Model Based Automated Diagnosis of Bearing Knock Faults in Internal Combustion Engines

Engine exhaust backpressure is a critical parameter in the calculation of the volumetric efficiency and exhaust gas recirculation flow of an internal combustion engine. The backpressure also needs to be controlled to a presetting limit under high speed and load engine operating conditions to avoid damaging a turbocharger. In this paper, a method is developed to estimate exhaust pressure for internal combustion engines equipped with variable geometry turbochargers. The method uses a model-based approach that applies a coordinate transformation to generate a turbine map for the estimation of exhaust pressure. This estimation can substitute for an expensive pressure sensor, thus saving significant cost for production vehicles. On the other hand, for internal combustion engines that have already installed exhaust pressure sensors, this estimation can be used to generate residual signals for model-based diagnostics. Cumulative sum algorithms are applied to residuals based on multiple sensor fusion, and with the help of signal processing, the algorithms are able to detect and isolate critical failure modes of a turbocharger system.

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Automatic Evaluation of Internal Combustion Engine Noise Based on an Auditory Model

Shock and Vibration ◽

10.1155/2019/2898219 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Kai Liang ◽

Haijun Zhao

Keyword(s):

Internal Combustion Engines ◽

Quality Evaluation ◽

Evaluation Model ◽

Objective Evaluation ◽

Internal Combustion ◽

Sound Quality ◽

Classification Model ◽

Combustion Engines ◽

Model Based ◽

Quality Evaluation Model

To improve the accuracy and efficiency of the objective evaluation of noise quality from internal combustion engines, an automatic noise quality classification model was constructed by introducing an auditory model-based acoustic spectrum analysis method and a convolutional neural network (CNN) model. A band-pass filter was also designed in the model to automatically extract the features of the noise samples, which were later used as input data. The adaptive moment estimation (Adam) algorithm was used to optimize the weights of each layer in the network, and the model was used to evaluate sound quality. To evaluate the predictive performance of the CNN model based on the auditory input, a back propagation (BP) sound quality evaluation model based on psychoacoustic parameters was constructed and used as a control. When processing the label values of the samples, the correlation between the psychoacoustic parameters of the objective evaluation and evaluation scores was analyzed. Four psychoacoustic parameters with the greatest correlation with subjective evaluation results were selected as the input values of the BP model. The results showed that the sound quality evaluation model based on the CNN could predict the sound quality of internal combustion engines more accurately, and the input evaluation score based on the auditory spectrum in the CNN classification model was more accurate than the short-time average energy input evaluation score based on the time domain.

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