Course12710100160 lectures
Application of AI in Reliability Engineering - Prognostics and Systems Health Management (PHM)
Prof. Prabhakar V Varde · IIT Bombay
About
Artificial Intelligence enabled Prognostics and Health Management (PHM) in general and its implementation at system level is relatively a new field in R&D and academics. The advantage of implementing PHM is it tracks the subject SSCs (System, Structure and Component) performance and thereby provide instantaneous information on system health and predicts the failure ‘instant’ along with uncertainty in prediction. This feature of PHM makes the SSCs operations & maintenance management a easier and finally reflects in reliability and safety of the plant. The available literature shows this approach has shown promising results.
Intended audience: Research scholar, Practitioners, Student interested to do projects in Machine learning project, reliability and safety young and middle level industry executives, engineering safety and reliability domain researchers, material degradation scientists, etc.
Prerequisites: MSc. / B.Tech./M.Tech/Ph.D in any engineering discipline
Lectures
Week 1
- Lecture 1 : Prognostics and Health Management: A Systems Approach
- Lecture 2 : Introduction and Background Aspects of Prognostics and Health Management Systems Approach
- Lecture 3 : Systems Approach- I
- Lecture 4 : Systems Approach (Continued / Risk-Based Engineering & ARCOM)
- Lecture 5 : A Brief Overview of Prognostics and Health Management
Week 2
- Lecture 6 : Artificial Intelligence and Machine Learning Enabled Reliability Assurance
- Lecture 7 : Advanced PHM Technologies and Approaches
- Lecture 8 : Artificial Intelligence and Machine Learning Enabled Reliability Assurance (Part 2)
- Lecture 9 : Advanced Machine Learning Techniques for PHM
- Lecture 10 : Data-Driven PHM: Implementation, Training, and ANN Configuration
Week 3
- Lecture 11 : Introduction to Systems Approach in Prognostics and Health Management (PHM)
- Lecture 12 : System Modeling Approaches in PHM: Deterministic vs. Probabilistic Frameworks and PRA Methods
- Lecture 13 : Fundamentals of Probabilistic Risk Assessment: Boolean Logic, Fault Tree Gates, Cut Sets, and Common Cause Failures
- Lecture 14 : Probability Distributions in Probabilistic Risk Assessment: Continuous and Discrete Models
- Lecture 15 : Statistical Estimation, Maximum Likelihood Estimation (MLE), and Bayesian Updating in PRA
Week 4
- Lecture 16 : Introduction to Sensors and Data Acquisition Systems
- Lecture 17 : Sensor Specifications, Functional Properties, and Application Areas
- Lecture 18 : Data Acquisition Systems (DAQ) Architecture and Signal Processing
- Lecture 19 : Sensor Systems for Electronics PHM and Common Cause Failure
- Lecture 20 : Sensor System Attributes, Performance Quantification, and Role of Process Parameters
Week 5
- Lecture 21 : Introduction to the Physics of Failure (PoF) Approach for Electronics
- Lecture 22 : Failure Mode, Mechanism, and Effect Analysis (FMMEA) and Time-to-Failure Models
- Lecture 23 : Fatigue Induced Degradation and Virtual Qualification Testing
- Lecture 24 : Potential of PoF for PHM and Life Consumption Monitoring
- Lecture 25 : Canary Devices for Early Warning in PHM
Week 6
- Lecture 26 : AI, Machine Learning, Deep Learning, and Role of Statistical Methods
- Lecture 27 : Supervised, Unsupervised, and Reinforced Learning Approaches
- Lecture 28 : Probabilistic Methods – Part A: Parametric Approaches
- Lecture 29 : Probabilistic Methods – Part B: Error Estimation and Interval Estimation
- Lecture 30 : Non-Parametric Methods
Week 7
- Lecture 31 : Advanced Prognostic Modeling Techniques and Data-Driven Approaches
- Lecture 32 : Remaining Useful Life (RUL) Estimation Methodologies
- Lecture 33 : Machine Learning and Neural Networks in Prognostics
- Lecture 34 : Physics-of-Failure (PoF) and Degradation Mechanisms
- Lecture 35 : System-Level Prognostics, Health Management Integration, and Case Studies
Week 8
- Lecture 36 : Introduction to Data Analytics for PHM
- Lecture 37 : Data Preprocessing and Feature Selection Techniques
- Lecture 38 : Feature Extraction Techniques: PCA, Autoencoders, and Case Studies
- Lecture 39 : Advanced Feature Extraction: LDA, Isomap, and Self-Organizing Maps (SOM)
- Lecture 40 : Fuzzy Data Analysis and Fuzzy Logic in PHM
Week 9
- Lecture 41: Introduction to Deep Learning in Prognostics and Health Management (PHM)
- Lecture 42: Deep Learning Considerations for PHM, Agentic AI, and Generative Approaches
- Lecture 43: Deep Learning Algorithms (Part A): CNN, RNN, and LSTM Architectures
- Lecture 44: Deep Learning Algorithms (Part B): DNN, GAN, DBN, and Deep Reinforcement Learning
- Lecture 45: Role of Uncertainty Prediction and Monte Carlo Simulation in PHM
Week 10
- Lecture 46 : Introduction to Systems Approach in PHM
- Lecture 47 : State-of-the-Art and Requirements of PHM
- Lecture 48 : Risk-Based Approach for System Modeling
- Lecture 49 : Identification and Prioritization for Implementing PHM
- Lecture 50 : Prognostics and Health Management Metrics
Week 11
- Lecture 51 : AI-Enabled Reliability Assurance and Prognostics
- Lecture 52 : Case Study and Data Set Understanding for Run-to-Failure Experiments
- Lecture 53 : Proposed Methodology: Deep Learning Framework for RUL Prediction
- Lecture 54 : Model Training, Evaluation Metrics, and Results Discussion
- Lecture 55 : Introduction to Loose Part Monitoring System (LPMS)
Week 12
- Lecture 56 : Introduction to Anomaly Detection in PHM
- Lecture 57 : Techniques for Anomaly Detection
- Lecture 58 : Case Study: General Description & Characterization of Aluminum Electrolytic Capacitors (AEC)
- Lecture 59 : Failure Modes, Criteria & Degradation Modeling of AECs
- Lecture 60 : Experimental Observations, Anomaly Detection & Conclusion on AECs
Syllabus and materials
At NPTEL, as the course publishes them.
Credits and licence
- Institution
- IIT Bombay
- Department
- Multidisciplinary
- Teacher
- Prof. Prabhakar V Varde
- Level
- Undergraduate
- Language
- English
- Subjects
- Medicine and health, Engineering, Business and management
- Source
- NPTEL
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only. NPTEL's terms
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- NPTEL gives no rating. Invisible Network shows it to Teens profiles and older, judged from what the source says about it. How courses are placed
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