A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems

Beghi, Alessandro; Cecchinato, Luca; Corazzol, Chiara; Rampazzo, Mirco; Simmini, Francesco; Susto, Gian Antonio

doi:10.3182/20140824-6-za-1003.02382

Cited by 56 publications

(15 citation statements)

References 5 publications

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“…PCA is an unsuperivsed feature reduction method that decomposes the given data into the model principal component space and the unmodelled residual component space, such that the first PCs represent most of the data variability. Feature extraction respective feature reduction is necessary scince utilizing high dimensional datasets to train data-driven models lead to high computational costs and memory requirements [13]. Moreover, high dimensional input data can lead to poor understanding of the resulting model [19, p. 32].…”

Section: Methodological Approachesmentioning

confidence: 99%

“…Thereby, Hotelling's T² distribution of the PCS and the squared prediction error (SPE) of the residual space were used for fault detection and diagnosis. Both Li et al [5] and Beghi et al [13] conclude that indeed the models trained in the RCS may yield higher classification performance rather than in the PCS.…”

Section: Related Workmentioning

confidence: 99%

“…Beghi et al [13] have implemented a similar approach by utilizing the same dataset, with the crucial difference of removing the most relevant principal components before training and fit the classifier using the residual component subspace (RCS) spanned by the residual components (PCA-R-OCSVM). They demonstrated that chiller faults can be reliably detected in the RS.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers

Falk

Sand

Corasaniti

et al. 2021

TH Wildau Eng. Nat. Sci. Proc.

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Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.

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Section: Methodological Approachesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers

Falk

Sand

Corasaniti

et al. 2021

TH Wildau Eng. Nat. Sci. Proc.

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show abstract

“…This formulation is useful when it is difficult to collect sufficient anomalous samples in advance or to obtain all possible anomaly patterns. Real-world examples of such scenarios include video surveillance [1], medical diagnosis [2], equipment failure detection [3], and manufacturing inspection [4].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Anomaly Detection Using Style Distillation

Chung¹,

Park²,

Keum³

et al. 2020

IEEE Access

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“…Smart production monitoring is a fundamental activity in semiconductor manufacturing for quality [1], [2], control [3]- [5] and maintenance [6] purposes. Advanced Monitoring Systems (AMSs) aim at detecting anomalies and trends; anomalies are data patterns that have different data characteristics from normal instances [7], while trends are tendencies of production to move in a particular direction over time. A statistical definition of anomaly is outlier, an observation that is distant from other observations; on the other hand, non-anomaly observations are referred as inliers.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection through on-line isolation Forest: An application to plasma etching

Susto

Beghi²,

McLoone

2017

2017 28th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC)

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Advanced Monitoring Systems are fundamental in advanced manufacturing for control, quality and maintenance purposes. Nowadays, with the increasing availability of data in production and equipment, the need for high-dimensional Anomaly Detection techniques is thriving; anomalies are data patterns that have different data characteristics from normal production instances and that may be associated with faults or drifts in production. Tools for dealing with high-dimensional monitoring problems are provided by Machine Learning: in this paper, we test the performance of a state-of-the-art anomaly detection technique, called Isolation Forest, on a real industrial dataset related to Etching, one of the most important semiconductor manufacturing process. The monitoring has been performed exploiting Optical Spectroscopy Data.

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A One-Class SVM Based Tool for Machine Learning Novelty Detection in HVAC Chiller Systems

Cited by 56 publications

References 5 publications

A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers

A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers

Unsupervised Anomaly Detection Using Style Distillation

Anomaly detection through on-line isolation Forest: An application to plasma etching

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