Manutenção de motores diesel marítimos: qualidade do óleo lubrificante

Abstract

Monitoring lubricating oil in maritime diesel engines plays a crucial role in predictive maintenance and reliable engine performance. Dedicated sensors capture data on oil viscosity, level, and quality, as well as system temperature and pressure. This data is continuously acquired and stored in a database, where machine learning algorithms are applied to analyze trends and anomalies. The importance of this monitoring lies in preventing catastrophic failures, increasing operational efficiency, and reducing maintenance costs. The use of machine learning enables the prediction of wear and potential issues, allowing for corrective actions to be taken before significant damage occurs. Thus, the combination of sensors, data acquisition, databases, and machine learning provide a comprehensive and powerful system for proactive lubricating oil management and engine performance optimization.