Acoustic Anomaly Detection Using Convolutional Autoencoders in Industrial Processes

Duman, Taha Berkay; Bayram, Barış; İnce, Gökhan

doi:10.1007/978-3-030-20055-8_41

Cited by 26 publications

(15 citation statements)

References 18 publications

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“…However, this situation has improved significantly in recent years with the release of AAD datasets such as MIMII [ 1 ], and ToyADMOS [ 8 ]. Traditionally, autoencoders have been the cornerstone for anomaly detection [ 2 , 9 , 10 , 11 ]. In the context of AAD, autoencoders are typically trained in an unsupervised manner on the normal operating sounds of machines.…”

Section: Related Workmentioning

confidence: 99%

“…Autoencoders are typically trained on the features extracted from raw audio signals, with spectral features such as MFCCs [ 2 ] and Mel-spectrograms [ 2 , 9 , 10 ] amongst the most popular. A Mel-spectogram is similar to a conventional spectogram, with the major difference being that the sound is represented on the Mel-scale, which measures the pitch as perceived by humans.…”

Section: Related Workmentioning

confidence: 99%

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OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Abbasi

Famouri²,

Shafiee

et al. 2021

Sensors

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Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Abbasi

Famouri²,

Shafiee

et al. 2021

Sensors

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“…However, there are several anomaly detection works based on AEs, used in various environments, but industrial one, explained in the next section. In industrial manufacturing processes, our previous work (Duman, Bayram, & _ Ince, 2019) presents an approach based on an AE to detect the abnormal events using audio features. Also, it is shown that this approach demonstrates better AAD performance compared to both OCSVM, and a hybrid model of OCSVM and CAE.…”

Section: Anomaly Detection For Industrial Purposesmentioning

confidence: 99%

“…However, in the studies, the anomaly detection was not applied for industrial purposes. Therefore, in our previous work (Duman et al, 2019), we presented the use of AE for AAD in industrial tasks, and compared with OCSVM using original audio datasets. In that work, it was shown that a deep network, CAE provides better AAD results than OCSVM, but it was an offline framewise solution not capable of working in real time.…”

Section: Related Workmentioning

confidence: 99%

Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

2020

Self Cite

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Development of intelligent systems with the pursuit of detecting abnormal events in real world and in real time is challenging due to difficult environmental conditions, hardware limitations, and computational algorithmic restrictions. As a result, degradation of detection performance in dynamically changing environments is often encountered. However, in the next-generation factories, an anomaly detection system based on acoustic signals is especially required to quickly detect and interfere with the abnormal events during the industrial processes due to the increased cost of complex equipment and facilities. In this study we propose a real time Acoustic Anomaly Detection (AAD) system with the use of sequence-to-sequence Autoencoder (AE) models in the industrial environments. The proposed processing pipeline makes use of the audio features extracted from the streaming audio signal captured by a single-channel microphone. The reconstruction error generated by the AE model is calculated to measure the degree of abnormality of the sound event. The performance of Convolutional Long Short-Term Memory AE (Conv-LSTMAE) is evaluated and compared with sequential Convolutional AE (CAE) using sounds captured from various industrial manufacturing processes. In the experiments conducted with the real time AAD system, it is shown that the Conv-LSTMAE-based AAD demonstrates better detection performance than CAE model-based AAD under different signal-to-noise ratio conditions of sound events such as explosion, fire and glass breaking. K E Y W O R D S acoustic anomaly detection, audio feature extraction, convolutional autoencoder, convolutional long short-term memory autoencoder, industrial processes 1 | INTRODUCTION The usage of smart systems in homes, factories, cities, and so forth, become more popular to ease the life of humans, especially in surveillance and monitoring tasks. Therefore, a wide variety of sensory information of different type and nature stemming from vision, audition, force/torque, temperature, energy consumption, power, network, and so forth, are individually or jointly utilized in monitoring tasks. However, processing the signals in real time is a challenging problem for abnormal event detection in dynamically changing environments. The aim of anomaly detection is to distinguish abnormal events from the usual ones. For the new generation of industrial manufacturing systems, monitoring of production with a focus on anomalies is one of the significant capabilities, since abnormal events can affect the quality of manufactured products, deteriorate the continuity and the reliability of the processes and assets (Panfilenko, Poller, Sonntag, Zillner, & Schneider, 2016). Even worse, some anomalies in production processes can endanger the safety of people who use industrial machines in the

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“…In industrial processes, the early detection of operating machines with a defects by using ML can potentially [25,31]: reduce maintenance time and costs; prevent or reduce production stops, and increase the safety of human operators that operate the machines. In this work, we focus on ML methods for Acoustic Anomaly Detection (AAD) [8], which aims to detect abnormal behaviours using audio data. In particular, we aim to automatically detect, beforehand, if a given industrial machine is not working correctly, by using only the sound produced by it.…”

Section: Introductionmentioning

confidence: 99%

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

Coelho

Pereira

Matos

et al. 2021

IFIP Advances in Information and Communication Technology

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Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the two proposed deep autoencoders, when combined with a mel-spectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Overall, a high-quality class discrimination level was achieved, ranging from 72% to 92%.

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Acoustic Anomaly Detection Using Convolutional Autoencoders in Industrial Processes

Cited by 26 publications

References 18 publications

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Real time detection of acoustic anomalies in industrial processes using sequential autoencoders

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

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