• Title/Summary/Keyword: Averaging Approach

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Remaining Useful Life Estimation based on Noise Injection and a Kalman Filter Ensemble of modified Bagging Predictors

  • Hung-Cuong Trinh;Van-Huy Pham;Anh H. Vo
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.17 no.12
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    • pp.3242-3265
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    • 2023
  • Ensuring reliability of a machinery system involve the prediction of remaining useful life (RUL). In most RUL prediction approaches, noise is always considered for removal. Nevertheless, noise could be properly utilized to enhance the prediction capabilities. In this paper, we proposed a novel RUL prediction approach based on noise injection and a Kalman filter ensemble of modified bagging predictors. Firstly, we proposed a new method to insert Gaussian noises into both observation and feature spaces of an original training dataset, named GN-DAFC. Secondly, we developed a modified bagging method based on Kalman filter averaging, named KBAG. Then, we developed a new ensemble method which is a Kalman filter ensemble of KBAGs, named DKBAG. Finally, we proposed a novel RUL prediction approach GN-DAFC-DKBAG in which the optimal noise-injected training dataset was determined by a GN-DAFC-based searching strategy and then inputted to a DKBAG model. Our approach is validated on the NASA C-MAPSS dataset of aero-engines. Experimental results show that our approach achieves significantly better performance than a traditional Kalman filter ensemble of single learning models (KESLM) and the original DKBAG approaches. We also found that the optimal noise-injected data could improve the prediction performance of both KESLM and DKBAG. We further compare our approach with two advanced ensemble approaches, and the results indicate that the former also has better performance than the latters. Thus, our approach of combining optimal noise injection and DKBAG provides an effective solution for RUL estimation of machinery systems.

Bagged Auto-Associative Kernel Regression-Based Fault Detection and Identification Approach for Steam Boilers in Thermal Power Plants

  • Yu, Jungwon;Jang, Jaeyel;Yoo, Jaeyeong;Park, June Ho;Kim, Sungshin
    • Journal of Electrical Engineering and Technology
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    • v.12 no.4
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    • pp.1406-1416
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    • 2017
  • In complex and large-scale industries, properly designed fault detection and identification (FDI) systems considerably improve safety, reliability and availability of target processes. In thermal power plants (TPPs), generating units operate under very dangerous conditions; system failures can cause severe loss of life and property. In this paper, we propose a bagged auto-associative kernel regression (AAKR)-based FDI approach for steam boilers in TPPs. AAKR estimates new query vectors by online local modeling, and is suitable for TPPs operating under various load levels. By combining the bagging method, more stable and reliable estimations can be achieved, since the effects of random fluctuations decrease because of ensemble averaging. To validate performance, the proposed method and comparison methods (i.e., a clustering-based method and principal component analysis) are applied to failure data due to water wall tube leakage gathered from a 250 MW coal-fired TPP. Experimental results show that the proposed method fulfills reasonable false alarm rates and, at the same time, achieves better fault detection performance than the comparison methods. After performing fault detection, contribution analysis is carried out to identify fault variables; this helps operators to confirm the types of faults and efficiently take preventive actions.

Sensor Mat using POF for Medical Application (의료용 플라스틱 광섬유 센서 매트)

  • Choi, Kyoo-Nam
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.44 no.4 s.316
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    • pp.74-78
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    • 2007
  • Novel concept of sensor mat and its signal processing method is proposed for patient monitoring in medical application. Proposed sensor mat structure has sensing inner layer which has cross-linked arrangement using plastic optical fiber(POF). Large core diameter of plastic optical fiber behaved as band pass filter by averaging the noise component caused by unwanted environmental factors. Signal processor followed by sensor output added noise immune performance by filtering out unwanted component. Fail-proof patient breath monitoring scheme was realized by using intelligent decision algorithm. Unlike the conventional approach by using mechanical sensor, which have high sensitivity both to signal and to environmental noise, our approach provided reliable breath motion detection.

State-Space Analysis on The Stability of Limit Cycle Predicted by Harmonic Balance

  • Lee, Byung-Jin;Yun, Suk-Chang;Kim, Chang-Joo;Park, Jung-Keun;Sung, Sang-Kyung
    • Journal of Electrical Engineering and Technology
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    • v.6 no.5
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    • pp.697-705
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    • 2011
  • In this paper, a closed-loop system constructed with a linear plant and nonlinearity in the feedback connection is considered to argue against its planar orbital stability. Through a state space approach, a main result that presents a sufficient stability criterion of the limit cycle predicted by solving the harmonic balance equation is given. Preliminarily, the harmonic balance of the nonlinear feedback loop is assumed to have a solution that determines the characteristics of the limit cycle. Using a state-space approach, the nonlinear loop equation is reformulated into a linear perturbed model through the introduction of a residual operator. By considering a series of transformations, such as a modified eigenstructure decomposition, periodic averaging, change of variables, and coordinate transformation, the stability of the limit cycle can be simply tested via a scalar function and matrix. Finally, the stability criterion is addressed by constructing a composite Lyapunov function of the transformed system.

Investigation of Turbulence Structures and Development Turbulence Model Based upon a Higher Order Averaging Method (고차평균법에 의한 난류구조의 규명 및 난류모델의 개발)

  • 여운광;편종근
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.4 no.4
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    • pp.201-207
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    • 1992
  • The averaged non-linear term in the turbulence equations, suggested by Yeo (1987), is analyzed theoretically and experimentally. It was formulated by applying the filtering concepts to the convolution integral average definition with the Gaussian response function. This filtering approach seems to be superior to the conventional averaging methods in which all four terms at the doubly average vol must be defined separately, and it also gives a very useful tool in understanding the turbulence structures. By theoretically analyzing the newly derived description for the averaged non-linear terms, it is found that the vortex stretching can be explicitly accounted for. Furthermore, comparisons of the correlation coefficients based on the experimental data show that the vortex stretching acts most significantly on the turbulence residual stress. Thus, it strongly supports the claim that the vortex stretching is essential in the transfer of turbulence. In addition. a general form of turbulent energy models in LES is derived, by which it is recognized that the Smagorinsky, the vorticity and the SGS energy models are not distinctive.

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Experimental Study on Oscillatory Behavior of Hydraulic Jump Roller (도수 롤러의 거동 분석을 위한 실험 연구)

  • Park, Moonhyung;Kim, Hyung Suk;Choi, Seohye;Ryu, Yonguk
    • Journal of Korean Society of Coastal and Ocean Engineers
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    • v.30 no.6
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    • pp.319-325
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    • 2018
  • This study conducted an experimental investigation on oscillatory behavior of the hydraulic jump roller. Based on the similarity of the hydraulic jump and tidal bore, the behavior of the front face of hydraulic jump with increasing downstream water depth was studied focusing on profile and fluctuation. In this study, for statistical approach, the ensemble averaging was applied to obtain relevant front profile and compared with the time averaging. The front profile gets mildly sloped and the fluctuation of the starting point of hydraulic jump decreases as the downstream water depth increases.

Development of Machining Simulation System using Enhanced Z Map Model (Enhanced Z map을 이용한 절삭 공정 시뮬레이션 시스템의 개발)

  • 이상규;고성림
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • 2002.05a
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    • pp.551-554
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    • 2002
  • The paper discusses new approach for machining operation simulation using enhanced Z map algorithm. To extract the required geometric information from NC code, suggested algorithm uses supersampling method to enhance the efficiency of a simulation process. By executing redundant Boolean operations in a grid cell and averaging down calculated data, presented algorithm can accurately represent material removal volume though tool swept volume is negligibly small. Supersampling method is the most common form of antialiasing and usually used with polygon mesh rendering in computer graphics. The key advantage of enhanced Z map model is that the data structure is same with conventional Z map model, though it can acquire higher accuracy and reliability with same or lower computation time. By simulating machining operation efficiently, this system can be used to improve the reliability and efficiency of NC machining process as well as the quality of the final product.

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AN EFFICIENT IMAGE SEGMENTATION TECHNIQUE TO IDENTIFY TARGET AREAS FROM LARGE-SIZED MONOCHROME IMAGES

  • Yoon Young-Geun;Lee Seok-Lyong;park Ho-Hyun;Chung Chin-Wan
    • Proceedings of the KSRS Conference
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    • 2005.10a
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    • pp.571-574
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    • 2005
  • In this paper, we propose an efficient image segmentation technique for large-sized monochrome images using a hybrid approach which combines threshold and region-based techniques. First, an image is partitioned into fixed-size blocks and for each block the representative intensity is determined by averaging pixel intensities within the block. Next, the neighborhood blocks that have similar characteristics with respect to a specific threshold are merged in order to form candidate regions. Finally, those candidate regions are refined to get final target object regions by merging regions considering the spatial locality and certain criteria. We have performed experiments on images selected from various domains and showed that our technique was able to extract target object regions appropriately from most images.

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Bayesian Method for Combining Results from Different Poisson Experiments

  • Cho, Jang Sik;Kim, Dal Ho
    • Communications for Statistical Applications and Methods
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    • v.7 no.2
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    • pp.533-540
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    • 2000
  • The problem of information related to I poission experiments, each having a distinct failure rate $\theta$i I=1,2,…,I, is considered. Instead of using a standard exchangeable prior for $\theta$=($\theta$1,$\theta$2,…,$\theta$I), we consider a partition of the experiments and take the $\theta$i's belonging to the same partition subgroup to be exchangeable and the $\theta$i's belonging to distinct subgroups to be independent. And we perform Gibbs sampling approach for Bayesian inference on $\theta$ conditional on a partition. Numerical study using real data is provided.

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Adaptive ${\alpha}-{\beta}$ Tracker for TWS Radar System

  • Kim, Byung-Doo;Lee, Ja-Sung
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.506-509
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    • 2005
  • An adaptive ${\alpha}-{\beta}$ tracker is proposed for tracking maneuvering targets with a track-while-scan radar system. The tracker gain is updated on-line corresponding to the adjusted process noise variance which is obtained via time averaging of the process over a sliding window. The adjusted process noise variance is used to compute the maneuverability index for the tracker gain based on the steady-state Kalman filter equation for each epoch. It is shown via simulation that the proposed approach provides robust and accurate position estimates during the target maneuver while the performance of the conventional ${\alpha}-{\beta}$ tracker is shown much degraded.

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