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.
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
RESIDUAL LIFE PREDICTION OF ELECTROLYTIC CAPACITOR
One of the major aspects for electronic engineers regarding capacitor is to predict its remaining useful life in order to protect it from sudden failures and prevent the complete system breakdown. The life of electrolytic capacitors is mostly relies on various environmental and electrical factors where environmental factors are temperature, humidity, atmospheric pressure and vibration and electrical factors are operating voltage, ripple current and dissipation factor.
Out of these factors, temperature (ambient temperature) is the critical factor while estimating the life of aluminum electrolytic capacitors whereas; conditions such as vibration, shock and humidity have little impact on the actual life of the capacitor [7-8].
Lifetime Acceleration Factors
Electrolytic capacitors are by assessed by accelerated life tests. The accelerated life tests contain four components (one for temperature, voltage and ripple current) which are given by the accompanying equation [9-10]:
LP = LT * LV * LR* LVIB * LH
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.
- Arbeit zitieren
- Cherry Bhargava (Autor:in), Shivani Gulati (Autor:in), 2017, Remaining Useful Life (RUL) Prediction of electrolytic Capacitor using Artificial Intelligence, München, GRIN Verlag, https://www.grin.com/document/427692
