scholarly journals An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1519 ◽  
Author(s):  
Dengyun Wu ◽  
Jianwen Wang ◽  
Hong Wang ◽  
Hongxing Liu ◽  
Lin Lai ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Frequency Ratio ◽  
Characteristic Frequency ◽  
Automatic Monitoring ◽  
Ensemble Empirical Mode Decomposition ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method

Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.

scholarly journals A Hydraulic Pump Fault Diagnosis Method Based on the Modified Ensemble Empirical Mode Decomposition and Wavelet Kernel Extreme Learning Machine Methods

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2599
Author(s):  
Zhenbao Li ◽  
Wanlu Jiang ◽  
Sheng Zhang ◽  
Yu Sun ◽  
Shuqing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Recognition Accuracy ◽  
Ensemble Empirical Mode Decomposition ◽  
Hydraulic Pump ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

To address the problem that the faults in axial piston pumps are complex and difficult to effectively diagnose, an integrated hydraulic pump fault diagnosis method based on the modified ensemble empirical mode decomposition (MEEMD), autoregressive (AR) spectrum energy, and wavelet kernel extreme learning machine (WKELM) methods is presented in this paper. First, the non-linear and non-stationary hydraulic pump vibration signals are decomposed into several intrinsic mode function (IMF) components by the MEEMD method. Next, AR spectrum analysis is performed for each IMF component, in order to extract the AR spectrum energy of each component as fault characteristics. Then, a hydraulic pump fault diagnosis model based on WKELM is built, in order to extract the features and diagnose faults of hydraulic pump vibration signals, for which the recognition accuracy reached 100%. Finally, the fault diagnosis effect of the hydraulic pump fault diagnosis method proposed in this paper is compared with BP neural network, support vector machine (SVM), and extreme learning machine (ELM) methods. The hydraulic pump fault diagnosis method presented in this paper can diagnose faults of single slipper wear, single slipper loosing and center spring wear type with 100% accuracy, and the fault diagnosis time is only 0.002 s. The results demonstrate that the integrated hydraulic pump fault diagnosis method based on MEEMD, AR spectrum, and WKELM methods has higher fault recognition accuracy and faster speed than existing alternatives.

scholarly journals Fault Diagnosis for Analog Circuits by Using EEMD, Relative Entropy, and ELM

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jian Xiong ◽  
Shulin Tian ◽  
Chenglin Yang
Keyword(s):  
Fault Diagnosis ◽  
Relative Entropy ◽  
Analog Circuits ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Eemd Method ◽  
Learning Machine ◽  
Elm Classifier

This paper presents a novel fault diagnosis method for analog circuits using ensemble empirical mode decomposition (EEMD), relative entropy, and extreme learning machine (ELM). First, nominal and faulty response waveforms of a circuit are measured, respectively, and then are decomposed into intrinsic mode functions (IMFs) with the EEMD method. Second, through comparing the nominal IMFs with the faulty IMFs, kurtosis and relative entropy are calculated for each IMF. Next, a feature vector is obtained for each faulty circuit. Finally, an ELM classifier is trained with these feature vectors for fault diagnosis. Via validating with two benchmark circuits, results show that the proposed method is applicable for analog fault diagnosis with acceptable levels of accuracy and time cost.

An Ensemble Empirical Mode Decomposition-Based Lossy Signal Compression Method for a Remote and Wireless Bearing Condition Monitoring System

2012 ◽  
Author(s):  
Peter W. Tse ◽  
Wei Guo
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Signal Compression ◽  
Compression Method ◽  
Vibration Signals ◽  
Bearing Fault ◽  
Mode Decomposition ◽  
Bearing Vibration ◽  
Eemd Method

Rolling bearings are one of the most widely used and most likely to fail components in the vast majority of rotating machines. A remote and wireless bearing condition system allows the bearings to be inspected in remote or hazardous environments and increases the machine reliability. To minimize the transmission loads of enormous vibration data for accurate bearing fault diagnosis, a lossy compression method based on ensemble empirical mode decomposition (EEMD) method was proposed for bearing vibration signals in this paper. The EEMD method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different frequency bands of signal components called intrinsic mode functions (IMFs). After applying the EEMD method to the vibration signal, the impulsive signal component related to the faulty bearing is extracted. The noise and irrelevant signal components that are often embedded in the collected vibration signals were removed. In the bearing signal, the distribution for most of the extremes is around zero. Almost all meaningful extremes related to the defect are concentrated in a small fraction of the samples. Hence, this signal compression provides high compression ratio for the bearing vibration signal. To verify the effectiveness of this method, raw vibration signals were collected from an experimental motor and a real traction motor. The proposed lossy signal compression method was applied to these vibration signals to extract the bearing signals and compress them. A comparison of this compression method with the popular wavelet compression method was also conducted. Wirelessly transmitting these compressed data demonstrates that the proposed signal compression method provides high compression performance for bearing vibration signals. Furthermore, the fault diagnosis using the reconstructed signal indicates that most of the impulses relating to the bearing fault are retained, including their periodicity and amplitudes, which are vital for accurate bearing fault diagnosis. Therefore, the compression of the bearing vibration signal contributes not only on the decreases of the file size and the transmission time, but also on the extraction of faulty bearing features to improve the accuracy in signal analysis. With the help of this method, wireless data communication for the remote and wireless bearing condition monitoring system becomes highly efficient, even in a limited bandwidth environment and maintains accurate bearing fault detection without loss of features and the need of transmitting a large amount of vibration data.

Gear Fault Diagnosis Method Using EEMD Sample Entropy and Grey Incidence

2013 ◽  
Vol 694-697 ◽  
pp. 1151-1154
Author(s):  
Wen Bin Zhang ◽  
Ya Song Pu ◽  
Jia Xing Zhu ◽  
Yan Ping Su
Keyword(s):  
Fault Diagnosis ◽  
Ensemble Empirical Mode Decomposition ◽  
Sample Entropy ◽  
Morphological Filter ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Diagnosis Method ◽  
Gear Fault Diagnosis

In this paper, a novel fault diagnosis method for gear was approached based on morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey incidence. Firstly, in order to eliminate the influence of noises, the line structure element was selected for morphological filter to denoise the original signal. Secondly, denoised vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMF) and some containing the most dominant fault information were calculated the sample entropy. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

scholarly journals Analysis method of the cavitation vibration signals in poppet valve based on EEMD

2021 ◽  
Vol 13 (2) ◽  
pp. 168781402199811
Author(s):  
Beibei Li ◽  
Qiao Zhao ◽  
Huaiyi Li ◽  
Xiumei Liu ◽  
Jichao Ma ◽  
...  
Keyword(s):  
Ensemble Empirical Mode Decomposition ◽  
Center Frequency ◽  
Analysis Method ◽  
Vibration Signals ◽  
Poppet Valve ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Cavitation Intensity ◽  
Eemd Method ◽  
Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

scholarly journals Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

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