A New Method for Multi-Fault Diagnosis of Rotating Machinery Based on the Mixture Alpha Stable Distribution Model

2014 ◽  
Vol 977 ◽  
pp. 349-352 ◽  
Author(s):  
Gang Yu ◽  
Jian Kang
Keyword(s):  
Pattern Recognition ◽  
Fault Diagnosis ◽  
Stable Distribution ◽  
Estimation Algorithm ◽  
Rotating Machinery ◽  
Parameters Estimation ◽  
New Method ◽  
Distribution Model ◽  
Model Parameters ◽  
Alpha Stable Distribution

As one of the most important type of machinery, rotating machinery may malfunction due to various reasons. Sometimes the fault is a single one, but sometimes it maybe in multi-fault condition, this paper mainly focus on the latter. First, the paper gives a brief introduction of the study on multi-fault, then it introduces the mixture of Alpha stable distribution model, besides, it gives the model parameters estimation algorithm in detail, at last we use the SOM net to complete pattern recognition. The results prove that this modeling method is effective in multi-fault diagnosis in rotating machinery.

Adaptive Stochastic Resonance Method for Early Fault Detection of Rotating Machinery Based on Alpha Stable Distribution

2014 ◽  
Vol 618 ◽  
pp. 458-462
Author(s):  
Gang Yu ◽  
Ye Chen
Keyword(s):  
Fault Detection ◽  
Stochastic Resonance ◽  
Stable Distribution ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Rotating Machinery ◽  
Sliding Windows ◽  
Alpha Stable Distribution ◽  
Early Fault Detection ◽  
The Impact

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

scholarly journals Data Preprocessing Method and Fault Diagnosis Based on Evaluation Function of Information Contribution Degree

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Siyu Ji ◽  
Chenglin Wen
Keyword(s):  
Fault Diagnosis ◽  
Combined Cycle ◽  
Training Data ◽  
New Method ◽  
Model Parameters ◽  
Evaluation Function ◽  
Data Set ◽  
Standard Data ◽  
True Value ◽  
The Mean

Neural network is a data-driven algorithm; the process established by the network model requires a large amount of training data, resulting in a significant amount of time spent in parameter training of the model. However, the system modal update occurs from time to time. Prediction using the original model parameters will cause the output of the model to deviate greatly from the true value. Traditional methods such as gradient descent and least squares methods are all centralized, making it difficult to adaptively update model parameters according to system changes. Firstly, in order to adaptively update the network parameters, this paper introduces the evaluation function and gives a new method to evaluate the parameters of the function. The new method without changing other parameters of the model updates some parameters in the model in real time to ensure the accuracy of the model. Then, based on the evaluation function, the Mean Impact Value (MIV) algorithm is used to calculate the weight of the feature, and the weighted data is brought into the established fault diagnosis model for fault diagnosis. Finally, the validity of this algorithm is verified by the example of UCI-Combined Cycle Power Plant (UCI-ccpp) simulation of standard data set.

scholarly journals Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Xinghui Zhang ◽  
Jianshe Kang ◽  
Lei Xiao ◽  
Jianmin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Stable Distribution ◽  
Planetary Gearbox ◽  
Alpha Stable Distribution ◽  
Gear Fault

Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE) of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

scholarly journals Alpha-Stable Distribution and Multifractal Detrended Fluctuation Analysis-Based Fault Diagnosis Method Application for Axle Box Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Qing Xiong ◽  
Weihua Zhang ◽  
Yanhai Xu ◽  
Yiqiang Peng ◽  
Pengyi Deng
Keyword(s):  
Fault Diagnosis ◽  
Detrended Fluctuation Analysis ◽  
Stable Distribution ◽  
Integration Scheme ◽  
Railway Vehicle ◽  
Fluctuation Analysis ◽  
Multifractal Detrended Fluctuation Analysis ◽  
Alpha Stable Distribution ◽  
Diagnosis Method ◽  
Detrended Fluctuation

A railway vehicle’s key components, such as wheelset treads and axle box bearings, often suffer from fatigue failures. If these faults are not detected and dealt with in time, the running safety of the railway vehicle will be seriously affected. To detect these components’ early failure and extend their fatigue life, a regular maintenance becomes critical. Currently, the regular maintenance of axle box bearings mainly depends on manual off-line inspection, which has low working efficiency and precision of fault diagnosis. In order to improve the maintenance efficiency and effectiveness of railway vehicles, this study proposes a method of integrating the vibration monitoring system of the axle box bearing in the underfloor wheelset lathe, where the integration scheme and work flow of the system are introduced followed by the detailed fault diagnosis method and application examples. Firstly, the band-pass filter and envelope analysis is successively performed on the original signal acquired by an accelerometer. Secondly, the alpha-stable distribution (ASD) and multifractal detrended fluctuation analysis (MFDFA) analysis of the envelope signal are performed, and five characteristic parameters with significant stability and sensitivity are extracted and then brought into the least squares support vectors machine based on particle swarm optimization to determine the state of the bearing quantitatively. Finally, the effectiveness of the method is validated by bench test data. The results demonstrated that the proposed method can accomplish effective diagnosis of axle box bearings’ fault location and fault degree and can yield better diagnosis accuracy than the single method of ASD or MFDFA.

scholarly journals One New Method on ARMA Model Parameters Estimation

2009 ◽  
Vol 3 (5) ◽  
Author(s):  
Xiaoqin Cao ◽  
Rui Shan ◽  
Jing Fan ◽  
Peiliang Li
Keyword(s):  
Arma Model ◽  
Parameters Estimation ◽  
New Method ◽  
Model Parameters

Statistical Characters Analysis of LF Atmospheric Noise

2014 ◽  
Vol 556-562 ◽  
pp. 4073-4076
Author(s):  
Yan Jun Wu ◽  
Gang Fu ◽  
Qian He
Keyword(s):  
Probability Distribution ◽  
Quantum Interference ◽  
Stable Distribution ◽  
Low Frequency ◽  
Distribution Model ◽  
Channel Noise ◽  
Communication Performance ◽  
Alpha Stable Distribution ◽  
Atmospheric Noise ◽  
Noise Data

In low frequency (LF) communication, the main factor of affecting property of LF communication system is the atmospheric noise caused by lightning phenomenon. Amplitude probability distribution of this atmospheric noise is with non-Gaussian characters seriously. Due to influence of various factors, such as occurrence time, physical location and seasons, making analysis of LF atmospheric noise on the receiver communication performance becomes more complex. Therefore, the study of characteristics of atmospheric noise amplitude probability distribution is necessary. In this paper, Firstly, we are on the assumption that the amplitude probability distribution characteristics of atmospheric noise obey Alpha stable distribution; then introduce the Alpha stable distribution model; Finally, we use probability distribution function (pdf) and Quantile-Quantile (Q-Q) plot fitting for Alpha stable distribution according to real LF channel noise data acquired by a high sensitive superconducting quantum interference device (SQUID). Simulation results show the assumption is correct.

Research on Arrangement of Sensors on Rotating Machinery Base Based on ANSYS

2010 ◽  
Vol 34-35 ◽  
pp. 1010-1014
Author(s):  
Ji Gang Wu ◽  
Xue Jun Li ◽  
Bai Hui Yao
Keyword(s):  
Fault Diagnosis ◽  
Transient Analysis ◽  
Test Bench ◽  
Rotating Machinery ◽  
Vibration Response ◽  
New Method ◽  
Solid Model ◽  
Response Characteristics ◽  
Sensor Arrangement ◽  
Rotor Bearing

The faults of rotating machinery are monitored by fixing the sensors on rotor directly, which brings some problems such as difficulty in fixing the sensors, poor universality and so on, while brings some advantages such as rapidness, convenience and good universality and so on by fixing the sensors on the base. The arrangement of sensors on rotating machinery base was designed by taking advantage of the vibration transitivity of rotor-bearing-base, which will provide a new method for fault diagnosis of rotating machinery. The solid model of rotor test bench was built by combining with PROE and ANSYS. The transient analysis of rotor test bench was done, and the vibration response characteristics of base and the arrangement of sensors were obtained. Finally, the validity of the sensor arrangement was verified by experiments.

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