# Intelligent Fault Diagnosis of Type and Severity in Low-Frequency, Low Bit-Depth Signals

arXiv: https://arxiv.org/abs/2411.06299 · HTML: https://mlsanity.com/paper/2411.06299/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 9 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [An acoustic signal cavitation detection framework based on XGBoost with adaptive selection feature engineering](https://mlsanity.com/paper/2202.13226/) (2022)

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

- [FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation](https://mlsanity.com/paper/2507.16696/) (2025)

## Abstract (arXiv)

This study focuses on Intelligent Fault Diagnosis (IFD) in rotating machinery utilizing a single microphone and a data-driven methodology, effectively diagnosing 42 classes of fault types and severities. The research leverages sound data from the imbalanced MaFaulDa dataset, aiming to strike a balance between high performance and low resource consumption. The testing phase encompassed a variety of configurations, including sampling, quantization, signal normalization, silence removal, Wiener filtering, data scaling, windowing, augmentation, and classifier tuning using XGBoost. Through the analysis of time, frequency, mel-frequency, and statistical features, we achieved an impressive accuracy of 99.54% and an F-Beta score of 99.52% with just 6 boosting trees at an 8 kHz, 8-bit configuration. Moreover, when utilizing only MFCCs along with their first- and second-order deltas, we recorded an accuracy of 97.83% and an F-Beta score of 97.67%. Lastly, by implementing a greedy wrapper approach, we obtained a remarkable accuracy of 96.82% and an F-Beta score of 98.86% using 50 selected features, nearly all of which were first- and second-order deltas of the MFCCs.