# Bearings Fault Detection Using Hidden Markov Models and Principal Component Analysis Enhanced Features

arXiv: https://arxiv.org/abs/2104.10519 · HTML: https://mlsanity.com/paper/2104.10519/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders](https://mlsanity.com/paper/1912.01096/) (2019)
- [An Automated Auto-encoder Correlation-based Health-Monitoring and Prognostic Method for Machine Bearings](https://mlsanity.com/paper/1703.06272/) (2017)
- [Selecting the Number of States in Hidden Markov Models - Pitfalls, Practical Challenges and Pragmatic Solutions](https://mlsanity.com/paper/1701.08673/) (2017)

## Built on by (most cited first, 1 of 1)

- [A Comprehensive Survey on Rare Event Prediction](https://mlsanity.com/paper/2309.11356/) (2023)

## Abstract (arXiv)

Asset health monitoring continues to be of increasing importance on productivity, reliability, and cost reduction. Early Fault detection is a keystone of health management as part of the emerging Prognostics and Health Management (PHM) philosophy. This paper proposes a Hidden Markov Model (HMM) to assess the machine health degradation. using Principal Component Analysis (PCA) to enhance features extracted from vibration signals is considered. The enhanced features capture the second order structure of the data. The experimental results based on a bearing test bed show the plausibility of the proposed method.