Unsupervised Novelty Detection Methods Benchmarking with Wavelet Decomposition
This work addresses the problem of expensive labeled data for novelty detection in engineering fields by comparing unsupervised methods, but it is incremental as it focuses on benchmarking existing techniques.
The study benchmarked unsupervised machine learning algorithms for novelty detection in vibration sensing, using a new dataset and a continuous metric to quantify anomaly degrees, finding insights into their adaptability and robustness.
Novelty detection is a critical task in various engineering fields. Numerous approaches to novelty detection rely on supervised or semi-supervised learning, which requires labelled datasets for training. However, acquiring labelled data, when feasible, can be expensive and time-consuming. For these reasons, unsupervised learning is a powerful alternative that allows performing novelty detection without needing labelled samples. In this study, numerous unsupervised machine learning algorithms for novelty detection are compared, highlighting their strengths and weaknesses in the context of vibration sensing. The proposed framework uses a continuous metric, unlike most traditional methods that merely flag anomalous samples without quantifying the degree of anomaly. Moreover, a new dataset is gathered from an actuator vibrating at specific frequencies to benchmark the algorithms and evaluate the framework. Novel conditions are introduced by altering the input wave signal. Our findings offer valuable insights into the adaptability and robustness of unsupervised learning techniques for real-world novelty detection applications.