Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

Somehsaraei, Homam Nikpey; Ghosh, Susmita; Maity, Sayantan; Pramanik, Payel; De, S. K.; Assadi, Mohsen

doi:10.3390/en13143750

Cited by 13 publications

(8 citation statements)

References 25 publications

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“…Possible applications of DBSCAN are anomaly detection for aircraft trajectories [24][25][26] or fault detection of wind turbines [27]. In [28], the DBSCAN algorithm is used for identifying anomalies within engine data in order to exclude the corresponding data points from data-driven model building. One major drawback of DBSCAN is the difficulty to determine appropriate hyperparameters for multi-variate datasets [24], especially if several clusters exist with varying densities.…”

Section: Fault Detectionmentioning

confidence: 99%

Steady-State Fault Detection with Full-Flight Data

et al. 2022

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Aircraft engine condition monitoring is a key technology for increasing safety and reducing maintenance expenses. Current engine condition monitoring approaches use a minimum of one steady-state snapshot per flight. Whilst being appropriate for trending gradual engine deterioration, snapshots result in a detrimental latency in fault detection. The increased availability of non-mandatory data acquisition hardware in modern airplanes provides so-called full-flight data sampled continuously during flight. These datasets enable the detection of engine faults within one flight by deriving a statistically relevant set of steady-state data points, thus, allowing the application of machine-learning approaches. It is shown that low-pass filtering before steady-state detection significantly increases the success rate in detecting steady-state data points. The application of Principal Component Analysis halves the number of relevant dimensions and provides a coordinate system of principal components retaining most of the variance. Consequently, clusters of data points with and without engine fault can be separated visually and numerically using a One-Class Support Vector Machine. High detection rates are demonstrated for various component faults and even for a minimum instrumentation suite using synthesized datasets derived from full-flight data of commercially operated flights. In addition to the tests conducted with synthesized data, the algorithm is verified based on operational in-flight measurements providing a proof-of-concept. Consequently, the availability of continuously sampled in-flight measurements combined with machine-learning methods allows fault detection within a single flight.

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Section: Fault Detectionmentioning

confidence: 99%

Steady-State Fault Detection with Full-Flight Data

et al. 2022

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“…A DBSCAN clustering method was proposed in [58] to detect and filter outliers. The results showed that the introduced filtering method is reliable and fast, minimizing the time and resources for data processing.…”

Section: Anomaly Detectionmentioning

confidence: 99%

“…Weizhong Yan [62] 2016 ELM Combustor anomaly detection Hui Luo and Shisheng Zhong [61] 2017 AE Improvement of anomaly detection performance Homam Nikpey Somehsaraei et al [58] 2020 ANN Enhancing model performance by filtering and detecting outliers Geunbae Lee et al [59] 2020 CAE Anomalies' identification in a monitoring system Weizhong Yan [63] 2020 ELM Enhancement of anomaly detection in a combustor Rui Xu and Weizhong Yan [64] 2020 GAN Faulty data detection Song Fu et al [60] 2021 R-DAE Anomaly detection improvement with a novel model…”

Section: Year ML Model Applicationmentioning

confidence: 99%

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

2021

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Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machine-learning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.

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“…Then, DBSCAN iteratively finds the direct density-reachable and density-reachable objects from the core object. Points that do not belong to any cluster are considered outliers [26]. The key to the algorithm is how to determine MinPts and Eps adaptively.…”

Section: Dbscan Algorithmmentioning

confidence: 99%

A Quantification Method for Supraharmonic Emissions Based on Outlier Detection Algorithms

et al. 2021

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Based on outlier detection algorithms, a feasible quantification method for supraharmonic emission signals is presented. It is designed to tackle the requirements of high-resolution and low data volume simultaneously in the frequency domain. The proposed method was developed from the skewed distribution data model and the self-tuning parameters of density-based spatial clustering of applications with noise (DBSCAN) algorithm. Specifically, the data distribution of the supraharmonic band was analyzed first by the Jarque–Bera test. The threshold was determined based on the distribution model to filter out noise. Subsequently, the DBSCAN clustering algorithm parameters were adjusted automatically, according to the k-dist curve slope variation and the dichotomy parameter seeking algorithm, followed by the clustering. The supraharmonic emission points were analyzed as outliers. Finally, simulated and experimental data were applied to verify the effectiveness of the proposed method. On the basis of the detection results, a spectrum with the same resolution as the original spectrum was obtained. The amount of data declined by more than three orders of magnitude compared to the original spectrum. The presented method will benefit the analysis of quantification for the amplitude and frequency of supraharmonic emissions.

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Automated Data Filtering Approach for ANN Modeling of Distributed Energy Systems: Exploring the Application of Machine Learning

Cited by 13 publications

References 25 publications

Steady-State Fault Detection with Full-Flight Data

Steady-State Fault Detection with Full-Flight Data

Machine-Learning-Based Condition Assessment of Gas Turbines—A Review

A Quantification Method for Supraharmonic Emissions Based on Outlier Detection Algorithms

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