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International Conference on Predictive Maintenance and Data Mining in Engineering

26th Dec – 27th Dec 2026 Berlin, Germany Standard / Physical Participation

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ConferenceICPMDME

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Conference Session Tracks

SDG-Aligned Research Themes

International Conference on Predictive Maintenance and Data Mining in Engineering conference tracks support global knowledge exchange, innovation, and sustainable development priorities across diverse disciplines.

SDG 9 - Industry, Innovation and Infrastructure

SDG 12 - Responsible Consumption and Production

This track will explore the latest methodologies and technologies in predictive maintenance. Emphasis will be placed on innovative approaches that enhance equipment reliability and operational efficiency.

This session will focus on the application of data mining techniques within various engineering domains. Participants will discuss case studies that demonstrate the effectiveness of data-driven decision-making.

This track will cover the integration of machine learning algorithms in fault detection processes. Attendees will examine real-world applications and the impact of these technologies on maintenance strategies.

This session will delve into condition-based maintenance approaches that utilize real-time data for decision-making. Discussions will highlight the benefits of proactive maintenance in reducing downtime and costs.

This track will investigate the role of sensor analytics in monitoring equipment health. Participants will share insights on how sensor data can be leveraged to predict failures and optimize maintenance schedules.

This session will focus on the principles of reliability engineering as they pertain to maintenance optimization. Attendees will explore strategies to enhance system reliability and minimize maintenance costs.

This track will examine the impact of big data analytics on predictive maintenance practices. Discussions will center on how large datasets can be utilized to improve maintenance outcomes and operational performance.

This session will highlight innovative practices in industrial engineering that enhance maintenance processes. Participants will discuss the intersection of engineering principles and maintenance optimization.

This track will present case studies showcasing successful implementations of predictive maintenance across various industries. Insights gained from these examples will provide valuable lessons for future applications.

This session will address the challenges faced in applying data mining techniques to maintenance scenarios. Participants will discuss barriers to implementation and potential solutions to overcome these obstacles.

This track will explore emerging trends and future directions in the fields of maintenance and data mining. Discussions will focus on the evolving landscape of technology and its implications for engineering practices.