Intelligent Reliability Analysis Using MATLAB and AI

Perform Failure Analysis and Reliability Engineering using MATLAB and Artificial Intelligence (English Edition)

Dr. Cherry Bhargava; Dr. Pardeep Kumar Sharma

• Publisher: BPB Publications
• Year: 2021
• ISBN:
  9789390684656
• Topic: Computer Science
• Subtopic: Natural Language Processing
• Index: Computer Science

How to minimize the global problem of e-waste

Key Features
? Explore core concepts of Reliability Analysis, various smart models, different electronic components, and practical use of MATLAB.
? Cutting edge coverage on building intelligent systems for reliability analysis.
? Includes numerous techniques and methods to identify failure and reliability parameters.

Description
Intelligent Reliability Analysis using MATLAB and AI explains a roadmap to analyze and predict various electronic components' future life and performance reliability. Deeply narrated and authored by reliability experts, this book empowers the reader to deepen their understanding of reliability identification, its significance, preventive measures, and various techniques.The book teaches how to predict the residual lifetime of active and passive components using an interesting use case on electronic waste. The book will demonstrate how the capacity of re-usability of electronic components can benefit the consumer to reuse the same component, with the confidence of successful operations. It lists key attributes and ways to design experiments using Taguchi's approach, based on various acceleration factors.This book makes it easier for readers to understand reliability modeling of active and passive components using the Artificial Neural Network, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference System (ANFIS). The book keeps you engaged with a systematic and detailed explanation of step-wise MATLAB-based implementation of electronic components.

What you will learn
? Optimize various acceleration factors for exploring the residual life of components experimentally.
? Design an intelligent model to predict the upcoming faults and failures of electronic components and make provision for timely replacement of the fault components.
? Design experiments using Taguchi's approach.
? Understand reliability modeling of active and passive components using the Artificial Neural Network and Fuzzy Logic.

Who this book is for
This book is for current and aspiring emerging tech professionals, researchers, students, and anyone who wishes to understand and diagnose the product life of electronic components using the power of artificial intelligence and various experimental techniques.

Table of Contents
1. RELIABILITY FUNDAMENTALS
2. RELIABILITY MEASURES
3. REMAINING USEFUL LIFETIME ESTIMATION TECHNIQUES
4. INTELLIGENT MODELS FOR RELIABILITY PREDICTION
5. ACCELERATED LIFE TESTING
6. EXPERIMENTAL TESTING OF ACTIVE AND PASSIVE COMPONENTS
7. INTELLIGENT MODELING FOR RELIABILITY ASSESSMENT USING MATLAB

About the Authors
Dr Cherry Bhargava is working as an Associate Professor at the Department of Computer Science and Engineering, Symbiosis Institute of Technology, Pune, Maharashtra, India. She holds a Ph.D. (ECE) specialization in Artificial Intelligence, M. Tech (VLSI Design & CAD), and B. Tech (EIE) degrees. She is GATE qualified with All India Rank 428. She has authored about 50 technical research papers in SCI, Scopus indexed quality journals, and national/international conferences. She has 18 books to her credit. She has registered six copyrights and filed twenty-one patents. Her four Australian innovation patents are granted. LinkedIn Profile: https://www.linkedin.com/in/dr-cherry-bhargava-7315619/ Dr. Pardeep Kumar Sharma is working as an associate professor at Lovely Professional University, Punjab, India. He has more than 14 years of teaching experience in the field of applied chemistry, artificial intelligence, DOE, and nanotechnology. He has completed his Ph.D. from Lovely Professional University and his post-graduation (Applied Chemistry) from Guru Nanak Dev University, Amritsar. LinkedIn Profile: https://www.linkedin.com/in/dr-pardeep-kumar-sharma-28581818/

CHAPTER 1

Reliability Fundamentals

Introduction

The reliability theory was introduced during the World War II, and it plays a critical role in systems design and development. A valuable contribution was made to the development of the theory of reliability by the mathematicians like Gnedenkov, Belgayev, Solovyeu, Polovoko, Barlow, and Proschan.

Structure

In this chapter, we will discuss the following topics:

• Importance of reliability and life analysis
• Faults and failures
• Various configuration of reliability system
• Series, parallel, and hybrid arrangement
• Reliable data acquisition in wireless sensor networks

Objectives

After studying this chapter, students should be able to understand the basic concepts of reliability, application area, and its performance parameters. The need of reliability for electrical and electronics components and devices is specified in this chapter. It would help the students to differentiate between faults, defects, and failures. The fundamental techniques and methods for reliability assessment and condition monitoring are discussed.

Reliability

In the modern era of integration, millions of transistors and other electronic components are connected on a single chip. As the number of devices enhance, reliability and validity become a challenging and critical issue. In a series connection, if any one of the components fails or degrades its performance, the complete system shuts down immediately. So, reliability analysis of all the individual components are equally important and necessary for the long term quality performance of the complete device or system.

The efficiency of the system which is performing a specific task, is described by the terms such as reliability, survivability. Reliability means the ability of the system to perform its intended function satisfactorily. The term reliability was defined by the Advisory Group on Reliability of Electronic Equipment (AGREE) as the "probability of a product performing its intended function satisfactorily under given condition for a specified period of time". The reliability evaluation techniques were adopted mostly in military applications and aerospace industry for the improvement of quality. The improvement of the effectiveness of components of various kinds has received special attention concerning with numerous problems because of the advanced technology. Sometimes effectiveness is also referred to as quality in literature.

The concept of survivability is understood as the ability of the system to preserve the properties to serve its purpose under adverse conditions (viz, explosions, fine, inundation, and so on). The reliability of a system was determined by the properties of the system, namely, trouble proofness, reparability, and longevity. Trouble proofness is the property of a system to preserve its capability in the duration of a definite time under normal conditions. The prevention, detection, and elimination of failure are useful for improvement of the system that is called repairability. Longevity is the ability for a prolonged operation with the necessary technical maintenance including various kinds of repairs. Maintainability is defined as the probability that failed equipment is restored to operable condition in a specified time (known as down time). Availability is the measure of performance of repairable equipment. It is the combination of reliability and maintainability.

Failure

The word failure plays an important role in the context of reliability theory. The term failure is defined as the termination of the ability of an item to perform its intended function. The notion of failure is a useful characteristic of reliability analysis because it is mainly responsible for various numerical criteria of reliability analysis. Failures are classified into the following different ways – inherent weakness failure, sudden failure, gradual failure, catastrophic failure, and degradation failure. Some of the causes for failures of component in the system are poor designing of the components, lack of experience, poor maintenance policies, wrong manufacturing techniques, and human errors.

Role of probability laws in reliability theory

The reliability analysis of a system is based precisely on defined concepts like reliability function R (t), expected life E (T), hazard function h (t) and failure rate λ (t). A population of identical systems, which operate under identical conditions, fail at different duration of times and the failure phenomenon can only be described in probability terms. Thus, the reliability definition is based on the concept of probability theory. In practice, reliability evaluation is associated with some parameters which are described by probability distributions. The most useful continuous distributions in the theory of reliability are the exponential, weibull, gamma, normal, and log normal distributions, and the two most important discrete distributions are binomial and poisson.

Hazard rate

In the reliability theory, identifying failure model is an art. The concept of hazard rate is effectively used in reliability analysis to identify the failure distributions. For instance, if the hazard rate is more or less constant, then it would represent that time to failure follows exponential distribution. The exponential distribution is widely used in the theory of reliability. On the other hand, it is interesting to note that exponential distribution would arise as a particular case of weibull and gamma distributions which are most commonly used in life testing and reliability estimation.

Weibull distribution

Weibull distribution was named after the Swedish scientist Weibull, who proposed it for the first time in 1939 in connection with his studies on strength of materials. Weibull established that the distribution is also useful in describing the aging effect or wear-out failures KAO, proposing it as a failure model for vacuum tube failures, and Leiblin and Zelen used it for ball bearing failures. Mann et al has shown the number of situations where this model is applicable for other types of failure data. Normal distribution is also applicable as a failure model in the context of reliability. Davis observed that the failure data is fit to normal distribution in the case of incandescent lamps; Bozavsky also described the use of the normal distribution in reliability problems.

Some important measures related to reliability analysis

Reliability measures quantify the effectiveness of the system. In the reliability theory, some of the important measures are covered in the next sections.

Probability of survival

The probability of failure is similar to that of probability of survival. The probability of survival is defined as the probability of a system operating without failure for a required period of time Y, and is given by the following formula:

Here, T is a random variable representing time to failure of the system, F (t) indicates the probability of the system failing by the time Y, and is defined as failure distribution.

Mean Time Between Failures [E(T)]

The definition of Mean Time...