A Fault Diagnosis Method of Rolling Bearing through Wear Particle and Vibration Analyses

2010 ◽  
Vol 26-28 ◽  
pp. 676-681 ◽  
Author(s):  
Zhong Yu Huang ◽  
Zhi Qiang Yu ◽  
Zhi Xiong Li ◽  
Yuan Cheng Geng
Keyword(s):  
Fault Diagnosis ◽  
Vibration Analysis ◽  
Wear Particle ◽  
Rolling Bearing ◽  
Particle Analysis ◽  
Complex Nature ◽  
Chemical Compositions ◽  
Rolling Bearings ◽  
Classification Rate ◽  
Wear Particle Analysis

Wear particle and vibration analysis are the two main condition monitoring techniques for machinery maintenance and fault diagnosis in industry. Due to the complex nature of machinery, these two techniques can only diagnose about 30% to 40% of faults when used independently. Therefore, it is critical to integrate vibration analysis and wear particle analysis to provide a more effective maintenance program. This paper presents a new fault diagnosis approach of rolling bearings via the combination of vibration analysis and wear particle analysis. Both the tribological and vibrant information of the rolling bearings with typical faults were collected by an experimental test rig. Wear particle analysis was applied to the oil samples to obtain the wear particle number and size distribution, the particle texture and the chemical compositions, etc. Vibration analysis was used to get the time and frequency characteristics of the vibration data. Then, an intelligent data fusion method based on the genetic algorithm based fuzzy neural network was employed to identify the rolling bearing conditions. The analysis results suggest that the proposed method is more feasible and effective for the rolling bearing fault diagnosis than separated use of the two techniques with respect to the classification rate, and thus has application importance.

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.

Fault diagnosis through Ferrography (Wear Particle Analysis)

2021 ◽  
pp. 303-322
Author(s):  
Anadi Sinha
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Wear Particle ◽  
Stage Ii ◽  
Lubricating Oil ◽  
Particle Analysis ◽  
Wear Particle Analysis ◽  
Analysis Technique ◽  
Detection And Diagnosis

The purpose of Plant Predictive Maintenance (PDM) programme is to improve Reliability of machineries through early detection and diagnosis of equipment problems, and degradation prior to equipment failure. Ferrography (Wear Particle Analysis) is one of the PDM techniques which allows detection, identification and evaluation of the degradation at the very incipient stage so that degradation is timely attended and mitigatory actions initiated. Ferrography is a Wear Particle Analysis technique based upon systematic collection and analysis of sample of lubricating oil from rotating and reciprocating machines. Ferrography analysis is conducted in 2 phases: Stage I – Quantitative, and Stage II – Qualitative. After Stage II analysis, recommendation is issued based on wear rating (Normal, Marginal, or Critical) so that operator can take timely action. Presently, 21 Nuclear Power Plants are operational in India and Forced Shutdown is a very costly affair. Lube oil of around 60 equipment from Indian Nuclear Power Plants is examined quarterly for Ferrography analysis, and failure of several equipment is avoided due to timely action. This paper will elaborate on the basic principles of Ferrography, and how systematic implementation of Ferrography has helped in avoiding forced failure of equipment, and hence prevent Forced Shutdown.

Analysis of the Behaviour of Rolling Bearings in Contaminated Oil Using Some Condition Monitoring Techniques

2006 ◽  
Vol 220 (5) ◽  
pp. 447-453 ◽  
Author(s):  
T Akagaki ◽  
M Nakamura ◽  
T Monzen ◽  
M Kawabata
Keyword(s):  
Vibration Analysis ◽  
Wear Debris ◽  
Friction And Wear ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Deep Groove ◽  
Deep Groove Ball Bearing ◽  
Observation Results ◽  
Wear Debris Analysis ◽  
Worn Surfaces

Friction and wear behaviours of rolling bearing in contaminated oil containing white-fused alumina particles were studied. The friction and wear processes were monitored using wear debris analysis, such as ferrography and spectrometric oil analysis program, and vibration analysis. Test bearing was a deep groove ball bearing (6002P5); Wear debris and worn surfaces of the bearing components were observed with a scanning electron microscope (SEM). It was found that the friction coefficient in the contaminated oil became lower by about 0.001 than that in the new oil for the large contaminants. The results of wear debris analysis showed that the large contaminants caused the high wear rate in the bearing. Three types of wear debris were commonly observed: thread-like debris, cutting chip debris, and plate-like debris. On the basis of the SEM observation results of the worn surfaces, wear mechanisms of these wear debris were discussed. The results of vibration analysis showed that the probability density function of vibration waveform was normal distribution in both the new and contaminated oils. In the contaminated oil, it changed depending on the contaminant size and the runtime, i.e. the progress of wear in the bearing. The result of wear debris analysis was related to that of vibration analysis and discussed.

scholarly journals Fault Diagnosis of Rolling Bearing Based on a Novel Adaptive High-Order Local Projection Denoising Method

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Rui Yuan ◽  
Yong Lv ◽  
Gangbing Song
Keyword(s):  
Fault Diagnosis ◽  
Numerical Simulations ◽  
Rolling Bearing ◽  
High Order ◽  
Rolling Bearings ◽  
Theoretical Derivation ◽  
Denoising Method ◽  
Vibration Signals ◽  
Local Projection ◽  
Ring Fault

Rolling bearings are vital components in rotary machinery, and their operating condition affects the entire mechanical systems. As one of the most important denoising methods for nonlinear systems, local projection (LP) denoising method can be used to reduce noise effectively. Afterwards, high-order polynomials are utilized to estimate the centroid of the neighborhood to better preserve complete geometry of attractors; thus, high-order local projection (HLP) can improve noise reduction performance. This paper proposed an adaptive high-order local projection (AHLP) denoising method in the field of fault diagnosis of rolling bearings to deal with different kinds of vibration signals of faulty rolling bearings. Optimal orders can be selected corresponding to vibration signals of outer ring fault (ORF) and inner ring fault (IRF) rolling bearings, because they have different nonlinear geometric structures. The vibration signal model of faulty rolling bearing is adopted in numerical simulations, and the characteristic frequencies of simulated signals can be well extracted by the proposed method. Furthermore, two kinds of experimental data have been processed in application researches, and fault frequencies of ORF and IRF rolling bearings can be both clearly extracted by the proposed method. The theoretical derivation, numerical simulations, and application research can indicate that the proposed novel approach is promising in the field of fault diagnosis of rolling bearing.

scholarly journals A Denoising Autoencoder-Based Bearing Fault Diagnosis System for Time-Domain Vibration Signals

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yi Gu ◽  
Jiawei Cao ◽  
Xin Song ◽  
Jian Yao
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Bp Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Novel Approach ◽  
Fault Diagnosis System ◽  
Original Time

The condition monitoring of rotating machinery is always a focus of intelligent fault diagnosis. In view of the traditional methods’ excessive dependence on prior knowledge to manually extract features, their limited capacity to learn complex nonlinear relations in fault signals and the mixing of the collected signals with environmental noise in the course of the work of rotating machines, this article proposes a novel approach for detecting the bearing fault, which is based on deep learning. To effectively detect, locate, and identify faults in rolling bearings, a stacked noise reduction autoencoder is utilized for abstracting characteristic from the original vibration of signals, and then, the characteristic is provided as input for backpropagation (BP) network classifier. The results output by this classifier represent different fault categories. Experimental results obtained on rolling bearing datasets show that this method can be used to effectively diagnose bearing faults based on original time-domain signals.

A blind deconvolution algorithm based on backward automatic differentiation and its application to rolling bearing fault diagnosis

2021 ◽  
Author(s):  
Bo Fang ◽  
Hu Jianzhong ◽  
Cheng Yang ◽  
Yudong Cao ◽  
Minping Jia
Keyword(s):  
Fault Diagnosis ◽  
Automatic Differentiation ◽  
Blind Deconvolution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Convex Optimization Problem ◽  
Bearing Fault Diagnosis ◽  
Filter Performance ◽  
Calculation Process

Abstract Blind deconvolution (BD) is an effective algorithm for enhancing the impulsive signature of rolling bearings. As a convex optimization problem, the existing BDs have poor optimization performance and cannot effectively enhance the impulsive signature excited by weak faults. Moreover, the existing BDs require manual derivation of the calculation process, which brings great inconvenience to the researcher's personalized design of the maximization criterion. A new BD algorithm based on backward automatic differentiation (BAD) is proposed, which is named BADBD. The calculation process does not require manual derivation so a general solution of BDs based on different maximization criteria is realized. BADBD constructs multiple cascaded filters to filter the raw vibration signal, which makes up for the deficiency of single filter performance. The filter coefficients are determined by Adam algorithm, which improves the optimization performance of the proposed BADBD. BADBD is compared with classic BDs by synthesized and real vibration signals. The results reveal superior capability of BADBD to enhance the impulsive signature and the fault diagnosis performance is significantly better than the classic BDs.

scholarly journals A New Second-Order Tristable Stochastic Resonance Method for Fault Diagnosis

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 965 ◽  
Author(s):  
Lu Lu ◽  
Yu Yuan ◽  
Heng Wang ◽  
Xing Zhao ◽  
Jianjie Zheng
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Fitness Function ◽  
Resonance Method ◽  
Poor Performance ◽  
Rolling Bearing ◽  
Second Order ◽  
Weak Signals ◽  
Rolling Bearings ◽  
Sr Method

Vibration signals are used to diagnosis faults of the rolling bearing which is symmetric structure. Stochastic resonance (SR) has been widely applied in weak signal feature extraction in recent years. It can utilize noise and enhance weak signals. However, the traditional SR method has poor performance, and it is difficult to determine parameters of SR. Therefore, a new second-order tristable SR method (STSR) based on a new potential combining the classical bistable potential with Woods-Saxon potential is proposed in this paper. Firstly, the envelope signal of rolling bearings is the input signal of STSR. Then, the output of signal-to-noise ratio (SNR) is used as the fitness function of the Seeker Optimization Algorithm (SOA) in order to optimize the parameters of SR. Finally, the optimal parameters are used to set the STSR system in order to enhance and extract weak signals of rolling bearings. Simulated and experimental signals are used to demonstrate the effectiveness of STSR. The diagnosis results show that the proposed STSR method can obtain higher output SNR and better filtering performance than the traditional SR methods. It provides a new idea for fault diagnosis of rotating machinery.

Oil film lifetime and wear particle analysis of laser-patterned stainless steel surfaces

Wear ◽  
2015 ◽  
Vol 334-335 ◽  
pp. 1-12 ◽  
Author(s):  
Andreas Rosenkranz ◽  
Tobias Heib ◽  
Carsten Gachot ◽  
Frank Mücklich
Keyword(s):  
Stainless Steel ◽  
Wear Particle ◽  
Particle Analysis ◽  
Wear Particle Analysis ◽  
Oil Film ◽  
Steel Surfaces

Failure Analysis of Rolling Bearing Based on Oil Monitoring Techniques with Mechanics Basis

2012 ◽  
Vol 164 ◽  
pp. 401-404
Author(s):  
Xu Feng Jiang ◽  
Fang Liu ◽  
Peng Cheng Zhao
Keyword(s):  
Chemical Properties ◽  
Wear Particle ◽  
Rolling Bearing ◽  
Mechanical Failure ◽  
Particle Analysis ◽  
Monitoring Techniques ◽  
Lubricant Oil ◽  
Physico Chemical ◽  
Cause Of Failure ◽  
Oil Monitoring

To determine the failure cause of rolling bearing of a factory heating device, oil samples extracted from the recovery of lubricant oil and were tested by comprehensive oil monitoring techniques (including physico-chemical properties analysis, pollution degree detection and ferrography wear particle analysis). The results show that root cause of failure is the operating temperature of the bearing is too high. Failure form is burning. Therefore, the application of oil monitoring techniques to analyze the mechanical failure mechanism, to prevent mechanical failure and extend machine life is very helpful.

Sign in / Sign up

Export Citation Format

Share Document