# Health Monitoring of Industrial machines using Scene-Aware Threshold Selection

arXiv: https://arxiv.org/abs/2111.10897 · HTML: https://mlsanity.com/paper/2111.10897/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 21 Nov 2021.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification](https://mlsanity.com/paper/1608.04363/) (2016)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection](https://mlsanity.com/paper/1908.03299/) (2019)
- [Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma](https://mlsanity.com/paper/1810.09133/) (2018)
- [A general anomaly detection framework for fleet-based condition monitoring of machines](https://mlsanity.com/paper/1912.12941/) (2019)

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

- [Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbanks](https://mlsanity.com/paper/2409.05319/) (2024)

## Abstract (arXiv)

This paper presents an autoencoder based unsupervised approach to identify anomaly in an industrial machine using sounds produced by the machine. The proposed framework is trained using log-melspectrogram representations of the sound signal. In classification, our hypothesis is that the reconstruction error computed for an abnormal machine is larger than that of the a normal machine, since only normal machine sounds are being used to train the autoencoder. A threshold is chosen to discriminate between normal and abnormal machines. However, the threshold changes as surrounding conditions vary. To select an appropriate threshold irrespective of the surrounding, we propose a scene classification framework, which can classify the underlying surrounding. Hence, the threshold can be selected adaptively irrespective of the surrounding. The experiment evaluation is performed on MIMII dataset for industrial machines namely fan, pump, valve and slide rail. Our experiment analysis shows that utilizing adaptive threshold, the performance improves significantly as that obtained using the fixed threshold computed for a given surrounding only.