Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life

Sreenuch, T.; Alghassi, Ali; Perinpanayagam, Suresh

doi:10.14569/ijacsa.2014.050113

Cited by 26 publications

(21 citation statements)

References 12 publications

(14 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A way to predict trends in nonlinear systems is by using classical data-driven methods,such as stochastic models, an autoregressive model, the bilinear model, the projection pursuit, etc. Soft computing techniques that involve using various types of neural networks (NNs) and neural fuzzy (NF) systems have also been commonly adopted to deal with data-driven forecasting of a system state [7] [8]. This prognostic approach applies to applications that havecomplicated system architecture, i.e., systems that incur high amount of cost when developing an accurate prognostic model.…”

Section: A Data-driven Prognosticsmentioning

confidence: 99%

Principle of Duality on Prognostics

Samie¹,

Motlagh²,

Alghassi³

et al. 2014

ijacsa

View full text Add to dashboard Cite

Abstract-The accurate estimation of the remaining useful life (RUL) of various components and devices used in complex systems, e.g., airplanes remain to be addressed by scientists and engineers. Currently, there area wide range of innovative proposals put forward that intend on solving this problem. Integrated System Health Management (ISHM) has thus far seen some growth in this sector, as a result of the extensive progress shown in demonstrating feasible and viable techniques. The problems related to these techniques were that they often consumed time and were too expensive and resourceful to develop. In this paper we present a radically novel approach for building prognostic models that compensates and improves on the current prognostic models inconsistencies and problems. Broadly speaking, the new approach proposes a state of the art technique that utilizes the physics of a system rather than the physics of a component to develop its prognostic model. A positive aspect of this approach is that the prognostic model can be generalized such that a new system could be developed on the basis and principles of the prognostic model of another system. This paper will mainly explore single switch dc-to-dc converters which will be used as an experiment to exemplify the potential success that can be discovered from the development of a novel prognostic model that can efficiently estimate the remaining useful life of one system based on the prognostics of its dual system.

show abstract

Section: A Data-driven Prognosticsmentioning

confidence: 99%

Principle of Duality on Prognostics

Samie¹,

Motlagh²,

Alghassi³

et al. 2014

ijacsa

View full text Add to dashboard Cite

show abstract

“…Apply the Multilevel Monte Carlo method to reliability theory [15]. Proposes a probabilistic Monte Carlo method for modeling and predicting the age of electronic components [16]. Popose Markov Chain Monte Carlo simulation method for structural reliability analysis [17].…”

Section: Introduction *mentioning

confidence: 99%

Reliability Analysis of Primary and Purification Pumps in RSG-Gas Using Monte Carlo Simulation Approach

Hartini¹,

Adrial²,

pujiarta

2019

Tri Dasa Mega

View full text Add to dashboard Cite

Reliability and maintenance play an important role in ensuring successful operation of a system. Reliability analysis is often used to determine the probability whether or not a system is functioning. However, limited available data and information are causing uncertainties and inaccuracies on component parameters. The purpose of this study is to conduct component/system reliability analysis using Monte Carlo simulation-based method. This method enables us to estimate the reliability of components/systems including parameter uncertainty and imprecision. It is also useful to predict and evaluate maintenance decisions related to reliability. Monte Carlo method employs random number generation based on the probability of the distribution of processed data, of which then validated with real available data to ensure the simulation condition is relatively similar to real-life condition. The data used in this research is failure data on RSG-GAS components/systems for core configuration number of 81 to 95, accumulated from year 2013 to 2018. The results show that reliability values of components JE01/AP01-02 on TTF 233.619 is 0.579 while for components KBE01/AP-01-02 in TTF 185.38 is 0.368.The component reliability value is 60%, which implies that maintenance may be performed after 225 days and 100 days for componentsJE01/AP01-02 and KBE01/AP01-02, respectively.Keywords: Reliability, Monte Carlo, Component damage, RSG-GAS

show abstract

“…Power electronic components such as Insulated Gate Bipolar Transistor (IGBT) play a crucial role in household appliances, automotive, renewable energy, etc. IGBT is increasingly required in many electronic systems with high reliability characteristics, for example in automotive vehicles [1].…”

Section: Introductionmentioning

confidence: 99%

“…The gradual degradation of IGBT decreases the efficiency of an electronic system, and a failure of the device can cause failure of the whole system [1]. Unexpected or sudden failures of IGBT devices occurring in the products or systems can lead to excessive downtime and large losses such as high maintenance cost and lost revenue.…”

Section: Introductionmentioning

confidence: 99%

“…but overall the system does not provide high accuracy of RUL prediction and high performance when compared to other computational approaches. Sreenuch et al [1] have used Monte-Carlo simulation and collector emitter voltage to develop prognostics algorithm for the RUL prediction of IGBT. Gamma, exponential and Poisson distribution models are combined.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data driven prognostics for predicting remaining useful life of IGBT

Ahsan

Stoyanov

Bailey

2016

2016 39th International Spring Seminar on Electronics Technology (ISSE)

View full text Add to dashboard Cite

Power electronic devices such IGBT (Integrated Gate Bipolar Transistor) are used in wide range of applications such as automotive, aerospace and telecommunications. The ability to predict degradation of power electronic components can minimise the risk of their failure while in operation. Research in this area aims to develop prognostics strategies for predicting degradation behaviour, failure modes and mechanisms, and remaining useful life of these electronic components. In this paper, data driven prognostics approaches based on Neural Network (NN) and Adaptive Neuro Fuzzy Inference System (ANFIS) models are developed and used to predict the degradation of an IGBT device. IGBT life data under thermal overstress load condition with square signal gate voltage bias, available from NASA prognostics data repository, is used to demonstrate the proposed data-driven prognostics strategy. The monitored collector-emitter voltage is used to identify the pattern and duration of different phases in the applied voltage load. The NN and ANFIS models are trained with a subset of the test data to predict remaining useful life (RUL) of the IGBT device under varying load test profiles. The predictive capability and performance of the models is observed and analysed.

show abstract

Probabilistic Monte-Carlo Method for Modelling and Prediction of Electronics Component Life

Cited by 26 publications

References 12 publications

Principle of Duality on Prognostics

Principle of Duality on Prognostics

Reliability Analysis of Primary and Purification Pumps in RSG-Gas Using Monte Carlo Simulation Approach

Data driven prognostics for predicting remaining useful life of IGBT

Contact Info

Product

Resources

About