Intelligent Fault Diagnosis for Online Condition Monitoring of MV Overhead Distribution Networks

Hashmi, G.Murtaza; Aljohani, Khalid; Kamarudin, Jawahir

doi:10.1109/icaaid51067.2022.9799512

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…However, as described earlier, the approach of interest in this paper instead concerns the study of anomalies introduced by the faulty item in the power grid. For example, in [35], the authors monitor the Medium Voltage (MV) overhead distribution network to detect and locate faults in place. Doing so makes it possible to signal the problem in advance, according to the predictive maintenance paradigm.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

Carratù,

Gallo,

Iacono

et al. 2023

IEEE Trans. Instrum. Meas.

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The recent development of highly automated machinery and intelligent industrial plants has increasingly enabled the continuous monitoring of their efficiency and condition, with the aim of maintaining high production efficiency and minimal malfunctions. Typical condition monitoring and fault detection applications are often achieved using acoustic and vibrational techniques, but the availability of distributed electrical measurements opens new opportunities for industrial fault detection with minimal impact on electrical systems. Even if artificial intelligence-based approaches can be used to model industrial equipment by means of measures made on electrical systems to which they are connected, machine learning algorithms have been demonstrated to be particularly adequate for this purpose due to the huge amount of data produced by interconnected sensors and devices. In this context, the aim of this work is to propose a new unsupervised analysis methodology for detecting anomalies in industrial machinery using electrical current values and other parameters measured on the power grid. The proposed framework is aimed at incorporating the advantages of Machine Learning algorithms and those of traditional analysis, optimizing their operation to improve performance and execution time; this also incorporates a methodology for analyzing the temporal dynamics of the anomaly based on Short-time Fourier transform to strengthen the performance of the detection. The results obtained showed excellent performance, both compared to the evaluations of a technical expert and to other methodologies used in the literature, with zero false positives detected in all datasets tested and a negligible number of undetected outlier events, less than 4% of the total in the datasets.

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Section: B Literature Reviewmentioning

confidence: 99%

A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

Carratù,

Gallo,

Iacono

et al. 2023

IEEE Trans. Instrum. Meas.

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“…Given the extremely general nature of Anomaly Detection, which can be applied to electrical signals, vibrations, and manufacturing shapes and materials, it has always been very difficult to identify features that would give high reliability in reporting anomalies [15]- [19]. "MACRO meets NANO in Measurement for Diagnostics, Optimization and Control" Delft, The Netherlands, September [21][22]2023 One of the most widely used Anomaly Detection modes for monitoring industrial machinery is monitoring power grid loads. In particular, it is possible to monitor the operating status of three-phase electric motors, which are widely used in certain industrial processing and are characterized by an ohmic-inductive load [21]- [23].…”

Section: Introductionmentioning

confidence: 99%

“…"MACRO meets NANO in Measurement for Diagnostics, Optimization and Control" Delft, The Netherlands, September [21][22]2023 One of the most widely used Anomaly Detection modes for monitoring industrial machinery is monitoring power grid loads. In particular, it is possible to monitor the operating status of three-phase electric motors, which are widely used in certain industrial processing and are characterized by an ohmic-inductive load [21]- [23]. An example of anomalous Time Series is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A deep learning method for current anomaly detection

Carratù,

Gallo,

Pietrosanto

et al. 2023

Proceedings of the 19th IMEKO TC10 International Conference on Measurement for Diagnostics, Optimization and Control on Measure

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The development and spread of Machine Learning methodologies have also involved the field of anomaly detection, particularly focused on fault detection. This is one of the main goals of Industry 4.0, as it is necessary to optimize repair time and cost. In this regard, Machine Learning is needed to identify precursor features of possible failures that would be difficult for a human operator to discern. However, compared to Deep Learning methodologies, these cannot be fully automatic because of the need to make choices about the features identified by the system. This paper aims to propose a Machine Learning-based system for detecting electrical anomalies attributable to malfunctions in connected industrial machinery. Specifically, the proposal is a fusion of unsupervised learning and traditional methodology to minimize human intervention while maintaining an explainable, white-box approach, contrary to proposals based on Deep Learning. The results demonstrated better performance to techniques of the state of the art.

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Intelligent Fault Diagnosis for Online Condition Monitoring of MV Overhead Distribution Networks

Cited by 2 publications

References 6 publications

A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

A Novel Methodology for Unsupervised Anomaly Detection in Industrial Electrical Systems

A deep learning method for current anomaly detection

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