Residual life prediction is the technique which demonstrates how reliable a particular electronic system or component works under in specific operating conditions. The remaining useful life relies on the failure rate of a component and on the operating conditions of a device. This failure rate drifts for the duration of the life of the item with time. Life is an important aspect while choosing the electronic hardware. Residual life estimation and life prediction are two distinct terms. The importance of life estimation is to evaluate the remaining useful life of a specific component under the different stress parameters. As an increasing number of components are integrated on to a chip, the chances of failure increase, as the different parts have their own stress factors and different working conditions. So the condition monitoring strategies are utilized which enhances the reliability of a component and a suitable move to be made before any harmful breakdown happens. The electronic circuits need a failure estimation technique to protect the system from unavoidable failures.
Residual life estimation of electronic components is an important fact these days as electronic components and devices becomes a great need of society. Residual life prediction is predicting the remaining useful life of a component or device based on various failure factors of any component and it also depends on the operating conditions. Many methods for predicting the life of electronic components have been developed. The life of electronic components can be predicted by creating an intelligent system for the failure analysis. The capability to predict the life of electronic components is a key to prevent the sudden costly failure and it will increase the overall performance and reliability of a system. So, remaining useful life prediction is an important factor for every active and passive electronic component such as resistor, capacitor and diode etc.
Inhaltsverzeichnis (Table of Contents)
- CHAPTER 1: INTRODUCTION
- RESIDUAL LIFE PREDICTION OF ELECTRONIC 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 2: 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 3: 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 4: 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 5: 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
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This work aims to predict the remaining useful life of various electronic components, specifically capacitors, resistors, and diodes. The research investigates the use of artificial intelligence techniques, such as artificial neural networks, fuzzy inference systems, and adaptive neuro-fuzzy inference systems, to develop reliable and accurate models for life prediction.
- Residual life prediction of electronic components
- Acceleration life testing (ALT) methods
- Artificial intelligence techniques for life estimation
- Design and development of decision support systems
- Comparison of different life prediction methods
Zusammenfassung der Kapitel (Chapter Summaries)
The introductory chapter establishes the importance of residual life prediction in ensuring the reliability of electronic systems and components. It defines the concept of residual life and explores the impact of various factors, including operating conditions and failure rates, on component lifetime. This chapter also provides an overview of different methods for predicting component life, including analytical and experimental approaches.
Chapter 2 presents a comprehensive literature review, summarizing existing research on residual life prediction of electronic components. This chapter explores various techniques, including artificial intelligence methods, and analyzes their strengths and limitations. It also highlights the importance of considering the specific characteristics of different component types.
Chapter 3 delves into the development of a robust methodology for estimating the remaining useful life of electronic components. It discusses the selection of components for life estimation, the utilization of experimental and analytical methods, and the integration of artificial intelligence techniques. This chapter also outlines the design of a decision support system and discusses the scope of the study.
Chapter 4 presents the practical implementation of the proposed methodology. It details the life estimation of capacitors, resistors, and diodes using both experimental (ALT) and artificial intelligence modeling approaches. This chapter provides insights into the effectiveness of various techniques and their application in real-world scenarios.
Chapter 5 focuses on the analysis of results obtained from different life prediction techniques. It compares the performance of analytical, experimental, and artificial intelligence methods and discusses the strengths and limitations of each approach. This chapter provides a comprehensive evaluation of the effectiveness of the developed decision support system.
Schlüsselwörter (Keywords)
This work focuses on residual life prediction, electronic components, artificial intelligence, life estimation, acceleration life testing, decision support systems, and fuzzy logic.
- Citation du texte
- Cherry Bhargava (Auteur), Shivani Gulati (Auteur), 2017, Remaining Useful Life (RUL) Prediction of electrolytic Capacitor using Artificial Intelligence, Munich, GRIN Verlag, https://www.grin.com/document/427692
