Rotating Machinery Diagnosing in Non-Stationary Conditions with Empirical Mode Decomposition-Based Wavelet Leaders Multifractal Spectra

Diagnosing the condition of rotating machines by non-invasive methods is based on the analysis of dynamic signals from sensors mounted on the machine—such as vibration, velocity, or acceleration sensors; torque meters; force sensors; pressure sensors; etc. The article presents a new method combining the empirical mode decomposition algorithm with wavelet leader multifractal formalism applied to diagnosing damages of rotating machines in non-stationary conditions. The development of damage causes an increase in the level of multifractality of the signal. The multifractal spectrum obtained as a result of the algorithm changes its shape. Diagnosis is based on the classification of the features of this spectrum. The method is effective in relation to faults causing impulse responses in the dynamic signal registered by the sensors. The method has been illustrated with examples of vibration signals of rotating machines recorded on a laboratory stand, as well as on real objects.

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An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing

Advances in Mechanical Engineering ◽

10.1155/2014/676205 ◽

2014 ◽

Vol 6 ◽

pp. 676205 ◽

Cited By ~ 17

Author(s):

Meijiao Li ◽

Huaqing Wang ◽

Gang Tang ◽

Hongfang Yuan ◽

Yang Yang

Keyword(s):

Fault Diagnosis ◽

Empirical Mode Decomposition ◽

Early Stage ◽

Rolling Bearing ◽

Ensemble Empirical Mode Decomposition ◽

Rolling Element Bearing ◽

Bearing Faults ◽

Outer Race ◽

Mode Decomposition ◽

Acceleration Sensors

In order to improve the effectiveness for identifying rolling bearing faults at an early stage, the present paper proposed a method that combined the so-called complementary ensemble empirical mode decomposition (CEEMD) method with a correlation theory for fault diagnosis of rolling element bearing. The cross-correlation coefficient between the original signal and each intrinsic mode function (IMF) was calculated in order to reduce noise and select an effective IMF. Using the present method, a rolling bearing fault experiment with vibration signals measured by acceleration sensors was carried out, and bearing inner race and outer race defect at a varying rotating speed with different degrees of defect were analyzed. And the proposed method was compared with several algorithms of empirical mode decomposition (EMD) to verify its effectiveness. Experimental results showed that the proposed method was available for detecting the bearing faults and able to detect the fault at an early stage. It has higher computational efficiency and is capable of overcoming modal mixing and aliasing. Therefore, the proposed method is more suitable for rolling bearing diagnosis.

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Modeling and Studying Acceleration-Induced Effects of Piezoelectric Pressure Sensors Using System Identification Theory

Sensors ◽

10.3390/s19051052 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1052 ◽

Cited By ~ 3

Author(s):

Fujing Xu ◽

Tiehua Ma

Keyword(s):

System Identification ◽

Empirical Mode Decomposition ◽

Pressure Sensor ◽

Pressure Sensors ◽

Shock Acceleration ◽

Transient Pressure ◽

Digital Compensation ◽

Mode Decomposition ◽

Inherent Frequency ◽

Identification Theory

Transient pressure testing is often accompanied by shock acceleration. Aiming at the acceleration-induced effects of pressure sensors, a dynamic compensation method combining empirical mode decomposition (EMD) with system identification theory (SIT) is proposed in this paper. This method is more effective at reducing the error of the acceleration-induced effects without affecting the sensor’s sensitivity and inherent frequency. The principle and theoretical basis of acceleration-induced effects is analyzed, and the static and dynamic acceleration-induced effects on the quartz crystal of a piezoelectric pressure sensor are performed. An acceleration-induced effects dynamic calibration system is built using a Machete hammer, which generates acceleration signals with larger amplitude and narrower pulse width, and an autoregressive exogenous (ARX)mathematical model of acceleration-induced effects is obtained using empirical mode decomposition-system identification theory (EMD-SIT). A digital compensation filter for acceleration-induced effects is designed on the basis of this model. Experimental results explain that the acceleration-induced effects of the pressure sensor were less than 11% after using the digital compensation filter. A series of test data verify the accuracy, reliability, and generality of the model.

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