Diagnosis for Engine Misfire Fault Based on Torsional Vibration and Neural-Network Analysis

2012 ◽  
Vol 433-440 ◽  
pp. 7240-7246
Author(s):  
Can Yi Du ◽  
Kang Ding ◽  
Zhi Jian Yang ◽  
Cui Li Yang
Keyword(s):  
Neural Network ◽  
Torsional Vibration ◽  
Bp Neural Network ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Correction Method ◽  
Operating Conditions ◽  
Diagnostic Model ◽  
Vibration Signal Analysis

Misfire is a common fault which affects the engine performances. Because the signal-to-noise ratio of torsional vibration signal is high, torsional vibration test and analysis for the engine were performed in a variety of operating conditions, including healthy condition and single-cylinder misfire condition. In order to improve the accuracy of analysis, energy centrobaric correction method was used to correct the amplitude. Taking the corrected amplitude of main order as the fault feature, and then a BP neural-network diagnostic model can be established for misfire diagnosis. The result shows that the method of combining torsional vibration signal analysis and neural-network can diagnose engine misfire fault correctly.

Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

2008 ◽  
Author(s):  
Ruqiang Yan ◽  
Robert X. Gao ◽  
Kang B. Lee ◽  
Steven E. Fick
Keyword(s):  
Time Series ◽  
Phase Space ◽  
Noise Reduction ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Multifractal Spectrum ◽  
Vibration Signal ◽  
Rolling Bearings ◽  
One Dimensional ◽  
Vibration Signal Analysis

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then analysis of bearing vibration signals is carried out as a case study. The LGP-based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

Embedded Sensing Design of Gearboxes for Vibration Monitoring

2010 ◽  
Vol 29-32 ◽  
pp. 264-268
Author(s):  
Z.S. Chen ◽  
Yong Min Yang ◽  
Z.X. Ge ◽  
C. Li
Keyword(s):  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Embedded Sensors ◽  
Analysis Method ◽  
Signal To Noise ◽  
Normal Functions ◽  
Embedded Sensing ◽  
Vibration Signal Analysis

Vibration signal analysis is one of the most effective ways for condition monitoring of gearboxes. Traditional way is often to mount additional accelerometer sensors on their cases, which has two unavoidable defects: signal-to-noise ratio is often low due to long signal travel paths and it may be not allowable due to space limitations. While embedded diagnostics (ED) can solve these two problems well by embedding sensors close to fault sources. However, embedded sensing design is a great challenge of ED because embedded sensors must have effects on the structure integrity of a gearbox. So it is necessary to determine how to embed sensors in order to ensure normal functions of a gearbox. In this paper, a finite element-based structure analysis method was proposed to perform embedded sensing design of bearings and gears to determine the optimal modified structure size.

scholarly journals Research on transformer condition evaluation technology based on vibration signal analysis

2021 ◽  
Vol 645 ◽  
pp. 012036
Author(s):  
Ma Hao ◽  
Yao Chuang ◽  
Duan Minghui ◽  
Wei Jufang ◽  
Zhang Xin ◽  
...  
Keyword(s):  
Signal Analysis ◽  
Vibration Signal ◽  
Condition Evaluation ◽  
Vibration Signal Analysis

Research on Roller Bearing with Fault Diagnosis Method Based on EMD and BP Neural Network

2014 ◽  
Vol 1014 ◽  
pp. 501-504 ◽  
Author(s):  
Shu Guo ◽  
You Cai Xu ◽  
Xin Shi Li ◽  
Ran Tao ◽  
Kun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Roller Bearing ◽  
Vibration Signal ◽  
Classification Performance ◽  
Outer Race ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Main Fault

In order to discover the fault with roller bearing in time, a new fault diagnosis method based on Empirical mode decomposition (EMD) and BP neural network is put forward in the paper. First, we get the fault signal through experiments. Then we use EMD to decompose the vibration signal into a series of single signals. We can extract main fault information from the single signals. The kurtosis coefficient of the single signals forms a feature vector which is used as the input data of the BP neural network. The trained BP neural network can be used for fault identification. Through analyzing, BP neural network can distinguish the fault into normal state, inner race fault, outer race fault. The results show that this method can gain very stable classification performance and good computational efficiency.

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