Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders.

Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.

Deferred Action for Childhood Arrivals (DACA): Maximizing Impacts in Medical Education and Health Care.

Introduction: Deferred Action for Childhood Arrivals (DACA) provides a path for individuals who are undocumented to join the physician workforce. Indeed, recipients of DACA can play an important role in addressing health inequities in medicine. Although DACA has been in place since 2012, many medical schools remain unaware of it or are hesitant to consider recipients for admission. In a similar vein, the premedical community, including those with DACA status, may be unaware of their eligibility and the steps necessary to pursue medicine. Further education and outreach are needed to achieve institutional policies conducive to the inclusion and success of those undocumented in medicine. Methods: We created an hour-long workshop to empower learners with key knowledge relevant to DACA policy and its impact on medicine. We evaluated the workshop through pre- and postworkshop questionnaires assessing participant knowledge and attitudes based on the theory of planned behavior (TPB). Results: A total of 112 participants engaged in our workshop. Ninety-one pretests and 61 posttests were completed by attendees. Data revealed a significant increase in performance on all knowledge-based and TPB questions, including intention to participate in future policy development. Moreover, participants reported appreciating the interactive nature of the session and expressed feelings of empowerment by their newfound knowledge base. Discussion: This workshop provides a promising foundation from which conversations and progress regarding DACA-related medical education policy can begin. Specifically, the workshop engages participants in the process of identifying actionable steps for overcoming barriers to inclusion and support.