Anomaly detection on industrial time series for retaining energy efficiency

Theumer, Philipp; Zeiser, Reinhard; Trauner, Ludwig; Reinhart, Gunther

doi:10.1016/j.procir.2021.03.006

Cited by 12 publications

(8 citation statements)

References 10 publications

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“…Most of these models are based on Autoencoders. Theumer et al (2021) proposed an autoencoder composed of Gated Recurrent Units to detect both abnormal points and subsequences. Chen et al (2020) used a Variational Autoencoder based on two dimensional convolutions to detect abnormal subsequences in multivariate time series.…”

Section: Related Workmentioning

confidence: 99%

One Class Support Subsequence Machine: Abnormal time series subsequences detection using One-Class SVM

Amine

Batton‐Hubert

Casoetto³

2023

Preprint

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In this paper, an approach is proposed for the detection of abnormal time series subsequences using the One-Class SVM with an application to defect detection of two automatic welding processes. The One Class-SVM has been used in many works for the same purpose but only after transforming the subsequences into a set of feature vectors. However, finding the relevant features that allow anomaly detection may be challenging. Methods dealing with the raw subsequences, on the other hand, can be easily generalized to other problems and are known to be efficient. Nevertheless, they often suffer from high computational cost since they involve computing the distances to all the reference subsequences, which may restrict their application, especially with the need of real time predictions in most of the applications. We show in this work that, contrary to what is indicated in the literature, the One- Class SVM can be used directly with the raw subsequences. This is achieved by employing distance substitution kernels, which are a class of kernels that allow to use dissimilarity measures in the One-class SVM formulation. The results show that the approach has a similar efficiency of those suggested in the literature that operate with raw subsequences in terms of accuracy. Furthermore, because it just requires the distances to the support vectors, the approach has a low time complexity in the prediction phase, making it more suitable for real-time monitoring. We further show that the One-Class SVM with non-metric dissimilarity remains efficient for aberrant subsequence identification.

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Section: Related Workmentioning

confidence: 99%

One Class Support Subsequence Machine: Abnormal time series subsequences detection using One-Class SVM

Amine

Batton‐Hubert

Casoetto³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Other approaches in predictive maintenance also focus on feature extraction [15] (FRESH), with the features then being possible to use as high-quality input to supervised machine learning models, provide high accuracy but the need for labeled training data, which might not always be available. Theumer et al [16] detect point and collective anomalies using sliding windows and autoencoders respectively. Machine learning based algorithms are also popular in the context of wear detection of machine tools [17].…”

Section: State Of the Artmentioning

confidence: 99%

Machine tool process monitoring by segmented timeseries anomaly detection using subprocess-specific thresholds

Netzer

Palenga

Fleischer

2022

Prod. Eng. Res. Devel.

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Time series data generated by manufacturing machines during processing is widely used in mass part production to assess if processes run without errors. Systems that make use of this data use machine learning approaches for flagging a time series as a deviation from normal behaviour. In single part production, the amount of data generated is not sufficient for learning-based classification. Here, methods often focus on global signal variance but have trouble finding anomalies that present as local signal deviations. The referencing of the process states of the machine is usually performed by state indexing which, however, is not sufficient in highly flexible production plants. In this paper, a system that learns granular patterns in time series based on mean shift clustering is used for detecting processing segments in varying machine conditions. An anomaly detection then finds deviating patterns based on the previously identified processing segments. The anomalies can then be labeled by a human-in-the-loop approach for enabling future anomaly classification using a combination of machine learning algorithms. The method of anomaly detection is validated using an industrial machine tool and multiple test series.

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“…Most of these models are based on Autoencoders. Theumer (2021) proposed an autoencoder composed of Gated Recurrent Units to detect both abnormal points and subsequences. Chen (2020) used a Variational Autoencoder based on two dimensional convolutions to detect abnormal subsequences in multivariate time series.…”

Section: Related Workmentioning

confidence: 99%

One Class Support Subsequence Machine: Abnormal time series subsequences detection using One-Class SVM

Amine

Batton‐Hubert

Casoetto³

2022

Preprint

View full text Add to dashboard Cite

In this paper, an approach is proposed for the detection of abnormal time series subsequences using the One-Class SVM with an application to defect detection of two automatic welding processes. The One Class-SVM has been used in many works for the same purpose but only after transforming the subsequences into a set of feature vectors. However, finding the relevant features that allow anomaly detection may be challenging. Methods dealing with the raw subsequences, on the other hand, can be easily generalized to other problems and are known to be efficient. Nevertheless, they often suffer from high computational cost since they involve computing the distances to all the reference subsequences, which may restrict their application, especially with the need of real time predictions in most of the applications. We show in this work that, contrary to what is indicated in the literature, the One-Class SVM can be used directly with the raw subsequences. This is achieved by employing distance substitution kernels, which are a class of kernels that allow to use dissimilarity measures in the One-class SVM formulation. The results show that the approach has a similar efficiency of those suggested in the literature that operate with raw subsequences in terms of accuracy. Furthermore, because it just requires the distances to the support vectors, the approach has a low time complexity in the prediction phase, making it more suitable for real-time monitoring. We further show that the One-Class SVM with non-metric dissimilarity remains efficient for aberrant subsequence identification.

show abstract

Anomaly detection on industrial time series for retaining energy efficiency

Cited by 12 publications

References 10 publications

One Class Support Subsequence Machine: Abnormal time series subsequences detection using One-Class SVM

One Class Support Subsequence Machine: Abnormal time series subsequences detection using One-Class SVM

Machine tool process monitoring by segmented timeseries anomaly detection using subprocess-specific thresholds

One Class Support Subsequence Machine: Abnormal time series subsequences detection using One-Class SVM

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