What is it about?
In this paper a 12-cylinder trainset diesel engine is investigated. The vibration of the engine is measured by two sensors. Then, the features of the signals are analyzed by a MLP neural network and wavelet transform. Several fault scenarios are considered and results show high accuracy of the method in fault detection.
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Why is it important?
As we know there is not any similar work on 12-cylinder diesel engine. Also, the data-set contains injector faults, and it contains engine without load and engine with a generator load. In addition, the results of wavelet and MLP neural network are compared.
Perspectives
In our opinion, the proposed method of this paper could be developed to classify a wider range of faults in future works.
Moosa Ayati
University of Tehran
Read the Original
This page is a summary of: Fault detection and diagnosis of a 12-cylinder trainset diesel engine based on vibration signature analysis and neural network, Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science, June 2018, SAGE Publications,
DOI: 10.1177/0954406218778313.
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