Remaining Useful Life (RUL) Prediction of electrolytic Capacitor using Artificial Intelligence

Table of Contents

CHAPTER

INTRODUCTION

RESIDUAL LIFE PREDICTIONOFELECTRONIC COMPONENTS

RESIDUAL LIFE PREDICTION OF ELECTROLYTIC CAPACITOR

Lifetime Acceleration Factors

RESIDUAL LIFE PREDICTION OF FIXED RESISTOR

Effects of Temperature on Life

Effects of operating voltage on Life

RESIDUAL LIFE PREDICTION OF DIODE

Effect of temperature on life

Effect of voltage on life

ARTIFICIAL INTELLIGENCE TECHNIQUES USED FOR RESIDUAL LIFE PREDICTION

Artificial neural network technique

Fuzzy Inference System

Adaptive Neuro-fuzzy Inference System (ANFIS)

CHAPTER

LITERATURE REVIEW

Salah-Al-Zubaidi, et.al:

Cherry Bhargava, et.al:

Salah Al-Zubaidi, et.al:

PrakashHPatil, et.al:

Zhigang Tian :

Seung Wu Lee, et.al:

Adithya Thaduri, et.al:

Garron KMorris, et.al:

Ajith Abraham, et.al:

Youmin Rong, et.al:

Prabhakar VVarde, et.al:

Vishu Madaan, et.al:

CHAPTER

REVIEW OF BASIC CONCEPTS AND DEVELOPMENT OF

Introduction

Outline of proposed work

Selection of components for life estimation

Methods for estimating the remaining useful life of electronic components

Experimental method (Using ALT)

Analytical method

Residual life estimation using artificial intelligence techniques

Artificial neural network technique

Fuzzy Inference System

Adaptive Neuro-fuzzy Inference System (ANFIS)

Design of decision support system

Tools Used:

MATLAB tool

Scope of the study

Residual life estimation using artificial intelligence techniques

Artificial neural network model

Fuzzy Inference System

Adaptive Neuro-fuzzy Inference System (ANFIS)

Design of fuzzy based decision support system

Modeling of fuzzy based decision support system

CHAPTER

WORK DONE

Residual life estimation of capacitor

Life estimation of electrolytic capacitor using analytical method

Residual life estimation of capacitor using ALT (acceleration life testing) approach

Residual life estimationof resistor

Residual life estimation of resistor using Acceleration life testing

Residual life estimation of resistor using artificial intelligence modeling

Residual life estimation of Diode

Residual life estimation of diode using ALT (acceleration life testing) method

Life prediction of diode using expert artificial intelligence modeling

Design of decision support system

CHAPTER

RESULTS AND DISCUSSION

Residual life estimation of capacitor using analytical method

Residual life of electrolytic capacitor obtained using artificial intelligence modeling

Residual life of electrolytic capacitor obtained using artificial neural network model

Residual life of electrolytic capacitor obtained using fuzzy model

Residual life of electrolytic capacitor obtained using ANFIS model

Comparison of output life obtained using various techniques

Life estimation of electrolytic capacitor using experimental method (using ALT)

Fuzzy based decision support system interfaces for electrolytic capacitor

Fuzzy based decision support system interfaces for resistor

Residual life estimation of diode using experimental method (ALT)

Fuzzy based decision support system interface for diode

CHAPTER

CONCLUSION & FUTURE SCOPE

CHAPTER

CONCLUSION & FUTURE SCOPE

CHAPTER

REFERENCES

ANNEXURE

Objective & Themes

The primary objective of this research is to develop an intelligent system for predicting the remaining useful life (RUL) of common electronic components—specifically electrolytic capacitors, fixed resistors, and diodes—under various operating conditions to prevent sudden system failures and enhance overall reliability.

• Application of analytical methods using lifetime acceleration factors.
• Implementation of experimental methods via Accelerated Life Testing (ALT).
• Utilization of artificial intelligence techniques including Artificial Neural Networks (ANN), Fuzzy Inference Systems, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS).
• Development of a GUI-based decision support system using MATLAB for real-time monitoring.
• Comparative performance analysis of different prediction models to identify the most accurate technique.

Excerpt from the Book

Summary of Chapters

INTRODUCTION: Provides an overview of the importance of residual life prediction in preventing failures and ensuring reliability in integrated electronic systems.

LITERATURE REVIEW: Examines various existing studies and methodologies related to reliability and life prediction, including statistical, analytical, and soft computing approaches.

REVIEW OF BASIC CONCEPTS AND DEVELOPMENT OF METHODOLOGY: Details the fundamental concepts of component life, reliability, and the proposed hybrid intelligent methodology for life estimation.

WORK DONE: Documents the practical application of experimental, analytical, and AI-based models on specific electronic components to gather failure data.

RESULTS AND DISCUSSION: Compares the outcomes of various prediction models (ANN, Fuzzy, ANFIS) with actual failure data and presents the developed decision support interfaces.

CONCLUSION & FUTURE SCOPE: Summarizes findings on the accuracy of different techniques, identifying ANFIS as the superior model, and suggests future research directions.

Keywords

Residual Life Prediction, Electrolytic Capacitor, Fixed Resistor, Diode, Accelerated Life Testing (ALT), Artificial Neural Network (ANN), Fuzzy Inference System, Adaptive Neuro-Fuzzy Inference System (ANFIS), Reliability, Condition Monitoring, MATLAB, Failure Analysis, Stress Factors, Predictive Maintenance, Intelligent Modeling

Frequently Asked Questions

What is the core focus of this research?

The research focuses on developing intelligent methodologies to estimate the Remaining Useful Life (RUL) of electronic components like capacitors, resistors, and diodes to prevent system downtime.

What are the central thematic fields covered?

The central themes include reliability engineering, life estimation through accelerated stress testing, and the application of artificial intelligence models to predict component degradation.

What is the primary research goal?

The goal is to increase system reliability and reduce costly, unexpected failures by creating an accurate predictive system that considers real-world operating conditions.

Which scientific methods are employed?

The research uses three main approaches: analytical methods (using acceleration factors), experimental methods (Accelerated Life Testing), and machine learning (ANN, Fuzzy Logic, and ANFIS).

What is addressed in the main part of the work?

The main part covers the formulation of mathematical models, the experimental testing of components, the design of AI-based predictive models in MATLAB, and the development of a user-friendly decision support interface.

Which keywords best characterize this work?

Key terms include Residual Life Prediction, Accelerated Life Testing, ANN, Fuzzy Inference System, ANFIS, Reliability, and Component Failure Analysis.

How does the ANFIS model improve upon standard techniques?

ANFIS combines the self-learning capabilities of ANN with the uncertainty-handling properties of Fuzzy logic, resulting in higher accuracy and fewer prediction errors compared to using the techniques individually.

What role does MATLAB play in this study?

MATLAB is used as the primary tool to design the intelligent models, implement Fuzzy rule bases, train neural networks, and develop the Graphical User Interface (GUI) for the decision support system.