Abstract
This paper aims to apply techniques of Big Data Analytics including K-Means Clustering to diagnose potential problems for offshore rotating machinery. The innovative methods are attempted in both Batch K-Means and Streaming K-Means. Their performances are compared with the conventional signal analysis method. Both KMeans models have a better performance on detecting significant mechanical faults as anomalies for offshore rotating machinery which can be considered as appropriate method for machine operational maintenance.
| Original language | English |
|---|---|
| Title of host publication | ICBDR 2019 - Proceedings of the 2019 3rd International Conference on Big Data Research |
| Publisher | Association for Computing Machinery |
| Pages | 30-36 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781450372015 |
| DOIs | |
| Publication status | Published - 20 Nov 2019 |
| Event | 3rd International Conference on Big Data Research, ICBDR 2019 - Cergy-Pontoise, France Duration: 20 Nov 2019 → 21 Nov 2019 |
Publication series
| Name | ACM International Conference Proceeding Series |
|---|
Conference
| Conference | 3rd International Conference on Big Data Research, ICBDR 2019 |
|---|---|
| Country/Territory | France |
| City | Cergy-Pontoise |
| Period | 20/11/19 → 21/11/19 |
Keywords
- Anomaly detection
- Big data
- K-means clustering
- Real-time processing
- Streaming K-means clustering
- Trouble diagnose
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Wang, Z., Zhou, Y., & Li, G. (2019). Anomaly detection for machinery by using Big Data Real-Time processing and clustering technique. In ICBDR 2019 - Proceedings of the 2019 3rd International Conference on Big Data Research (pp. 30-36). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3372454.3372480