@inproceedings{28ee731fd6d345588e632dcad8df573d,
title = "Anomaly detection for machinery by using Big Data Real-Time processing and clustering technique",
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.",
keywords = "Anomaly detection, Big data, K-means clustering, Real-time processing, Streaming K-means clustering, Trouble diagnose",
author = "Zhuo Wang and Yanghui Zhou and Gangmin Li",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 3rd International Conference on Big Data Research, ICBDR 2019 ; Conference date: 20-11-2019 Through 21-11-2019",
year = "2019",
month = nov,
day = "20",
doi = "10.1145/3372454.3372480",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "30--36",
booktitle = "ICBDR 2019 - Proceedings of the 2019 3rd International Conference on Big Data Research",
}