Unsupervised Novelty Detection Methods Benchmarking with Wavelet Decomposition

TL;DR

This paper benchmarks various unsupervised novelty detection algorithms using wavelet decomposition on vibration data, introducing a continuous anomaly metric and a new dataset with altered signals to evaluate robustness.

Contribution

It compares multiple unsupervised methods for vibration-based novelty detection, proposing a continuous anomaly metric and providing a new dataset for benchmarking.

Findings

Unsupervised algorithms show varying robustness to signal alterations.

Wavelet decomposition effectively captures features for novelty detection.

The continuous metric offers nuanced anomaly quantification.

Abstract

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.