Joo-Ho Choi

Practical options for selecting data-driven or physics-based prognostics algorithms with reviews

This paper is to provide practical options for prognostics so that beginners can select appropriate methods for their fields of application. To achieve this goal, several popular algorithms are first reviewed in the data-driven and physics-based prognostics methods. Each algorithm’s attributes and pros and cons are analyzed in terms of model definition, model parameter estimation and ability to handle noise and bias in data. Fatigue crack growth examples are then used to illustrate the characteristics of different algorithms. In order to suggest a suitable algorithm, several studies are made based on the number of data sets, the level of noise and bias, availability of loading and physical models, and complexity of the damage growth behavior. Based on the study, it is concluded that the Gaussian process is easy and fast to implement, but works well only when the covariance function is properly defined. The neural network has the advantage in the case of large noise and complex models but only with many training data sets. The particle filter and Bayesian method are superior to the former methods because they are less affected by noise and model complexity, but work only when physical model and loading conditions are available.

Warpage mechanism analyses of strip panel type PBGA chip packaging

The objective of this study is to analyze a warpage development mechanism by simulating a strip type packaging for plastic ball grid array. Molding compound and substrate materials were thermo-mechanically tested to obtain the mechanical properties by several test methods. Samples were fabricated using the same materials, and warpage developments were measured at room temperature after molding compound cure. Based on the tested materials property, the warpage developments were simulated by numerical calculations during cooldown process. The results were compared with the measurement data of the samples, and the warpage mechanism was investigated based on the elastic and viscoelastic simulation results. It was found that the relaxation behaviors of the molding compound and the substrate materials had significant effect on the warpage development. It was also found that the warpage development was dependent on the packaging geometry. The development mechanism was analyzed through the simulation calculations by combining different material properties modeling and geometries, and the results showed comprehensive consideration of the materials and the packaging design are essential to control the warpage.

A Comparison Study of Methods for Parameter Estimation in the Physics-based Prognostics

Prediction of remaining useful life of a system is important for safety and maintenance scheduling. In the physics-based prognostics, the accuracy of predicted remaining useful life is directly related to that of estimated model parameters. It, however, is not a simple task to estimate the model parameters because most real systems have multivariate model parameters, which are often correlated each other. This paper mainly discusses the difference in estimating model parameters among different prognostics methods: the particle filter method, the overall Bayesian method, and the incremental Bayesian method. These methods are based on the same theoretical foundation, Bayesian inference, but they are different from each other in the sampling scheme and/or uncertainty analysis process. A simple analytical example and the Paris model for crack growth are used to demonstrate the difference among the three methods in terms of prognostics metrics. The numerical results show that particle filter and overall Bayesian methods outperform the incremental Bayesian method. Even though the particle filter shows slightly better results in terms of prognostics metrics, the overall Bayesian method is efficient when batch data exist.

Options for Prognostics Methods: A review of data-driven and physics-based prognostics

Condition-based maintenance (CBM) is a cost effective maintenance strategy, in which maintenance schedules are predicted based on the results provided from diagnostics and prognostics. Although there are several reviews on diagnostics methods and CBM, a relatively small number of reviews on prognostics are available. Moreover, most of them either provide a simple comparison of different prognostics methods or focus on algorithms rather than interpreting the algorithms in the context of prognostics. The goal of this paper is to provide a practical review of prognostics methods so that beginners in prognostics can select appropriate methods for their field of applications in terms of implementation and prognostics performance. To achieve this goal, this paper introduces not only various prognostics algorithms, but also their attributes, pros and cons using simple examples.

Model-Based Fault Diagnosis of a Planetary Gear: A Novel Approach Using Transmission Error

Extensive prior studies aimed at the development of diagnostic methods for planetary gearboxes have mainly examined acceleration and acoustic emission signals. Recently, due to the relationship between gear mesh stiffness and transmission error (TE), TE-based techniques have emerged as a promising way to analyze dynamic behavior of spur and helical gears. However, to date, TE has not been used as a measure to detect faults in planetary gears. In this paper, we propose a new methodology for model-based fault diagnostics of planetary gears using TE signals. A lumped parametric model of planetary gear dynamics was built to extract simulated TE signals, while accounting for the planet phasing effect, which is a peculiar characteristic of the planetary gear. Next, gear dynamic analysis was performed using TE signals, and TE-based damage features were calculated from the processed TE signals to quantitatively represent health conditions. The procedures described aforesaid were then applied to a case study of a planetary gear in a wind turbine gear train. From the results, we conclude that TE signals can be used to detect the faults, while enhancing understanding of the complex dynamic behaviors of planetary gears.

A Survey on Prognostics and Comparison Study on the Model-Based Prognostics

In this paper, PHM (Prognostics and Health Management) techniques are briefly outlined. Prognostics, being a central step within the PHM, is explained in more detail, stating that there are three approaches - experience based, data-driven and model based approaches. Representative articles in the field of prognostics are also given in terms of the type of faults. Model based method is illustrated by introducing a case study that was conducted to the crack growth of the gear plate in UH-60A helicopter. The paper also addresses the comparison of the OBM (Overall Bayesian Method), which was developed by the authors with the PF (Particle Filtering) method, which draws great attention recently in prognostics, through the study on a simple crack growth problem. Their performances are examined by evaluating the metrics introduced by PHM society.

Development of a Fatigue Model for Low Alloy Steels Using a Cycle-Dependent Cohesive Zone Law

A fatigue model for SAE 4130 steels is developed using a cycle-dependent cohesive zone law. Reduction of fracture energy and degradation of stiffness are considered to describe failure resistance after certain number of cycles. The reduction rate of fracture energy is determined with experimental stress (S)- number of cycles to failure (N) scatter found in the literature. Three-dimensional finite element models containing a cohesive zone are generated with commercial software (ABAQUS). Calculated fatigue lives at different stress ratios are in good agreement with experimental ones. In addition, fatigue behavior of hardened SAE 4130 steels is predicted with that of normalized material.

Probabilistic Calibration of Computer Model and Application to Reliability Analysis of Elasto-Plastic Insertion Problem

: 표준편차 Key Words: Pyrotechnically Actuated Device(파이로작동기구), Elasto-Plastic Analysis(탄소성해석), Calibration (보정), Markov Chain Monte Carlo(마르코프체인 몬테카를로) 초록: 컴퓨터 해석모델은 물리현상을 바탕으로 단순화된 모델을 구축하고 해를 구하는 유용한 도구이나, 많은 경우 단순화 가정 또는 입력변수 정보의 미비나 불확실성으로 인해 실제와 차이가 발생한다. 본 연구에서는 이러한 문제에 대해 베이지안 확률이론을 이용하여 실측데이터를 통해 해석모델을 보정하는 방법을 소개하고 이를 파이로 작동기구의 탄소성 압착 문제에 적용한다. 파이로 작동기구는 고에너지의 재료를 원격으로 폭발시켜 작동하는 장치로 그 작동의 신속한 계산을 위해서 단순한 수학모델을 구축하고 실험데이터를 토대로 미지의 입력변수를 확률적으로 보정하였다. 이 때, 확률적 추정을 위해서는 현대적 계산통계기법의 하나인 Markov Chain Monte Carlo 기법을 이용하였으며, 최종적으로 그 결과를 압착거동해석에 활용하여 작동기구의 신뢰도를 평가하였다. Abstract: A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

Bayesian Parameter Estimation for Prognosis of Crack Growth under Variable Amplitude Loading

In this study, crack-growth model parameters subjected to variable amplitude loading are estimated in the form of a probability distribution using the method of Bayesian parameter estimation. Huangs model is employed to describe the retardation and acceleration of the crack growth during the loadings. The Markov Chain Monte Carlo (MCMC) method is used to obtain samples of the parameters following the probability distribution. As the conventional MCMC method often fails to converge to the equilibrium distribution because of the increased complexity of the model under variable amplitude loading, an improved MCMC method is introduced to overcome this shortcoming, in which a marginal (PDF) is employed as a proposal density function. The model parameters are estimated on the basis of the data from several test specimens subjected to constant amplitude loading. The prediction is then made under variable amplitude loading for the same specimen, and validated by the ground-truth data using the estimated parameters.

In-Situ Monitoring and Prediction of Progressive Joint Wear Using Bayesian Statistics

In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in-situ measurement data is presented. An instrumented slider-crank mechanism is built, which can measure the joint force and the relative motion between the pin and bushing during operation. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower link and the slide stage, as well as a prismatic joint for the linear slide, are used. Based on the relative motion between the centers of pin and bushing, the wear volumes are estimated at six different operating cycles. The Bayesian inference technique is used to update the distribution of wear coefficient, which incorporates in-situ measurement data to obtain the posterior distribution. In order to obtain the posterior distribution, Markov Chain Monte Carlo technique is employed, which effectively draws samples of the given distribution. The results show that it is possible to narrow the distribution of wear coefficient and to predict the future wear volume with reasonable confidences. The effect of prior distribution on the wear coefficient is discussed by comparing with non-informative case.© 2010 ASME