Anomaly detection via distributed sensing: a VAR modeling approach

Abbracciavento, Francesco; Formentin, Simone; Balocco, Jacopo; Rota, Andrea; Manzoni, Vincenzo; Savaresi, Sergio M.

doi:10.1016/j.ifacol.2021.08.339

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Online anomaly detection using LSTM-based AE on multivariate time series data is explored for Smart Manufacturing in [15]. Additionally, [16] addresses anomaly detection and prevention in modern manufacturing processes by leveraging sensor data. The study focuses on scenarios with distributed time series measurements, employing Vector Autoregressive (VAR) modeling for multivariate time series analysis.…”

Section: Approaches To Unsupervised Degradation Monitoringmentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Preprint

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In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoders, showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

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Section: Approaches To Unsupervised Degradation Monitoringmentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Online anomaly detection using LSTM-based AE on multivariate time series data is explored for Smart Manufacturing in [ 22 ]. Additionally, ref [ 23 ] addresses anomaly detection and prevention in modern manufacturing processes by leveraging sensor data. The study focuses on scenarios with distributed time series measurements, employing Vector Autoregressive (VAR) modeling for multivariate time series analysis.…”

Section: Background and Related Workmentioning

confidence: 99%

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Choudhary,

Vuković,

Mutlu

et al. 2024

Sensors

View full text Add to dashboard Cite

In a dynamic production processes, mechanical degradation poses a significant challenge, impacting product quality and process efficiency. This paper explores a novel approach for monitoring degradation in the context of viscose fiber production, a highly dynamic manufacturing process. Using causal discovery techniques, our method allows domain experts to incorporate background knowledge into the creation of causal graphs. Further, it enhances the interpretability and increases the ability to identify potential problems via changes in causal relations over time. The case study employs a comprehensive analysis of the viscose fiber production process within a prominent textile industry, emphasizing the advantages of causal discovery for monitoring degradation. The results are compared with state-of-the-art methods, which are not considered to be interpretable, specifically LSTM-based autoencoder, UnSupervised Anomaly Detection on Multivariate Time Series (USAD), and Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data (TranAD), showcasing the alignment and validation of our approach. This paper provides valuable information on degradation monitoring strategies, demonstrating the efficacy of causal discovery in dynamic manufacturing environments. The findings contribute to the evolving landscape of process optimization and quality control.

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“…Seven studies depended solely on noise sensors [39]- [42], [60]- [63]. Five papers utilized only electric current sensors [38], [68]- [71], while two used only temperature sensors [83], [84]. Finally, two papers solely relied on pressure sensors [88], [89].…”

Section: What Are the Types Of Sensors And Variables Employed For Det...mentioning

confidence: 99%

“…Outlier detection was another common method for finding anomalies in sensor data, used in 16 primary studies. On the other hand, time-series-based and density estimate approaches were less frequently used, with only 2 [83], [90] and 1 [69] primary studies, respectively.…”

Section: ) Algorithmsmentioning

confidence: 99%

Anomaly Detection in Industrial Machinery Using IoT Devices and Machine Learning: A Systematic Mapping

Chevtchenko,

Rocha,

Santos

et al. 2023

IEEE Access

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Anomaly detection is critical in the smart industry for preventing equipment failure, reducing downtime, and improving safety. Internet of Things (IoT) has enabled the collection of large volumes of data from industrial machinery, providing a rich source of information for Anomaly Detection (AD). However, the volume and complexity of data generated by the Internet of Things ecosystems make it difficult for humans to detect anomalies manually. Machine learning (ML) algorithms can automate anomaly detection in industrial machinery by analyzing generated data. Besides, each technique has specific strengths and weaknesses based on the data nature and its corresponding systems. However, a large portion of the existing systematic mapping studies on AD primarily focus on addressing network and cybersecurity-related problems, with limited attention given to the industrial sector. Additionally, the related literature do not cover the challenges involved in using ML for AD in industrial machinery within the context of the IoT ecosystems. Therefore, this paper presents a systematic mapping study on AD for industrial machinery using IoT devices and ML algorithms to address this gap. Our primary objective is to investigate the use of ML models for anomaly detection within an industrial setting, particularly within IoT ecosystems. The study comprehensively evaluates 84 relevant studies spanning from 2016 to 2023, providing an extensive review of AD research. Our findings identify the most commonly used algorithms, preprocessing techniques, and sensor types. Additionally, this review identifies application areas and points to future challenges and research opportunities.

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Anomaly detection via distributed sensing: a VAR modeling approach

Cited by 5 publications

References 14 publications

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Interpretability of Causal Discovery in Tracking Deterioration in a Highly Dynamic Process

Anomaly Detection in Industrial Machinery Using IoT Devices and Machine Learning: A Systematic Mapping

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