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Cao, F., Estert, M., Qian, W., &amp; Zhou, A. (2006, April). Density-based clustering over an evolving data stream with noise. In Proceedings of the 2006 SIAM international conference on data mining (pp. 328-339). Society for industrial and applied mathematics.

Fan, Y., Nowaczyk, S., &amp; Antonelo, E. A. (2016). Predicting air compressor failures with echo state networks. In PHM Society European Conference (Vol. 3, No. 1).

Fan, Y., Nowaczyk, S., &amp; Rögnvaldsson, T. (2020). Transfer learning for remaining useful life prediction based on consensus self-organizing models. Reliability Engineering &amp; System Safety, 203, 107098.

Ahmad, S., Lavin, A., Purdy, S., &amp; Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134-147.

Lavin, A., &amp; Ahmad, S. (2015, December). Evaluating real-time anomaly detection algorithms--the Numenta anomaly benchmark. In 2015 IEEE 14th international conference on machine learning and applications (ICMLA) (pp. 38-44). IEEE.

Wu, K., Zhang, K., Fan, W., Edwards, A., &amp; Philip, S. Y. (2014, December). Rs-forest: A rapid density estimator for streaming anomaly detection. In 2014 IEEE international conference on data mining (pp. 600-609). IEEE.

Hendrickx, K., Meert, W., Mollet, Y., Gyselinck, J., Cornelis, B., Gryllias, K., &amp; Davis, J. (2020). A general anomaly detection framework for fleet-based condition monitoring of machines. Mechanical Systems and Signal Processing, 139, 106585.</property:References>
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