# Novel Methodology for Condition Monitoring of
## 1 Introduction  
Sensors are important elements in automation and the new concept of industry 4.0 because they allow the monitoring and assessment of the physical conditions of equipment, in order to achieve a better capacity for the control, reliability and integrity of industrial equipment [1]. In recent years, sensing has become more important in the research field, resulting from technological advances in detection that allow multivariate sensor monitoring [2], generating significant improvement in the observability of industrial systems. However, multivariate monitoring in engineering systems provides a large amount of varied data, generating a challenge for data processing [3]. With the significantdevelopment of intelligent detection techniques and artificial intelligence, data analysis offers a promising approach to effectively learning complex multivariable data, which is why supervised and unsupervised learning techniques may be considered as two powerful tools for solving problems presented as a result of a large amount of data to be processed, allowing the reduction of information and a significant improvement in the analysis and the classification of data [4]. Thus, condition monitoring and fault assessment strategies play an important role in ensuring the availability of industrial processes; in this regard, it is desirable that most industrial systems will be monitored by different sensors for ensuring the integrity of the industrial equipment. The gearbox transmission system is one of the important elements due to the fact that it contains mechanical elements that are commonly included in a wide variety of industrial applications; the reason for its common use is because it is needed in order to handle different rotating speeds and maintain specific torques between the electric machines and the load mechanics. Although these components are robust, efficient, reliable and have a low cost, their monitoring and continuous assessment are necessary [5]. Even so, the occurrence of unexpected faults in gearboxes can occur at any time, causing production losses, system malfunctions or unwanted downtime, with significant financial losses and human efforts [6]. Various faults may occur within the gearbox transmission system. In the literature, it has been reported that the occurrence of faults in the gears represents 80% in transmission machinery systems and 10% in rotating machinery systems [7], resulting from several factors ranging from inadequate lubrication, fluctuating loads, decoupling, misalignment, poor cooling, and gear design, among others [8]. In the literature, different methodologies for monitoring and detecting gearbox faults have been reported based on different sensing techniques and methods for analyzing and classifying data. For example, acoustic emissions are used to detect angular misalignments of shafts in the gearbox [9]. Furthermore, the influence of the oil film thickness has been considered in the detection of failures in helical gearboxes [10], and also the application of the discrete wavelet transform and the use of artificial neural networks for acoustic signals have been taken into account for the detection of faults in gearboxes [11]. On the other hand, vibration-based analysis represents the most classical approach to perform fault detection in gearbox transmission systems; in this sense, a time-frequency signal analysis using a generalized synchronism transformation is applied to diagnose gearbox failures and bearing defects from vibration signals [12; 13], and intelligent diagnostic models for gearboxes have also been proposed from wavelet support vector machines and genetic algorithms using vibration signals [14]. Other physical magnitudes have been also considered; thus, the use of motor current signature analysis (MCSA (Table 1 shows the definition)) has also been a classical approach to identify the characteristic frequencies of gearbox failures [15] and also to diagnose eccentricity failures during transient speeds [16].