 Prof. Michael G. Pecht Phone: 301-405-5323
Email: pecht@umd.edu
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Class Timings
Mondays, 9:30 am to 12:10 pm US Eastern Time
Also online access is available
for qualified students (Classes start on Aug 26, 2024) |
 Prof. Jay Lee Phone: 301-405-5255 Email: leejay@umd.edu
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 Dr. Michael H. Azarian Phone: 301-405-7555 Email: mazarian@umd.edu
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Imagine a world where systems would never break down unexpectedly because they would notify the user whenever they needed maintenance – a world where you could board a plane or turn on an air conditioner and never have to worry that it will not be fully operational.
In recent years, the integration of machine learning and PHM for highly complex engineering systems has been gaining increased attention. Industrial AI (Artificial Intelligence) integrates data and domain knowledge from multiple disciplines in a systems engineering approach to augment PHM for improved performance and resilience. AI-augmented PHM can provide an early warning of failure, forecast maintenance, reduce maintenance cycles, assess the potential for life extensions, and improve future designs and qualification methods. In the future, this 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 Prognostics and Health Management (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
- Bayesian Statistics, Uncertainty Interpretation, Quantification and 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
Completing this course will give you the fundamental knowledge and skills to develop and implement PHM 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
- 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
- Identify and analyze failure precursors based on failure mechanisms
- Enable condition-based (predictive) maintenance
- Assess the cost and benefits of alternative implementations of predictive maintenance
- Increase system availability through an extension of maintenance cycles and/or timely repair actions
- Reduce the occurrence of no fault found (NFF)
- 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, contact Prof. Michael Pecht, Prof. Jay Lee, and Dr. 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, or 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 student is NOT a US citizen or permanent resident, contact International Student and Scholar Services to determine how to apply for Advanced Special Student status. US citizens or permanent residents with international credentials can apply for Advanced Special Student Status here.
- To apply for Advanced Special Student status, international applicants may be required to submit results of English proficiency tests (TOEFL, IELTS or PTE) unless the Advanced Special Student applicant holds a degree from one of the waived countries on this page. If an Advanced Special Student is not a native English speaker and doesn’t have a degree from one of those countries, then he/she MUST submit an English proficiency score to be considered for admission.
- Please visit the following website for more information. The DC Consortium Page 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 section RE01. If you have any questions, please contact the Mechanical Engineering Graduate Office. (megrad@umd.edu)
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