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https://hdl.handle.net/1959.11/61459
Title: | Investigating the effects of varying cluster numbers on anomalies detected in mining machines |
Contributor(s): | Fan, Zongwen (author); Chiong, Raymond (author) ; Hu, Zhongyi (author); Lin, Yuqing (author) |
Publication Date: | 2017 |
DOI: | 10.1109/ICONDA.2017.8270404 |
Handle Link: | https://hdl.handle.net/1959.11/61459 |
Abstract: | | Anomaly detection is very important for the mining industry. If anomalies in mining equipment can be correctly detected to predict machine breakdown, mining companies will be able to reduce the cost of maintaining their machines. However, anomaly detection in this context is quite difficult considering the large volume of sensor data involved and unlabelled nature of the data. Clustering techniques have therefore been applied to analyse this problem, by dividing the data into normal and abnormal clusters. In this paper, we investigate the influence of using different numbers of clusters in clustering models, which include k-means, fuzzy c-means and the self-organising map, to obtain useful data patterns and classify the data into normal and abnormal types. Our aim here is to reduce the trigger of false alarm in the anomaly detection process. The data used in this study is based on real-world grease cycle data from a mining company in Australia. Our experimental results show that with more clusters, the number of anomalies detected tends to decrease for the clustering models considered. This means false alarms can be reduced by increasing the number of clusters used.
Publication Type: | Conference Publication |
Conference Details: | IConDA 2017: 1st International Conference on Computer and Drone Applications, Kuching, Malaysia, 9th - 11th November, 2017 |
Source of Publication: | 1st International Conference on Computer and Drone Applications: Ethical Integration of Computer and Drone Technology for Humanity Sustainability, IConDA 2017, p. 82-86 |
Publisher: | IEEE |
Place of Publication: | United States of America |
Fields of Research (FoR) 2020: | 4602 Artificial intelligence |
Peer Reviewed: | Yes |
HERDC Category Description: | E1 Refereed Scholarly Conference Publication |
Appears in Collections: | Conference Publication School of Science and Technology
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