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.

A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine

2016 ◽  
Vol 232 (2) ◽  
pp. 369-380 ◽  
Author(s):  
Chao Zhang ◽  
Zhongxiao Peng ◽  
Shuai Chen ◽  
Zhixiong Li ◽  
Jianguo Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Dynamic State ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Diagnosis Method

During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.

scholarly journals Texture Feature Extraction Method for Ground Nephogram Based on Hilbert Spectrum of Bidimensional Empirical Mode Decomposition

2014 ◽  
Vol 31 (9) ◽  
pp. 1982-1994 ◽  
Author(s):  
Xiaoying Chen ◽  
Aiguo Song ◽  
Jianqing Li ◽  
Yimin Zhu ◽  
Xuejin Sun ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Intrinsic Mode Functions ◽  
Hilbert Spectrum ◽  
Feature Extraction Method ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Bidimensional Empirical Mode Decomposition

Abstract It is important to recognize the type of cloud for automatic observation by ground nephoscope. Although cloud shapes are protean, cloud textures are relatively stable and contain rich information. In this paper, a novel method is presented to extract the nephogram feature from the Hilbert spectrum of cloud images using bidimensional empirical mode decomposition (BEMD). Cloud images are first decomposed into several intrinsic mode functions (IMFs) of textural features through BEMD. The IMFs are converted from two- to one-dimensional format, and then the Hilbert–Huang transform is performed to obtain the Hilbert spectrum and the Hilbert marginal spectrum. It is shown that the Hilbert spectrum and the Hilbert marginal spectrum of different types of cloud textural images can be divided into three different frequency bands. A recognition rate of 87.5%–96.97% is achieved through random cloud image testing using this algorithm, indicating the efficiency of the proposed method for cloud nephogram.

scholarly journals Multiple Fault Detection of Rolling Bearing through Ensemble Empirical Mode Decomposition of Vibration Signal

2019 ◽  
Vol 9 (2) ◽  
pp. 2724-2726
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Ensemble Empirical Mode Decomposition ◽  
Data Driven ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Multiple Faults ◽  
Mode Decomposition

Generally, two or more faults occur simultaneously in the bearings. These Compound Faults (CF) in bearing, are most difficult type of faults to detect, by any data-driven method including machine learning. Hence, it is a primary requirement to decompose the fault vibration signals logically, so that frequencies can be grouped in parts. Empirical Mode Decomposition (EMD) is one of the simplest techniques of decomposition of signals. In this paper we have used Ensemble Empirical Mode Decomposition (EEMD) technique for compound fault detection/identification. Ensembled Empirical Mode Decomposition is found useful, where a white noise helps to detect the bearing frequencies. The graphs show clearly the capability of EEMD to detect the multiple faults in rolling bearings.

scholarly journals An Improvement in Representation of Audio Signal in Time-Frequency Plane using EMD-2TEMD Based Approach

2016 ◽  
Vol 44 ◽  
pp. 141-150
Author(s):  
Kazi Mahmudul Hassan ◽  
Md. Ekramul Hamid ◽  
Takayoshi Nakai
Keyword(s):  
Empirical Mode Decomposition ◽  
Source Separation ◽  
Audio Signal ◽  
Hilbert Spectrum ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Separation Result ◽  
Mode Decomposition ◽  
Recent Approach ◽  
Non Stationary Signal

This study proposed an enhanced time-frequency representation of audio signal using EMD-2TEMD based approach. To analyze non-stationary signal like audio, timefrequency representation is an important aspect. In case of representing or analyzing such kind of signal in time-frequency-energy distribution, hilbert spectrum is a recent approach and popular way which has several advantages over other methods like STFT, WT etc. Hilbert-Huang Transform (HHT) is a prominent method consists of Empirical Mode Decomposition (EMD) and Hilbert Spectral Analysis (HSA). An enhanced method called Turning Tangent empirical mode decomposition (2T-EMD) has recently developed to overcome some limitations of classical EMD like cubic spline problems, sifting stopping condition etc. 2T-EMD based hilbert spectrum of audio signal encountered some issues due to the generation of too many IMFs in the process where EMD produces less. To mitigate those problems, a mutual implementation of 2T-EMD & classical EMD is proposed in this paper which enhances the representation of hilbert spectrum along with significant improvements in source separation result using Independent Subspace Analysis (ISA) based clustering in case of audio signals. This refinement of hilbert spectrum not only contributes to the future work of source separation problem but also many other applications in audio signal processing.

Gearbox Fault Detection Using Empirical Mode Decomposition

2004 ◽  
Author(s):  
Xianfeng Fan ◽  
Ming J. Zuo
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Vibration Signal ◽  
Original Data ◽  
Intrinsic Mode Functions ◽  
Hilbert Huang Transform ◽  
Mode Decomposition ◽  
Stationary Vibration ◽  
Non Stationary Signal ◽  
Envelope Spectrum

Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special IMF’s envelope spectrum will become larger when fault impulses are present. Based on this idea, we propose a new fault detection method that combines EMD with Hilbert transform. The proposed method is compared with both the Hilbert-Huang transform and the wavelet transform using simulated signal and real signal collected from a gearbox. The results obtained show that the proposed method is effective in capturing the hidden fault impulses.

scholarly journals An Improved Empirical Mode Decomposition Method for Vibration Signal

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaohan Liu ◽  
Guangfeng Shi ◽  
Weina Liu
Keyword(s):  
Signal Processing ◽  
Power Spectrum ◽  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Vibration Signal ◽  
Vibration Monitoring ◽  
Blasting Vibration ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method

With the development of electronic measurement and signal processing technology, nonstationary and nonlinear signal characteristics are widely used in the fields of error diagnosis, system recognition, and biomedical instruments. Whether these features can be extracted effectively usually affects the performance of the entire system. Based on the above background, the research purpose of this paper is an improved vibration empirical mode decomposition method. This article introduces a method of blasting vibration signal processing—Differential Empirical Mode Decomposition (DEMD), combined with phosphate rock engineering blasting vibration monitoring test, and Empirical Mode Decomposition (EMD) to compare and analyze the frequency screening of blasting vibration signals, the aliasing distortion, and the power spectrum characteristics of the decomposed signal. The results show that compared with EMD, DEMD effectively suppresses signal aliasing and distortion, and from the characteristics of signal power spectrum changes, DEMD extracts different dominant frequency components, and the frequency screening effect of blasting vibration signals is superior to EMD. It can bring about an obvious improvement in accuracy, and the calculation time is about 4 times that of the EMD method. Based on the ground analysis of ground motion signals, this paper uses the EMD algorithm to analyze measured ground blast motion signals and study its velocity characteristics and differential time, which provides a new way of studying motion signals.

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.

Faults Monitoring and Diagnosis of Ball Bearing Based on Hilbert-Huang Transformation

2005 ◽  
Vol 291-292 ◽  
pp. 649-654 ◽  
Author(s):  
H. Li ◽  
H.Q. Zheng ◽  
L.W. Tang
Keyword(s):  
Energy Spectrum ◽  
Machine Tool ◽  
Empirical Mode Decomposition ◽  
Ball Bearing ◽  
Intrinsic Mode Function ◽  
Vibration Signals ◽  
Monitoring And Diagnosis ◽  
Mode Function ◽  
Mode Decomposition ◽  
Faults Diagnosis

A novel scheme for ball bearing faults detection is presented based on Hilbert-Huang transformation and its energy spectrum. The basic method is introduced in detail. The energy spectrum is applied in the research of the faults diagnosis for the ball bearing of machine tool. Firstly, the analyzed vibration signals are separated into a series of intrinsic mode function using the empirical mode decomposition. Then, the energy spectrum is applied to the intrinsic mode function. The experimental results show that this method based on Hilbert-Huang transformation and energy spectrum can effectively diagnosis the faults of the ball bearing.

scholarly journals Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery

2018 ◽  
Vol 8 (9) ◽  
pp. 1441 ◽  
Author(s):  
Liang Fang ◽  
Hongchun Sun
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Single Channel ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Vibration Signals ◽  
Mode Decomposition ◽  
Fault Features ◽  
Kernel Independent Component Analysis

A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode decomposition (EMD), variational mode decomposition (VMD) and EEMD, the better performance of this method is proven with three experimental analyses of faulty gear, a faulty rolling bearing and a faulty shaft. The results demonstrate that the proposed method is efficient to extract the fault features of single-channel vibration signals of rotating machinery.

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