Identification of Aging Effects in Common Rail Diesel Injectors Using Geometric Classifiers and Neural Networks

Aging effects such as coking or cavitation in the nozzle of common rail (CR) diesel injectors deteriorate combustion performance. This is of particular relevance when it comes to complying with emission legislation and demonstrates the need for detecting and compensating aging effects during operation. The first objective of this paper is to analyze the influence of worn nozzles on the injection rate. Therefore, measurements of commercial solenoid common rail diesel injectors with different nozzles are carried out using an injection rate analyzer of the Bosch type. Furthermore, a fault model for typical aging effects in the nozzle of the injector is presented together with two methods to detect and identify these effects. Both methods are based on a multi-domain simulation model of the injector. The needle lift, the control piston lift and the pressure in the lower feed line are used for the fault diagnosis. The first method is based on signal characteristics in the time domain to generate normalized reference maps. For determining the aging parameters a geometric classification method is used. The second method is a multi-net implementation of an artificial neural network. The methods show good results regarding the detection and identification of the investigated aging effects and will therefore be the basis for the development of compensation methods in future investigations.