scholarly journals Bearing Health Monitoring Based on the Orthogonal Empirical Mode Decomposition

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
C. Delprete ◽  
E. Brusa ◽  
C. Rosso ◽  
F. Bruzzone
Keyword(s):  
Empirical Mode Decomposition ◽  
Power Transmission ◽  
Vibration Signal ◽  
Power Transmission Line ◽  
Industrial Equipment ◽  
Test Rig ◽  
Service Monitoring ◽  
Mode Decomposition ◽  
Crucial Component ◽  
Set Up

Bearing is a crucial component of industrial equipment, since any fault occurring in this system usually affects the functionality of the whole machine. To manage this problem, some currently available technologies enable the remote prognosis and diagnosis of bearings, before that faults compromise the system function and safety, respectively. A system for the in-service monitoring of bearing, to detect any inner fault or damage of components and material, allows preventing undesired machine stops. Moreover, it even helps in performing an out-monitoring action, aimed at revealing any anomalous behaviour of the system hosting bearings, through their dynamic response. The in-monitoring can be based on the vibration signal measurement and exploited to detect the presence of defects in material. In this paper, the orthogonal empirical mode decomposition is analysed and tested to investigate how it could be effectively exploited in a lean in-service monitoring operation and remote diagnosis. The proposed approach is validated on a test rig, where an elementary power transmission line was set up. The activity highlights some main properties and practical issues of the technological implementation, as well as the precision of the Orthogonal Empirical Mode Decomposition, as a compact approach for an effective detection of bearing faults in operation.

scholarly journals Bearing Fault Classification Using Ensemble Empirical Mode Decomposition and Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1248
Author(s):  
Rafia Nishat Toma ◽  
Cheol-Hong Kim ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Original Signal ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Reconstructed Signal

Condition monitoring is used to track the unavoidable phases of rolling element bearings in an induction motor (IM) to ensure reliable operation in domestic and industrial machinery. The convolutional neural network (CNN) has been used as an effective tool to recognize and classify multiple rolling bearing faults in recent times. Due to the nonlinear and nonstationary nature of vibration signals, it is quite difficult to achieve high classification accuracy when directly using the original signal as the input of a convolution neural network. To evaluate the fault characteristics, ensemble empirical mode decomposition (EEMD) is implemented to decompose the signal into multiple intrinsic mode functions (IMFs) in this work. Then, based on the kurtosis value, insignificant IMFs are filtered out and the original signal is reconstructed with the rest of the IMFs so that the reconstructed signal contains the fault characteristics. After that, the 1-D reconstructed vibration signal is converted into a 2-D image using a continuous wavelet transform with information from the damage frequency band. This also transfers the signal into a time-frequency domain and reduces the nonstationary effects of the vibration signal. Finally, the generated images of various fault conditions, which possess a discriminative pattern relative to the types of faults, are used to train an appropriate CNN model. Additionally, with the reconstructed signal, two different methods are used to create an image to compare with our proposed image creation approach. The vibration signal is collected from a self-designed testbed containing multiple bearings of different fault conditions. Two other conventional CNN architectures are compared with our proposed model. Based on the results obtained, it can be concluded that the image generated with fault signatures not only accurately classifies multiple faults with CNN but can also be considered as a reliable and stable method for the diagnosis of fault bearings.

Study on Vibration Signal Marginal Spectrum of Paper-Transferring System in Printing Press Based on EMD

2010 ◽  
Vol 139-141 ◽  
pp. 2464-2468
Author(s):  
Yi Ming Wang ◽  
Shao Hua Zhang ◽  
Zhi Hong Zhang ◽  
Jing Li
Keyword(s):  
Empirical Mode Decomposition ◽  
Optimization Design ◽  
Impact Load ◽  
Vibration Signal ◽  
Key Factors ◽  
Mode Function ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Periodic Signals ◽  
The Moment

The precision of transferring paper is key factors to decide the print overprint accuracy, and vibration has an important impact on paper transferring accuracy. Empirical mode decomposition (EMD) can be used to extract the features of vibration test signal. According to the intrinsic mode function (IMF) by extracted, it is useful to analyze the dynamic characteristics of swing gripper arm on motion state. Due to the actual conditions of printing, the vibration signal of Paper-Transferring mechanism system is complex quasi periodic signals. Hilbert-Huang marginal spectrum that is based on empirical mode decomposition can solve the problem which is modals leakage by FFT calculated in frequency domain. Through the experimental research, the phase information of impact load at the moment of grippers opening or closing, which can be used for the optimization design of Paper-Transferring system and the improvement in the accuracy of swing gripper arm.

scholarly journals Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3125
Author(s):  
Zou ◽  
Chen ◽  
Liu
Keyword(s):  
Empirical Mode Decomposition ◽  
Field Experiments ◽  
Low Frequency ◽  
Vibration Signal ◽  
Micro Electro Mechanical System ◽  
Ensemble Empirical Mode Decomposition ◽  
Reconstruction Method ◽  
Monitoring Methods ◽  
Dynamic Displacement ◽  
Mode Decomposition

Considering the lack of precision in transforming measured micro-electro-mechanical system (MEMS) accelerometer output signals into elevation signals, this paper proposes a bridge dynamic displacement reconstruction method based on the combination of ensemble empirical mode decomposition (EEMD) and time domain integration, according to the vibration signal traits of a bridge. Through simulating bridge analog signals and verifying a vibration test bench, four bridge dynamic displacement monitoring methods were analyzed and compared. The proposed method can effectively eliminate the influence of low-frequency integral drift and high-frequency ambient noise on the integration process. Furthermore, this algorithm has better adaptability and robustness. The effectiveness of the method was verified by field experiments on highway elevated bridges.

scholarly journals Research on Multidomain Fault Diagnosis of Large Wind Turbines under Complex Environment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Rong Jia ◽  
Fuqi Ma ◽  
Jian Dang ◽  
Guangyi Liu ◽  
Huizhi Zhang
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Empirical Mode Decomposition ◽  
Wind Turbines ◽  
Vibration Signal ◽  
Characteristic Parameters ◽  
Feature Extraction Method ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Large Wind

Under the complicated environment of large wind turbines, the vibration signal of a wind turbine has the characteristics of coupling and nonlinearity. The traditional feature extraction method for the signal is hard to accurately extract fault information, and there is a serious problem of information redundancy in fault diagnosis. Therefore, this paper proposed a multidomain feature fault diagnosis method based on complex empirical mode decomposition (CEMD) and random forest theory (RF). Firstly, this paper proposes a novel method of complex empirical mode decomposition by using the correlation information between two-dimensional signals and utilizing the idea of ensemble empirical mode decomposition (EEMD) by adding white noise to suppress the problem mode mixing in empirical mode decomposition (EMD). Secondly, the collected vibration signals are decomposed into IMFs by CEMD. Then, calculate 11 time domain characteristic parameters and 13 frequency domain characteristic parameters of the vibration signal, and calculate the energy and energy entropy of each IMF components. Make all the characteristic parameters as the multidomain feature vectors of wind turbines. Finally, the redundant feature vectors are eliminated by the importance of each feature vector which has been calculated, and the feature vectors selected are input to the random forest classifier to achieve the fault diagnosis of large wind turbines. Simulation and experimental results show that this method can effectively extract the fault feature of the signal and achieve the fault diagnosis of wind turbines, which has a higher accuracy of fault diagnosis than the traditional classification methods.

A method of eliminating the vibration signal noise of hydropower unit based on NA-MEMD and approximate entropy

2016 ◽  
Vol 231 (2) ◽  
pp. 317-328 ◽  
Author(s):  
Xueli An ◽  
Junjie Yang
Keyword(s):  
Empirical Mode Decomposition ◽  
Real World ◽  
Approximate Entropy ◽  
Vibration Signal ◽  
Signal Denoising ◽  
Intrinsic Mode Functions ◽  
Denoising Method ◽  
Multivariate Empirical Mode Decomposition ◽  
Mode Decomposition ◽  
Simulation Signal

A new vibration signal denoising method of hydropower unit based on noise-assisted multivariate empirical mode decomposition (NA-MEMD) and approximate entropy is proposed. Firstly, the NA-MEMD is used to decompose the signal into a number of intrinsic mode functions. Then, the approximate entropy of each component is computed. According to a preset threshold of approximate entropy, these components are reconstructed to denoise vibration signal of hydropower unit. The analysis results of simulation signal and real-world signal show that the proposed method is adaptive and has a good denoising performance. It is very suitable for online denoising of hydropower unit's vibration signal.

An Enhanced Extraction Method Based on EEMD for Processing a Bearing Vibration Signal With Multiple Vibration Sources

2014 ◽  
Author(s):  
Wei Guo
Keyword(s):  
Empirical Mode Decomposition ◽  
Early Stage ◽  
Similarity Criterion ◽  
Vibration Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Combination Technique ◽  
Mode Decomposition ◽  
Rolling Element ◽  
Eemd Method ◽  
Effective Signal

Condition monitoring and fault diagnosis for rolling element bearings is an imperative part for preventive maintenance procedures and reliability improvement of rotating machines. When a localized fault occurs at the early stage of real bearing failures, the impulses generated by the defect are relatively weak and usually overwhelmed by large noise and other higher-level macro-structural vibrations generated by adjacent machine components and machines. To indicate the bearing faulty state as early as possible, it is necessary to develop an effective signal processing method for extracting the weak bearing signal from a vibration signal containing multiple vibration sources. The ensemble empirical mode decomposition (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 bands of simple signal components. However, the energy dispersion and many redundant components make the decomposition result obtained by the EEMD losing the physical significance. In this paper, to enhance the decomposition performance of the EEMD method, the similarity criterion and the corresponding combination technique are proposed to determine the similar signal components and then generate the real mono-component signals. To validate the effectiveness of the proposed method, it is applied to analyze raw vibration signals collected from two faulty bearings, each of which involves more than one vibration sources. The results demonstrate that the proposed method can accurately extract the bearing feature signal; meanwhile, it makes the physical meaning of each IMF clear.

Diagnosis of Ball Screw Preload Loss by Vibration Signals through the Hilbert-Huang Transform and Multiscale Measure

2011 ◽  
Vol 58-60 ◽  
pp. 636-641
Author(s):  
Yan Chen Shin ◽  
Yi Cheng Huang ◽  
Jen Ai Chao
Keyword(s):  
Machine Tool ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Ball Screw ◽  
Multiscale Entropy ◽  
Vibration Signals ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Diagnosis Method

This paper proposes a diagnosis method of ball screw preload loss through the Hilbert-Huang Transform (HHT) and Multiscale entropy (MSE) process when machine tool is in operation. Maximum dynamic preload of 2% and 4% ball screws are predesigned, manufactured and conducted experimentally. Vibration signal patterns are examined and revealed by Empirical Mode Decomposition (EMD) with Hilbert Spectrum. Different preload features are extracted and discriminated by using HHT. The irregularity development of ball screw with preload loss is determined and abstracting via MSE based on complexity perception. The experiment results successfully show preload loss can be envisaged by the proposed methodology.

Fault Diagnosis for Wind Turbine Gearboxes Based on EMD and the Energy Operator

2013 ◽  
Vol 281 ◽  
pp. 10-13 ◽  
Author(s):  
Xian You Zhong ◽  
Liang Cai Zeng ◽  
Chun Hua Zhao ◽  
Xian Ming Liu ◽  
Shi Jun Chen
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Empirical Mode Decomposition ◽  
Energy Operator ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Wind Turbine Gearbox ◽  
Mode Decomposition ◽  
The Cost ◽  
Fault Information

Wind turbine gearbox is subjected to different sorts of failures, which lead to the increasement of the cost. A approach to fault diagnosis of wind turbine gearbox based on empirical mode decomposition (EMD) and teager kaiser energy operator (TKEO) is presented. Firstly, the original vibration signal is decomposed into a number of intrinsic mode functions (IMFs) using EMD. Then the IMF containing fault information is analyzed with TKEO, The experimental results show that EMD and TKEO can be used to effectively diagnose faults of wind turbine gearbox.

Rolling Bearing Localized Defect Evaluation by Multiscale Signature via Empirical Mode Decomposition

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
Qingbo He ◽  
Peng Li ◽  
Fanrang Kong
Keyword(s):  
Health Information ◽  
Empirical Mode Decomposition ◽  
Multiple Scales ◽  
Regression Line ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Rolling Element

Measured vibration signals from rolling element bearings with defects are generally nonstationary, and are multiscale in nature owing to contributions from events with different localization in time and frequency. This paper presents a novel approach to characterize the multiscale signature via empirical mode decomposition (EMD) for rolling bearing localized defect evaluation. Vibration signal measured from a rolling element bearing is first adaptively decomposed by the EMD to achieve a series of usable intrinsic mode functions (IMFs) carrying the bearing health information at multiple scales. Then the localized defect-induced IMF is selected from all the IMFs based on a variance regression approach. The multiscale signature, called multiscale slope feature, is finally estimated from the regression line fitted over logarithmic variances of the IMFs excluding the defect IMF. The presented feature reveals the pattern of energy transfer among multiple scales due to localized defects, representing an inherent self-similar signature of the bearing health information that is embedded on multiple analyzed scales. Experimental results verify the performance of the proposed multiscale feature, and further discussions are provided.

EMD and Envelope Spectrum Based Bearing Fault Detection

2012 ◽  
Vol 459 ◽  
pp. 233-237 ◽  
Author(s):  
Zhen Tao Li ◽  
Hui Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Fault Detection And Diagnosis ◽  
Mode Decomposition ◽  
Analysis Technique ◽  
Oscillation Modes ◽  
Intrinsic Oscillation ◽  
Detection And Diagnosis ◽  
Envelope Spectrum

A novel method to fault diagnosis of bearing based on empirical mode decomposition (EMD) and envelope spectrum is presented. EMD method is self-adaptive to non-stationary and non-linear signal. The methodology developed in this paper decomposes the original vibration signal in intrinsic oscillation modes, using the empirical mode decomposition. Then the envelope spectrum is applied to the selected intrinsic mode function that stands for the bearing faults. The basic principle is firstly introduced in detail. Then the EMD is applied in the research of the fault detection and diagnosis of the bearing. The experimental results show that the proposed method based on EMD and envelope spectrum analysis technique can effectively diagnose the faults of bearing.

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