AE Fault Diagnosis of Rolling Bearing Based on Base

2011 ◽  
Vol 143-144 ◽  
pp. 664-668
Author(s):  
D.L. Yang ◽  
X.J. Li
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Dimensionality Reduction ◽  
Rolling Bearing ◽  
Acoustic Emission Sensors ◽  
Ae Signal ◽  
Acoustic Emission Test ◽  
Fault Source ◽  
Original Feature

In the acoustic emission fault diagnosis, the acoustic emission sensors was installed on the bearing pedestal where near from the fault source so that can collected stronger fault AE signal, however ,sometimes, it is inconvenience for AE sensor installation. This paper proposed that install the AE sensor on the base for collect the fault AE signal, but the signal was weak, so carried on EMD first, and selected the former 8 IMF to construct the original feature, than carried on KPCA for dimensionality reduction and get the optimized feature. In this paper, taking bearing acoustic emission test for example, by compared the base fault feature with the bearing pedestal fault feature, verified that the method that the AE sensor install on the base is feasible.

scholarly journals MEMS Acoustic Emission Sensors

2020 ◽  
Vol 10 (24) ◽  
pp. 8966
Author(s):  
Didem Ozevin
Keyword(s):  
Acoustic Emission ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Frequency Bandwidth ◽  
Micro Electro Mechanical Systems ◽  
Design Variables ◽  
Transduction Mechanisms ◽  
Acoustic Emission Sensors ◽  
Ae Signal ◽  
Constitutive Properties

This paper presents a review of state-of-the-art micro-electro-mechanical-systems (MEMS) acoustic emission (AE) sensors. MEMS AE sensors are designed to detect active defects in materials with the transduction mechanisms of piezoresistivity, capacitance or piezoelectricity. The majority of MEMS AE sensors are designed as resonators to improve the signal-to-noise ratio. The fundamental design variables of MEMS AE sensors include resonant frequency, bandwidth/quality factor and sensitivity. Micromachining methods have the flexibility to tune the sensor frequency to a particular range, which is important, as the frequency of AE signal depends on defect modes, constitutive properties and structural composition. This paper summarizes the properties of MEMS AE sensors, their design specifications and applications for detecting the simulated and real AE sources and discusses the future outlook.

Study and Analysis of On-Line Detection and Fault Diagnosis System of Rolling Bearing

2013 ◽  
Vol 443 ◽  
pp. 218-222
Author(s):  
Jing Zi Wei ◽  
Ran Zhang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fault Detection And Diagnosis ◽  
Detection Methods ◽  
Line Detection ◽  
Diagnosis System ◽  
Bearing Fault ◽  
On Line ◽  
Fault Diagnosis System

This paper first of all gives an introduction to the structure of rolling bearing, development stage of fault and the main fault types; then, it makes an analysis of the common detection methods and the technologies involved in rolling bearing fault; at last, based on the emphasis on the rolling bearing on-line detection and fault diagnosis system of acoustic emission technology, it elaborates the basic principles of acoustic emission, rolling bearing fault detection and diagnosis system experiment setting. Meanwhile, it introduces modern signal processing technology into acoustic emission information feature extraction and state recognition, such as wavelet analysis and wavelet packet analysis.

A Multi-Fault Diagnosis Method of Rolling Bearing Based on Wavelet-PCA and Fuzzy K-Nearest Neighbor

2010 ◽  
Vol 29-32 ◽  
pp. 1602-1607 ◽  
Author(s):  
Xiang Shun Chen ◽  
Hu Biao Zeng ◽  
Zhi Xiong Li
Keyword(s):  
Fault Diagnosis ◽  
Nearest Neighbor ◽  
Rolling Bearing ◽  
Fault Detection And Diagnosis ◽  
Operating Conditions ◽  
K Nearest Neighbor ◽  
Rolling Bearings ◽  
Classification Rate ◽  
Wavelet Pca ◽  
Original Feature

Rolling bearings are widely used in various areas including aircraft, mining, manufacturing, and agriculture, etc. The breakdowns of the rotational machinery resulted from the rolling bearing failures account for 30%. It is therefore imperative to monitor the rolling bearing conditions in time in order to prevent the malfunctions of the plants. In the present paper is described a fault detection and diagnosis technique for rolling bearing multi-faults based on wavelet-principle component analysis (PCA) and fuzzy k-nearest neighbor (FKNN). In the diagnosis process, the wavelet analysis was firstly employed to decompose the vibration data of the rolling bearings under eight different operating conditions, and for each sample its energy of each sub-band was calculated to obtain the original feature space. Then, the PCA was used to reduce the dimensionality of the original feature vector and hence the most important features could be gotten. Lastly, the FKNN algorithm was employed in the pattern recognition to identify the conditions of the bearings of interest. The experimental results suggest that the sensitive fault features can be extracted efficiently after the wavelet-PCA processing, and the proposed diagnostic system is effective for the rolling bearing multi-fault diagnosis. In addition, the proposed method can achieve higher performance than that without PCA with respect to the classification rate.

De-Noising Method of Acoustic Emission Signal for Rolling Bearing Based on Adaptive Wavelet Correlation Analysis

2013 ◽  
Vol 273 ◽  
pp. 188-192
Author(s):  
Xin Li ◽  
Xue Jun Li ◽  
Guang Bin Wang
Keyword(s):  
Acoustic Emission ◽  
Correlation Analysis ◽  
Rolling Bearing ◽  
Threshold Method ◽  
Adaptive Wavelet ◽  
Wavelet Entropy ◽  
Emission Signal ◽  
Rotary Machine ◽  
Dyadic Wavelet ◽  
Ae Signal

In acoustic emission (AE) detection technique, to avoid the serious noise disturbance in the fault diagnosis of rotary machine, a de-noising method based on adaptive wavelet correlation analysis to be applied to the AE signal is proposed. First, AE signals are decomposed by dyadic wavelet transform and at the same time the AE signal is divided into available coefficients and noise coefficients. Secondly, the available coefficients are reconstructed to restore the original real signal after de-noising process. Finally, the de-noising threshold is set by adaptive threshold method based on wavelet entropy. On the simulation of AE signal and the bearing fault measured AE signal using wavelet entropy correlation de-noising method, the traditional wavelet de-noising method and the traditional lifting wavelet de-noising method three kinds of de-noising methods are compared, the results show that the wavelet entropy correlation de-noising method can greatly improve the rolling bearing AE signal de-noising effect.

scholarly journals Multi-Domain Entropy-Random Forest Method for the Fusion Diagnosis of Inter-Shaft Bearing Faults with Acoustic Emission Signals

Entropy ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 57 ◽  
Author(s):  
Jing Tian ◽  
Lili Liu ◽  
Fengling Zhang ◽  
Yanting Ai ◽  
Rui Wang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Random Forest ◽  
High Sensitivity ◽  
Generalization Ability ◽  
Bearing Faults ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Ae Signal ◽  
Ae Signals

Inter-shaft bearing as a key component of turbomachinery is a major source of catastrophic accidents. Due to the requirement of high sampling frequency and high sensitivity to impact signals, AE (Acoustic Emission) signals are widely applied to monitor and diagnose inter-shaft bearing faults. With respect to the nonstationary and nonlinear of inter-shaft bearing AE signals, this paper presents a novel fault diagnosis method of inter-shaft bearing called the multi-domain entropy-random forest (MDERF) method by fusing multi-domain entropy and random forest. Firstly, the simulation test of inter-shaft bearing faults is conducted to simulate the typical fault modes of inter-shaft bearing and collect the data of AE signals. Secondly, multi-domain entropy is proposed as a feature extraction approach to extract the four entropies of AE signal. Finally, the samples in the built set are divided into two subsets to train and establish the random forest model of bearing fault diagnosis, respectively. The effectiveness and generalization ability of the developed model are verified based on the other experimental data. The proposed fault diagnosis method is validated to hold good generalization ability and high diagnostic accuracy (~0.9375) without over-fitting phenomenon in the fault diagnosis of bearing shaft.

Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

2017 ◽  
Vol 17 (2) ◽  
pp. 156-168 ◽  
Author(s):  
Cheng-Wei Fei ◽  
Yat-Sze Choy ◽  
Guang-Chen Bai ◽  
Wen-Zhong Tang
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Distance Method ◽  
Bearing Faults ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Entropy Distance

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

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