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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 FANG LIU1, WEIXING SU1*,
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;
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