scholarly journals Instantaneous Amplitude-Frequency Feature Extraction for Rotor Fault Based on BEMD and Hilbert Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Chuanjin Huang ◽  
Haijun Song ◽  
Wenping Lei ◽  
Zhanya Niu ◽  
Yajun Meng
Keyword(s):  
Feature Extraction ◽  
Hilbert Transform ◽  
Vibration Signal ◽  
Three Dimensions ◽  
Intrinsic Mode Functions ◽  
Instantaneous Amplitude ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
The Hilbert Transform

The vibration signals propagating in different directions from rotating machines can contain a variety of characteristic information. A novel feature extraction method based on bivariate empirical mode decomposition (BEMD) for rotor is proposed to comprehensively extract the fault features. In this work, the number of signal projection directions is determined through simulation, and the energy end condition based on the energy threshold is increased using BEMD to enhance the decomposition quality. Mixed vibration signals are generated along two orthogonal directions. Then, the acquired vibration signal can be decomposed into several intrinsic mode functions (IMFs) at the rotational speed using the BEMD method. Furthermore, the instantaneous frequency and instantaneous amplitude of the real signals and the imaginary part of the IMF signals are obtained using the Hilbert transform. The fault features along two and three dimensions can be investigated, providing more comprehensive information to aid in the fault diagnosis of rotor. Experimental results on oil film oscillation, the oil whirl, the bistability of the rotor, and looseness and rotor rubbing composite fault indicate the effectiveness of the proposed method.

Feature Extraction of Rolling Bearing Based on LMD Energy Feature

2017 ◽  
Vol 868 ◽  
pp. 363-368
Author(s):  
Bang Sheng Xing ◽  
Le Xu
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Small Samples ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Local Mean ◽  
Energy Feature

For the situation that it is difficult to diagnose rolling bearings fault effectively for small samples, so it proposes a feature extraction method of rolling bearing based on local mean decomposition (LMD) energy feature. Due to the frequency domain distribution of vibration signals will change when different faults occur in rolling bearings, so it can use LMD energy feature method to extract the fault features of rolling bearings. The instances analysis and extracted results show that the LMD energy feature can extract the vibration signal fault feature of rolling bearings effectively.

scholarly journals A Novel Feature Extraction Method for Power Transformer Vibration Signal Based on CEEMDAN and Multi-Scale Dispersion Entropy

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1319
Author(s):  
Haikun Shang ◽  
Junyan Xu ◽  
Yucai Li ◽  
Wei Lin ◽  
Jinjuan Wang
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Vibration Signal ◽  
Practical Significance ◽  
Stable Operation ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Multi Scale

Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-scale dispersion entropy (MDE) was proposed. In this paper, CEEMDAN method is used to decompose the original transformer vibration signal. Additionally, then MDE is used to capture multi-scale fault features in the decomposed intrinsic mode functions (IMFs). Next, the principal component analysis (PCA) method is employed to reduce the feature dimension and extract the effective information in vibration signals. Finally, the simplified features are sent into density peak clustering (DPC) to get the fault diagnosis results. The experimental data analysis shows that CEEMDAN-MDE can effectively extract the information of the original vibration signals and DPC can accurately diagnose the types of transformer faults. By comparing different algorithms, the practicability and superiority of this proposed method are verified.

Study on Feature Extraction Method for Diesel Engine Valve Faults

2016 ◽  
Author(s):  
Hongzi Fei ◽  
Long Liu ◽  
Xuemin Li ◽  
Xiuzhen Ma
Keyword(s):  
Feature Extraction ◽  
Diesel Engine ◽  
Vibration Signal ◽  
Intrinsic Mode Functions ◽  
Stationary Characteristic ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Fault Features ◽  
Empirical Mode Decomposition Method ◽  
Faults Diagnosis

Valve faults diagnosis technique of a diesel engine is studied deeply in this paper. The experiment of valve clearance and air leakage faults are done in a diesel engine, and cylinder head vibration and transient speed signals are measured synchronously on normal and fault conditions respectively. These signals are used to feature extraction. In order to avoid the leakage and aliasing of vibration signal’s frequent spectrum, resample method based on order tracking is proposed, and vibration signal was transformed from time domain to crank angle domain accurately. Considering the non-stationary characteristic of vibration signal, a series of intrinsic mode functions with different scales were obtained using the empirical mode decomposition method, and fault features parameters were extracted through 3D Hilbert spectrums of the intrinsic mode functions. Experimental results show that the method can effectively extract fault features of diesel engine and use them to realize the valve system faults diagnosis further.

scholarly journals A Novel Feature Extraction Method for the Condition Monitoring of Bearings

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2322
Author(s):  
Abdenour Soualhi ◽  
Bilal El Yousfi ◽  
Hubert Razik ◽  
Tianzhen Wang
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Health Indicator ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Marginal Spectrum ◽  
Spectrum Density ◽  
The Hilbert Transform

This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings.

Tool Breakage Feature Extraction in PCB Micro-Hole Drilling Using Vibration Signals

2012 ◽  
Vol 497 ◽  
pp. 126-131 ◽  
Author(s):  
Zhen Hua Ren ◽  
Xiao Hu Zheng ◽  
Qing Long An ◽  
Cheng Yong Wang ◽  
Ming Chen
Keyword(s):  
Feature Extraction ◽  
Printed Circuit Board ◽  
Vibration Signal ◽  
Circuit Board ◽  
Hole Drilling ◽  
Features Selection ◽  
Tool Breakage ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Micro Hole

Tool breakage monitoring is crucial to automation fabrication, especially for high-density hole machining, such as PCB (Printed Circuit Board). A tool breakage feature extraction method in PCB micro-hole drilling is presented in this paper. The vibration signal is analyzed by wavelet transform. The decomposed signals energy ratio at each frequency band is computed as monitoring features. The monitoring performance of different features selection is given. The vibration signals are observed to provide the capability in distinguishing micro drill breakage with proper features extraction and classifier design.

Fault Analysis of a Dust-Removing Blower in a Sintering Plant Based on Envelope Analysis

2015 ◽  
Vol 779 ◽  
pp. 145-150
Author(s):  
Zi Wang ◽  
Yu Dong Yang ◽  
Jing Liu ◽  
Xiao Ping Qu ◽  
Yan Yang Zhou
Keyword(s):  
Hilbert Transform ◽  
Vibration Signal ◽  
Fault Analysis ◽  
Envelope Analysis ◽  
Fault Features ◽  
Diagnosis Method ◽  
The Hilbert Transform ◽  
Very High ◽  
Dynamic Unbalance ◽  
Sintering Plant

Dust-removing blower is a key equipment in sintering plants, which can provide enough wind and negative pressure. It can also improve the efficiency of dust-removing. The vibration level of a dust-removing blower in a sintering plant is very high, which is beyond its normal value. Due to the complex working condition and strong background noise, it is difficult to extract fault features from the vibration signal of the dust-removing blower. Therefore, fault analysis of the blower is very difficult. Since the modulation phenomenon existed in the vibration signal of the blower is found, the envelope analysis based on the Hilbert transform is proposed to demodulate the vibration signal. The frequency spectrum of the demodulated signal shows that the first order frequency characteristic is obvious, which can effectively reveal the dynamic unbalance of the rotor system is the main reason of the abnormal vibration of the blower. According to this diagnosis, some possible reasons for the unbalance are proposed, as well as advices regarding to the repair of the blower system. Moreover, the test and analysis are conducted on the repaired blower system. The results show that the unbalance problem is eliminated and the blower can work normally, which can validate the accuracy and reliability of the proposed diagnosis method for fault analysis of the dust-removing blower.Keywords: dynamic unbalance; modulation; dust-removing blower; Hilbert Transform

Fault Feature Extraction of High-Speed Automaton Based on Motion Morphology Decomposition

2013 ◽  
Vol 347-350 ◽  
pp. 224-227
Author(s):  
Ai Yu Wang ◽  
Hong Xia Pan ◽  
Hui Ling Liu
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Characteristic Parameters ◽  
Feature Extraction Method ◽  
Fault Features ◽  
Fault State ◽  
Fault Feature Extraction ◽  
Fault Characteristic

In order to obtain the characteristic parameters reflecting fault state of high-speed automaton (HSA), the fault feature extraction method based on motion morphology decomposition and wavelet packet transform (WPT) was presented. According to the movement law of the automaton, the vibration signal generated in three bursts of fire was decomposed into three separate signals, then the response signal in each shooting is a separate signal. Then using WPT to respectively extract wavelet packet energy from three separate signals as the fault characteristic parameters of HSA. By the example, the results show that the extracted fault features can well reflect the working conditions of automaton. Thus the proposed method could be used to extract the fault feature of automaton for monitoring the condition and diagnosing the fault of automaton.

scholarly journals Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

2018 ◽  
Vol 5 (5) ◽  
pp. 180087 ◽  
Author(s):  
Xiang Chen ◽  
Jingchao Li ◽  
Hui Han ◽  
Yulong Ying
Keyword(s):  
Feature Extraction ◽  
Hilbert Transform ◽  
Radiation Source ◽  
Box Dimension ◽  
Instantaneous Amplitude ◽  
Box Counting ◽  
Box Counting Dimension ◽  
Source Signal ◽  
The Hilbert Transform ◽  
Subtle Feature

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.

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.

scholarly journals A Feature Extraction Method for Vibration Signal of Bearing Incipient Degradation

2016 ◽  
Vol 16 (3) ◽  
pp. 149-159 ◽  
Author(s):  
Haifeng Huang ◽  
Huajiang Ouyang ◽  
Hongli Gao ◽  
Liang Guo ◽  
Dan Li ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Signal To Noise Ratio ◽  
Experimental Studies ◽  
Vibration Signal ◽  
Noise Sources ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Feature Extraction Method ◽  
Spectral Kurtosis

Abstract Detection of incipient degradation demands extracting sensitive features accurately when signal-to-noise ratio (SNR) is very poor, which appears in most industrial environments. Vibration signals of rolling bearings are widely used for bearing fault diagnosis. In this paper, we propose a feature extraction method that combines Blind Source Separation (BSS) and Spectral Kurtosis (SK) to separate independent noise sources. Normal, and incipient fault signals from vibration tests of rolling bearings are processed. We studied 16 groups of vibration signals (which all display an increase in kurtosis) of incipient degradation after they are processed by a BSS filter. Compared with conventional kurtosis, theoretical studies of SK trends show that the SK levels vary with frequencies and some experimental studies show that SK trends of measured vibration signals of bearings vary with the amount and level of impulses in both vibration and noise signals due to bearing faults. It is found that the peak values of SK increase when vibration signals of incipient faults are processed by a BSS filter. This pre-processing by a BSS filter makes SK more sensitive to impulses caused by performance degradation of bearings.

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