A Copula-based sampling method for data-driven prognostics and health management

Xi, Zhimin; Jing, Rong; Wang, Pingfeng; Hu, Chao

doi:10.1109/icphm.2013.6621450

Cited by 11 publications

(5 citation statements)

References 42 publications

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“…Publication ID (T. Wang, Yu, Siegel, & Lee, 2008) 1 (Heimes, 2008) 2 (Peel, 2008) 3 (Coble & Hines, 2008) 4 (Coble, 2010) (Coble & Hines, 2011) (Siegel, 2009) 5 (Ramasso, 2009) 6 (T. Wang, 2010) 7 (Riad, Elminir, & Elattar, 2010) 8 (Abbas, 2010) 9 (Ramasso & Gouriveau, 2010) 10 (Ramasso & Gouriveau, 2013) (Sarkar, Jin, & Ray, 2011) 11 (Xue, Williams, & Qiu, 2011) 12 (Zhao, P., & Willett, 2011) 13 (El-Koujok, Gouriveau, & Zerhouni, 2011) 14 (Liao & Sun, 2011) 15 (P. 16 (Son, Fouladirad, & Barros, 2012) 17 (Richter, 2012) 18 (Sun, Zuo, Wang, & Pecht, 2012) 19 (Peng, Wang, Wang, Liu, & Peng, 2012) 20 (Hu, Youn, Wang, & Yoon, 2012) 21 (Peng, Xu, Liu, & Peng, 2012) 22 (Javed, Gouriveau, Zemouri, & Zerhouni, 2012) 23 (Serir, Ramasso, & Zerhouni, 2012) 24 25 (Yu, 2013) 26 (Ramasso, Rombaut, & Zerhouni, 2013) 27 (Liu, Gebraeel, & Shi, 2013) 28 (Son, Fouladirad, Barros, Levrat, & Iung, 2013) 29 (Xi, Jing, Wang, & Hu, 2013) 30 (Lin, Chen, & Zhou, 2013) 31 (Javed, Gouriveau, & Zerhouni, 2013) 32 (Ramasso & Denoeux, 2013) 33 (Li, Qian, & Wang, 2013) 34 (Tamilselvan & Wang, 2013) 35 (Ishibashi & Nascimento Junior, 2013) 36 (Gouriveau, Ramasso, & Zerhouni, 2013) 37 (Jianzhong, Hongfu, Haibin, & Pecht, 2010) 38…”

Section: Appendixmentioning

confidence: 99%

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Ramasso,

Saxena

2014

PHM_CONF

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Benchmarking of prognostic algorithms has been challenging due to limited availability of common datasets suitable for prognostics. In an attempt to alleviate this problem, several benchmarking datasets have been collected by NASA’s prognostic center of excellence and made available to the Prognostics and Health Management (PHM) community to allow evaluation and comparison of prognostics algorithms. Among those datasets are five C-MAPSS datasets that have been extremely popular due to their unique characteristics making them suitable for prognostics. The C-MAPSS datasets pose several challenges that have been tackled by different methods in the PHM literature. In particular, management of high variability due to sensor noise, effects of operating conditions, and presence of multiple simultaneous fault modes are some factors that have great impact on the generalization capabilities of prognostics algorithms. More than 70 publications have used the C-MAPSS datasets for developing data-driven prognostic algorithms. The C-MAPSS datasets are also shown to be well-suited for development of new machine learning and pattern recognition tools for several key preprocessing steps such as feature extraction and selection, failure mode assessment, operating conditions assessment, health status estimation, uncertainty management, and prognostics performance evaluation. This paper summarizes a comprehensive literature review of publications using C-MAPSS datasets and provides guidelines and references to further usage of these datasets in a manner that allows clear and consistent comparison between different approaches.

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Section: Appendixmentioning

confidence: 99%

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Ramasso,

Saxena

2014

PHM_CONF

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“…While the move toward big data solutions for APC systems is critical and necessary, 8 semiconductor manufacturers build data‐driven approaches to study system behavior by using a large volume of historical data from equipment sensors. Data‐driven approaches are increasingly used for equipment behavior modeling and monitoring 9–11 and equipment failure diagnoses 12,13 . These approaches are the most commonly used methods in literature and cover a wide selection of regression models, multivariate analytics, and black‐boxed artificial neural networks (ANNs).…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the physical model‐ and knowledge‐based approaches, data‐driven approaches allow for a good trade‐off between accuracy and applicability. They perform prognosis and diagnosis by training a set of run‐to‐failure units to build a model of system deterioration in which the deterioration features are extracted from raw signals 9 …”

Section: Introductionmentioning

confidence: 99%

Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing

et al. 2021

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Condition‐based monitoring (CBM) as a new control scheme suggests characterizing the machine condition and triggering the corresponding control actions. CBM includes prognostic and diagnostic modules. In this study, the framework of equipment deterioration modeling and monitoring for batch processes is proposed with two objectives in the semiconductor industry. The first one is to characterize equipment behavior by exploiting the temporal data of batch processes. The second one is to model the deterioration trend with the most related causes. With the best‐fitted mother wavelet, wavelet packet decomposition transforms the temporal data into macro and micro level domains to identify two types of deterioration. The determinant of the correlation matrix of the decomposed signals is calculated as the equipment condition, and the factors that account for the deterioration are identified through a stepwise searching algorithm. A case study shows that the proposed methodology can identify influencing factors and model deterioration.

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“…However, the uncertain nature of degradation and failures can make maintenance scheduling a challenge (Grall et al, 2002). Recent advances in maintenance strategy focus on prognostics and health management (PHM) methods (Engel et al, 2000; Leão et al, 2010; Xi et al, 2013) to detect, diagnose, and predict system degradation and failures, and condition-based maintenance (CBM; Maillart & Pollock, 2002; Jardine et al, 2006; Wang et al, 2010) that utilizes PHM information to maximize system availability and minimize operational costs (Youn et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A maintenance-focused approach to complex system design

Honda

Zubair

et al. 2016

AIEDAM

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Maintenance plays a critical role in reducing operating cost and maximizing reliability of a complex engineering system. This paper proposes a novel maintenance-focused, system-level design framework that attempts to capture the interactions between maintenance strategies and system-level design parameters overlooked in current modeling approaches. The goal of this maintenance-focused approach is to help designers better understand the interconnectedness of system architecture, choice of maintenance strategy, and uncertainties in a design. Application of the proposed design framework is demonstrated through a case example of a power plant condenser system. Results show that using an integrated approach can reveal the many nonobvious interactions between subsystems, and produce system designs that have lower life-cycle cost compared to traditional sequential design approaches.

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A Copula-based sampling method for data-driven prognostics and health management

Cited by 11 publications

References 42 publications

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Review and Analysis of Algorithmic Approaches Developed for Prognostics on CMAPSS Dataset

Equipment deterioration modeling and cause diagnosis in semiconductor manufacturing

A maintenance-focused approach to complex system design

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