Predictive maintenance has been employed to reduce maintenance costs and production losses and to prevent any failure before it occurs. The framework proposed in this work performs diesel engine prognosis by evaluating the absolute value of the failure severity using random forest (RF) and multilayer perceptron (MLP) neural networks. A database was implemented with 3500 failure scenarios to overcome the problem of inducing destructive failures in diesel engines. Diesel engine failure signals were developed with the zero-dimensional thermodynamic model inside a cylinder coupled with the crankshaft torsional vibration model. Artificial neural networks and random forest regression models were employed for classifying and quantifying failures. The methodology was applied alongside an engine simulator to assess effectiveness and accuracy. The best-fitting performance was obtained with the random forest regressor with an RMSE value of 0.10 ± 0.03%.

Sign language is the main type of communication of the deaf community. However, most people do not know this language, which causes communication problems for many people. Many technological solutions have been proposed to overcome this issue. Some use the concept of wearable devices, but gesture recognition in a video sequence is a cheaper and less intrusive solution. In this work, we address the problem of gesture recognition in video. To do so, we employ a two-step method with feature space mapping and classification. First, the body parts of each subject in a video are segmented through a deep neural network architecture. Then, we use Gait Energy Image to encode the motion of the body parts in a compact feature space. Small datasets are usually a problem in this type of application, leading to sparse representation in the feature space. To contour this problem, we evaluate SMOTE as a data augmentation technique in the feature space and classical dimensionality reduction techniques. We evaluate our method on three challenging Brazilian sign language (Libras) datasets, CEFET/RJ-Libras, MINDS-Libras, and LIBRAS-UFOP, achieving global accuracies of 85.40 ± 3.13 %, 84.66 ± 1.78 %, and 64.91 ± 3.79 %, respectively, with singular value decomposition and support vector machine.