Identification of start of combustion based on contribution level of principal component in vibration signal of cylinder head surface

2022 ◽  
Vol 189 ◽  
pp. 108632
Author(s):  
Bangxiong Pan ◽  
Xiaodan Zhao ◽  
Limei Wang ◽  
Xiuliang Zhao
Keyword(s):  
Cylinder Head ◽  
Principal Component ◽  
Vibration Signal ◽  
Head Surface ◽  
Start Of Combustion ◽  
Contribution Level

scholarly journals Sensor Distribution Optimization for Structural Impact Monitoring Based on NSGA-II and Wavelet Decomposition

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4264 ◽  
Author(s):  
Peng Li ◽  
Liuwei Huang ◽  
Jiachao Peng
Keyword(s):  
Wavelet Decomposition ◽  
Principal Component ◽  
Experimental System ◽  
Vibration Signal ◽  
The Finite Element Method ◽  
Nsga Ii ◽  
Impact Monitoring ◽  
Impact Location ◽  
Average Accuracy ◽  
The Impact

Optimal sensor placement is a significant task for structural health monitoring (SHM). In this paper, an SHM system is designed which can recognize the different impact location and impact degree in the composite plate. Firstly, the finite element method is used to simulate the impact, extracting numerical signals of the structure, and the wavelet decomposition is used to extract the band energy. Meanwhile, principal component analysis (PCA) is used to reduce the dimensions of the vibration signal. Following this, the non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the placement of sensors. Finally, the experimental system is established, and the Product-based Neural Network is used to recognize different impact categories. Three sets of experiments are carried out to verify the optimal results. When three sensors are applied, the average accuracy of the impact recognition is 59.14%; when the number of sensors is four, the average accuracy of impact recognition is 76.95%.

KPCA Denoising and its Application in Machinery Fault Diagnosis

2011 ◽  
Vol 103 ◽  
pp. 274-278 ◽  
Author(s):  
Ling Li Jiang ◽  
Zong Qun Deng ◽  
Si Wen Tang
Keyword(s):  
Time Series ◽  
Principal Component ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Kernel Principal Component Analysis ◽  
Reconstruction Method ◽  
Denoising Method ◽  
One Dimensional ◽  
Multidimensional Time Series ◽  
Machinery Fault Diagnosis

This paper proposes a kernel principal component analysis (KPCA)-based denoising method for removing the noise from vibration signal. Firstly, one-dimensional time series is expanded to multidimensional time series by the phase space reconstruction method. Then, KPCA is performed on the multidimensional time series. The first kernel principal component is the denoised signal. A rolling bearing denoising example verify the effectiveness of the proposed method

Multi-fault Condition Monitoring of Slurry Pump with Principle Component Analysis and Sequential Hypothesis Test

2019 ◽  
Vol 34 (07) ◽  
pp. 2059019 ◽  
Author(s):  
Hanxin Chen ◽  
Wenjian Huang ◽  
Jinmin Huang ◽  
Chenghao Cao ◽  
Liu Yang ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Hypothesis Test ◽  
Wavelet Packet ◽  
Principal Component ◽  
Vibration Signal ◽  
Component Analysis ◽  
Ratio Test ◽  
Vibration Signals ◽  
Time Domain Signal ◽  
Sequential Probability

A new method about the multi-fault condition monitoring of slurry pump based on principal component analysis (PCA) and sequential probability ratio test (SPRT) is proposed. The method identifies the condition of the slurry pump by analyzing the vibration signal. The experimental model is established using the normal impeller and the faulty impellers where the collected vibration signals were preprocessed using wavelet packet transform (WPT). The characteristic parameters of the vibration signals are extracted by time domain signal analysis and the dimension of data was reduced by PCA. The principal components with the largest contribution rate are chosen as the inputted signal to SPRT to assess the proposed algorithm. The new methodology is reasonable and practical for the multi-fault diagnosis of slurry pump.

scholarly journals An Integrated Cumulative Transformation and Feature Fusion Approach for Bearing Degradation Prognostics

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Lixiang Duan ◽  
Fei Zhao ◽  
Jinjiang Wang ◽  
Ning Wang ◽  
Jiwang Zhang
Keyword(s):  
Feature Fusion ◽  
Model Performance ◽  
Principal Component ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Kernel Principal Component Analysis ◽  
Machine Model ◽  
Monotonic Trend ◽  
Learning Machine

Aimed at degradation prognostics of a rolling bearing, this paper proposed a novel cumulative transformation algorithm for data processing and a feature fusion technique for bearing degradation assessment. First, a cumulative transformation is presented to map the original features extracted from a vibration signal to their respective cumulative forms. The technique not only makes the extracted features show a monotonic trend but also reduces the fluctuation; such properties are more propitious to reflect the bearing degradation trend. Then, a new degradation index system is constructed, which fuses multidimensional cumulative features by kernel principal component analysis (KPCA). Finally, an extreme learning machine model based on phase space reconstruction is proposed to predict the degradation trend. The model performance is experimentally validated with a whole-life experiment of a rolling bearing. The results prove that the proposed method reflects the bearing degradation process clearly and achieves a good balance between model accuracy and complexity.

Approach to Detect Air Tightness of Engine Cylinder by Vibration Analysis

2012 ◽  
Vol 443-444 ◽  
pp. 50-53 ◽  
Author(s):  
Xin Yong Qiao ◽  
Xiao Yang Xie ◽  
Jian Min Liu ◽  
Xiao Ming Zhang
Keyword(s):  
Neural Network ◽  
Cylinder Head ◽  
Rbf Neural Network ◽  
Vibration Signal ◽  
Time Domain Analysis ◽  
Compression Pressure ◽  
Engine Cylinder ◽  
Air Tightness ◽  
Cylinder Compression ◽  
Set Up

Cylinder compression pressure reflects the air tightness of engine. A method for measuring the compression pressure of cylinder indirectly through measuring the vibration signal of cylinder head was studied and then to detect the air tightness. The air pressure signal in cylinder and vibration signals of cylinder head were measured at the same time when the diesel engine was driven by the motor. According to port timing, the vibration signal excited by cylinder pressure was separated using time domain analysis. A RBF neural network model was set up to build the relation between compression pressure and cylinder head vibration. So the air tightness of cylinder can be detected after calculating the compression pressure by use of neural network.

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