A Fault Diagnosis Method for Valve Train of Diesel Engine Considering Incomplete Feature Set

2019 ◽  
Author(s):  
Zhinong Jiang ◽  
Yuehua Lai ◽  
Zijia Wang ◽  
Jinjie Zhang
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Valve Train ◽  
Diagnosis Method

Research on the Fault Diagnosis Technology of Diesel Engine Based on the Instantaneous Speed

2009 ◽  
Vol 413-414 ◽  
pp. 547-552 ◽  
Author(s):  
Yi Hu ◽  
Rui Ping Zhou ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Vibration Amplitude ◽  
Good Effect ◽  
Cylinder Pressure ◽  
Angular Vibration ◽  
Diagnosis Method ◽  
Som Neural Network ◽  
Instantaneous Speed

The instantaneous speed signals of diesel contain lots of information about machine states, which is useful for fault diagnosis of diesel engine. Mixed fault diagnosis method of diesel engine based on the instantaneous speed has been proposed, which combines with the lower order angular vibration amplitude and SOM neural network to diagnose the cylinder pressure fault, then extracts three feature parameters of instantaneous speed to locate the fault cylinder. The method can detect the cylinder pressure fault accurately in diesel engine and locate the fault cylinder. The experimental confirmation shows that it has good effect on fault diagnosis of diesel engine.

scholarly journals A Variational Stacked Autoencoder with Harmony Search Optimizer for Valve Train Fault Diagnosis of Diesel Engine

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 223 ◽  
Author(s):  
Kun Chen ◽  
Zhiwei Mao ◽  
Haipeng Zhao ◽  
Zhinong Jiang ◽  
Jinjie Zhang
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Harmony Search ◽  
Operating Conditions ◽  
Valve Train ◽  
Fine Tuning ◽  
Stacked Autoencoder ◽  
Adaptive Adjustment ◽  
High Level ◽  
Coupling Fault

Diesel engine fault diagnosis is vital due to enhanced reliability and economic efficiency requirements. The extracted features in traditional fault diagnosis are constructed manually, which is very cumbersome because of the requirement for lots of expertise. To handle this issue, this paper proposed a variational stacked autoencoder (VSAE) to adaptively extract features from angular domain signals. As an unsupervised algorithm, VSAE can extract high-level features with the help of multiple encoding layers. Layer-wise pre-training and fine-tuning are introduced to get a better network initialization value. Moreover, the dropout technique and the batch normalization technique are carried out to prevent over-fitting and implement fast convergence. Finally, the harmony search optimizer (HSO) algorithm is introduced to get an appropriate hyper-parameter setting in the VSAE model, as well as make adaptive adjustment of the network structure. In order to verify the proposed method, the valve train fault data is collected on the diesel engine test rig under twelve operating conditions. The results indicate that the proposed scheme can effectively diagnose different degrees of intake valve fault, exhaust valve fault, and coupling fault under various operating conditions. Furthermore, the classification accuracy improved from 94.10% to 98.85%VSAE compared with stacked autoencoder (SAE) and some other traditional fault diagnosis algorithms.

Diesel Engine Fault Diagnosis Method Based on GA-PNN

1969 ◽  
Vol 9 (11) ◽  
Author(s):  
Manjiang Hu ◽  
Hongmao Qing ◽  
Long Ying ◽  
Renkai Ding
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Diagnosis Method

The Engine Fault Diagnosis with RBFNN Based on AIC

2012 ◽  
Vol 468-471 ◽  
pp. 1066-1069
Author(s):  
Qiang Huang ◽  
Xiao Zhuo Ouyang ◽  
Cheng Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Rbf Neural Network ◽  
Information Criterion ◽  
Vibration Signals ◽  
The Neural Network ◽  
Rotary Machines ◽  
Diagnosis Method ◽  
Diagnosis Model

In this paper, an engine diagnosis method with high precision and quickly response is proposed. Firstly, the Akaike Information Criterion (AIC) is used to improve the performance of the neural network to build the fault diagnosis model. Then the vibration signals are analyzed to estimate the states of the diesel engine. Finally, the five states of diesel engine are set to validate the veracity of diagnosis method. According to experiment and simulation researches, it indicates that the diagnosis method with RBF neural network based on AIC is effective. The veracity of identification is 100% to the single fault. It is a valuable reference to the vibration diagnosis for other complex rotary machines.

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