munotes®

B.E Artificial Intelligence and Machine Learning Reliability Engineering Syllabus - Mumbai University

This is the Fourth Year AI-ML syllabus under REV-2019 'C' Scheme, in force from the academic year 2023-24. The University has not yet published an NEP 2020 syllabus for this year of the degree, and this is the scheme its examinations are set on.

Reliability-Engineering.pdf
Semester 7 · Fourth Year AI-ML

Loading syllabus...

Syllabus for Reliability Engineering

Semester 7 · Fourth Year AI-ML

Module 1: Probability theory: Probability: Standard definitions and concepts; Conditional Probability, Baye’s Theorem.

  • Probability Distributions: Central tendency and Dispersion; Binomial, Normal, Poisson, Weibull, Exponential, relations between them and their significance. Measures of Dispersion: Mean, Median, Mode, Range, Mean Deviation, Standard Deviation, Variance, Skewness and Kurtosis.

Module 2: Reliability Concepts: Reliability definitions, Importance of Reliability, Quality Assurance and Reliability, Bath Tub Curve.

  • Failure Data Analysis: Hazard rate, failure density, Failure Rate, Mean Time To Failure (MTTF), MTBF, Reliability Functions. Reliability Hazard Models: Constant Failure Rate, Linearly increasing, Time Dependent Failure Rate, Weibull Model. Distribution functions and reliability analysis.

Module 3

  • System Reliability: System Configurations: Series, parallel, mixed configuration, k out of n structure, Complex systems.

Module 4: Reliability Improvement: Redundancy Techniques: Element redundancy, Unit redundancy, Standby redundancies. Markov analysis.

  • System Reliability Analysis – Enumeration method, Cut-set method, Success Path method, Decomposition method.

Module 5: Maintainability and Availability: System downtime, Design for Maintainability: Maintenance requirements, Design methods: Fault Isolation and self-diagnostics, Parts standardization and Interchangeability, Modularization and Accessibility, Repair Vs Replacement.

  • Availability – qualitative aspects.

Module 6: Failure Mode, Effects and Criticality Analysis: Failure mode effects analysis, severity/criticality analysis, FMECA examples. Fault tree construction, basic symbols, development of functional reliability block diagram, Fau1t tree analysis and Event tree Analysis

  • Assessment consists of two tests out of which; one should be compulsory class test and the other is either a class test or assignment on live problems or course project.

REFERENCES

  • 1 L.S. Srinath, “Reliability Engineering”, Affiliated East-Wast Press (P) Ltd., 1985.
  • 2 Charles E. Ebeling, “Reliability and Maintainability Engineering”, Tata McGraw Hill.
  • 3 B.S. Dhillion, C. Singh, “Engineering Reliability”, John Wiley & Sons, 1980.
  • 4 P.D.T. Conor, “Practical Reliability Engg.”, John Wiley & Sons, 1985.
  • 5 K.C. Kapur, L.R. Lamberson, “Reliability in Engineering Design”, John Wiley & Sons.
  • 6 Murray R. Spiegel, “Probability and Statistics”, Tata McGraw-Hill Publishing Co. Ltd.

Reproduced from the University of Mumbai syllabus for B.E. (Artificial Intelligence and Machine Learning) under REV-2019 'C' Scheme, in force from the academic year 2023-24. Wording is as printed in that syllabus. Module numbering is as printed there too.

The complete syllabus

This subject is cut from the University circular for its year. Open a document here if you want the whole thing rather than a single subject.

PDF 7.8 (R-A) B.E. (Artificial Intelligence and Machine Learning) Sem I & II (Revised, NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.21 (N) B.E. (Artificial Intelligence and Machine Learning) Sem III & IV (NEP 2020) NEP 2020 syllabus Read full PDF Read
PDF 6.42 (R) B.E. (Artificial Intelligence and Machine Learning) Third Year, Sem V & VI (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
PDF B.E. (Artificial Intelligence and Machine Learning) Fourth Year, Sem VII & VIII (REV-2019 'C' Scheme) REV-2019 'C' Scheme syllabus Read full PDF Read
Report or request
Done!