Authors
Abstract
Maintaining machine health is crucial for optimizing operational efficiency and minimizing downtime in industrial applications. This paper explores recent advancements in vibration analysis, focusing on developments from the past decade that have transformed the field. Emerging technologies such as machine learning (ML), advanced signal processing, and the Internet of Things (IoT) have reshaped how vibration data is collected, analyzed, and interpreted. These innovations enable real-time data monitoring and more precise fault detection, allowing for early diagnosis and predictive maintenance, which improves machinery reliability and reduces unexpected failures. By incorporating ML and IoT, industries can now implement more advanced predictive maintenance strategies that significantly lower operational costs and enhance machine performance. Furthermore, the use of advanced algorithms allows for more accurate interpretation of complex vibration signals, offering deeper insights into potential machine issues. However, despite these technological strides, there remains a critical need for industry-wide standardization in data analysis methods and reporting practices to ensure consistency and accuracy across applications. This paper provides a comprehensive review of these technological advancements, highlighting both the benefits and challenges they present, and stresses the importance of continued research to fully harness their potential. Ultimately, the findings underscore the transformative impact of these innovations on improving machine reliability and operational efficiency.