Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologies
Filtering is the process of defining, recognizing, and correcting flaws in given data so that the influence of inaccuracies in input data on subsequent studies is minimized. This paper aims to discuss the characteristics of some filtering methods from various topics. Wavelet transform and frequency...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
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| Series: | Maritime Transport Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666822X22000132 |
| _version_ | 1811295239925661696 |
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| author | Januwar Hadi Dimitrios Konovessis Zhi Yung Tay |
| author_facet | Januwar Hadi Dimitrios Konovessis Zhi Yung Tay |
| author_sort | Januwar Hadi |
| collection | DOAJ |
| description | Filtering is the process of defining, recognizing, and correcting flaws in given data so that the influence of inaccuracies in input data on subsequent studies is minimized. This paper aims to discuss the characteristics of some filtering methods from various topics. Wavelet transform and frequency (Fourier) transform are considered for the decomposition methodologies whereas descriptive statistics is used for the statistical methodology. The Kalman filter and autoencoder neural network are also explored for the predictive methodologies. All the aforementioned methodologies are discussed empirically using two metrics of R-squared and mean absolute error. This paper aims to study the effectiveness of these filtering techniques in filtering noisy data collected from mass flowmeter reading in an unconventional situation i.e., on a tugboat while in operation to measure fuel consumption. Finally, the performance of various filtering methods is assessed, and their effectiveness in filtering noisy data is compared and discussed. It is found that the Haar wavelet transforms, Kalman filter and the descriptive statistics have a better performance as compared to their counterparts in filtering out spikes found in the mass flow data. |
| first_indexed | 2024-04-13T05:29:40Z |
| format | Article |
| id | doaj.art-a30a4a3d1fa343148aa4b875f3069313 |
| institution | Directory Open Access Journal |
| issn | 2666-822X |
| language | English |
| last_indexed | 2024-04-13T05:29:40Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Maritime Transport Research |
| spelling | doaj.art-a30a4a3d1fa343148aa4b875f30693132022-12-22T03:00:29ZengElsevierMaritime Transport Research2666-822X2022-01-013100063Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologiesJanuwar Hadi0Dimitrios Konovessis1Zhi Yung Tay2Engineering Cluster, Singapore Institute of Technology, 10 Dover Drive, 534038 SingaporeDepartment of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, 100 Montrose St, Glasgow G4 0LZ, United KingdomEngineering Cluster, Singapore Institute of Technology, 10 Dover Drive, 534038 Singapore; Corresponding author.Filtering is the process of defining, recognizing, and correcting flaws in given data so that the influence of inaccuracies in input data on subsequent studies is minimized. This paper aims to discuss the characteristics of some filtering methods from various topics. Wavelet transform and frequency (Fourier) transform are considered for the decomposition methodologies whereas descriptive statistics is used for the statistical methodology. The Kalman filter and autoencoder neural network are also explored for the predictive methodologies. All the aforementioned methodologies are discussed empirically using two metrics of R-squared and mean absolute error. This paper aims to study the effectiveness of these filtering techniques in filtering noisy data collected from mass flowmeter reading in an unconventional situation i.e., on a tugboat while in operation to measure fuel consumption. Finally, the performance of various filtering methods is assessed, and their effectiveness in filtering noisy data is compared and discussed. It is found that the Haar wavelet transforms, Kalman filter and the descriptive statistics have a better performance as compared to their counterparts in filtering out spikes found in the mass flow data.http://www.sciencedirect.com/science/article/pii/S2666822X22000132Fuel oil consumptionFuel efficiencyClimate changeData filteringStatistical analysisDecomposition |
| spellingShingle | Januwar Hadi Dimitrios Konovessis Zhi Yung Tay Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologies Maritime Transport Research Fuel oil consumption Fuel efficiency Climate change Data filtering Statistical analysis Decomposition |
| title | Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologies |
| title_full | Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologies |
| title_fullStr | Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologies |
| title_full_unstemmed | Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologies |
| title_short | Filtering harbor craft vessels’ fuel data using statistical, decomposition, and predictive methodologies |
| title_sort | filtering harbor craft vessels fuel data using statistical decomposition and predictive methodologies |
| topic | Fuel oil consumption Fuel efficiency Climate change Data filtering Statistical analysis Decomposition |
| url | http://www.sciencedirect.com/science/article/pii/S2666822X22000132 |
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