research-article

Under Sampling Bagging Shapelet Transformation for Hydraulic System

Authors:

Yohan Joo,

Jongpil Jeong,

Sangmin BaeAuthors Info & Claims

COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems

Pages 13 - 18

https://doi.org/10.1145/3312614.3312621

Published: 05 May 2019 Publication History

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Abstract

In order to preserve the prediction of machine defects, fault conditions and normal conditions must be classified from time series data collected in real time from various sensors. For predictive maintenance of machine defects, fault conditions and normal conditions must be classified from time series data collected in real time from various sensors. Using the amount of time series data as it is classified is too large and inefficient. Therefore, a variety of studies has been conducted regarding effective classification. Above all, Shapelet transformation is known as a feature extraction method that enables effective classification from time series data based on information acquisition. In general, sampling and ensemble methods used to reduce noise and overfitting are expected to make shapelet transformations more stable. In this paper, we propose a framework to effectively classify time series data by adding Under Sampling Bagging technique to the shapelet transformation method.

References

[1]

L. Breiman. 1996. Bagging predictors. Mach. Learn 24, 2 (Aug. 1996), 123--140.

Digital Library

Google Scholar

[2]

L. Breiman. 2001. Random forest. Mach. Learn 45, 1 (Oct. 2001), 5--32.

Digital Library

Google Scholar

[3]

Y. Freund and R. E. Schapire. 1997. A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci 55, 1 (March 1997), 119--139.

Digital Library

Google Scholar

[4]

W. Y. Wang H. Han and B. H. Mao. 2005. Borderline-smote: a new oversampling method in imbalanced data sets learning. in Proc. Int. Conf. Adv. Intell. Comput. Lecture Notes Comput. Sci (Jan. 2005), 878--887.

Digital Library

Google Scholar

[5]

J. Hills et al J. Lines, L. M. Davis. 2012. A shapelet transform for time series classification. Proc. 18th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining (Aug. 2012), 289--297.

Digital Library

Google Scholar

[6]

C. Lu L. Zhu and Y. Sun. 2016. Time series shapelet classification based online short-term volt-age stability assessment. IEEE Trans. Power Syst 31, 2 (March 2016), 1430--1439.

Crossref

Google Scholar

[7]

Z. Yang Dong L. Zhu, C. Lu and C. Hong. 2017. Imbalance Learning Machine-Based Power System Short-Term Voltage Stability Assessment. IEEE Transactions on Industrial Informatics 13, 1 (Oct. 2017), 2533--2543.

Google Scholar

[8]

J. Laurikkala. 2001. Improving identification of difficult small classes by balancing class distri-bution. in Proc. Conf. Artif. Intell. Med. Europe (June 2001), 63--66.

Digital Library

Google Scholar

[9]

E. Pignanelli N. Helwig and A. Schutze. 2015. Condition Monitoring of a Complex Hydraulic System using Multivariate Statistics. Proceedings of IEEE Instrumentation and Measurement Technology Conference(I2MTC) (July 2015), 210--215.

Google Scholar

[10]

L. O. Hall N. V. Chawla, A. Lazarevic and K. W. Bowyer. 2003. SMOTE-Boost: Improving prediction of the minority class in boosting. in Proc. 7th Eur. Conf. Principles Pract. Knowl. Discov. Databases (Jan. 2003), 107--119.

Google Scholar

[11]

L.O. Hall N.V. Chawla, K.W. Bowyer and W.P. Kegelmeyer. 2002. SMOTE: Synthetic minor-ity oversampling technique. J. Artif. Intell. Res 16 (June 2002), 321--357.

Digital Library

Google Scholar

[12]

L. Ye and E. Keogh. 2009. Time series shapelets: A new primitive for data mining. in Proc. 15th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining (June 2009), 947--955.

Digital Library

Google Scholar

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Sun JDing HLi NSun XDong X(2024)Intelligent Fault Diagnosis of Hydraulic System Based on Multiscale One-Dimensional Convolutional Neural Networks with Multiattention MechanismSensors10.3390/s2422726724:22(7267)Online publication date: 14-Nov-2024
https://doi.org/10.3390/s24227267
Zhang PHu WCao WChen LWu M(2024)Multi-fault diagnosis and fault degree identification in hydraulic systems based on fully convolutional networks and deep feature fusionNeural Computing and Applications10.1007/s00521-024-09548-736:16(9125-9140)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09548-7
Huang KWu SLi FYang CGui W(2022)Fault Diagnosis of Hydraulic Systems Based on Deep Learning Model With Multirate Data SamplesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308340133:11(6789-6801)Online publication date: Nov-2022
https://doi.org/10.1109/TNNLS.2021.3083401

Index Terms

Under Sampling Bagging Shapelet Transformation for Hydraulic System
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Classification and regression trees

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COINS '19: Proceedings of the International Conference on Omni-Layer Intelligent Systems

May 2019

241 pages

ISBN:9781450366403

DOI:10.1145/3312614

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 05 May 2019

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COINS '19: INTERNATIONAL CONFERENCE ON OMNI-LAYER INTELLIGENT SYSTEMS

May 5 - 7, 2019

Crete, Greece

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Sun JDing HLi NSun XDong X(2024)Intelligent Fault Diagnosis of Hydraulic System Based on Multiscale One-Dimensional Convolutional Neural Networks with Multiattention MechanismSensors10.3390/s2422726724:22(7267)Online publication date: 14-Nov-2024
https://doi.org/10.3390/s24227267
Zhang PHu WCao WChen LWu M(2024)Multi-fault diagnosis and fault degree identification in hydraulic systems based on fully convolutional networks and deep feature fusionNeural Computing and Applications10.1007/s00521-024-09548-736:16(9125-9140)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09548-7
Huang KWu SLi FYang CGui W(2022)Fault Diagnosis of Hydraulic Systems Based on Deep Learning Model With Multirate Data SamplesIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2021.308340133:11(6789-6801)Online publication date: Nov-2022
https://doi.org/10.1109/TNNLS.2021.3083401

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Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction

Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles

Using boosting to prune bagging ensembles

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Under Sampling Adaboosting Shapelet Transformation for Time Series Feature Extraction

Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles

Using boosting to prune bagging ensembles

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