Challenges in Reliability Assessment for Electronics

2010 ◽  
Vol 118-120 ◽  
pp. 419-423 ◽  
Author(s):  
Qing Chuan He ◽  
Wen Hua Chen ◽  
Jun Pan ◽  
Shi Jiao Wang
Keyword(s):  
Life Cycle ◽  
Failure Mechanism ◽  
Health Management ◽  
Reliability Assessment ◽  
Model Identification ◽  
Catastrophic Failure ◽  
Prognostics And Health Management ◽  
Enabling Technology ◽  
Actual Application ◽  
Reliability Of Electronics

There has been a growing interest in assessing the ongoing reliability of electronics and systems in order to predict failures and provide warning to avoid catastrophic failure. Methods based on prognostics and health management shows an enabling technology to assess the reliability of electronics and systems under its actual application conditions. However, many challenges in implementation of methods based on PHM still remain including: environmental and usage profiles for life-cycle loads, identification of failure mechanism, identification of failure PoF model, identification of parameters to be monitored, approaches to anomaly detection. These challenges were presented and discussed, and would be carried out by developing methodologies and techniques.

A Survey of Sensor Technologies for Prognostics and Health Management of Electronic Systems

2014 ◽  
Vol 602-605 ◽  
pp. 2229-2232 ◽  
Author(s):  
Wen Xue Yang ◽  
Zhe Chen ◽  
Feng Yang
Keyword(s):  
Life Cycle ◽  
Early Warning ◽  
Health Management ◽  
Life Cycle Costs ◽  
Electronic Systems ◽  
Sensor Systems ◽  
Prognostics And Health Management ◽  
Electronic Products ◽  
System Failures

Recently, the field of Prognostics and Health Management (PHM) for electronic products and systems has received increasing attention due to the potentialities to provide early warning of system failures, reduce life cycle costs, and forecast maintenance as needed. This paper introduces the sensors and their sensor technologies. The required attributes of sensors for the development for PHM of electronics are discussed. Finally, their trends in sensor systems are presented.

scholarly journals PHM Survey : Implementation of Diagnostic Methods for Monitoring Industrial Systems

2019 ◽  
Vol 10 (2) ◽  
Author(s):  
Abdenour Soualhi ◽  
Bilal Elyousfi ◽  
Yasmine Hawwari ◽  
Kamal Medjaher ◽  
Guy Clerc ◽  
...  
Keyword(s):  
Life Cycle ◽  
Health Management ◽  
Fault Isolation ◽  
Diagnostic Methods ◽  
Condition Based Maintenance ◽  
Prognostics And Health Management ◽  
Isolation And Identification ◽  
Industrial Systems ◽  
Industrial Sectors ◽  
Processing Step

The modernization of industrial sectors involves the use of complex industrial systems and therefore requires condition based maintenance. This one aims at increasing the operational availability and reducing the life-cycle while increasing the reliability and life expectancy of industrial systems. This maintenance also called predictive maintenance is a part of an emerging philosophy called PHM ‘Prognostics and Health Management’. In this paper, the PHM will be emphasized on the existing diagnostic methods used for fault isolation and identification. This depicts an important part of the PHM as it exploits the data given by the signal-processing step and its output is treated by the prognostic part. The diagnostic is mainly classified in three categories that will be highlighted in this paper.

scholarly journals Prognostics and Health Management System for Electric Vehicles with a Hierarchy Fusion Framework: Concepts, Architectures, and Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Cheng Wang ◽  
Tongtong Ji ◽  
Feng Mao ◽  
Zhenpo Wang ◽  
Zhiheng Li
Keyword(s):  
Life Cycle ◽  
Fault Diagnosis ◽  
Real Time ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Health Management ◽  
Research Progress ◽  
Prognostics And Health Management ◽  
Time Data ◽  
Real Time Data

The prognostics and health management (PHM) of electric vehicles is an important guarantee for their safety and long-term development. At present, there are few studies researching about life cycle PHM system of electric vehicles. In this paper, we first summarize the research progress and key methods of PHM. Then, we propose a three-level PHM system with a hierarchy fusion architecture for electric vehicles based on the structure, data source of them. In the PHM system, we introduce a database consisting of the factory data, real-time data, and detection data. The electric vehicle's factory parameters are used for determining the life curve of the electric vehicle and its components, the real-time data are used for predicting the remaining useful lifetime (RUL) of the electric vehicle and its components, and the detection data are used for fault diagnosis. This health management database is established to help make condition-based maintenance decisions for electric vehicles. In this way, a complete electric vehicle PHM system is formed, which can realize the whole-life-cycle life prediction and fault diagnosis of electric vehicles.

Testability Design of the PHM System for Aero-Engines

2012 ◽  
Vol 544 ◽  
pp. 94-98
Author(s):  
Zhen Hua Wen ◽  
Yuan Peng Liu ◽  
Xin Yin
Keyword(s):  
Life Cycle ◽  
Health Management ◽  
Management Strategies ◽  
Prognostics And Health Management ◽  
Technical Approach ◽  
Test Program ◽  
Fusion Methods ◽  
Aero Engines ◽  
Total Life ◽  
Monitoring Information

PHM (Prognostics and Health Management) for Aero-engines is an effective technical approach to balance the economy and safety of the flight in the total life cycle. In this paper, we mainly analyze the popular issues in the process of designing PHM system for aero-engines including the testability design concept, the scheme of condition monitoring and the utilization extent of condition information. Then presents some useful solutions and advices for the testability design respectively; and analyzes the influence of testability on health management strategies and the main source of uncertainty; then propose a roadmap for making test program based on the PHM requirements and evaluating test program, for improve the utilizing degree of monitoring information, we lastly presented common data fusion methods and some typical examples is illustrated.

scholarly journals A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings

2020 ◽  
Vol 10 (16) ◽  
pp. 5639
Author(s):  
Jinwoo Sim ◽  
Seokgoo Kim ◽  
Hyung Jun Park ◽  
Joo-Ho Choi
Keyword(s):  
Health Management ◽  
Practical Importance ◽  
Feature Engineering ◽  
Catastrophic Failure ◽  
Prognostics And Health Management ◽  
New Developments ◽  
The Public ◽  
Basic Concepts ◽  
Public Datasets ◽  
Selection Of

Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which is known as feature engineering, a prerequisite step for the prognostics and health management (PHM) of these components. While there have been many new developments in this field, no studies have addressed the tutorial aspects of features engineering to aid engineers in solving problems by their own effort, which is of practical importance for successful PHM. The paper aims at helping beginners learn the basic concepts, and implement the algorithms using the public datasets as well as those made by the authors. Matlab codes are provided for them to implement the process by their own hands.

A Review on Prognostics and Health Management : 2013~2018

2019 ◽  
Vol 19 (1) ◽  
pp. 68-84 ◽  
Author(s):  
Hyun Su Sim ◽  
Jun-Gyu Kang ◽  
Yong Soo Kim
Keyword(s):  
Health Management ◽  
Prognostics And Health Management

Remaining useful life estimation: a review on stochastic process-based approaches

2020 ◽  
Vol 14 ◽  
Author(s):  
Dangbo Du ◽  
Jianxun Zhang ◽  
Xiaosheng Si ◽  
Changhua Hu
Keyword(s):  
Stochastic Process ◽  
Health Management ◽  
Remaining Useful Life ◽  
Future Trends ◽  
Prognostics And Health Management ◽  
Inverse Gaussian ◽  
Life Estimation ◽  
Inverse Gaussian Process ◽  
Pros And Cons ◽  
Useful Life

Background: Remaining useful life (RUL) estimation is the central mission to the complex systems’ prognostics and health management. During last decades, numbers of developments and applications of the RUL estimation have proliferated. Objective: As one of the most popular approaches, stochastic process-based approach has been widely used for characterizing the degradation trajectories and estimating RULs. This paper aimed at reviewing the latest methods and patents on this topic. Methods: The review is concentrated on four common stochastic processes for degradation modelling and RUL estimation, i.e., Gamma process, Wiener process, inverse Gaussian process and Markov chain. Results: After a briefly review of these four models, we pointed out the pros and cons of them, as well as the improvement direction of each method. Conclusion: For better implementation, the applications of these four approaches on maintenance and decision-making are systematically introduced. Finally, the possible future trends are concluded tentatively.

Sign in / Sign up

Export Citation Format

Share Document