Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset

Tziolas, Theodoros; Παπαγεωργιου, Κωνσταντινοσ; Theodosiou, Theodosios; Papageorgiou, Elpiniki I.; Mastos, Theofilos; Papadopoulos, Angelos

doi:10.3390/engproc2022018023

Cited by 17 publications

(14 citation statements)

References 20 publications

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“…After collecting the data, a convolutional-based encoder-decoder network to determine where anomalies exist in the workpiece was trained. The ability to detect anomalies in time series by applying convolutions was successfully demonstrated by multiple publications [1,[7][8][9]]. An undercomplete autoencoder is a feedforward deep neural network that tries to reconstruct its input 𝒙 ϵ ℝ 𝑝 and consists of two parts being an encoder and a decoder [10].…”

Section: Methodsmentioning

confidence: 99%

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Multivariate Time Series Dataset of Milling 16MnCr5 for Anomaly Detection

Schlagenhauf¹,

Wolf²,

Puchta³

2022

Preprint

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Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself did not get the same attention by researchers. That is why in this article, the authors present a pub-licly available multivariate time series dataset which was recorded during milling of 16MnCr5. Due to artificially introduced, though realistic anomalies in the workpiece the dataset can be ap-plied for anomaly detection. By using a convolutional autoencoder as a first model good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learn-ing. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics like anomaly detection and transfer learning.

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Section: Methodsmentioning

confidence: 99%

“…In process anomaly detection is an important topic in machine learning research and has a huge potential to further decrease manufacturing costs on the way towards zerodefect manufacturing [1]. With regards to milling lots of research has been done to detect anomalous cutting tool behavior.…”

mentioning

confidence: 99%

Multivariate Time Series Dataset of Milling 16MnCr5 for Anomaly Detection

Schlagenhauf¹,

Wolf²,

Puchta³

2022

Preprint

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“…Reconstruction loss is the term for this function. Typical examples are mean squared error and cross-entropy loss [ 64 , 65 ]. The breakdown process and architecture for the proposed model are described in Figure 3 .…”

Section: Proposed Anomaly Detection Modelmentioning

confidence: 99%

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

Seok

Kareem

Hur

2023

Sensors

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Industry 5.0, also known as the “smart factory,” is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

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“…embedding 𝑧 ∈ 𝑅 , with 𝑑 < 𝑛; the decoder then tries to reconstruct 𝑥 from 𝑧, producing an output vector 𝑥 ∈ 𝑅 . They have been used for dimensionality reduction [57][58][59], classification [60][61][62], and anomaly detection [63,64]. We implemented a convolutional autoencoder (CAE) coupled with an LSTM backend, following an idea similar to that described in [65]: first, the autoencoder was trained to learn good embeddings of the input data; then, we then passed these learned embeddings to an LSTM architecture like the one described in the above section.…”

Section: Cae-lstmmentioning

confidence: 99%

A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection

et al. 2023

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Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information.

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Autoencoders for Anomaly Detection in an Industrial Multivariate Time Series Dataset

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 17 publications

References 20 publications

Multivariate Time Series Dataset of Milling 16MnCr5 for Anomaly Detection

Multivariate Time Series Dataset of Milling 16MnCr5 for Anomaly Detection

LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)

A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection

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