Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning
An engine control system is responsible for controlling the combustion parameters of an internal combustion engine to increase the efficiency of the engine. An optimized parameter setting of an engine control system is highly influenced by the engine load. Therefore, with a change in engine load, th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/14/3172 |
| _version_ | 1798004992224788480 |
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| author | Syed Maaz Shahid Sunghoon Ko Sungoh Kwon |
| author_facet | Syed Maaz Shahid Sunghoon Ko Sungoh Kwon |
| author_sort | Syed Maaz Shahid |
| collection | DOAJ |
| description | An engine control system is responsible for controlling the combustion parameters of an internal combustion engine to increase the efficiency of the engine. An optimized parameter setting of an engine control system is highly influenced by the engine load. Therefore, with a change in engine load, the parameter settings need to be updated for higher engine efficiency. Hence, to optimize parameter settings during operation, engine load information is necessary. In this paper, we propose a real-time engine load classification from sensed signals. For the classification, an artificial neural network is used and trained using processed, real, measured data. To that end, a magnetic pickup sensor extracts the rotational speed of the prime mover of a four-stroke V12 marine diesel engine. The measured signal is then converted into a crank angle degree (CAD) signal that shows the behavior of the combustion strokes of firing cylinders at a particular engine load. The CAD signals are considered an input feature to the designed network for classification of engine loads. For verification, we considered five classes of engine load, and the trained network classifies these classes with an accuracy of 99.4%. |
| first_indexed | 2024-04-11T12:32:12Z |
| format | Article |
| id | doaj.art-4686eed7555948f69b85767134e0938c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:32:12Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-4686eed7555948f69b85767134e0938c2022-12-22T04:23:43ZengMDPI AGSensors1424-82202019-07-011914317210.3390/s19143172s19143172Real-Time Classification of Diesel Marine Engine Loads Using Machine LearningSyed Maaz Shahid0Sunghoon Ko1Sungoh Kwon2School of Electrical Engineering, University of Ulsan, Ulsan 44610, KoreaHyundai Heavy Industries, Ulsan 44032, KoreaSchool of Electrical Engineering, University of Ulsan, Ulsan 44610, KoreaAn engine control system is responsible for controlling the combustion parameters of an internal combustion engine to increase the efficiency of the engine. An optimized parameter setting of an engine control system is highly influenced by the engine load. Therefore, with a change in engine load, the parameter settings need to be updated for higher engine efficiency. Hence, to optimize parameter settings during operation, engine load information is necessary. In this paper, we propose a real-time engine load classification from sensed signals. For the classification, an artificial neural network is used and trained using processed, real, measured data. To that end, a magnetic pickup sensor extracts the rotational speed of the prime mover of a four-stroke V12 marine diesel engine. The measured signal is then converted into a crank angle degree (CAD) signal that shows the behavior of the combustion strokes of firing cylinders at a particular engine load. The CAD signals are considered an input feature to the designed network for classification of engine loads. For verification, we considered five classes of engine load, and the trained network classifies these classes with an accuracy of 99.4%.https://www.mdpi.com/1424-8220/19/14/3172diesel enginecylinder banksload classificationreal measured datacrank angleneural networkmachine learning |
| spellingShingle | Syed Maaz Shahid Sunghoon Ko Sungoh Kwon Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning Sensors diesel engine cylinder banks load classification real measured data crank angle neural network machine learning |
| title | Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning |
| title_full | Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning |
| title_fullStr | Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning |
| title_full_unstemmed | Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning |
| title_short | Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning |
| title_sort | real time classification of diesel marine engine loads using machine learning |
| topic | diesel engine cylinder banks load classification real measured data crank angle neural network machine learning |
| url | https://www.mdpi.com/1424-8220/19/14/3172 |
| work_keys_str_mv | AT syedmaazshahid realtimeclassificationofdieselmarineengineloadsusingmachinelearning AT sunghoonko realtimeclassificationofdieselmarineengineloadsusingmachinelearning AT sungohkwon realtimeclassificationofdieselmarineengineloadsusingmachinelearning |