Three-phase induction motors (IMs) are considered an essential part of electromechanical systems. Despite the fact that IMs operate efficiently under harsh environments, there are many cases where they indicate deterioration. A crucial type of fault that must be diagnosed early is stator winding faults as a consequence of short circuits. Motor current signature analysis is a promising method for the failure diagnosis of power systems. Wavelets are ideal for both time- and frequency-domain analyses of the electrical current of nonstationary signals. In this paper, the signal data are obtained from simulations of an induction motor for various stator winding fault conditions and one normal operating condition. Our main contribution is the presentation of a fault diagnostic system based on a hybrid discrete wavelet–CNN method. First, the time series of the currents are processed with discrete wavelet analysis. In this way, the harmonic frequencies of the faults are successfully captured, and features can be extracted that comprise valuable information. Next, the features are fed into a convolutional neural network (CNN) model that achieves competitive accuracy and needs significantly reduced training time. The motivations for integrating CNNs into wavelet analysis results for fault diagnosis are as follows: (1) the monitoring is automated, as no human operators are needed to examine the results; (2) deep learning algorithms have the potential to identify even more indistinguishable and complex faults than those that human eyes could.

Vessels frequently encounter challenging marine conditions that expose the propeller-hull to corrosive water and marine fouling. These challenges necessitate innovative approaches to optimize propeller-hull performance. This study aims to assess a method for predicting propeller-hull degradation. The proposed solution revolves around an innovative Key Performance Indicator (KPI) based on Artificial Neural Networks (ANNs). Our objective is to validate the findings; thus, a thorough comparison is conducted between the proposed method and the baseline solution derived from the ISO-19030. Emphasis is placed on determining the optimal parameters for computing the KPI, which involves applying various features, filters, and pre-processing techniques. The proposed method is tested on real data collected by an Internet of Things (IoT) system installed in different types of vessels. Four distinct experiments with ANNs are conducted. Results demonstrate that the ANN-based indicator offers greater accuracy in predicting propeller-hull degradation compared to the baseline method. Additionally, it is demonstrated that selecting a diverse set of features and implementing consistent filtering and preprocessing techniques enhance the performance of the traditional indicator. The utilization of Deep Learning (DL) in the maritime industry is of great significance, as it enables a comprehensive and dynamic assessment of predictive maintenance of the propeller-hull. The DL index method holds potential for diverse maintenance applications, providing a holistic platform with anticipated environmental and financial benefits.

In recent years, the concentration levels of various air pollutants have been constantly increasing, primarily due to the high vehicle flow. In 2020, however, severe lockdowns in Greece were imposed to reduce the spread of the COVID-19 pandemic, which led to a rapid reduction in the concentration levels of air pollutants such as PM_2.5 and PM₁₀ in the atmosphere. Initially, this paper seeks to identify the correlation between the concentration levels of PM₁₀ and the traffic flow by acquiring data from low-cost IoT devices which were placed in Thessaloniki, Greece, from March to August 2020. The correlation and the linearity between the two parameters were further investigated by applying descriptive analytics, regression techniques, Pearson correlation, and independent T-testing. The obtained results indicate that the concentration levels of PM₁₀ are strongly correlated to the vehicle flow. Therefore, the results hint that the decrease in the vehicle flow could result in improving the quality of environmental air. Finally, the acquired results point out that the temperature and humidity are weakly correlated with the concentration levels of PM₁₀ present in the atmosphere.