# Novel Methodology for Condition Monitoring of
## 1 Introduction
Yet, although some investigations have focused on the analysis of faulty conditions in gearbox transmission systems, there exists a lack of analysis to diagnose the occurrence of incipient faults, such as uniform wear in the gears, in such systems. \begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Definition** \\ \hline AC & Alternative Current \\ AI & Artificial Intelligence \\ ANN & Artificial Neural Network \\ DC & Direct Current \\ HTL & Healthy \\ k-NN & k-Nearest Neighbor \\ LDA & Linear Discriminant Analysis \\ LTSA & Load Torque Signature Analysis \\ MCSA & Motor Current Signature Analysis \\ MLP & Multilayer Perceptron \\ RTD & Resistance Temperature Detector \\ SVM & Support Vector Machine \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acronyms and definitions. Gears are the main part of the gearbox transmission system because they are responsible for transmitting the circular motion through the contact of cogwheels inside of them. In spite of this, their continuous operation makes the occurrence of incipient failures inherent in this type of element. Indeed, the friction between the gears leads to reduction in the mechanical properties and accelerates the degradation of the gears; the appearance of uniform wear in gears is known as the most common and initial fault condition [17]. On the other hand, the most common faults that have been reported are related to the presence of irregularities in the teeth of the gears such as tooth breakage; chipping and cracks in the root; and chipping, pitting and damage to the surface of the tooth [18; 19]. To detect these problems, adaptive wavelet filters are effectively applied to vibration signals acquired from the monitoring of the gearbox [20]. In addition, diagnosis methodologies based on vibration signals are proposed for the identification of wear in gears [7], with MCSA and load torque signature analysis (LTSA) being applied to detect faults such as misalignment, wear and mass imbalances of gears [21]. Nevertheless, few reported papers analyze and study gradual and uniform wear in the gears of the transmission system. Moreover, most techniques used for monitoring the condition in gearboxes have their limitations, with the vibration and the acoustic signal being affected by environmental noise and the location of the sensors [22]. MCSA is not a direct measurement of the equipment; it is based on the current of the electric motor, which can cause confusion when it comes to condition monitoring and failure detection in a gearbox [23]. Also, when a gear presents a fault, it manifests with an increase in temperature. One technique widely used for temperature monitoring is infrared thermography, and this may be a complementary and helpful method in conjunction with the most used and proven techniques for monitoring and detecting faults in a gearbox transmission system. Thereby, infrared imaging is a noninvasive and nondestructive technique that efficiently monitors temperature and possesses a wide range of monitoring and the possibility to visualize and locate hot spots [24] through the increase in temperature caused by the faults present in the gearbox transmission system. Although this technology was expensive in the beginning, low-cost cameras and cores have emerged that make it more accessible and used in various areas such as medicine, manufacturing, and electrical engineering, among others [25; 26; 27]. Besides that, there are multivariable and multiple sensors, in which each pixel of the image represents a temperature value as well as a color intensity value. In the literature, few papers are reported that use infrared imaging for monitoring and diagnosis of gearbox failures. A thermal analysis was proposed based on MLP neural networks for the diagnosis of failures in helical gears [28].