# An ensemble of convolution-based methods for fault detection using vibration signals

arXiv: https://arxiv.org/abs/2305.05532 · HTML: https://mlsanity.com/paper/2305.05532/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 8 of 8)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline](https://mlsanity.com/paper/1611.06455/) (2016)
- [ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels](https://mlsanity.com/paper/1910.13051/) (2019)
- [MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification](https://mlsanity.com/paper/2012.08791/) (2020)
- [Feature-based time-series analysis](https://mlsanity.com/paper/1709.08055/) (2017)
- [Non-linear Functional Modeling using Neural Networks](https://mlsanity.com/paper/2104.09371/) (2021)
- [A Functional approach for Two Way Dimension Reduction in Time Series](https://mlsanity.com/paper/2301.00357/) (2023)

## Built on by (most cited first, 3 of 3)

- [Machine Learning Approaches for Diagnostics and Prognostics of Industrial Systems Using Open Source Data from PHM Data Challenges: A Review](https://mlsanity.com/paper/2312.16810/) (2023)
- [Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance](https://mlsanity.com/paper/2403.00975/) (2024)
- [Exploring LLM-based Frameworks for Fault Diagnosis](https://mlsanity.com/paper/2509.23113/) (2025)

## Abstract (arXiv)

This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig. Various traditional machine learning and deep learning methods have been proposed for multivariate time-series classification, including distance-based, functional data-oriented, feature-driven, and convolution kernel-based methods. Recent studies have shown using convolution kernel-based methods like ROCKET, and 1D convolutional neural networks with ResNet and FCN, have robust performance for multivariate time-series data classification. We propose an ensemble of three convolution kernel-based methods and show its efficacy on this fault detection problem by outperforming other approaches and achieving an accuracy of more than 98.8%.