Published Paper Details:

In an era dominated by data-driven decision making, understanding and leveraging the insights derived from recent online searches is paramount for optimizing maintenance strategies, employing machine learning algorithm. The study emphasizes the transformation from reactive to proactive maintenance, capitalizing on the analysis of patterns and trends extracted from the wealth of data generated daily. This proactive approach enables timely maintenance, reducing downtime and mitigating economic losses. The data for analysis is collected from various sources (ex: Cloud Architectures), including sensors (WSNs), machine PLCs, and communication protocols (IoT). These data are then processed on various cloud architectures, allowing for the identification of patterns and anomalies that may indicate potential failures. As organizations navigate the dynamic landscape of technology and data analytics, the integration of historical search data emerges as a pivotal tool for making informed decisions, allocating resources judiciously, and extending the lifespan of critical assets. The exploration underscores the paradigm shift towards predictive maintenance as a cornerstone for industrial and manufacturing sectors. By embracing the synergy between data analytics and maintenance practices, businesses are propelled into an era where downtime is minimized, and productivity is maximized. Predictive maintenance not only ensures smoother operations but also fosters a culture of continuous improvement and innovation.