# Novel Methodology for Condition Monitoring of
## 7 Discussion  
As presented in the literature review, different condition monitoring strategies have focused on detecting the occurrence of faulty conditions in gearbox transmission systems; indeed, the occurrence of most of the addressed conditions is related to inadequate lubrication, eccentricity failures, fluctuating loads, decoupling, misalignment, poor cooling, and gear design, among others [8; 9; 10; 11]. However, most of the reported methodologies are limited to the diagnosis of gearbox faults as a single gear tooth damage, whereas most analyzed conditions 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. [7; 19; 20; 21]. In fact, from an industrial viewpoint, these faulty conditions may be considered as a critical condition in gearbox transmission systems since their occurrence may produce critical damage to the entire transmission system, leading to the machine breakdown. Therefore, as the literature review depicts, few works have focused on analysis for monitoring and assessing the appearance of incipient faults, such as uniform wear, in gearbox transmission systems, which is the main objective of this proposed work.  
In this regard, [39] proposes a multidimensional hybrid intelligent method for gear fault diagnosis; this proposal includes the estimation of time-domain, frequency-domain and time-frequency-domain features, from acquired vibration signals, through the Hilbert transform, the wavelet packet transform (WPT) and the empirical mode decomposition (EMD); moreover, by means of multiple classifiers combined with a genetic algorithm (GA), different levels of damage are identified in the tooth root. Yet, although different severities of incipient damage in the tooth root are identified, the estimation of frequency-domain and time-frequency-domain features requires additional knowledge since their estimation is performed through complex signal processing techniques. In this sense, for the proposed work, the consideration of only statistical time-domain features leads to a high-performance characterization of the acquired thermal images.  
On the other hand, a preview research work that assessed uniform wear in gears has also been performed by our research group [7]; in this study, the assessment of the uniform wear in gears was carried out by means of vibration signals. The novelty of this work includes the consideration of feature selection and feature reduction stages, and the final diagnosis outcome was performed by means of a fuzzy-based classifier, resulting in an average of 97.27% for the global classification ratio. Yet, although the detection of different levels of uniform wear condition is performed, the application of such a proposed method includes the installation of vibration sensors that represent a critical limitation in industrial environments. Thereby, in order to face the consideration of invasive sensors, such as accelerometers, the proposed condition monitoring presented in this work represents a suitable option that leads to the obtainment of accurate responses during the assessment of incipient faults in gearbox transmission systems.  
Additionally, an improvement in the diagnosis and fault identification is also obtained in the proposed work by taking into account continuous monitoring through infrared images; thus, the obtained results make the proposed methodology appropriate to be considered as an attractive, alternative tool for classical infrared imaging inspection procedures and for techniques used for the diagnosis and classification of different levels of uniform wear in gears, such as an incipient failure in a gearbox transmission system.