Bearing Fault Diagnosis Based on Statistical Feature Extraction in Time and Frequency Domain and Neural Network

2017 ◽  
Vol 8 (4) ◽  
Author(s):  
L.S. Dhamande ◽  
M.B. Chaudhari
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Vibration Signal ◽  
Angular Speed ◽  
Machine Intelligence ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Set Up

Bearing is an important component of almost every mechanical system used in industrial environment. Hence the defect in bearing must be detected in advance to avoid catastrophic failure. This paper aims to diagnose the defect in bearing automatically using machine intelligence. A condition monitoring setup is designed for analyzing the defects in outer race, inner race and rolling element of bearing. MATLAB is used for feature extraction and neural network is used for diagnosis. It is found that the amplitude at defect frequencies may not always clearly indicate the increment; hence statistical analysis of bearing signature is a better alternative. The work presents an experimental investigation carried out on an experimental set-up for the study of bearing fault at same angular speed and load. This paper proposes an approach of damage detection in which defects in bearing are accurately analysed using vibration signal and neural network.

scholarly journals A Bearing Fault Diagnosis Method Using Multi-Branch Deep Neural Network

Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 345
Author(s):  
Van-Cuong Nguyen ◽  
Duy-Tang Hoang ◽  
Xuan-Toa Tran ◽  
Mien Van ◽  
Hee-Jun Kang
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Deep Neural Network ◽  
Image Representation ◽  
Vibration Signal ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Multiple Domain

Feature extraction from a signal is the most important step in signal-based fault diagnosis. Deep learning or deep neural network (DNN) is an effective method to extract features from signals. In this paper, a novel vibration signal-based bearing fault diagnosis method using DNN is proposed. First, the measured vibration signals are transformed into a new data form called multiple-domain image-representation. By this transformation, the task of signal-based fault diagnosis is transferred into the task of image classification. After that, a DNN with a multi-branch structure is proposed to handle the multiple-domain image representation data. The multi-branch structure of the proposed DNN helps to extract features in multiple domains simultaneously, and to lead to better feature extraction. Better feature extraction leads to a better performance of fault diagnosis. The effectiveness of the proposed method was verified via the experiments conducted with actual bearing fault signals and its comparisons with well-established published methods.

Feature Extraction of Rolling Bearing Fault Diagnosis

2012 ◽  
Vol 190-191 ◽  
pp. 993-997
Author(s):  
Li Jie Sun ◽  
Li Zhang ◽  
Yong Bo Yang ◽  
Da Bo Zhang ◽  
Li Chun Wu
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Attribute Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Mechanical Equipment ◽  
Characteristic Parameters ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Experimental Trials

Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis. This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening. The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, "zoom" method and vibration signal as the characteristic parameters. The latter includes two methods: sensitivity extraction and data mining methods to select attributes. Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

scholarly journals Rolling Element Bearing Fault Diagnosis Based on Deep Belief Network and Principal Component Analysis

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Guangxing Niu ◽  
Bin Zhang ◽  
Paul Ziehl ◽  
Frank Ferrese ◽  
Michael Golda
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Principal Component ◽  
Operating Conditions ◽  
Belief Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Ae Signal

Rolling element bearings are critical components in industrial rotating machines. Faults and failures of bearings can cause degradation of machine performance or even a catastrophe. Bearing fault diagnosis is therefore essential and significant to safe and reliable operation of systems. For bearing condition monitoring, acoustic emission (AE) signals attract more and more attention due to its advantages on sensitivity over the extensively used vibration signal. In bearing fault diagnosis and prognosis, feature extraction is a critical and tough work, which always involves complex signal processing and computation. Moreover, features greatly rely on the characteristics, operating conditions, and type of data. With consideration of changes in operating conditions and increase of data complexity, traditional diagnosis approaches are insufficient in feature extraction and fault diagnosis. To address this problem, this paper proposes a Deep Belief Network (DBN) and Principal Component Analysis (PCA) based fault diagnosis approach using AE signal. This proposed approach combines the advantages of deep learning and statistical analysis, DBN automatically extracts features from AE signal, PCA is applied to dimensionality reduction. Different bearing fault modes are identified by least squares support vector machine (LS-SVM) using the extracted features. An experimental case is conducted with a tapered roller bearing to verify the proposed approach. Experimental results demonstrate that the proposed approach has excellent feature extraction ability and high fault classification accuracy.

Fault Diagnosis of Bearings Based on Time-Delayed Correlation Demodulation and Fuzzy Clustering

2011 ◽  
Vol 211-212 ◽  
pp. 510-514 ◽  
Author(s):  
Pan Fu ◽  
Wei Lin Li ◽  
Wei Qing Cao
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Failure Diagnosis ◽  
Rolling Element Bearing ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Fuzzy C Means Clustering ◽  
Important Means ◽  
Set Up

As one of the most common parts of various rolling mechanical equipments, rolling element bearing is vulnerable. Therefore, great attentions have been attributed to the theories, failure diagnosis methods and their applications for rolling bearings. Vibration analysis is also a very important means for bearing fault diagnosis. This paper aims at the research on the methods of signal processing and pattern recognition. An experimental platform was set up for the failure diagnosis of rolling bearings, on which we have done a lot of experiments. Then the vibration signals of normal rolling bearings, rolling bearings with failure on the outer and inner race were collected. Time-delayed correlation demodulation was applied and the features of vibration signal were effectively extracted. Fuzzy C-means clustering system was established to carry out the recognition of the fault of bearings. Experimental results have proved the developed fault diagnostic architecture is reliable and effective.

scholarly journals A Novel Method for Adaptive Multiresonance Bands Detection Based on VMD and Using MTEO to Enhance Rolling Element Bearing Fault Diagnosis

2016 ◽  
Vol 2016 ◽  
pp. 1-20 ◽  
Author(s):  
Xingxing Jiang ◽  
Shunming Li ◽  
Chun Cheng
Keyword(s):  
Fault Diagnosis ◽  
Energy Operator ◽  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Teager Energy Operator ◽  
Rolling Element ◽  
Element Bearing ◽  
Teager Energy

Vibration signals of the defect rolling element bearings are usually immersed in strong background noise, which make it difficult to detect the incipient bearing defect. In our paper, the adaptive detection of the multiresonance bands in vibration signal is firstly considered based on variational mode decomposition (VMD). As a consequence, the novel method for enhancing rolling element bearing fault diagnosis is proposed. Specifically, the method is conducted by the following three steps. First, the VMD is introduced to decompose the raw vibration signal. Second, the one or more modes with the information of fault-related impulses are selected through the kurtosis index. Third, Multiresolution Teager Energy Operator (MTEO) is employed to extract the fault-related impulses hidden in the vibration signal and avoid the negative value phenomenon of Teager Energy Operator (TEO). Meanwhile, the physical meaning of MTEO is also discovered in this paper. In addition, an idea of combining the multiresonance bands is constructed to further enhance the fault-related impulses. The simulation studies and experimental verifications confirm that the proposed method is effective for identifying the multiresonance bands and enhancing rolling element bearing fault diagnosis by comparing with Hilbert transform, EMD-based demodulation, and fast Kurtogram analysis.

A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis

2012 ◽  
Vol 227 (6) ◽  
pp. 1362-1370 ◽  
Author(s):  
Changqing Shen ◽  
Qingbo He ◽  
Fanrang Kong ◽  
Peter W Tse
Keyword(s):  
Fault Diagnosis ◽  
Morphological Analysis ◽  
Rolling Element Bearing ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Bearing Defect ◽  
Rolling Element ◽  
Element Bearing ◽  
Fault Characteristic

The research in fault diagnosis for rolling element bearings has been attracting great interest in recent years. This is because bearings are frequently failed and the consequence could cause unexpected breakdown of machines. When a fault is occurring in a bearing, periodic impulses can be revealed in its generated vibration frequency spectrum. Different types of bearing faults will lead to impulses appearing at different periodic intervals. In order to extract the periodic impulses effectively, numerous techniques have been developed to reveal bearing fault characteristic frequencies. In this study, an adaptive varying-scale morphological analysis in time domain is proposed. This analysis can be applied to one-dimensional signal by defining different lengths of the structure elements based on the local peaks of the impulses. The analysis has been first validated by simulated impulses, and then by real bearing vibration signals embedded with faulty impulses caused by an inner race defect and an outer race defect. The results indicate that by using the proposed adaptive varying-scale morphological analysis, the cause of bearing defect could be accurately identified even the faulty impulses were partially covered by noise. Moreover, compared to other existing methods, the analysis can be functioned as an efficient faulty features extractor and performed in a very fast manner.

Research of Rolling Bearing Fault Feature Extraction Based on EMD and Choi-Williams

2013 ◽  
Vol 694-697 ◽  
pp. 1377-1381
Author(s):  
Xing Chun Wei ◽  
Yu Lin Tang ◽  
Tao Chen
Keyword(s):  
Feature Extraction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Outer Ring ◽  
Intrinsic Mode Functions ◽  
Linear Superposition ◽  
Time Frequency ◽  
Bearing Fault ◽  
Rolling Element ◽  
Bearing Vibration

Aiming at rolling bearing fault signal of the non stationary feature, Apply a new method to the rolling bearing vibration signal of feature extraction, which is combined the Empirical Mode Decomposition (EMD) and the Choi-Williams distribution. Firstly, original signals were decomposed into a series of intrinsic mode functions (IMF) of different scales. To the decomposed each IMF component for Choi-Williams time-frequency analysis, Then take the linear superposition, finally obtained the rolling bearing vibration signal of Choi-Williams distribution. After the analyses of the rolling bearing inner ring, outer ring and rolling element fault signal ,the results show that this method can effectively suppress the frequency aliasing and interference caused by cross terms. And be able to accurately extract the fault frequency of the bearing inner ring, outer ring and rolling element, lay the foundation for the subsequent rolling bearing state recognition.

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