On-line detection method for outliers of dynamic instability measurement data in geological exploration control process

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 p...

Full description

Bibliographic Details
Main Authors:	Liu, Fang, Su, Weixing, Zhao, Jianjun, Liang, Xiaodan
Format:	Article
Language:	English
Published:	Penerbit Universiti Kebangsaan Malaysia 2017
Online Access:	http://journalarticle.ukm.my/11688/ http://journalarticle.ukm.my/11688/ http://journalarticle.ukm.my/11688/1/22%20SM46%2011.pdf

id	ukm-11688
recordtype	eprints
spelling	ukm-116882018-05-28T00:55:51Z http://journalarticle.ukm.my/11688/ On-line detection method for outliers of dynamic instability measurement data in geological exploration control process Liu, Fang Su, Weixing Zhao, Jianjun Liang, Xiaodan 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. Penerbit Universiti Kebangsaan Malaysia 2017-11 Article PeerReviewed application/pdf en http://journalarticle.ukm.my/11688/1/22%20SM46%2011.pdf Liu, Fang and Su, Weixing and Zhao, Jianjun and Liang, Xiaodan (2017) On-line detection method for outliers of dynamic instability measurement data in geological exploration control process. Sains Malaysiana, 46 (11). pp. 2205-2213. ISSN 0126-6039 http://www.ukm.my/jsm/english_journals/vol46num11_2017/contentsVol46num11_2017.htm
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Kebangasaan Malaysia
building	UKM Institutional Repository
collection	Online Access
language	English
description	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.
format	Article
author	Liu, Fang Su, Weixing Zhao, Jianjun Liang, Xiaodan
spellingShingle	Liu, Fang Su, Weixing Zhao, Jianjun Liang, Xiaodan On-line detection method for outliers of dynamic instability measurement data in geological exploration control process
author_facet	Liu, Fang Su, Weixing Zhao, Jianjun Liang, Xiaodan
author_sort	Liu, Fang
title	On-line detection method for outliers of dynamic instability measurement data in geological exploration control process
title_short	On-line detection method for outliers of dynamic instability measurement data in geological exploration control process
title_full	On-line detection method for outliers of dynamic instability measurement data in geological exploration control process
title_fullStr	On-line detection method for outliers of dynamic instability measurement data in geological exploration control process
title_full_unstemmed	On-line detection method for outliers of dynamic instability measurement data in geological exploration control process
title_sort	on-line detection method for outliers of dynamic instability measurement data in geological exploration control process
publisher	Penerbit Universiti Kebangsaan Malaysia
publishDate	2017
url	http://journalarticle.ukm.my/11688/ http://journalarticle.ukm.my/11688/ http://journalarticle.ukm.my/11688/1/22%20SM46%2011.pdf
first_indexed	2023-09-18T20:00:55Z
last_indexed	2023-09-18T20:00:55Z
_version_	1777406842021871616

On-line detection method for outliers of dynamic instability measurement data in geological exploration control process

Similar Items