of
## 1 Introduction
Further, there was a review published discussing different studies with infrared imaging to detect failures in gearboxes [29], and infrared imaging was used as a complement to other techniques such as vibration signals and acoustic signals for the diagnosis of failures in a gearbox [30]. Even so, the papers reported in the literature that use infrared imaging only focus on failures in the gearbox and do not study the wear in the gears of the gearbox transmission system. Furthermore, not all the information generated by infrared imaging is used, and the analysis and classification of data may be chaotic. In addition, the use of artificial intelligence (AI) techniques has increased as an emerging field in industrial applications; in this regard, techniques such as k-Nearest Neighbor (k-NN), Naive Bayes Classifier, Support Vector Machine (SVM) and Deep Learning are used to represent an effective solution for the recognition and automatic diagnosis of failures in rotary machines. However, although most of them may be included in condition monitoring schemes, sometimes their improper application can produce several limitations; for example, k-NN needs a lot of storage space, Naive Bayes must have combinatorial and calculation problems and there is a need for prior probability, SVM has low efficiency, and Deep Learning needs large samples and training for a long time, plus it only focuses on failures in the gearbox. The proposal and choice of an AI technique such as an Artificial Neural Network (ANN) may lead to the performance of automatic fault diagnosis and condition assessment of the incipient occurrence of wear in gears, providing a high classification performance [31]. For this reason, the main contribution of this paper lies in the proposal of a novel noninvasive methodology that combines the analysis of thermographic images, supervised learning and artificial neural network (ANN) for the automatic diagnosis and assessment of different levels of uniform wear conditions in a gearbox transmission system. Also, it helps to contribute and complement all the techniques used for the continuous diagnosis and identification of incipient faults in industrial machinery composed of gearboxes. Additionally, the novelty of this work includes the use of thermal images that come from a thermographic camera, which is used for the analysis of the thermal behavior of the gearbox. The thermal matrix of the thermography is used to extract a set of suitable features and to perform their dimensionality reduction through LDA to obtain a visual representation of the considered conditions; in addition, the implementation of an ANN allows the automatic classification and final diagnosis outcome. Thereby, the proposed methodology consists in detecting the hot spots of the infrared imaging produced by the uniform wear in the gears, and then extracting thermal statistical features that characterize the different levels of wear in the gears. Based on the extracted data sets, linear discriminant analysis (LDA) is applied for the reduction of dimensionality features. An ANN is proposed for the classification and detection of uniform wear in the gears. A novel methodology is applied to the study of four states in the gears: healthy (HTL), and with 25%, 50%, and 75% uniform wear in the gear. Finally, the novel methodology is compared to vibration signals, obtaining an improvement for the diagnosis and classification of faults by uniform wear in the gears of the gearbox transmission system when using infrared imaging.