Sains Malaysiana 46(11)(2017): 2205-2213

http://dx.doi.org/10.17576/jsm-2017-4611-22

On-line Detection Method for Outliers of Dynamic Instability Measurement Data in Geological Exploration Control Process

(Kaedah Pengesanan atas Talian untuk Persilan Luar Pengukuran Data Ketidakstabilan

Dinamik dalam Proses Penerokaan Kawalan Geologi)

FANG LIU¹, WEIXING SU^1*, JIANJUN ZHAO² & XIAODAN LIANG¹

¹School of Computer Science & Software Engineering, Tianjin Polytechnic University, Tianjin 300387, China

²Bei Jing General Research Institute of Mining & Metallurgy, Beijing 100160, China

Received: 3 January 2017/Accepted: 14 May 2017

ABSTRACT

Considering the characteristics of the vibration data detected by the unstable regulation process in the grinding and grading control system and the shortcomings of the traditional wavelet anomaly detection method, an online anomaly detection method combining autoregressive and wavelet analysis is proposed. By introducing the improved robust AR model, this method can overcome the problem that the time and frequency of traditional anomaly detection using wavelet analysis method cannot be well balanced and ensure the rationality of normal detection of process data. Considering the characteristics of parameter change and dynamic characteristics in the process of grinding and grading, the proposed method has the ability of on-line detection and parameter updating in real time, which ensures the control parameters of time-varying process control system. In order to avoid the problem that the traditional anomaly detection method needs to set the detection threshold, introduce the HMM to analyse the wavelet coefficients and update the HMM parameters online, which can ensure that the HMM can well reflect the distribution of the abnormal value of the process data. Through the experiment and application, it is proven that the anomaly data detection method proposed in this paper is more suitable for the detection data in the process of unstable regulation.

Keywords: Auto-regression; HMM; outlier detection; time series; wavelet

ABSTRAK

Dengan mengambil kira ciri data getaran yang dikesan melalui proses pengaturan yang tidak stabil dalam sistem kawalan pengisaran dan penggredan serta kelemahan kaedah pengesanan anomali tradisi gelombang kecil, kaedah pengesanan anomali atas talian yang menggabungkan autoregresi dan analisis gelombang kecil adalah dicadangkan. Dengan memperkenalkan model AR mantap diperbaik, kaedah ini boleh mengatasi masalah tidak boleh seimbangkan masa dan kekerapan anomali tradisi menggunakan kaedah analisis gelombang kecil dan memastikan rasionaliti pengesanan biasa dalam pemprosesan data. Dengan mengambil kira ciri perubahan parameter dan ciri dinamik dalam proses mengisar dan penggredan, kaedah yang dicadangkan mempunyai keupayaan pengesanan atas talian dan pengemaskinian parameter masa nyata dan memastikan parameter kawalan untuk sistem kawalan proses perubahan masa. Bagi mengelakkan masalah yang dihadapi oleh kaedah pengesanan anomali tradisi adalah perlu menetapkan tahap pengesanan dengan memperkenalkan HMM untuk menganalisis pekali gelombang kecil dan mengemaskini parameter HMM secara atas talian yang boleh memastikan bahawa HMM dapat menunjukkan pengagihan nilai data proses yang tidak normal dalam pemprosesan data. Melalui uji kaji dan aplikasinya, dibuktikan bahawa kaedah pengesanan anomali data yang dicadangkan dalam kertas ini adalah lebih sesuai untuk pengesanan data dalam proses peraturan yang tidak stabil.

Kata kunci: Auto-regresi; gelombang kecil; HMM; pengesanan pensilan luar; siri masa

RUJUKAN

Alex, A., Haralambos, S. & George, B. 2003. Anew algorithm for online structure and parameter adaptation of RBF networks. Neural Networks 16: 1003-1017.

Bharti, S. & Pattanaik, K.K. 2016. Gravitational outlier detection for wireless sensor networks. International Journal of Communication 29(13): 2015-2027.

Barnet, V. & Lewis, T. 1994. Outlier in Statistical Data. Chichester: John Wiley & Sons.

Dai, W., Chai, T.Y. & Yang, S.X. 2015. Data-driven optimization control for safety operation of hematite grinding process. IEEE Transactions on Industrial Electronics 62(5): 2930-2941.

Durocher, M., Lee, T.S., Ouarda, T.B.M.J. & Chebana, F. 2016. Hybrid signal detection approach for hydro-meteorological variables combining EMD and cross-wavelet analysis. International Journal of Climatology 36(4): 1600-1613.

Griffiths, K.R., Hicks, B.J. & Keogh, P.S. 2016. Wavelet analysis to decompose a vibration simulation signal to improve pre-distribution testing of packaging. Mechanical Systems and Signal Processing 76-77: 780-795.

Grubbs, F.E. 1969. Procedures for detecting outlying observations in samples. Technometrics 11(1): 1-21.

Han, J.W. & Micheline, K. 2001. 2nd ed. Data Mining Concepts and Techniques. Massachusetts: Morgan Kaufmann Publishers. pp. 254-257.

Jeff, A.B. 2006. What HMMs Can Do? IEICE-- Transactions on Information and Systems E89-D(3): 869-891.

Knorr, E.M. & Ng, R.T. 1999. Finding intensional knowledge of distance-based outliers. Proceedings of the Twenty-Fifth International Conference on Very Large Data Bases. pp. 211-222.

Knorr, E.M. & Ng, R.T. 1998. Algorithms for mining distance-based outliers. Proceedings of the Twenty- Fourth International Conference on Very Large Data Bases. pp. 392-403.

le Roux, J.D. & Craig, I.K. 2013. Reducing the number of size classes in a cumulative rates model used for process control of a grinding mill circuit. Powder Technology 246: 169-181.

le Roux, J.D., Craig, I.K. & Hulbert, D.G. 2013. Analysis and validation of a run-of-mine ore grinding mill circuit model for process control. Minerals Engineering 43- 44: 121-134.

Lindang, H.U., Tarmudi, Z.H. & Jawan, A. 2017. Assessing water quality index in river basin: Fuzzy inference system approach. Malaysian Journal of Geoscience 1(1): 27-31.

Lou, H.L. 1995. Implementing the viterbi algorithm-fundamentals and real-rime issues for processor designers. IEEE Signal processing Magazing. pp. 42- 52.

Lu, S. W. 2016. Acceleration of kinetic Monte Carlo simulation of particle breakage process during grinding with controlled accuracy. Powder Technology 301: 186-196.

Mallat, S. & Hwang, W.L. 1992. Singularity detection and processing with wavelets. IEEE Transactions on Information Theory 38(2): 617-642.

Othman, R., Isa, N. & Othman, A. 2015. Precipitated calcium carbonate from industrial waste for paper making. Sains Malaysiana 44(11): 1561-1565.

Pittner, S. & Kamarthi, S.V. 1999. Feature extraction from wavelet coefficients for pattern recognition tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(1): 83-88.

Rabiner, L.R. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2): 257-286.

Rahman, Z.U., Khan, Z.M., Khattak, Z., Abbas, M.A. & Ishfaque, M. 2017. Microfacies analysis and reservoir potential of Sakesar Limestone, Nammal Gorge (Western Salt Range), Upper Indus Basin, Pakistan. Pakistan Journal of Geology 1(1): 12-17.

Ramaswamy, S., Rastogi, R. & Sim, K.S. 2000. Efficient algorithms for mining outliers from large data sets. Proceeding of the ACM SIGMOD International Conference on Management of Data Dallas, Teas: ACM Press. pp. 427-438.

Seo, H.S. 2016. A sequential outlier detecting method using a clustering algorithm. The Korean Journal of Applied Statistics 29(4): 699-706.

Su, W.X., Zhu, Y.L. & Liu, F. 2013. An online outlier detection method based on wavelet technique and robust RBF network. Transactions of the Institute of Measurement and Control 35(8): 1046-1057.

Takeuchi, J.I. & Yamanishi, K. 2006. A unifying framework for detecting out19liers and change points from time series. IEEE Transactions on Knowledge and Data Engineering 18(4).

Xu, C.Y. & Shin, Y.C. 2007. Control of cutting force for creep-feed grinding processes using a multi-level fuzzy controller. Journal of Dynamic Systems Measurement and Control-Transactions of the ASME 129(4): 480-492.

Zhang, C.L., Huang, Y.Z., Ma, X.X., Lu, W.Z. & Wang, G.X. 1998. A new approach to detect transformer inrush current by applying wavelet transform. In Proc. Powercon ’98 2: 1040-1044.

Zhang, Q., Wang, C.X. & Zhao, J. 2012. Outlier detecting algorithm based on clustering and local information. Journal of Jilin University (Science Edition) 50(6): 1214-1217.

Zhou, P., Chai, T.Y. & Wang, H. 2009. Intelligent optimal-setting control for grinding circuits of mineral processing process. IEEE Transactions on Automation Science and Engineering 6(4): 730-743.

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