Transmission components are prone to fatigue damage due to high and intermittent loading cycles, that cause premature failure of gearboxes. Recently, several vibration-based diagnostics approaches using Machine Learning (ML) and Deep Learning (DL) algorithms have been proposed to identify gearboxes faults. However, most of them rely on a large amount of training data collection from physical experiments, which is often associated with high costs. This paper offers an ML and DL classification performance comparison of several algorithms to diagnose faults in a gearbox based on realistic simulated vibration data. A dynamic model of a single-stage gearbox was developed to generate data for different health conditions. Generated datasets were fed to ML and DL algorithms and accuracy results were compared. Results revealed the superiority of Convolutional Neural Network compared to other classifiers. This research contributes to the prevention of catastrophic failures in gearboxes by early crack detection and maintenance schedule optimization.

Early diagnosis in rotating machinery has been a challenge when looking toward the concept of intelligent machines. A crucial and critical component in these systems is the lubricated journal bearing, subjected to wear fault by abrasive removing of material in its inner wall, mainly during run-ups and run-downs. In extreme conditions, wear faults can cause unexpected shutdowns in rotating systems. Consequently, advanced condition monitoring is an essential procedure in the wear diagnosis of journal bearings. Although an increasing number of data-driven condition monitoring approaches for rotating machines have been proposed in the past decade, they mostly rely on substantial amounts of experimental data for training, which is expensive and time-consuming to obtain. The objective of this work is to develop a framework using a deep learning algorithm to classify wear faults in hydrodynamic journal bearings using simulated vibrations signals. Numerically simulated data sets under different wear severity levels and operating conditions were used to train and test the diagnostics framework. The results show that the proposed framework can be a promising tool to diagnose wear faults in journal bearings.

The improvements in wind energy infrastructure have been a constant process throughout many decades. There are new advancements in technology that can further contribute toward the prognostics and health management (PHM) in this industry. These advancements are driven by the need to fully explore the impact of uncertainty, quality and quantity of data, physics-based machine learning (PBML), and digital twin (DT). All these aspects need to be taken into consideration to perform an effective PHM of wind energy infrastructure. To address these aspects, four research questions were formulated. What is the role of uncertainty in machine learning (ML) in diagnostics and prognostics? What is the role of data augmentation and quality of data for ML? What is the role of PBML? What is the role of the DT in diagnostics and prognostics? The methodology used was Preferred Reporting Items for Systematic Review and Meta-Analysis. A total of 143 records, from the last five years, were analyzed. Each of the four questions was answered by discussion of literature, definitions, critical aspects, benefits and challenges, the role of aspect in PHM of wind energy infrastructure systems, and conclusion.

The advancement of medical 3D printing technology includes several enhancements, such as decreasing the length of surgical procedures and minimizing anesthesia exposure, improving preoperative planning, creating personalized replicas of tissues and bones specific to individual patients, bioprinting, and providing alternatives to human organ transplants. The range of materials accessible for 3D printing within the healthcare industry is significantly narrower when compared with conventional manufacturing techniques. Liquid silicone rubber (LSR) is characterized by its remarkable stability, outstanding biocompatibility, and significant flexibility, thus presenting substantial opportunities for manufacturers of medical devices who are engaged in 3D printing. The main objective of this study is to develop, refine, and assess a 3D printer that can employ UV-cured silicone for the fabrication of aortic heart valves. Additionally, the research aims to produce a 3D-printed silicone aortic heart valve and evaluate the feasibility of the final product. A two-level ANOVA experimental design was utilized to investigate the impacts of print speed, nozzle temperature, and layer height on the print quality of the aortic heart valve. The findings demonstrated that the 3D-printed heart valve’s UV-cured silicone functioned efficiently, achieving the target flow rates of 5 L/min and 7 L/min. Two distinct leaflet thicknesses (LT) of the heart valve, namely 0.8 mm and 1.6 mm, were also analyzed to simulate calcium deposition on the leaflets.