基于大数据分析的大型燃气发动机实时失火检测

2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY) Pub Date : 2018-09-01 DOI:10.1109/SISY.2018.8524725

J. Z. Szabó, P. Bakucz

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引用次数: 1

摘要

本文介绍了模糊深度学习算法在Deutz mwm8缸大型燃气发动机振动信号分类中的应用。本文的主要贡献是识别了实时嵌入式微控制器环境下的失火特性。调度模糊系统的实质是一个分层的深度神经网络，它从模糊和神经表示中获取信息。从这两种不同的观点中学习到的失火特征被卷积，形成最终的分类信号。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Real-Time Misfire Detection of Large Gas Engine Using Big Data Analytics

In this work application of fuzzy deep learning algorithm to classify vibration signals of a Deutz MWM 8 cylinder large gas engine is presented. The main contribution is to identify the misfire characteristics in realtime embedded microcontroller environment. The substance of the scheduled fuzzy system is a stratified deep neural network that obtains information from both fuzzy and neural representations. The misfire characteristics learnt from these two various views are convolved, forming the ultimate signal to be classified.

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2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY)

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