Learn · Application of AI in Reliability Engineering - Prognostics and Systems Health Management (PHM)
Lecture 16 of 60Week 4
Lecture 16 : Introduction to Sensors and Data Acquisition Systems
Prof. Prabhakar V Varde · IIT Bombay
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- Application of AI in Reliability Engineering - Prognostics and Systems Health Management (PHM), IIT Bombay (at NPTEL)
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All the lectures
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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)
- 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