Hybrid Conferencee

International Conference on Predictive Maintenance Analytics in Engineering (ICPMAE - 26)

26th - 27th June 2026 | Manila, Philippines
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Conference Brochure
Sample Full Paper
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Conference Notifications:

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Call for Papers Extended:
"The deadline for full paper submissions has been extended for the Research Plus International Conference in Manila. Submit your research by today to participate in one of the top conferences."
Certificate of Presentation:
"Present your research and receive a Certificate of Presentation to recognise your valuable contribution to the conference."
Abstract Submissions Open:
"Abstract submissions for the Manila event are now open! Don’t miss the chance to present your research. Submit now."
Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Manila conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Manila, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Manila."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Call for Paper

The ICPMAE aims to explore emerging trends and future directions in research and innovation. It provides a collaborative platform for researchers and professionals to share ideas that shape the future of their respective domains.

The conference highlights advancements in Data Science, encouraging innovative, solution-oriented research that addresses global challenges and technological evolution.

Authors are invited to submit papers addressing, but not limited to, the following areas:

01
Predictive maintenance strategies in engineering
02
Machine learning for equipment failure prediction
03
Data analytics for maintenance optimization
04
Real-time monitoring in predictive maintenance
05
Challenges in implementing predictive maintenance
06
Case studies of predictive maintenance success
07
AI-driven insights for maintenance planning
08
Statistical methods for maintenance analytics
09
Impact of IoT on predictive maintenance
10
Data-driven decision-making in engineering
11
Machine learning algorithms for maintenance
12
Future trends in predictive maintenance
13
Integration of maintenance data sources
14
Visualization techniques for maintenance data
15
Ethical considerations in predictive maintenance
16
Adaptive maintenance strategies using AI
17
Collaborative approaches to maintenance analytics
18
Benchmarking predictive maintenance models
19
User experience in maintenance applications
20
Machine learning frameworks for predictive maintenance