scholarly journals Potential of Empirical Mode Decomposition for Hilbert Demodulation of Acoustic Emission Signals in Gearbox Diagnostics

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Spectrum ◽  
Ae Signals

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals.

Gear Fault Diagnosis Based on Harmonic Wavelet Packet and BP Neural Network

2012 ◽  
Vol 217-219 ◽  
pp. 2683-2687 ◽  
Author(s):  
Chen Jiang ◽  
Xue Tao Weng ◽  
Jing Jun Lou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Fault Detection And Identification ◽  
Gear Fault ◽  
Harmonic Wavelet ◽  
Gear Fault Diagnosis

The gear fault diagnosis system is proposed based on harmonic wavelet packet transform (WPT) and BP neural network techniques. The WPT is a well-known signal processing technique for fault detection and identification in mechanical system，In the preprocessing of vibration signals, WPT coefficients are used for evaluating their energy and treated as the features to distinguish the fault conditions.In the experimental work, the harmonic wavelets are used as mother wavelets to build and perform the proposed WPT technique. The experimental results showed that the proposed system achieved an average classification accuracy of over 95% for various gear working conditions.

Condition Monitoring of PMSM and Stator-inter Turn Fault Diagnosis by Advanced Signal Processing Technique

2021 ◽  
Vol 23 (07) ◽  
pp. 1419-1430
Author(s):  
Khadim Moin Siddiqui ◽  
◽  
Farhad Ilahi Bakhsh ◽  
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Dynamic Performance ◽  
Processing Technique ◽  
Industrial Applications ◽  
Fault Analysis ◽  
Industrial Plant ◽  
Signal Processing Technique ◽  
Permanent Magnet Synchronous Motors

In the present time, Permanent Magnet Synchronous Motors (PMSMs) are extensively used in many industrial applications due to its advantages over conventional synchronous motor. The PMSM is compact and efficient with high dynamic performance, thus having more advantages such as light weight, small size and bulky burden ability. When PMSMs are failed during the operation then large revenue losses occurs for industries. Hence, it is essential to diagnose these faults before occurring, for protection of any industrial plant. In the paper, firstly a comprehensive review of condition monitoring has been done for PMSM faults and their diagnostics techniques. From review, it is found that the stator inter-turn fault diagnosis has been the challenging task for many researchers. Hence, the work has been extended for fault analysis of stator inter-turn under transient conditions, which is effectively analyzed with the help of advanced signal processing technique.

scholarly journals A Reservoir Information Extraction Method Based on Improved Hilbert-Huang Transform

2019 ◽  
pp. 85-90
Author(s):  
Qingmi Yang
Keyword(s):  
Signal Processing ◽  
Seismic Signal ◽  
Ensemble Empirical Mode Decomposition ◽  
Processing Technique ◽  
Intrinsic Mode Functions ◽  
Signal Processing Technique ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Non Stationary Signal

Hilbert-Huang transform (HHT) is a nonlinear non-stationary signal processing technique, which is more effective than traditional time-frequency analysis methods in complex seismic signal processing. However, this method has problems such as modal aliasing and end effect. The problem causes the accuracy of signal processing to drop. Therefore, this paper introduces the method of combining the Ensemble Empirical Mode Decomposition (EEMD) and the Normalized Hilbert transform (NHT) to extract the instantaneous properties. The specific process is as follows: First, the EEMD method is used to decompose the seismic signal to a series of Intrinsic Mode Functions (IMF), and then The IMFs is screened by using the relevant properties, and finally the NHT is performed on the IMF to obtain the instantaneous properties.

scholarly journals MODWT, PCA and Decision Tree based Fault diagnosis of Gear

2021 ◽  
Vol 23 (07) ◽  
pp. 376-386
Author(s):  
Mansi Mansi ◽  
◽  
Sukhdeep S. Dhami ◽  
Vanraj Vanraj ◽  
◽  
...  
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Decision Tree ◽  
Processing Technique ◽  
Classification Performance ◽  
Performance Comparison ◽  
Vibration Monitoring ◽  
Fault Classification ◽  
Discrete Wavelet ◽  
Signal Processing Technique

A gearbox is an important power transmission equipment. Its maintenance is a top requirement because it is prone to a variety of failures. For gearbox fault diagnosis, techniques such as vibration monitoring have been widely used. Also, when it comes to machine Condition monitoring and fault diagnostics, feature extraction is the crucial step. For a classifier to perform accurately, it must have the appropriate discriminative information or features. Hence, this paper proposes a signal processing methodology based on Maximal overlap discrete wavelet transform (MODWT) and a dimensionality reduction technique i.eprincipal component analysis (PCA) to reduce the dimensionality of the feature space and obtain an ideal subspace for machine fault classification. Firstly, the raw vibration signature is denoised with the help of a state-of-the-art MODWT signal processing technique to identify the hidden fault signatures. Then various traditional statistical features are extracted from this denoised signal. These multi-dimensional features are then processed with PCA and further, the Decision Tree is used for fault classification. Performance comparison of the proposed method with traditional raw analysis and without application of PCA is presented and the proposed method outperforms at every level.

scholarly journals Vibration Signal Processing-Based Detection of Short-Circuited Turns in Transformers: A Nonlinear Mode Decomposition Approach

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 575 ◽  
Author(s):  
Jose R. Huerta-Rosales ◽  
David Granados-Lieberman ◽  
Juan P. Amezquita-Sanchez ◽  
David Camarena-Martinez ◽  
Martin Valtierra-Rodriguez
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Signal Processing Technique ◽  
Correct Operation ◽  
Decomposition Approach ◽  
Vibration Signals ◽  
Nonlinear Mode ◽  
Mode Decomposition ◽  
Fault Severity ◽  
Low Levels

Transformers are vital and indispensable elements in electrical systems, and therefore, their correct operation is fundamental; despite being robust electrical machines, they are susceptible to present different types of faults during their service life. Although there are different faults, the fault of short-circuited turns (SCTs) has attracted the interest of many researchers around the world since the windings in a transformer are one of the most vulnerable parts. In this regard, several works in literature have analyzed the vibration signals that generate a transformer as a source of information to carry out fault diagnosis; however this analysis is not an easy task since the information associated with the fault is embedded in high level noise. This problem becomes more difficult when low levels of fault severity are considered. In this work, as the main contribution, the nonlinear mode decomposition (NMD) method is investigated as a potential signal processing technique to extract features from vibration signals, and thus, detect SCTs in transformers, even in early stages, i.e., low levels of fault severity. Also, the instantaneous root mean square (RMS) value computed using the Hilbert transform is proposed as a fault indicator, demonstrating to be sensitive to fault severity. Finally, a fuzzy logic system is developed for automatic fault diagnosis. To test the proposal, a modified transformer representing diverse levels of SCTs is used. These levels consist of 0 (healthy condition), 5, 10, 15, 20, and 25 SCTs. Results demonstrate the capability of the proposal to extract features from vibration signals and perform automatic fault diagnosis.

Gear Fault Diagnosis Using Synchro-Squeezing Transform Based Feature Analysis

2013 ◽  
Vol 569-570 ◽  
pp. 449-456 ◽  
Author(s):  
Budhaditya Hazra ◽  
Sriram Narasimhan
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Mathematical Structure ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Gear Fault Diagnosis ◽  
The Rich ◽  
The Individual ◽  
Non Stationary Signal

Synchro-squeezing transform has recently emerged as a powerful signal processing tool in non-stationary signal processing. Premised upon the concept of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane and extract the individual components of a non-stationary multi-component signal, akin to empirical mode decomposition (EMD). The rich mathematical structure based on continuous wavelet transform (CWT) makes synchro-squeezing powerful for gear fault diagnosis, as faulty gear signal is frequently constituted out of multiple amplitude-modulated and frequency-modulated signals embedded in noise. This work utilizes the decomposing power of synchro-squeezing transform to extract the IMFs from a gear signal followed by the application of standard gearbox condition indicators which promises greater prognostic power than that can be achieved by applying condition indictors directly to the inherently complex gear signals. The efficacy and the robustness of the algorithm are demonstrated with the aid of practical experimental data obtained from a helicopter gear box.

Vibration Signal Processing for Gear Fault Diagnosis Based on Empirical Mode Decomposition and Nonlinear Blind Source Separation

2011 ◽  
Vol 42 (10) ◽  
pp. 55-61 ◽  
Author(s):  
Yu Jiang ◽  
Li Qin ◽  
Yuelei Zhang ◽  
Jingping Wu
Keyword(s):  
Signal Processing ◽  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Blind Source Separation ◽  
Single Channel ◽  
Source Separation ◽  
Vibration Signal ◽  
Normal Operation ◽  
Mode Decomposition ◽  
Gear Fault

Gear failures happen frequently in the gear mechanisms, and an unexpected serious gear fault may cause severe damage on the machinery. Hence, precise gear fault detection at the early stage is imperative to ensure the normal operation of the machinery. Independent component analysis (ICA) has been paid more and more attention for its powerful ability of separating the useful vibration source from the multi-sensor observations to enhance the fault feature extraction. This is the so called blind source separation (BSS) procedure. However, the popular ICA model may suffer from two limitations. One is the linear mixture assumption, and the other is the lack of sensor channels. Up to now, only limited research considered the nonlinear ICA model in the field of mechanic fault diagnosis, and techniques for the situation where the number of sensor channels is less than the number of independent sources for gear defect detection are scarce. In order to extract the useful source involved with the gear fault characteristics in single-channel vibration signal processing, this work presents a new method based on the empirical mode decomposition (EMD) and nonlinear ICA. The EMD was firstly employed to decompose the vibration signal into a number of intrinsic mode functions (IMFs), and then these IMFs were taken as the multi-channel observations. The post-nonlinear (PNL) ICA model based on the radial basis function (RBF) neural network was applied to the nonlinear BSS procedure on the IMFs. The experimental vibration data acquired from the gear fault test-bed were processed for the validation of the proposed method. The nonlinear ICA method has been compared with the linear ICA and non-ICA based approaches. The analysis results show that the sensitive characteristics of the gear meshing vibration can be separated from the single channel measurement by the proposed method, and the fault diagnosis precision can be enhanced significantly. The detection rate can be increased by 3.75% or better when the ICA based preprocessing is carried out, and the proposed nonlinear ICA outperforms the linear ICA detection model.

A Novel Signal Mechanical Signal Processing Technique for Detection of Perturbations in Coupled Microresonators

2010 ◽  
Author(s):  
M. Sadegh Hajhashemi ◽  
Behraad Bahreyni
Keyword(s):  
Signal Processing ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Micromechanical Resonators ◽  
Sensing Applications ◽  
Signal Processing Method ◽  
Mechanical Signal ◽  
Novel Method ◽  
Quantities Of Interest

In this paper we present a novel method for processing of the signals from sensor systems based on coupled micromechanical resonators. Microfabricated resonators typically have electrostatic or piezoelectric interfaces. Properly designed microresonators can be mechanically coupled to each other in order to realize a bandpass filter transfer function. We will demonstrate that it is possible to use coupled microresonators for sensing applications and propose a novel mechanical signal processing method to detect quantities of interest.

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