Aluminium process fault detection by Multiway Principal Component Analysis

Majid, Noriza; Taylor, Marilyn; Chen, John J. J.; Stam, Marco A.; Mulder, Albert; Young, Brent R.

doi:10.1016/j.conengprac.2010.12.005

Cited by 48 publications

(24 citation statements)

References 12 publications

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“…Obviously, the sampling signal relative to the sampling frequency does not satisfy the sampling theorem [4] which indicates that the sampling part in a fake frequency form, which produces aliasing phenomenon [5]. In order to reduce the impact of this phenomenon, we use a third-order Butterworth low-pass filter to filter out the high-frequency noise in the original analog voltage signals.…”

Section: B the Hardware Design And Implementation Of Gap Voltage Meamentioning

confidence: 99%

“…At present, there are no sensors or equipment that can detect the alumina concentration on line [4]. Most of companies acquire the alumina concentration by analyzing the electrolyte samples through X-ray fluorescence spectrograph offline [4,2]. To solve the online problem, by using the electrical signals of anode rods, a novel method of alumina concentration measurement based on ELM algorithm is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Fast and accurate method of alumina concentration measurement becomes a difficult problem as the site environment of aluminum reduction cell has the characteristics of high temperature, strong magnetic field, coupled parameters, and severe nonlinearity et.al. At present, there are no sensors or equipment that can detect the alumina concentration on line [4]. Most of companies acquire the alumina concentration by analyzing the electrolyte samples through X-ray fluorescence spectrograph offline [4,2].…”

Section: Introductionmentioning

confidence: 99%

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A novel on-line alumina concentration measurement system

Zhang

Yang

Yin

et al. 2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

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The concentration of alumina in the electrolyte is of great significance during the production of aluminum; it may affect the stability of aluminum reduction cell and the current efficiency. However, the concentration of alumina is hard to be detected online because of the special circumstance in the aluminum reduction cell. At present, there is lack of fast and accurate soft sensing methods for alumina concentration and existing methods can not meet the needs for online measurement. In this paper, a novel soft sensing method based on the voltage between the anode and the cathode, the current of the anode rod and the modified extreme learning machine for online measurement of the alumina concentration is proposed. In order to achieve the soft sensing scheme, considering the problems on the work spot, such as anode effect, the paper mainly develops a system that can realize the online measurement of the voltage between the anode and the cathode. The hardware of the system is composed of three parts including protection circuit, filter circuit and data acquisition card. When the anode effect occurs, the protection circuit which is used to ensure the safe use of the acquisition device will switch off the connection between the voltage and the data collection card. When the voltage returns to normal level, it will switch on the connection. The modified ELM algorithm is based on the enhanced random search which is called incremental extreme learning machine in some references. It randomly chooses the input weights and analytically determines the output weights without manual intervention. The experimental results show that the approach can give accurate estimations of alumina concentration within 6 percent relative error.

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Section: B the Hardware Design And Implementation Of Gap Voltage Meamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel on-line alumina concentration measurement system

Zhang

Yang

Yin

et al. 2015

2015 IEEE International Conference on Mechatronics and Automation (ICMA)

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“…Tucker1 or Multiway PCA (MPCA) is the first tensor model used for TAD in many applications [25,25,27,27,39,39,44,47,94,110,134,135,[147][148][149] . Tucker1 is used when variance is only important in one dimension.…”

Section: Tucker1mentioning

confidence: 99%

“…a random PC). This rule is used for model order estimation of Tucker1 for anomaly detection [33,134] .…”

Section: Cumulative Sum Of the Percentage Of Eigenvalues Or Explainedmentioning

confidence: 99%

Tensor-based anomaly detection: An interdisciplinary survey

Fanaee-T

Gama

2016

Knowledge-Based Systems

125

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Traditional spectral-based methods such as PCA are popular for anomaly detection in a variety of problems and domains. However, if data includes tensor (multiway) structure (e.g. space-time-measurements), some meaningful anomalies may remain invisible with these methods. Although tensor-based anomaly detection (TAD) has been applied within a variety of disciplines over the last twenty years, it is not yet recognized as a formal category in anomaly detection. This survey aims to highlight the potential of tensor-based techniques as a novel approach for detection and identification of abnormalities and failures. We survey the interdisciplinary works in which TAD is reported and characterize the learning strategies, methods and applications; extract the important open issues in TAD and provide the corresponding existing solutions according to the state-of-the-art.

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Adaptive control and signal processing literature survey (No. 25)

2011

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that even though the list has been broadly categorized, these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please email Erfu Yang at erfu.yang@gmail.com.

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Aluminium process fault detection by Multiway Principal Component Analysis

Cited by 48 publications

References 12 publications

A novel on-line alumina concentration measurement system

A novel on-line alumina concentration measurement system

Tensor-based anomaly detection: An interdisciplinary survey

Adaptive control and signal processing literature survey (No. 25)

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