Anomaly detection plays an essential role in health monitoring and reliability assurance of
complex system. However, previous researches suffer from distraction by outliers in training
and extensively relying on empiric-based feature engineering, leading to many limitations
in the practical application of detection methods. In this paper, we propose an unsupervised
anomaly detection method that combines random convolution kernels with isolation forest to
tackle the above problems in equipment state monitoring. The random convolution kernels
are applied to generate cross-dimensional and multi-scale features for multi-dimensional
time series, with combining the time series decomposing method to select abnormally sensitive features for automatic feature extraction. Then, anomaly detection is performed on the
obtained features using isolation forests with low requirements for purity of training sample.
The verification and comparison on different types of datasets show the performance of the
proposed method surpass the traditional methods in accuracy and applicability.
CITATIONS(3):
1.
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