Abstract
Aiming at the characteristics of the vibration data collected by the regulation system during the unstable regulation process and the deficiency of the traditional wavelet anomaly detection method, an on-line anomaly detection method combining the autoregressive and the wavelet analysis is proposed to detect the abnormal data of the regulation system. 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 abnormal value detection of process data. Considering the general parameters of the regulation system is time-varying and has strong dynamic characteristics, the method proposed in this paper has the ability of online detection and real-time updating of parameters to ensure that the control parameters of time-varying control system; In order to avoid the problem that the traditional anomaly detection method needs to set the detection threshold in advance, HMM is introduced to analyze the wavelet coefficients and update the HMM parameters online, which can ensure that the HMM can well reflect the actual distribution of process data anomalies. It is proved that the method of anomaly data detection proposed in this paper is more suitable for the unstable regulation process data and has certain practicability through experiment and application.
Similar content being viewed by others
References
Liu, S., Hou, Z., Yin, C.: Data-driven modeling for UGI gasification processes via an enhanced genetic BP neural network with link switches [J]. IEEE Trans. Neural Netw. Learn. 27(12), 2718–29 (2016)
Yan, J., Li, K., Bai, E.: Special condition wind power forecasting based on gaussian process and similar historical data [C]. General Meeting of the IEEE-Power-and-Energy-Society, Denver (2015)
Bourgin, F., Ramos, M.H.: Investigating the interactions between data assimilation and post-processing in hydrological ensemble forecasting [J]. J. Hydrol. 519, 2775–2784 (2014)
Peng, K., Li, Q., Zhan, K.: Quality-related process monitoring for dynamic non-Gaussian batch process with multi-phase using a new data-driven method [J]. Neurocomputing 214, 318–28 (2016)
Zhang, H., Albin, S.L., Wagner, S.R.: Determining statistical process control baseline periods in long historical data streams [J]. J. Qual. Technol. 42(1), 21–35 (2010)
Keller, S., Popenheim, A.: Analysis process for different polymere electrolyte fuel cell stacks to derive a control stratgy based impedance data [J]. Fuel Cells 14(5), 758–768 (2014)
Grubbs, F.E.: Procedures for detecting outlying observations in samples [J]. Technometrics 11(1), 1–21 (1969)
Barnet, V., Lewis, T.: Outlier in Statistical Data [M]. Wiley, Chichester (1994)
Knorr, E.M., Ng R.T.: Finding Intensional Knowledge of Distance-based Outliers [C]. In: Proceedings of the Twenty-Fifth International Conference on Very Large Data Bases, pp. 211–222 (1999)
Knorr, E.M., Ng, R.T.: Algorithms for Mining Distance-based Outliers [C]. In: Proceedings of the Twenty-fourth International Conference on Very Large Data Bases, pp. 392–403 (1998)
Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets [C]. In: Proceeding of the ACM SIGMOD International Conference on Management of Data Dallas, ACM Press, Teas, pp. 427–438 (2000)
Seo, H.S.: A sequential outlier detecting method using a clustering algorithm [J]. Korean J. Appl. Stat. 29(4), 699–706 (2016)
Zhang, Q., Wang, C., Zhao, J.: Outlier detecting algorithm based on clustering and local information [J]. J. Jilin Univ. 50(6), 1214–17 (2012)
Bharti, S., Pattanaik, K.K.: Gravitational outlier detection for wireless sensor networks [J]. Int. J. Commun. 29(13), 2015–2027 (2016)
Su, W., Zhu, Y., Liu, F.: An online outlier detection method based on wavelet technique and robust RBF network [J]. Trans. Inst. Meas. Control 35(8), 1046–1057 (2013)
Pittner, S., Kamarthi, S.V.: Feature extraction from wavelet coefficients for pattern recognition tasks [J]. IEEE Trans. Pattern Anal. Mach. Intell. 21(1), 83–88 (1999)
Mallat, S., Hwang, W.L.: Singularity detection and processing with wavelets [J]. IEEE Trans. Inf. Theory 38(2), 617–642 (1992)
Griffiths, K.R., Hicks, B.J., Keogh, P.S.: Wavelet analysis to decompose a vibration simulation signal to improve pre-distribution testing of packaging [J]. Mech. Syst. Signal Process. 76–77, 780–795 (2016)
Durocher, M., Lee, T.S., Ouarda, T.B.M.J.: Hybrid signal detection approach for hydro-meteorological variables combining EMD and cross-wavelet analysis [J]. Int. J. Climatol. 36(4), 1600–1613 (2016)
Han, J., Kamber, M.: Data Mining Concepts and Techniques [M], vol. 8, pp. 254–257. Machinery Industry Press, Beijing (2001)
Takeuchi, J., Yamanishi, K.: A unifying framework for detecting outliers and change points from time series [J]. IEEE Trans. Knowl. Data Eng. 18(4), 482–492 (2006)
Chuanli, Z., Yizhuang, H., Xiaoxu, M., Wenzhe, L., Guoxing, W.: A new approach to detect transformer inrush current by applying wavelet transform [J]. Proc. POWERCON ’98 2, 1040–1044 (1998)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition [J]. Proc. IEEE 77(2), 257–286 (1989)
Bilmes, J.A.: What HMMs can do [J]. IEICE Trans. Inf. Syst. E89–D(3), 869–891 (2006)
Hui, L.L.: Implementing the Viterbi Algorithm—Fundamentals and real-rime issues for processor designers. IEEE Signal Process. Mag. 1053–5888, 42–52 (1995)
Alexandridis, A., Sarimveis, H., Bafas, G.: Anew algorithm for online structure and parameter adaptation of RBF networks [J]. Neural Netw. 16, 1003–1017 (2003)
Acknowledgements
This research is partially supported by National Natural Science Foundation of China under Grant 51607122. This research is partially supported by State Key Laboratory of Process Automation in Mining & Metallurgy/Beijing Key Laboratory of Process Automation in Mining & Metallurgy Research Fund Project BGRIMM-KZSKL-2017-01. This research is partially supported by the basic scientific research business funded projects of Tianjin TJPUZK20170129 and by Tianjin Province Science and Technology projects 16ZLZDZF00150.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Su, W., Liu, F., Zhao, J. et al. An on-line detection method for outliers of dynamic unstable measurement data. Cluster Comput 22 (Suppl 4), 7831–7839 (2019). https://doi.org/10.1007/s10586-017-1458-3
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1458-3