Diagnosing faults in aluminium processing by using multivariate statistical approaches

Majid, Noriza; Taylor, Marilyn; Chen, John J. J. J.; Yu, Wei; Young, Brent R.

doi:10.1007/s10853-011-5884-y

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…Information about the current process and the results of the diagnosis that were provided in textual form were put together with the charts. In this research [30,31], a mixture of text and graphics incorporated with suitable colour (red and green) and user control boxes such as a combo box for selecting cells was used instead of selecting only one mode in order to demonstrate clearly abnormal events. In Figure 4, for example, the operator's screen indicated this situation by a change in the colour of button for cell 2004 from green to red, the status of the process from "IN CONTROL" to "OUT OF CONTROL, " and the status of the anode effect detection from "NO" to "YES. "…”

Section: Results Presentationmentioning

confidence: 99%

“…The cascade fault detection and diagnosis system [30,31] was designed to detect any faults and then diagnose faults that are related to anode effect, anode spike, block feeder, and low alumina dissolution. This system is presented as an example of how faults such as an anode effect can be detected and diagnosed with multivariate statistical techniques as can be seen in Figure 4.…”

Section: Cascade Fault Detection and Diagnosis Systemmentioning

confidence: 99%

“…These charts are Hotelling's 2 chart and the SPE chart. The abnormal events detected by these charts were then diagnosed using MPLS in order to classify patterns related to these abnormal events [31].…”

Section: Fault Detection and Diagnostic (Fdd) Techniquesmentioning

confidence: 99%

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Aluminium Process Fault Detection and Diagnosis

Majid

Taylor

Chen

et al. 2015

Advances in Materials Science and Engineering

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The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments.

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Section: Results Presentationmentioning

confidence: 99%

Section: Cascade Fault Detection and Diagnosis Systemmentioning

confidence: 99%

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Aluminium Process Fault Detection and Diagnosis

Majid

Taylor

Chen

et al. 2015

Advances in Materials Science and Engineering

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“…Aluminum metal is widely used in aerospace, construction, power electronics, automotive and other fields, and aluminum electrolysis is the main way to obtain aluminum metal in the aluminum industry [1]. However, the occurrence of the anode effect (AE) [2][3][4] will directly lead to low efficiency of aluminum electrolysis, large energy consumption [5], environmental pollution and equipment damage [6,7].…”

Section: Introductionmentioning

confidence: 99%

“…The other is based on a data-driven method, which relies on a large amount of historical data (these data are selected or calculated from the historical values of parameters which can be collected from aluminum electrolytic cells) to train models, instead of building models based on the fault mech anisms [16][17][18][19][20]. In early studies, much work related to AE was based on principal component analysis (PCA) [21][22][23] or multivariate statistical methods [1,24]. In recent years, neural networks [5,25,26], extreme gradient boosting (XGBoost) [2] and hybrid algorithms (for example, a hybrid algorithm based on support vector machine (SVM) and k nearest neighbor [27]) have been applied to AE prediction.…”

Section: Introductionmentioning

confidence: 99%

A modified neighborhood mutual information and light gradient boosting machine-based long-term prediction approach for anode effect

Pan

Kong

Chen

et al. 2019

Meas. Sci. Technol.

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The anode effect (AE) often occurs in the aluminum electrolysis process, which seriously affects production efficiency and causes large energy consumption. Therefore, predicting AE in advance has become very important. However, traditional cell resistance-based methods have the disadvantages of low accuracy and short predicting time, and the data-driven methods have not fully considered the relevance between other features and AE, causing the low accuracy for long-term prediction. In this paper, a long-term prediction approach for AE based on a modified neighborhood mutual information and light gradient boosting machine (MNMI-LGBM) is proposed. The MNMI is used to measure the relevance between features and AE for feature selection, and a derivative feature of the cell resistance is fed into the LGBM as a key feature to predict AE. The predicting time in the range of 0–50 min is considered. The results show that the proposed approach has an accuracy above 89% over the entire 0–50 min predicting time range. Specifically, the accuracy achieves 93.6% when predicting time is 50 min. The proposed approach only requires six features as the input of LGBM and can be applied for real-time AE prediction.

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Detection of Local Cell Conditions Based On Individual Anodecurrent Measurements

et al. 2016

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The application of individual anode current measurements in the Hall-Hé roult cells has been investigated to aid cell monitoring, in addition to the conventional use of cell voltage measurements. Its advantages are significant, especially in high amperage cells, where information from the voltage signal is heavily diluted. One common use of individual anode current measurements is to identify process faults that can cause a significant change in the local current flow paths between anode busbar and the metal pad. While this detection is simple and direct, there are other consequential changes in cell conditions that impact the current distribution in the vicinity of a problem anode. This paper presents a Moving Window Kernel PCA-based method to extract spatial information from the individual anode current signals by incorporating other known process variables.

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Diagnosing faults in aluminium processing by using multivariate statistical approaches

Cited by 15 publications

References 18 publications

Aluminium Process Fault Detection and Diagnosis

Aluminium Process Fault Detection and Diagnosis

A modified neighborhood mutual information and light gradient boosting machine-based long-term prediction approach for anode effect

Detection of Local Cell Conditions Based On Individual Anodecurrent Measurements

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