Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Coelho, Gabriel; Pereira, Pedro; Matos, Luís Miguel; Ribeiro, António P.; Nunes, Eduardo C.; Ferreira, André; Cortez, Paulo; Pilastri, André

doi:10.1007/978-3-030-79150-6_27

Cited by 4 publications

(10 citation statements)

References 28 publications

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“…The proposed model made it possible to achieve a significant improvement in anomaly detection according to the data of machine sensors according to AUC 95.45%, compared to the previously proposed models [7,8,10,13]. Densenet+XGBoost improved by about 8% over the PCA model [7] applied to the log spectrogram of the audio signal combined with LOF and GMM on the MIMII dataset.…”

Section: Discussionmentioning

confidence: 99%

“…Densenet+XGBoost gave comparable results (AUC 95.5%) on the reviewed MIMII dataset. Coelho et al [10] used CNN and Dense network in combination with an autoencoder for the task of unsupervised acoustic anomaly detection, where the results averaged 72.0%, 73.1%, 91.8%, and 78.9% for the fan, pump, slider, and valve, respectively. The accuracy of the proposed method is 93.1% for the fan, 97.3% for the pump, 98.4% for the slider, and 93.0% for the valve, which is significantly higher than the above result.…”

Section: Discussionmentioning

confidence: 99%

“…The difficulty here is to obtain information from several sensors that differ in their specific acoustic properties [6]. Researchers propose new methods and expand existing algorithms for detecting industrial equipment faults [6][7][8][9][10][11][12][13].…”

Section: Review Of the Literaturementioning

confidence: 99%

“…The application of autoencoder deep learning architectures for unsupervised acoustic anomaly detection based on Dense and convolutional neural networks (CNN) was considered in [10]. The energy features of the melspectrogram were extracted from the raw sounds.…”

Section: Review Of the Literaturementioning

confidence: 99%

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An Intelligent Model Based on Deep Transfer Learning for Detecting Anomalies in Cyber-Physical Systems

Sukhostat

2021

RIC

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Context. The problem of detecting anomalies from signals of cyber-physical systems based on spectrogram and scalogram images is considered. The object of the research is complex industrial equipment with heterogeneous sensory systems of different nature. Objective. The goal of the work is the development of a method for signal anomalies detection based on transfer learning with the extreme gradient boosting algorithm. Method. An approach based on transfer learning and the extreme gradient boosting algorithm, developed for detecting anomalies in acoustic signals of cyber-physical systems, is proposed. Little research has been done in this area, and therefore various pre-trained deep neural model architectures have been studied to improve anomaly detection. Transfer learning uses weights from a deep neural model, pre-trained on a large dataset, and can be applied to a small dataset to provide convergence without overfitting. The classic approach to this problem usually involves signal processing techniques that extract valuable information from sensor data. This paper performs an anomaly detection task using a deep learning architecture to work with acoustic signals that are preprocessed to produce a spectrogram and scalogram. The SPOCU activation function was considered to improve the accuracy of the proposed approach. The extreme gradient boosting algorithm was used because it has high performance and requires little computational resources during the training phase. This algorithm can significantly improve the detection of anomalies in industrial equipment signals. Results. The developed approach is implemented in software and evaluated for the anomaly detection task in acoustic signals of cyber-physical systems on the MIMII dataset. Conclusions. The conducted experiments have confirmed the efficiency of the proposed approach and allow recommending it for practical use in diagnosing the state of industrial equipment. Prospects for further research may lie in the application of ensemble approaches based on transfer learning to various real datasets to improve the performance and fault-tolerance of cyber-physical systems.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

Section: Review Of the Literaturementioning

confidence: 99%

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An Intelligent Model Based on Deep Transfer Learning for Detecting Anomalies in Cyber-Physical Systems

Sukhostat

2021

RIC

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“…This work is set within a larger R&D project that addresses an unsupervised in-vehicle intelligence using multiple data sources (e.g., images, sound, particles) and that includes the Bosch Car Multimedia S.A. (BCM) company. This paper presents the full research related with the in-vehicle AAD component of the R&D project, which corresponds to a large extended version of a previously published conference paper [18]. The main goal is to detect invehicle AAD, such as passengers arguing, coughing or an accidental breaking of glass.…”

Section: Introductionmentioning

confidence: 99%

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

Coelho

Matos

Pereira

et al. 2022

Neural Comput & Applic

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The growing usage of digital microphones has generated an increased interest in the topic of Acoustic Anomaly Detection (AAD). Indeed, there are several real-world AAD application domains, including working machines and in-vehicle intelligence (the main target of this research project). This paper introduces three deep AutoEncoders (AE) for unsupervised AAD tasks, namely a Dense AE, a Convolutional Neural Network (CNN) AE and Long Short-Term Memory Autoencoder (LSTM) AE. To tune the deep learning architectures, development data was adopted from public domain audio datasets related with working machines. A large set of computational experiments was held, showing that the three proposed deep autoencoders, when combined with a melspectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Next, on a second experimental stage, aiming to address the final in-vehicle passenger safety goal, the three AEs were adapted to learn from in-vehicle normal audio, assuming three realistic scenarios that were generated by a Deep Autoencoders for Acoustic Anomaly Detection synthetic audio mixture tool. In general, a high quality AAD discrimination was obtained: working machine data -72% to 91%; and in-vehicle audio -78% to 81%. In conjunction with an automotive company, an in-vehicle AAD intelligent system prototype was further developed, aiming to test a selected model (LSTM AE) during a pilot demonstration event that targeted the cough anomaly. Interesting results were obtained, with the AAD system presenting a high cough classification accuracy (e.g., 100% for front seat locations).

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TinyVers: A Tiny Versatile System-on-Chip With State-Retentive eMRAM for ML Inference at the Extreme Edge

Jain

Giraldo

Roose

et al. 2023

IEEE J. Solid-State Circuits

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Extreme edge devices or Internet-of-thing nodes require both ultra-low power always-on processing as well as the ability to do on-demand sampling and processing. Moreover, support for IoT applications like voice recognition, machine monitoring, etc., requires the ability to execute a wide range of ML workloads. This brings challenges in hardware design to build flexible processors operating in ultra-low power regime. This paper presents TinyVers, a tiny versatile ultra-low power ML system-on-chip to enable enhanced intelligence at the Extreme Edge. TinyVers exploits dataflow reconfiguration to enable multi-modal support and aggressive on-chip power management for duty-cycling to enable smart sensing applications. The SoC combines a RISC-V host processor, a 17 TOPS/W dataflow reconfigurable ML accelerator, a 1.7 µW deep sleep wake-up controller, and an eMRAM for boot code and ML parameter retention. The SoC can perform up to 17.6 GOPS while achieving a power consumption range from 1.7 µW-20 mW. Multiple ML workloads aimed for diverse applications are mapped on the SoC to showcase its flexibility and efficiency. All the models achieve 1-2 TOPS/W of energy efficiency with power consumption below 230 µW in continuous operation. In a duty-cycling use case for machine monitoring, this power is reduced to below 10 µW.

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Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Cited by 4 publications

References 28 publications

An Intelligent Model Based on Deep Transfer Learning for Detecting Anomalies in Cyber-Physical Systems

An Intelligent Model Based on Deep Transfer Learning for Detecting Anomalies in Cyber-Physical Systems

Deep autoencoders for acoustic anomaly detection: experiments with working machine and in-vehicle audio

TinyVers: A Tiny Versatile System-on-Chip With State-Retentive eMRAM for ML Inference at the Extreme Edge

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