Fatigue Crack Length Estimation and Prediction using Trans-fitting with Support Vector Regression

A method is described in this paper for crack propagation prediction using only the initial crack length of the target specimen. The proposed method consists of two parts: (1) crack length estimation using support vector regression (SVR) and (2) crack length prediction using a new trans-fitting method. Features based on the filtered wave signals were defined and a model was constructed using the SVR method to estimate the crack length. The hyper-parameters of the SVR model were selected based on a grid search algorithm. Prediction of the crack length was based on the previous crack length, which was estimated based on the wave signals. In this step, a newly proposed trans-fitting method was applied. The proposed trans-fitting method updated the selected candidate function to translocate the trend of crack propagation based on the training dataset. By translocating the trends to the estimated crack length of the target specimen, the crack propagation could be predicted. The proposed method was validated by comparison with given specimens. The results show that the proposed method can estimate and predict the crack length accurately

MSEC2006-21087 VARIATION PROPAGATION ANALYSIS ON COMPLIANT ASSEMBLIES CONSIDERING CONTACT INTERACTION

ABSTRACT Dimensional variation is inherent to any manufacturing process. In order to minimize its impact on assembly products is important to understand how it propagates through the assembly process. Unfortunately, manufacturing processes are complex and in many cases highly non-linear. Traditional assembly models have represented assembly as a linear process. However, assemblies that include the contact between their components and tools show a highly non-linear response. This paper presents a new assembly methodology considering the contact effect. In addition, an efficient to predict output response is presented. The enhance dimension reduction method (eDR) is used to accurately and efficiently predict the statistical response of the assembly to variation on the input parameters

Variation propagation analysis on compliant assemblies considering contact interaction

Dimensional variation is inherent to any manufacturing process. In order to minimize its impact on assembly products it is important to understand how the variation propagates through the assembly process. Unfortunately, manufacturing processes are complex and in many cases highly nonlinear. Traditionally, assembly process modeling has been approached as a linear process. However, many assemblies undergo highly complex nonlinear physical processes, such as compliant deformation, contact interaction, and welding thermal deformation. This paper presents a new variation propagation methodology considering the compliant contact effect, which will be analyzed through nonlinear frictional contact analysis. Its variation prediction will be accurately and efficiently conducted using an enhanced dimension reduction method. A case study is presented to show the applicability of the proposed methodology

Risk Prediction of Engineering Assets: An Ensemble of Part Lifespan Calculation and Usage Classification Methods

For the 2014 Prognostics and Health Management (PHM) Data Challenge Competition, the PHM Society proposed a problem surrounding risk prediction of engineering assets. We worked to address this problem by statistically analyzing the maintenance records, extracting key data features, and proposing an ensemble method for accurate prediction of imminent failure of assets. The data analysis of maintenance records provided two key pieces of information: 1) parts and part replacement reasons were able to be classified into corrective and scheduled maintenance actions, and 2) a linear relation was found between failure frequency and usage time. Based on this information, we proposed two risk-prediction methods, namely, a method based on part lifespan calculation and a method based on usage classification. Further work showed that the ensemble approach, which combined these two methods with a risk assignment formulation, provided more accurate risk prediction. The score predicted by the ensemble approach ranked in the second place in the 2014 PHM Data Challenge Competition

A Comprehensive Review of Digital Twin -- Part 1: Modeling and Twinning Enabling Technologies

As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented attention because of its promise to further optimize process design, quality control, health monitoring, decision and policy making, and more, by comprehensively modeling the physical world as a group of interconnected digital models. In a two-part series of papers, we examine the fundamental role of different modeling techniques, twinning enabling technologies, and uncertainty quantification and optimization methods commonly used in digital twins. This first paper presents a thorough literature review of digital twin trends across many disciplines currently pursuing this area of research. Then, digital twin modeling and twinning enabling technologies are further analyzed by classifying them into two main categories: physical-to-virtual, and virtual-to-physical, based on the direction in which data flows. Finally, this paper provides perspectives on the trajectory of digital twin technology over the next decade, and introduces a few emerging areas of research which will likely be of great use in future digital twin research. In part two of this review, the role of uncertainty quantification and optimization are discussed, a battery digital twin is demonstrated, and more perspectives on the future of digital twin are shared

TDR-based Multiple Leak Detection System using an S-parameter Transmission Line Model for Long-Distance Pipelines

Leaks in water distribution systems should be detected to avoid economic, environmental, and social problems. Existing Bayesian Inference based time-domainreflectometry (TDR) methods for leak detection have a limitation for real applications due to the lengthy time in building sample data. As the pipeline distance becomes longer and multiple leaks must be considered in long distance pipelines, the computational time for building training data gets larger. This paper proposes a scattering-parameter-based forward model to relieve computational burden of the existing TDR methods. It was shown that the proposed model outperformed the existing RLGC-based forward model in terms of computational time. The proposed model that is combined with Bayesian inference and TDR signal modeling is validated with an experimental pipeline, leak detectors, transmission line, and TDR instrument for leak detection. In summary, the proposed method is promising for leak detection in long pipelines as well as multiple leaks

L-shape triple defects in a phononic crystal for broadband piezoelectric energy harvesting

Abstract This study proposes a phononic crystal (PnC) with triple defects in an L-shape arrangement for broadband piezoelectric energy harvesting (PEH). The incorporation of defects in PnCs has attracted significant attention in PEH fields owing to properties such as energy localization and amplification near the defect. Several studies have been conducted to enhance output electric power of PnC-based PEH systems with single defects. However, it is susceptible to the limitations of narrow bandwidth. Recently, double-defect-incorporated systems have been proposed to widen the PEH bandwidth via defect-band splitting. Nevertheless, the PEH performance rapidly decreases in the frequency range between the split defect bands. The limitations of single- and double-defect-incorporated systems can be resolved by the incorporation of the proposed design concept, called the L-shape triple defects in a PnC. The isolated single defect at the top vertex of the letter L compensates for the limitations of double-defect-incorporated systems, whereas the double defects at the bottom vertices compensate for the limitations of the single-defect-incorporated systems. Hence, the proposed design can effectively confine and harvest elastic-wave energy over broadband frequencies while enhancing the application of single and double defects. The effectiveness of the proposed design concept is numerically validated using the finite element method. In the case of a circular hole-type PnC, it is verified that the PnC with L-shape triple defects broadens the bandwidth, and improves the output voltage and electric power compared with those of single- and double-defect-incorporated systems. This study expands the design space of defect-incorporated PnCs and might shed light on other engineering applications of thefrequency detector and elastic wave power transfer

Fault Log Recovery Using an Incomplete-data-trained FDA Classifier for Failure Diagnosis of Engineered Systems

In the 2015 PHM Data Challenge Competition, the goal of the competition problem was to diagnose failure of industrial plant systems using incomplete data. The available data consisted of sensor measurements, control reference signals, and fault logs. A detailed description of the plant system of interest was not revealed, and partial fault logs were eliminated from the dataset. This paper presents a fault log recovery method using a machine-learning-based fault classification approach for failure diagnosis. For optimal performance, it was critical to be able to utilize a set of incomplete data and to select relevant features. First, physical interpretation of the given data was performed to select proper features for a fault classifier. Second, Fisher discriminant analysis (FDA) was employed to minimize the effect of outliers in the incomplete data sets. Finally, the type of the missing fault logs and the duration of the corresponding faults were recovered. The proposed approach, based on the use of an incomplete-data-trained FDA classifier, led to the second-highest score in the 2015 PHM Data Challenge Competition

Predictive carbon nanotube models using the Eigenvector Dimension Reduction (EDR) method

It has been reported that a carbon nanotube (CNT) is one of the strongest materials with their high failure stress and strain. Moreover, the nanotube has many favorable features, such as high toughness, great flexibility, low density, and so on. This discovery has opened new opportunities in various engineering applications, for example, a nanocomposite material design. However, recent studies have found a substantial discrepancy between computational and experimental material property predictions, in part due to defects in the fabricated nanotubes. It is found that the nanotubes are highly defective in many different formations (e.g., vacancy, dislocation, chemical, and topological defects). Recent parametric studies with vacancy defects have found that the vacancy defects substantially affect mechanical properties of the nanotubes. Given random existence of the nanotube defects, the material properties of the nanotubes can be better understood through statistical modeling of the defects. This paper presents predictive CNT models, which enable to estimate mechanical properties of the CNTs and the nanocomposites under various sources of uncertainties. As the first step, the density and location of vacancy defects will be randomly modeled to predict mechanical properties. It has been reported that the Eigenvector Dimension Reduction (EDR) method performs probability analysis efficiently and accurately. In this paper, Molecular Dynamics (MD) simulation with a modified Morse potential model is integrated with the EDR method to predict the mechanical properties of the CNTs. To demonstrate the feasibility of the predicted model, probabilistic behavior of mechanical properties (e.g., failure stress, failure strain, and toughness) is compared with the precedent experiment results. Copyright © 2007 by ASME