## 5 Conclusions  
In this work, a non-contact and novel methodology was developed to detect multiple faults in induction motors and gearboxes through CNN. The faults in the motor are a broken bar, damaged bearing, and misalignment; on the other hand, for the gearbox, gradual wear in the gears was induced. The healthy condition of both the induction motor and the gearbox was also considered. Based on this, it can be inferred that the use of infrared thermography combined with artificial intelligence can be used to develop high-performance intelligent defect detection systems since temperature is an indicator of the presence of any alteration in the system. The major contributions are described below:  
* An initial database of 600 images with multiple engine failures and gearbox wear was created. Likewise, the database was expanded to 4800 images by performing basic transformations, such as mirroring or illumination changes. The pre-processing involves simulating possible scenarios in a real environment during the time of image acquisition for the thermographic images. * For the CNN configuration, ablation tests were performed based on the variation in the number of filters and the pooling layer, which allowed us to obtain the best configuration. It is important to mention that a basic configuration was obtained. Therefore, both the computational load and the processing time were low. The performance indicators showed an accuracy of 98.5%, a recall of 100%, a precision of 98%, and a F1-Score of 99%. * Finally, the main advantage of the CNN and thermographic imaging-based method compared to other traditional contact-based methods for detecting motor faults and gearbox wear is its ability to perform multi-fault diagnosis in a kinematic chain in a fast, non-contact, and low computational way. The results obtained in this work suggest that this methodology is easy to use and suitable for the classification of multiple faults in asynchronous motors and wear on the teeth of a gearbox. It should be noted that, as with any methodology, this approach has limitations. It does not directly detect vibration, which may result in some initial mechanical failures going undetected until they generate sufficient heat. It should be noted that the interpretation is susceptible to external factors such as ambient temperature or inadequate cooling. Although more expensive initially, infrared thermography provides a comprehensive, non-invasive, and rapid view of the engine's condition, making it ideal for a more global approach or systems where thermal problems are common. To maximize the benefits of the method and control costs, it is often best to combine infrared thermography with other technologies, such as vibration, acoustics, thermography, electrical signals, and so on, depending on the specific needs of the system and the resources available. In future work, a combination of technologies, including vibration, electrical signals, ultrasound, and infrared thermography, will be selected according to the specific requirements of the system and the resources available for the detection and classification of faults in electromechanical systems. In addition, some other common faults in the electromechanical system could be studied, and different intelligent state classification algorithms could be tested, such as autoencoders. In addition, the use of an embedded system is proposed to allow the automatic monitoring, detection, and classification of multiple faults in a single electronic device. In addition, by integrating new technologies, it is possible to create a digital twin of the electromechanical system to analyze different types of faults using virtual reality and to test different conditions safely without putting the equipment and the operator at risk. Finally, it is suggested that further tests be carried out with other types of transformations or noise in the original thermal images to replicate other conditions that may occur, such as Gaussian noise, Poisson noise, or speckle noise. Conceptualization, E.R.-O. and I.A.C.-A.