Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle

A Namigtle-Jiménez; R F Escobar-Jiménez; J F Gómez-Aguilar; C D García-Beltrán; A C Téllez-Anguiano

doi:10.1016/j.isatra.2019.11.003

Online ANN-based fault diagnosis implementation using an FPGA: Application in the EFI system of a vehicle

ISA Trans. 2020 May:100:358-372. doi: 10.1016/j.isatra.2019.11.003. Epub 2019 Nov 11.

Authors

A Namigtle-Jiménez¹, R F Escobar-Jiménez², J F Gómez-Aguilar³, C D García-Beltrán⁴, A C Téllez-Anguiano⁵

Affiliations

¹ Posgrado del Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico.
² Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico. Electronic address: esjiri@cenidet.edu.mx.
³ Conacyt-Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico.
⁴ Tecnológico Nacional de México / CENIDET. Int. Internado Palmira S/N, Palmira C.P.62490, Cuernavaca, Morelos, Mexico.
⁵ Tecnológico Nacional de México / Instituto Tecnológico de Morelia, Av. Tecnológico 1500. Col. Lomas de Santiaguito. C.P. 58120, Morelia, Mich., Mexico.

PMID: 31733892
DOI: 10.1016/j.isatra.2019.11.003

Abstract

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.

Keywords: Artificial neural networks; Electronic fuel injection rail system; FPGA; Fault detection and diagnosis scheme.