Application of AI in Reliability: Prognostics and Systems Health Management (PHM): Prof. Jay Lee - Joins the Team in Fall 2024 (2024)

Prof. Michael G. Pecht Phone: 301-405-5323 Email: pecht@umd.edu	Class Timings Mondays, 9:30 am to 12:10 pm Alsoonline class is available for qualified students (Class starts from Aug 26, 2024)
Prof.Jay Lee Phone: 301-405-5255 Email:leejay@umd.edu
Dr. Michael H. Azarian Phone: 301-405-7555 Email:mazarian@umd.edu

Prognostics and health management (PHM) is an enabling discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risk. In recent years, integrating machine learning and PHM for highly complex engineering systems is gaining growing attention. Industrial AI (Artificial Intelligence) integrates data, domain, and disciplines as a system engineering to augment PHM for improved performance and resilience, including providing an early warning of failure, forecasting maintenance as needed, reducing maintenance cycles, assessing the potential for life extensions, and to improve future designs and qualification methods. In the future, AI-augmented PHM will enable systems to assess their own real-time performance (self-cognizant health management and diagnostics) under actual usage conditions and adaptively enhance life cycle sustainment with risk-mitigation actions that will virtually eliminate unplanned failures.

Some of the topics covered in this course include:

• Fundamentals of PHM
• Data Pre-processing (Data Cleansing, Feature Extraction, Feature Selection, Feature Learning)
• Internet of Things, Big Data, and Sensors for PHM
• Physics-of-Failure Approach to Prognostics
• Machine Learning and Artificial Intelligence for Anomaly Detection, Diagnostics, and Prognostics
• Industrial AI and Non-traditional Machine Learning
• Bayesian Statistics, Uncertainty Interpretation, Quantificationand Management in Prognostics
• PHM Cost and Return on Investment
• Valuation and Optimization of PHM-enabled Maintenance Decisions
• Software Tools for PHM
• Predictive Maintenance
• PHM Applications in Industry
• Challenges and Opportunities in PHM

This is an interdisciplinary course, and students in many areas, including aerospace, civil, electrical, mechanical engineering, and engineering management, are welcome. Students will get the opportunity to learn the basic scientific foundations that enable PHM and work on its implementation for real-life applications through projects. Experts from industry, government, and academia will teach guest lectures in this course.

The knowledge of PHM methodologies and technologies will prepare students to develop and implement PHM. Completing this course will give you the fundamental knowledge and skills to develop and implement PHM concepts for aerospace, civil, electrical, electro-mechanical, electronic, and mechanical systems. Specifically, you will have the knowledge needed to:

• Assess methods for damage estimation of components and systems due to field loading conditions
• Assess the cost and benefits of prognostic implementations
• Develop algorithms and models for data processing and feature engineering
• Develop novel methods for in-situ monitoring of products and systems in actual life-cycle conditions
• Enable condition-based (predictive) maintenance
• Identify and analyze failure precursors based on failure mechanisms
• Increase system availability through an extension of maintenance cycles and/or timely repair actions
• Reduce the occurrence of no fault found (NFF)
• Subtract life-cycle costs of equipment from the reduction in inspection costs, downtime, and inventory
• Understand data analytics (machine learning) methods used for anomaly detection, diagnostics, and prognostics
• Understand the logistics and supply-chain challenges in PHM implementation.

For more information, contactProf. Michael Pecht, Prof. Jay Lee, andDr. Michael H. Azarian.

Frequently Asked Questions for Advanced Special Students

1. Criteria for admission:

There are three options to take UMD graduate courses without pursuing a graduate degree: Advanced Special Student Status, Visiting Graduate Student Status, or Golden Identification Cardholder Status (for Senior Citizens). Among these three options, the Advanced Special Student option is the most suitable option for most practicing engineers.

2.Applicants must hold a baccalaureate degree from a regionally accredited institution, with a cumulative 3.0 grade point average, and must satisfy ONE of the following requirements:

• Submit official transcripts covering all credits used in satisfying the baccalaureate degree requirements, OR
• If the applicant holds a master's or doctoral degree from a regionally accredited institution, submit an official transcript showing the award of a master's or doctoral degree, OR
• Achieve a score that places the applicant in the upper 50th percentile of appropriate national standardized aptitude examinations including the Graduate Record Examination Aptitude Test, the Miller Analogies Test, and the Graduate Management Admissions Test, (where different percentiles are possible, the Graduate School will determine which score is acceptable), OR
• Provide a strong letter of support from the Graduate Director of the program in which the applicant plans to take a course.

3. To apply, applicants must:

Submit a completed online application, which includes uploading official transcripts showing a bachelor’s degree from a regionally accredited institution and a personal statement. After successfully submitting the application, please send out an email to the Mechanical Engineering Graduate Office informing them that you have completed the application for taking the class.

4. Other information:

• If the interested prospective students are NOT US citizens or permanent residents, contactInternational Student and Scholar Servicesto determine how to apply for Advanced Special Student status. US citizens or permanent residents with international credentials, can apply for Advanced Special Student Statushere.
• To apply for Advanced Special Student status, international applicants may be required tosubmit results ofEnglish proficiency tests(TOEFL, IELTS or PTE) unless the Advanced Special Student applicant holds a degree from one of the waived countries (on previous link). If an Advanced Special Student is not a native English speaker and doesn’t have a degree from of those countries, then he/she MUST submit an English proficiency score to be considered for admission.
• Please visit the followingwebsitefor more information, and theDC ConsortiumPage link is here.

5. Fall 2024 application deadline (Advanced Special Student): The deadline for application is the first day of classes, August 26th, 2024.

6. Course Registration:

Advanced Special Students should register to sectionRE01. If there is a confusion, please contact theMechanical Engineering Graduate Office. (megrad@umd.edu)