scholarly journals Bearing Fault Diagnosis Based on Collaborative Representation Using Projection Dictionary Pair

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Dan Ma ◽  
Yixiang Lu ◽  
Yushun Zhang ◽  
Hua Bao ◽  
Xueming Peng
Keyword(s):  
Learning Strategy ◽  
Vibration Signal ◽  
Collaborative Representation ◽  
Rolling Bearings ◽  
Frequency Spectra ◽  
Similar Frequency ◽  
Vibration Signals ◽  
Redundant Dictionary ◽  
Learned Dictionary

In state analysis of rolling bearings using collaborative representation theory, how to construct an excellent redundant dictionary to collaboratively represent the acquired normal or abnormal data has been being a significant issue. Thus, a new method for fault detection and classification of rolling bearings is proposed in this paper. The proposed algorithm mainly consists of three components. First, a wavelet transform is employed to extract features, which takes advantage of the observation that vibration signals under different conditions have similar frequency spectra. This similarity ensures that we can collaboratively represent any test sample by using training samples. Second, under the similarity assumption, a dictionary pair learning strategy is employed to build an overcomplete dictionary pair, which is used to realize an optimal representation of the vibration signal. Meanwhile, the sparse constraint is also taken into account during dictionary training to enhance the robustness of the classification. Finally, the learned dictionary combined with collaborative representation is used to intelligently perform pattern classification of rolling bearings. The effectiveness and superiority of the method are verified by applying the proposed algorithm on the simulated and real vibration signals. The results show that, for different fault categories generated from different fault size and motor loads, our method can rapidly and accurately identify the fault category to which the input sample belongs.

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.

Health Monitoring of a Gear Box Using Vibration Signal Analysis

2015 ◽  
Vol 813-814 ◽  
pp. 1012-1017 ◽  
Author(s):  
M.R. Praveen ◽  
M. Saimurugan
Keyword(s):  
Frequency Domain ◽  
Health Monitoring ◽  
Crucial Role ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Vibration Signals ◽  
Nearest Neighbours ◽  
Gear Box ◽  
Vibration Signal Analysis

A gear plays a crucial role in the performance of a gear box. The faults in a gear reduces the gear life and if problem arises in shaft it affects bearing. Gear box is finally affected due to these faults. Vibration signals carries information about condition of a gear box which are captured using piezoelectric accelerometer. In this paper, features are extracted and classified using K nearest neighbours (KNN) algorithms for both time and frequency domain. The effectiveness of KNN in classification of gear faults for both time and frequency domain is discussed and compared.

scholarly journals RESEARCH OF FEATURES OF DEVELOPMENT OF BEARING DEFECTS ON THE BASIS OF WAVELET ANALYSIS

2020 ◽  
pp. 5-13
Author(s):  
Nataliya Veselovska ◽  
Serhiy Shargorodskiy ◽  
Bohdan Bratslavets ◽  
Olha Yalina
Keyword(s):  
Wavelet Analysis ◽  
Vibrational Energy ◽  
Vibration Signal ◽  
The Body ◽  
Technical Diagnostics ◽  
Industrial Complex ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Bearing Defects ◽  
Wide Range

Today the vibrodiagnostic method achieves the highest efficiency and manufacturability for the operation of the technical condition of the technological equipment of the agro-industrial complex. At the same time, this method is one of the most modern methods of technical diagnostics, indicating the kinematic warehouses of diagnostic objects. Vibration analysis is a fundamental tool for diagnostic monitoring of bearings. The vibration signal of defective rolling bearings and its spectrum contain characteristic features by which it is possible to fairly correctly identify the type and location of the defect. At the moment the defective element passes through the loaded zone of the rolling bearing, a pronounced peak, an energy impulse, appears in the vibration. Thus, when a bearing with internal defects is operating, characteristic components appear in vibration - harmonics with natural frequencies, the numerical values of which can be calculated using theoretical formulas using the geometric dimensions of the bearing elements and the rotor speed of the mechanism. In a loaded bearing, four characteristic frequencies can be distinguished that are used for diagnostics - the frequency of the outer bearing cage, the frequency of the inner cage, the cage frequency and the rolling element frequencies. The complexity of the analysis of vibration signals of rolling bearings for the purpose of their diagnostics lies in the fact that the signs of a defective bearing are distributed over a wide range of frequencies, have low vibrational energy and are somewhat random in nature. In addition, the vibration signal is, of course, removed from the body of the equipment containing the bearing, and therefore contains not only information useful from the point of view of bearing diagnostics, but also noise - vibrations produced by other parts of the mechanism. The analysis of methods for diagnosing bearing defects based on wavelet analysis of their vibration signals allows us to single out the most promising direction, which consists in the fact that the bearing vibration signal is decomposed into coefficients using wavelet analysis, after which the most significant coefficients are selected from these coefficients.

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 Fuzzy C-Means Based Clustering and Rule Formation Approach for Classification of Bearing Faults Using Discrete Wavelet Transform

Computation ◽  
2019 ◽  
Vol 7 (4) ◽  
pp. 54 ◽  
Author(s):  
Anbu ◽  
Thangavelu ◽  
Ashok
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Fuzzy Rule ◽  
Discrete Wavelet ◽  
Clustering Method ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

The Wavelet De-Noising of Vibration Signals for Aircraft Rolling Bearings

2014 ◽  
Vol 912-914 ◽  
pp. 873-877 ◽  
Author(s):  
Feng Kui Cui ◽  
Fei Fei Lv ◽  
Xiao Qiang Wang ◽  
Dong Ying Zhang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Noise Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Noise Interference ◽  
Low Snr ◽  
Contrast Analysis

Aiming at air rolling bearing vibration signals low SNR and nonstationary characteristics, taking wavelet theory and principles of the wavelet noise reduction for air vibration signals of rolling bearings to conduct wavelet noise reduction processing.By means of the simulation signal wavelet noise reduction processing and fast Fourier transform, the contrast analysis of the vibration signals after wavelet noise reduction and FFT transform and the original signal directly to the result of the fast Fourier transform, and thus prove the validity of the vibration signal wavelet noise reduction. Through the actual vibration signals of bearing conductnoise reduction processing, the result is a further indication of the superiority of wavelet noise reduction in eliminate noise interference.

Bearing fault diagnosis using a speed-adaptive network based on vibro-speed data fusion and majority voting

2021 ◽  
Author(s):  
Zonghao Yuan ◽  
Zengqiang Ma ◽  
Li Xin ◽  
Dayong Gao ◽  
Fu Zhipeng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Majority Voting ◽  
Variable Speed ◽  
Accuracy Rate ◽  
Rolling Bearings ◽  
Adaptive Network ◽  
Vibration Signals ◽  
Rotating Speed ◽  
Multiple Variable

Abstract Fault diagnosis of rolling bearings is key to maintain and repair modern rotating machinery. Rolling bearings are usually working in non-stationary conditions with time-varying loads and speeds. Existing diagnosis methods based on vibration signals only don’ t have the ability to adapt to rotational speed. And when the load changes, the accuracy rate of them will be obviously reduced. A method is put forward which fuses multi-modal sensor signals to fit speed information. Firstly, the features are extracted from raw vibration signals and instantaneous rotating speed signals, and fused by 1D-CNN-based networks. Secondly, to improve the robustness of the model when the load changes, a majority voting mechanism is proposed in the diagnosis stage. Lastly, Multiple variable speed samples of four bearings under three loads are obtained to evaluate the performance of the proposed method by analyzing the loss function, accuracy rate and F1 score under different variable speed samples. It is empirically found that the proposed method achieves higher diagnostic accuracy and speed-adaptive ability than the algorithms based on vibration signal only. Moreover, A couple of ablation studies are also conducted to investigate the inner mechanism of the proposed speed-adaptive network.

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.

scholarly journals Applying Energy-Equal Entropy of Wavelet Packet to Diagnose Circuit Breaker Faults

2014 ◽  
Vol 8 (1) ◽  
pp. 445-452
Author(s):  
Liu Mingliang ◽  
Wang Keqi ◽  
Sun Laijun ◽  
Zhang Jianfeng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Wavelet Packet ◽  
Circuit Breaker ◽  
Back Propagation ◽  
Vibration Signal ◽  
Characteristic Parameter ◽  
Vibration Signals

Aiming to better reflect features of machinery vibration signals of high-voltage (HV) circuit breaker (CB), a new method is proposed on the basis of energy-equal entropy of wavelet packet(WP). First of all, three-layer wavelet packet decomposes vibration signal, reconstructing 8 nodes of signals in the 3rd layer. Then, the vector is extracted with energy-equal entropy of reconstructed signals. At last, the simple back-propagation (BP) neural network for fault diagnosis contributes to classification of the characteristic parameter. This technology is the basis of a number of patents and patents pending, which is experimentally demonstrated by the significant improvement of diagnose faults.

scholarly journals Model of the Vibration Signal of the Vibrating Sieving Screen Suspension for Condition Monitoring Purposes

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 213
Author(s):  
Anna Michalak ◽  
Jacek Wodecki ◽  
Michał Drozda ◽  
Agnieszka Wyłomańska ◽  
Radosław Zimroz
Keyword(s):  
Mining Industry ◽  
Vibration Signal ◽  
External Noise ◽  
Diagnostic Process ◽  
Gaussian Component ◽  
Vibration Signals ◽  
Stable Component ◽  
Proposed Model ◽  
Deterministic Components

Diagnostics of industrial machinery is a topic related to the need for damage detection, but it also allows to understand the process itself. Proper knowledge about the operational process of the machine, as well as identification of the underlying components, is critical for its diagnostics. In this paper, we present a model of the signal, which describes vibrations of the sieving screen. This particular type is used in the mining industry for the classification of ore pieces in the material stream by size. The model describes the real vibration signal measured on the spring set being the suspension of this machine. This way, it is expected to help in better understanding how the overall motion of the machine can impact the efforts of diagnostics. The analysis of real vibration signals measured on the screen allowed to identify and parameterize the key signal components, which carry valuable information for the following stages of diagnostic process of that machine. In the proposed model we take into consideration deterministic components related to shaft rotation, stochastic Gaussian component related to external noise, stochastic α-stable component as a model of excitations caused by falling rocks pieces, and identified machine response to unitary excitations.

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