Namigtle-Jiménez A, Escobar-Jiménez R F, Gómez-Aguilar J F, García-Beltrán C D, Téllez-Anguiano A C
Posgrado del Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico.
Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico.
ISA Trans. 2020 May;100:358-372. doi: 10.1016/j.isatra.2019.11.003. Epub 2019 Nov 11.
In this research, fault detection and diagnosis (FDD) scheme for isolating the damaged injector of an internal combustion engine is formulated and experimentally applied. The FDD scheme is based on a temporal analysis (statistical methods), as well as in a frequency analysis (fast Fourier transform) of the fuel rail pressure. The arrangement of the scheme consists of three coupled artificial neural networks (ANNs) to classify the faulty injector correctly. The ANNs were trained considering five different scenarios, one scenario without fault in the injection system, and the other four scenarios represent a fault per injector (1 to 4). The Levenberg-Marquardt (LM), BFGS quasi-Newton, gradient descent (GD), and extreme learning machine (ELM) algorithms were tested to select the best training algorithm to classify the faults. Experimental results obtained from the implementation in a VW four-cylinder CBU 2.5L vehicle in idle operating conditions (800 rpm) show the effectiveness of the proposed FDD scheme.
在本研究中，制定了用于隔离内燃机损坏喷油器的故障检测与诊断（FDD）方案并进行了实验应用。该FDD方案基于时间分析（统计方法）以及燃油轨压力的频率分析（快速傅里叶变换）。该方案的架构由三个耦合的人工神经网络（ANN）组成，用于正确分类故障喷油器。考虑了五种不同的工况对人工神经网络进行训练，一种工况是喷射系统无故障，另外四种工况分别代表每个喷油器出现一种故障（1至4号喷油器）。对Levenberg-Marquardt（LM）、BFGS拟牛顿法、梯度下降（GD）和极限学习机（ELM）算法进行了测试，以选择用于故障分类的最佳训练算法。在大众四缸CBU 2.5L车辆怠速运行条件（800转/分钟）下实施该方案所获得的实验结果表明了所提出的FDD方案的有效性。
