A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

Nentwich, Corbinian; Reinhart, Günther

doi:10.3390/app112110403

Cited by 5 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another common limitation of existing CPD algorithms is that they require batched data to function and are thus illsuited for real-time applications Aminikhanghahi and Cook (2017); Rakthanmanon et al (2011); Kawahara et al (2007); Itoh and Kurths (2010) such as detecting change-points in a patient's vital signs Yang et al (2006) or continuously monitoring the wear and tear of industrial robots Nentwich and Reinhart (2021). In contrast, LS-USS can be efficiently updated every time a new data point is added to the time series, allowing it to run in real-time.…”

Section: Related Workmentioning

confidence: 99%

“…(2007) ; Itoh and Kurths (2010) such as detecting change-points in a patient’s vital signs Yang et al. (2006) or continuously monitoring the wear and tear of industrial robots Nentwich and Reinhart (2021) . In contrast, LS-USS can be efficiently updated every time a new data point is added to the time series, allowing it to run in real-time.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Latent space unsupervised semantic segmentation

2023

View full text Add to dashboard Cite

The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To effectively and efficiently analyze continuously recorded and multidimensional time series at scale, the ability to perform meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to easily work with both online and batch data. Latent Space Unsupervised Semantic Segmentation addresses the challenge of multivariate change-point detection by utilizing an autoencoder to learn a 1-dimensional latent space on which change-point detection is then performed. To address the challenge of real-time time series segmentation, this work introduces the Local Threshold Extraction Algorithm (LTEA) and a “batch collapse” algorithm. The “batch collapse” algorithm enables Latent Space Unsupervised Semantic Segmentation to process streaming data by dividing it into manageable batches, while Local Threshold Extraction Algorithm is employed to detect change-points in the time series whenever the computed metric by Latent Space Unsupervised Semantic Segmentation exceeds a predefined threshold. By using these algorithms in combination, our approach is able to accurately segment time series data in real-time, making it well-suited for applications where timely detection of changes is critical. When evaluating Latent Space Unsupervised Semantic Segmentation on a variety of real-world datasets the Latent Space Unsupervised Semantic Segmentation systematically achieves equal or better performance than other state-of-the-art change-point detection algorithms it is compared to in both offline and real-time settings.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Latent space unsupervised semantic segmentation

2023

View full text Add to dashboard Cite

show abstract

“…Another common limitation of existing CPD algorithms is that they require being used on batched data and are thus ill-suited for real-time applications [2,15,16,18] such as detecting change-points in a patient's vital signs [19] or continuously monitoring the wear and tear of industrial robots [20]. In contrast, LS-USS can be efficiently updated every time a new data point is added to the time series allowing it to run in real-time.…”

Section: Related Workmentioning

confidence: 99%

Latent Space Unsupervised Semantic Segmentation

Strømmen¹,

Tørresen²,

Côté-Allard³

2022

Preprint

View full text Add to dashboard Cite

The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To analyze these continuously recorded, and often multidimensional, time series at scale, being able to conduct meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify changepoints within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to work easily with both online and batch data. When comparing LS-USS against other state-of-the-art change-point detection algorithms on a variety of real-world datasets, in both the offline and real-time setting, LS-USS systematically achieves on par or better performances.

show abstract

Comparison of Data Sources for Robot Gear Condition Monitoring

Nentwich

Daub

2022

Procedia CIRP

View full text Add to dashboard Cite

A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

Cited by 5 publications

References 33 publications

Latent space unsupervised semantic segmentation

Latent space unsupervised semantic segmentation

Latent Space Unsupervised Semantic Segmentation

Comparison of Data Sources for Robot Gear Condition Monitoring

Contact Info

Product

Resources

About