Infrared thermography (IRT) has become an interesting alternative for performing condition assessments of different types of induction motor (IM)-based equipment when it operates under harsh conditions. The reported results from state-of-the-art articles that have analyzed thermal images do not consider (1): the presence of more than one fault, and (2) the inevitable noise-corruption the images suffer. Bearing in mind these reasons, this paper presents a convolutional neural network (CNN)-based methodology that is specifically designed to deal with noise-corrupted images for detecting the failures that have the highest incidence rate: bearing and broken bar failures; moreover, rotor misalignment failure is also considered, as it can cause a further increase in electricity consumption. The presented results show that the proposal is effective in detecting healthy and failure states, as well as identifying the failure nature, as a 95% accuracy is achieved. These results allow considering the proposal as an interesting alternative for using IRT images obtained in hostile environments.

Effective diabetes management heavily relies on appropriate insulin administration, which strongly depends on the correct administration strategy. In this sense, insulin administration plays a fundamental role, as its use depends on the patient’s clinical condition and diabetes type. Traditional syringe-based methods require proper training to ensure that insulin is successfully delivered into the subcutaneous tissue, where it can be absorbed and metabolized; however, it is desirable to develop an insulin applicator that does not require training for its appropriate use. Aiming to provide support solutions that help patients to develop a correct administration technique, a biodesign-based methodology, coupled with biomimetic concepts, is employed to design a device that assists the user in creating a stable skin fold and guiding needle orientation during injection without requiring exhaustive training for device usage. A three-step approach is employed for the design, where computational fluid dynamics (CFD) and finite element analysis (FEA) methods are employed to ensure that the device produces a laminar insulin flow and the device strength is tested. It should be pointed out both methods are required since complications produced by sudden flows must be avoided, with CFD allowing assessment of the device mechanical properties in terms of the device strength. Initial functional evaluation indicates that the proposed approach does not require extensive training or complex operational procedures, facilitating its integration into everyday use. The device design is validated from the results obtained for the CFD analysis, as no turbulent flow is produced, whereas the FEA indicates that the geometrical form can handle the stresses produced by the folding generation without generating excessive deformations. Moreover, an infrared thermography analysis is also carried out to find out if the folding force generation is located in the zone of interest, the results of which indicate that the device operates in the desired physical zone.