scholarly journals Lubrication State Recognition Based on Energy Characteristics of Friction Vibration with EEMD and SVM

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Hai-jie Yu ◽  
Hai-jun Wei ◽  
Jing-ming Li ◽  
Da‐ping Zhou ◽  
Li‐dui Wei ◽  
...  
Keyword(s):  
Frequency Band ◽  
Ensemble Empirical Mode Decomposition ◽  
Experimental Results ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Energy Characteristics ◽  
State Recognition ◽  
Band Energy ◽  
Frequency Band Energy

In order to identify different lubrication states, lubrication experiments were carried out on a Bruker UMT-3 tester. The experimental results show that the frequency band energy characteristics of friction vibration signals are different under different lubrication states. Based on this, a lubrication state recognition method with ensemble empirical mode decomposition (EEMD) and support vector machine (SVM) was proposed. The vibration signals were decomposed into a finite number of stationary intrinsic mode functions (IMFs) with the EEMD method. The first six IMF components containing the main friction information were retained to calculate the energy ratio and construct the feature vector. The experimental results show that the mixed lubrication state can be identified by hundred percent, and there is a slight confusion between boundary lubrication and dry friction. The results show that frequency band energy of friction vibration signals is an effective feature to identify different lubrication states, and the proposed method can be used to identify different lubrication states.

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.

Application of Wavelet Packet Energy Spectrum in Micro Motor Fault Diagnosis

2013 ◽  
Vol 726-731 ◽  
pp. 3159-3162
Author(s):  
Sheng Yi Chen ◽  
Gui Tang Wang ◽  
Shou Lei Sun ◽  
Qiang Zhou
Keyword(s):  
Fault Diagnosis ◽  
Energy Spectrum ◽  
Frequency Band ◽  
Wavelet Packet ◽  
Reconstruction Algorithm ◽  
Mechanical Equipment ◽  
Vibration Signals ◽  
Band Energy ◽  
Status Information ◽  
Frequency Band Energy

To diagnosis vibration signals of micro motor in several different fault types a method based on wavelet packet energy spectrum is presented, the energy on each Sub-frequency band, which are Calculated by Wavelet packet decomposition and reconstruction algorithm, are used to normalization process.Under both circumstances of normal working and unmoral working of mechanical equipment,there exist evident differences among the Sub-frequency band energy after the decomposition of wavelet packet, which energy contains a wealth of micro motor running status information and the eigenvectors is structured by the Sub-frequency band energy spectrum can establish energy and Fault mapping relationship.The preliminary experimental results show that it is effective to use the wavelet packet-energy spectrum in micro motor fault diagnosis .

scholarly journals The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yanyan Zhang ◽  
Gang Wang ◽  
Chaolin Teng ◽  
Zhongjiang Sun ◽  
Jue Wang
Keyword(s):  
Frequency Band ◽  
Classification Accuracy ◽  
Relative Frequency ◽  
Wavelet Packet ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Band Energy ◽  
Frequency Band Energy ◽  
Semg Signals

For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis (PCA) and the Davies-Bouldin (DB) index were used to perform the feature selection. Lastly, the support vector machine (SVM) was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.

Frequency Band Energy Statistics Method and the Applications in Monitoring the Work Conditions for Mechanical Equipments

2010 ◽  
Vol 29-32 ◽  
pp. 78-83
Author(s):  
Yun Long Yuan ◽  
Chao Yong Yuan ◽  
Chao Zhen Yang
Keyword(s):  
Fault Diagnosis ◽  
Energy Distribution ◽  
Frequency Band ◽  
Present Method ◽  
Vibration Signal ◽  
Work Conditions ◽  
Current Work ◽  
Vibration Signals ◽  
Band Energy ◽  
Frequency Band Energy

A new manner, named frequency band energy statistics method, has been proposed for the analysis of the spectra from the vibration signals. The recorded vibration signals were first divided into multi-segments. Then each segment was calculated via the FFT transformation and 1/3 octave spectrum to obtained the characteristics of energy distribution, by making the histogram maps of the obtained features of the frequency energies. Consequently, we can monitor the work conditions and fault diagnosis for the mechanical equipments by the compared analysis of the corresponding histogram maps of equipments with normal and abnormal work conditions. The results show that present method exhibits a very strong sensitivity to the changes of vibration signal, leading to the fine detection of the minor changes form the equipment work conditions. Current work might provide a novel and facile method for definitely monitoring the work conditions and fault diagnosis of the mechanical equipments.

scholarly journals Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 209 ◽  
Author(s):  
Shaohua Xue ◽  
Jianping Tan ◽  
Lixiang Shi ◽  
Jiwei Deng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Sensitivity Index ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
New Perspective

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

scholarly journals Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve

2020 ◽  
Vol 10 (4) ◽  
pp. 1323 ◽  
Author(s):  
Jun Peng ◽  
Xuanheng Tang ◽  
Bin Chen ◽  
Fu Jiang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Frequency Band ◽  
High Speed ◽  
Current Data ◽  
Solenoid Valve ◽  
Data Set ◽  
Energy Characteristics ◽  
Band Energy ◽  
Failure Type ◽  
Braking System ◽  
Frequency Band Energy

A high-speed solenoid valve is a key component of the braking system. Accurately predicting the failure type of the solenoid valve is an important guarantee for safe operation of the braking system. However, electrical, magnetic, and mechanical coupling aging mechanism; individual differences; and uncertainty of aging processes have remained major challenges. To address this problem, a method combining physical indices and data features is proposed to predict the failure type of solenoid valve. Firstly, the mechanism model of the solenoid valve is established and five physical indices are extracted from the driven current curve. Then, the frequency band energy characteristics are obtained from the current change rate curve of the solenoid valve by wavelet packet decomposition. Combining physical indices and frequency band energy characteristics into a comprehensive feature vector, we applied random forest to both predict and classify the failure type. We generate a data set consisting of 60 high-speed solenoid valves periodically switched under accelerated aging test conditions, including driven current, final failure type, and switching cycles. The prediction result shows that the proposed method achieves 95.95% and 94.68% precision for the two failures using the driven current data of the 3000th cycle and has better prediction performance than other algorithms.

scholarly journals Feature Extraction Strategy with Improved Permutation Entropy and Its Application in Fault Diagnosis of Bearings

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Jiang ◽  
Zhencai Zhu ◽  
Wei Li ◽  
Bo Wu ◽  
Zhe Tong ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Optimal Number ◽  
Permutation Entropy ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Time Frequency ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

Feature extraction is one of the most difficult aspects of mechanical fault diagnosis, and it is directly related to the accuracy of bearing fault diagnosis. In this study, improved permutation entropy (IPE) is defined as the feature for bearing fault diagnosis. In this method, ensemble empirical mode decomposition (EEMD), a self-adaptive time-frequency analysis method, is used to process the vibration signals, and a set of intrinsic mode functions (IMFs) can thus be obtained. A feature extraction strategy based on statistical analysis is then presented for IPE, where the so-called optimal number of permutation entropy (PE) values used for an IPE is adaptively selected. The obtained IPE-based samples are then input to a support vector machine (SVM) model. Subsequently, a trained SVM can be constructed as the classifier for bearing fault diagnosis. Finally, experimental vibration signals are applied to validate the effectiveness of the proposed method, and the results show that the proposed method can effectively and accurately diagnose bearing faults, such as inner race faults, outer race faults, and ball faults.

A Modified EEMD Decomposition for the Detection of Rolling Bearing Faults

2014 ◽  
Vol 548-549 ◽  
pp. 369-373
Author(s):  
Yuan Cheng Shi ◽  
Yong Ying Jiang ◽  
Hai Feng Gao ◽  
Jia Wei Xiang
Keyword(s):  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Bearing Faults ◽  
Mode Decomposition ◽  
Fault Features ◽  
Rolling Element ◽  
Inner Race

The vibration signals of rolling element bearings are non-linear and non-stationary and the corresponding fault features are difficult to be extracted. EEMD (Ensemble empirical mode decomposition) is effective to detect bearing faults. In the present investigation, MEEMD (Modified EEMD) is presented to diagnose the outer and inner race faults of bearings. The original vibration signals are analyzed using IMFs (intrinsic mode functions) extracted by MEEMD decomposition and Hilbert spectrum in the proposed method. The numerical and experimental results of the comparison between MEEMD and EEMD indicate that the proposed method is more effective to extract the fault features of outer and inner race of bearings than EEMD.

scholarly journals Mechanical Fault Diagnosis for HV Circuit Breakers Based on Ensemble Empirical Mode Decomposition Energy Entropy and Support Vector Machine

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Jianfeng Zhang ◽  
Mingliang Liu ◽  
Keqi Wang ◽  
Laijun Sun
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Circuit Breaker ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
Time Segment ◽  
Vibration Signals ◽  
Equal Time ◽  
Mode Decomposition

During the operation process of the high voltage circuit breaker, the changes of vibration signals can reflect the machinery states of the circuit breaker. The extraction of the vibration signal feature will directly influence the accuracy and practicability of fault diagnosis. This paper presents an extraction method based on ensemble empirical mode decomposition (EEMD). Firstly, the original vibration signals are decomposed into a finite number of stationary intrinsic mode functions (IMFs). Secondly, calculating the envelope of each IMF and separating the envelope by equal-time segment and then forming equal-time segment energy entropy to reflect the change of vibration signal are performed. At last, the energy entropies could serve as input vectors of support vector machine (SVM) to identify the working state and fault pattern of the circuit breaker. Practical examples show that this diagnosis approach can identify effectively fault patterns of HV circuit breaker.

Sign in / Sign up

Export Citation Format

Share Document