scholarly journals Monitoring of Valve Gap in Diesel Engine Based on Vibration Response Feature Extraction

2021 ◽  
Vol 33 (x) ◽  
pp. 1
Author(s):  
Chaoming Huang ◽  
Jie Li ◽  
Xin Wang ◽  
Jianbin Liao ◽  
Hongliang Yu ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Diesel Engine ◽  
Vibration Response ◽  
Response Feature

Characteristic Study on the Dynamic Response of Block Based on the EMD Technique of Feature Extraction

2011 ◽  
Vol 88-89 ◽  
pp. 93-98
Author(s):  
Xian Feng Du ◽  
Zhi Jun Li ◽  
Fong Rong Bi ◽  
Jun Hong Zhang ◽  
Xia Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Original Data ◽  
Response Characteristic ◽  
Vibration Response ◽  
Engine Block ◽  
Intrinsic Mode Functions ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Block Based ◽  
The Fourier Transform

A feature extraction method for engine block using the empirical mode decomposition (EMD) technique has been proposed in this paper. The EMD technique is developed to break the limitations of conventional signal processing techniques in some extent and to perform further decomposition of signals. In order to extract feature information of engine block, the vibration response will first be processed by the EMD to generate the intrinsic mode functions (IMFs), and then identified by the Fourier transform. Then the same procedure will be adopted to extract the vibration response characteristic from FEM model of block, which is compared between the original and improved engine block. To verify the feasibility of such an approach, the vibration response generated by the finite element simulation will be analyzed, with results compared with the experimental ones. The results demonstrated that the EMD technique made the vibration characteristic more visible by the sifting process, and using the IMFs computed from vibration response, rather than based on the original data, the vibration sources of engine can be successfully identified. And we can also further confirm the structural weak regions of engine block and the main vibration sources, which are benefited to the engine block optimization.

The Research on Weak Fault Time Domain Feature Extraction of Diesel Engine Instantaneous Speed

2014 ◽  
Vol 687-691 ◽  
pp. 1026-1029
Author(s):  
Yun Ling Sun ◽  
Sheng Jie Wang ◽  
Hong Xiang Tian
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Time Domain ◽  
Information Source ◽  
Mathematic Model ◽  
Exhaust Valve ◽  
Occurrence Rate ◽  
Weak Fault ◽  
Instantaneous Speed

Instantaneous speed signal is a new information source for diesel fault diagnosis. It has been used in critical fault diagnosis successfully such as misfire. But the occurrence rate of early weak diesel fault is high, and can’t be diagnosed easily by other information source, so it has high value to research on the weak fault feature extraction method of transient speed. Due to the difficulty in setting up effect and accurate mathematic model for diesel engine instantaneous speed under weak fault conditions, so in this paper the early fault simulated experiments are proceed in 6-135 type diesels. Many early faults are simulated by adjusting cylinder fuel eject quantity, adjusting exhaust valve clearance, simulating exhaust valve leak in differ degree. Through analyzing the variation rule of instantaneous speed signal in different status, the direct time domain fault feature is extracted. It is the instantaneous speed unitary rise amplitude ratio of every cylinder, the value of each cylinder is named as Pi. The experiments outcome shows that if Pi descend over 10%, then the No.i cylinder exist fault. The feature is very sensitive to some fault which influence gas press in cylinder such as fuel ejects quantity not uniformity and valve leaking.

Study on Feature Extraction Method for Diesel Engine Valve Faults

2016 ◽  
Author(s):  
Hongzi Fei ◽  
Long Liu ◽  
Xuemin Li ◽  
Xiuzhen Ma
Keyword(s):  
Feature Extraction ◽  
Diesel Engine ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Stationary Characteristic ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Fault Features ◽  
Empirical Mode Decomposition Method ◽  
Faults Diagnosis

Valve faults diagnosis technique of a diesel engine is studied deeply in this paper. The experiment of valve clearance and air leakage faults are done in a diesel engine, and cylinder head vibration and transient speed signals are measured synchronously on normal and fault conditions respectively. These signals are used to feature extraction. In order to avoid the leakage and aliasing of vibration signal’s frequent spectrum, resample method based on order tracking is proposed, and vibration signal was transformed from time domain to crank angle domain accurately. Considering the non-stationary characteristic of vibration signal, a series of intrinsic mode functions with different scales were obtained using the empirical mode decomposition method, and fault features parameters were extracted through 3D Hilbert spectrums of the intrinsic mode functions. Experimental results show that the method can effectively extract fault features of diesel engine and use them to realize the valve system faults diagnosis further.

scholarly journals Analysis of torsional vibration effect on the diesel engine block vibration

2020 ◽  
Vol 21 (5) ◽  
pp. 522
Author(s):  
Shiwei Ni ◽  
Yibin Guo ◽  
Binglin Lv ◽  
Donghua Wang ◽  
Wanyou Li ◽  
...  
Keyword(s):  
Finite Element Method ◽  
Diesel Engine ◽  
Torsional Vibration ◽  
Coupling Effect ◽  
Fem Simulation ◽  
Vibration Response ◽  
Engine Block ◽  
Torsional Vibrations ◽  
Low Speed ◽  
Vibration Effect

In this paper, the coupling effect between the crankshaft torsional vibration and the low speed diesel engine block vibration is investigated. Using finite element method (FEM), a model of low speed diesel engine is established to study the vibration response. The mode results of FEM agree very well with the test. The additional torques acting on the low-speed diesel engine caused by torsional vibration are discussed in detail. The vibration response of the low-speed diesel engine considering the effect of torsional vibration is studied using FEM simulation. By analyzing the effect of torsional vibration, the results demonstrate that the crankshaft torsional vibrations have important effects on the engine block vibration. The results from this study can provide useful theoretical guidance to predict vibration of low-speed diesel engine.

scholarly journals Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension

2018 ◽  
Vol 31 (1) ◽  
Author(s):  
Jian Zhang ◽  
Chang-Wen Liu ◽  
Feng-Rong Bi ◽  
Xiao-Bo Bi ◽  
Xiao Yang
Keyword(s):  
Feature Extraction ◽  
Fractal Dimension ◽  
Diesel Engine ◽  
Fault Feature Extraction

Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network

2019 ◽  
Vol 19 (3) ◽  
pp. 873-884
Author(s):  
Yue Si ◽  
Zhousuo Zhang ◽  
Chuiqing Kong ◽  
Shujuan Li ◽  
Guigeng Yang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Sandwich Structure ◽  
Detection Method ◽  
Sandwich Structures ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Vibration Response ◽  
Permutation Entropy ◽  
Response Signal ◽  
Complex Wavelet

It is significant to perform looseness condition detection of viscoelastic sandwich structures to avoid serious accidents. Due to the multilayer characteristic of the viscoelastic sandwich structure, the vibration response signal of such structures is nonlinear and nonstationary. Furthermore, the looseness condition feature signal contained in the vibration response signal is very puny. Condition feature extraction has become a challenging task in the looseness condition detection of viscoelastic sandwich structures. Therefore, a novel method called dual-tree complex wavelet packet-based deep autoencoder network is proposed for this task. First, the vibration response signal of the viscoelastic sandwich structure is decomposed by dual-tree complex wavelet packet transform and the sub-band signals which contain rich energy are extracted. Then, the energies of the extracted sub-band signals are calculated to form a feature set. Finally, a deep autoencoder network is established to fuse the feature set, and the fused feature is viewed as the detection index to detect the looseness condition of the viscoelastic sandwich structure. The proposed method is applied to the connecting bolt looseness condition detection of the viscoelastic sandwich structure to validate its effectiveness. Compared with the detection method based on dual-tree complex wavelet packet transform and energy and the detection method based on dual-tree complex wavelet packet transform and permutation entropy, the results indicate that the effectiveness of the proposed method in this article is more superior to that of the other two methods.

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