# Unsupervised Novelty Detection Methods Benchmarking with Wavelet Decomposition

arXiv: https://arxiv.org/abs/2409.07135 · HTML: https://mlsanity.com/paper/2409.07135/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 36 references that are in the map.
- First posted on arXiv 11 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Anomaly Detection in Additive Manufacturing Processes using Supervised Classification with Imbalanced Sensor Data based on Generative Adversarial Network](https://mlsanity.com/paper/2210.17274/) (2022)
- [Physics-Informed Real NVP for Satellite Power System Fault Detection](https://mlsanity.com/paper/2405.17339/) (2024)
- [Vibration Analysis in Bearings for Failure Prevention using CNN](https://mlsanity.com/paper/2005.07057/) (2020)
- [Semi-Supervised Novelty Detection for Precise Ultra-Wideband Error Signal Prediction](https://mlsanity.com/paper/2404.05351/) (2024)

## Abstract (arXiv)

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.