Intelligent Anomaly Detection of Machine Tools based on Mean Shift Clustering

Netzer, Markus; Michelberger, Jonas; Fleischer, Jürgen

doi:10.1016/j.procir.2020.03.043

Cited by 15 publications

(5 citation statements)

References 17 publications

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“…However, the process requires joint training of data dimensionality reduction, reconstruction error, and density estimation, which is much more complicated and requires a lot of time and computing resources. e clustering methods are one important type of methods for anomaly detection and density estimation, including K-Means, Mean-Shift, DBSCAN, Gaussian mixture model, and multivariate mixture model [12,[21][22][23][24][25][26]. Due to the limitation of high-dimensional data [27], the above-mentioned methods cannot be directly applied to anomaly detection of high-dimensional data.…”

Section: Related Workmentioning

confidence: 99%

“…Mean-Shift is also a density-based clustering algorithm [30,31]. e algorithm updates the centroid to the average value of the specified area through iteration to achieve the purpose of clustering [23,24]. Due to the distance between the sample and the offset point being different, the contribution of the corresponding offset to the mean offset vector is also different.…”

Section: Mean-shiftmentioning

confidence: 99%

See 1 more Smart Citation

Unsupervised Anomaly Detection Based on Deep Autoencoding and Clustering

Zhang

Liu

Chen

et al. 2021

Security and Communication Networks

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The unsupervised anomaly detection task based on high-dimensional or multidimensional data occupies a very important position in the field of machine learning and industrial applications; especially in the aspect of network security, the anomaly detection of network data is particularly important. The key to anomaly detection is density estimation. Although the methods of dimension reduction and density estimation have made great progress in recent years, most dimension reduction methods are difficult to retain the key information of original data or multidimensional data. Recent studies have shown that the deep autoencoder (DAE) can solve this problem well. In order to improve the performance of unsupervised anomaly detection, we propose an anomaly detection scheme based on a deep autoencoder (DAE) and clustering methods. The deep autoencoder is trained to learn the compressed representation of the input data and then feed it to clustering approach. This scheme makes full use of the advantages of the deep autoencoder (DAE) to generate low-dimensional representation and reconstruction errors for the input high-dimensional or multidimensional data and uses them to reconstruct the input samples. The proposed scheme could eliminate redundant information contained in the data, improve performance of clustering methods in identifying abnormal samples, and reduce the amount of calculation. To verify the effectiveness of the proposed scheme, massive comparison experiments have been conducted with traditional dimension reduction algorithms and clustering methods. The results of experiments demonstrate that, in most cases, the proposed scheme outperforms the traditional dimension reduction algorithms with different clustering methods.

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Section: Related Workmentioning

confidence: 99%

Section: Mean-shiftmentioning

confidence: 99%

Unsupervised Anomaly Detection Based on Deep Autoencoding and Clustering

Zhang

Liu

Chen

et al. 2021

Security and Communication Networks

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“…These candidates are then shifted towards regions of the highest density, identified using a kernel density estimate. In power system applications, Mean Shift could be beneficial for detecting areas of high energy consumption or demand hotspots [321], [322], [323], providing valuable insights for power distribution and load management strategies [324], [325], [326].…”

Section: ) Mean-shift Clusteringmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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“…If the position segment appears again, clusters can be generated across these other channels. An approach for clustering these signals was previously presented using Mean-Shift Clustering in [24], subsequently, the approach was extended by extensive data pre-processing, involving smoothing of the positional time series and offset corrections to further improve the matching with previously appearing positional signals of the same type.…”

Section: Pattern Recognitionmentioning

confidence: 99%

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

Netzer

Palenga

Fleischer

2022

Prod. Eng. Res. Devel.

Self Cite

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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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Intelligent Anomaly Detection of Machine Tools based on Mean Shift Clustering

Cited by 15 publications

References 17 publications

Unsupervised Anomaly Detection Based on Deep Autoencoding and Clustering

Unsupervised Anomaly Detection Based on Deep Autoencoding and Clustering

K-Means and Alternative Clustering Methods in Modern Power Systems

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

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