Improving ship yard ballast pumps’ operations: A PCA approach to predictive maintenance. Kimera, D. & Nangolo, F. N. Maritime Transport Research, 1:100003, January, 2020. ![Improving ship yard ballast pumps’ operations: A PCA approach to predictive maintenance [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper investigates a predictive maintenance approach for marine mechanical systems via an early warning system. A machine learning methodology was used to process and analyze the dock pump back pressure, flow rate, amperage and suction pressure data. Operating parameters for a dock pump were monitored for 40 weeks and the values were manually input into the tool. Unsupervised machine learning was used in order to draw inferences from data via MATLAB. A principal component analysis (PCA) algorithm was used to improve on the selection of the key operating parameters of the dock pumps. The dock pump flow rate and suction pressure, were the principal components that were 99.707% sufficient to explain the variation in the data. Using the dataset explained by the PCA, two data classes were later used in the SVM algorithm for a binary classification approach. The developed tool predicted that the dock pump may fail/requires maintenance between seventh and eighth weeks. This prediction deviated from the actual ten weeks that it took the dock pump to fail. A prediction deviation from the actual failure time to failure could be attributed to the quality of the historical failure and maintenance data. Nevertheless, with less ambiguity of the data, the maintenance prediction tool can be used as a basis before sensor technology on the dock pumps is implemented.
@article{kimera_improving_2020,
title = {Improving ship yard ballast pumps’ operations: {A} {PCA} approach to predictive maintenance},
volume = {1},
issn = {2666-822X},
shorttitle = {Improving ship yard ballast pumps’ operations},
url = {http://www.sciencedirect.com/science/article/pii/S2666822X20300034},
doi = {10.1016/j.martra.2020.100003},
abstract = {This paper investigates a predictive maintenance approach for marine mechanical systems via an early warning system. A machine learning methodology was used to process and analyze the dock pump back pressure, flow rate, amperage and suction pressure data. Operating parameters for a dock pump were monitored for 40 weeks and the values were manually input into the tool. Unsupervised machine learning was used in order to draw inferences from data via MATLAB. A principal component analysis (PCA) algorithm was used to improve on the selection of the key operating parameters of the dock pumps. The dock pump flow rate and suction pressure, were the principal components that were 99.707\% sufficient to explain the variation in the data. Using the dataset explained by the PCA, two data classes were later used in the SVM algorithm for a binary classification approach. The developed tool predicted that the dock pump may fail/requires maintenance between seventh and eighth weeks. This prediction deviated from the actual ten weeks that it took the dock pump to fail. A prediction deviation from the actual failure time to failure could be attributed to the quality of the historical failure and maintenance data. Nevertheless, with less ambiguity of the data, the maintenance prediction tool can be used as a basis before sensor technology on the dock pumps is implemented.},
language = {en},
urldate = {2020-11-16},
journal = {Maritime Transport Research},
author = {Kimera, David and Nangolo, Filemon N.},
month = jan,
year = {2020},
keywords = {Ballast pumps, Floating docks, Machine learning, Predictive maintenance, Principle component analysis},
pages = {100003},
}
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Unsupervised machine learning was used in order to draw inferences from data via MATLAB. A principal component analysis (PCA) algorithm was used to improve on the selection of the key operating parameters of the dock pumps. The dock pump flow rate and suction pressure, were the principal components that were 99.707% sufficient to explain the variation in the data. Using the dataset explained by the PCA, two data classes were later used in the SVM algorithm for a binary classification approach. The developed tool predicted that the dock pump may fail/requires maintenance between seventh and eighth weeks. This prediction deviated from the actual ten weeks that it took the dock pump to fail. A prediction deviation from the actual failure time to failure could be attributed to the quality of the historical failure and maintenance data. Nevertheless, with less ambiguity of the data, the maintenance prediction tool can be used as a basis before sensor technology on the dock pumps is implemented.","language":"en","urldate":"2020-11-16","journal":"Maritime Transport Research","author":[{"propositions":[],"lastnames":["Kimera"],"firstnames":["David"],"suffixes":[]},{"propositions":[],"lastnames":["Nangolo"],"firstnames":["Filemon","N."],"suffixes":[]}],"month":"January","year":"2020","keywords":"Ballast pumps, Floating docks, Machine learning, Predictive maintenance, Principle component analysis","pages":"100003","bibtex":"@article{kimera_improving_2020,\n\ttitle = {Improving ship yard ballast pumps’ operations: {A} {PCA} approach to predictive maintenance},\n\tvolume = {1},\n\tissn = {2666-822X},\n\tshorttitle = {Improving ship yard ballast pumps’ operations},\n\turl = {http://www.sciencedirect.com/science/article/pii/S2666822X20300034},\n\tdoi = {10.1016/j.martra.2020.100003},\n\tabstract = {This paper investigates a predictive maintenance approach for marine mechanical systems via an early warning system. A machine learning methodology was used to process and analyze the dock pump back pressure, flow rate, amperage and suction pressure data. Operating parameters for a dock pump were monitored for 40 weeks and the values were manually input into the tool. Unsupervised machine learning was used in order to draw inferences from data via MATLAB. A principal component analysis (PCA) algorithm was used to improve on the selection of the key operating parameters of the dock pumps. The dock pump flow rate and suction pressure, were the principal components that were 99.707\\% sufficient to explain the variation in the data. Using the dataset explained by the PCA, two data classes were later used in the SVM algorithm for a binary classification approach. The developed tool predicted that the dock pump may fail/requires maintenance between seventh and eighth weeks. This prediction deviated from the actual ten weeks that it took the dock pump to fail. 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