Fault diagnosis of marine 4-stroke diesel engines using a one-vs-one extreme learning ensemble. (January 2017)

• Record Type: Journal Article
• Authors: Kowalski, Jerzy; Krawczyk, Bartosz; Woźniak, Michał
• Is Part Of: Engineering applications of artificial intelligence. Volume 57(2016:Sep.)
• Page Start: 134
• Page End: 141
• Publication Date: 2017-01
• Subjects: Marine engine -- Fault diagnosis -- Fault detection -- Diesel engine -- Machine learning -- Ensemble learning -- Extreme learning machines -- Multi-class decomposition; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.10.015
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper proposes a novel approach for intelligent fault diagnosis for stroke Diesel marine engines, which are commonly used in on-road and marine transportation. The safety and reliability of a ship's work rely strongly on the performance of such an engine; therefore, early detection of any type of failure that affects the engine is of crucial importance. Automatic diagnostic systems are of special importance because they can operate continuously in real time, thereby providing efficient monitoring of the engine's performance. We introduce a fully automatic machine learning-based system for engine fault detection. For this purpose, we monitor various signals that are emitted by the engine, and we use them as an input for a pattern classification algorithm. This action is realized by an ensemble of Extreme Learning Machines that work in a decomposition mode. Because we address 14 different faults and a correct operation mode, we must handle a 15-class problem. We tackle this task by binarization in one-vs-one mode, where each Extreme Learning Machine is trained on a pair of classes. Next, Error-Correcting Output Codes are used to reconstruct the original multi-class task. The results from experiments that were conducted on a real-life dataset demonstrate that the proposed approach delivers superior classification accuracy and a low response time in comparison with a number of state-of-the-art methods and thus is a suitable choice for a real-life implementation on board a ship.