The Error Caused by Order Analysis for Rolling Bearing Fault Signal

2013 ◽  
Vol 819 ◽  
pp. 254-258 ◽  
Author(s):  
Wei Dong Cheng ◽  
Robert X. Gao ◽  
Jin Jiang Wang ◽  
Tian Yang Wang ◽  
Wei Gang Wen ◽  
...  
Keyword(s):  
High Speed ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rate Of Change ◽  
Rolling Element Bearings ◽  
Factors Affecting ◽  
Order Analysis ◽  
Rolling Element ◽  
Angular Interval ◽  
The Time Domain

Defect diagnosis of rolling element bearings operating under time-varying rotational speeds entails order tracking and analysis techniques that convert a vibration signal from the time domain to the angle domain to eliminate the effect of speed variations. When a signal is resampled at a constant angular increment, the amount of data padded into each data segment will vary, depending on the rate of change in the rotational speeds. This leads to changes in the distance between the adjacent impulse peaks, and consequently, the result of order analysis. This paper presents a quantitative analysis of key factors affecting the accuracy of order analysis on rolling element bearings under variable speeds. An analytical model is established and simulated. The effects of speed variation, instantaneous speed, angular interval between impulses, and the rising time of impulse are specified. It is concluded that the results of order analysis will be smaller as the rotational speed increases, and becomes larger when the speed decreases. Furthermore, the error is larger under low speeds than high speed.

scholarly journals The Hybrid KICA-GDA-LSSVM Method Research on Rolling Bearing Fault Feature Extraction and Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Jiyong Li ◽  
Shunming Li ◽  
Xiaohong Chen ◽  
Lili Wang
Keyword(s):  
Feature Extraction ◽  
High Speed ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Least Square ◽  
Support Vector ◽  
Rolling Element Bearings ◽  
Kernel Independent Component Analysis ◽  
Rolling Element ◽  
Independent Features

Rolling element bearings are widely used in high-speed rotating machinery; thus proper monitoring and fault diagnosis procedure to avoid major machine failures is necessary. As feature extraction and classification based on vibration signals are important in condition monitoring technique, and superfluous features may degrade the classification performance, it is needed to extract independent features, so LSSVM (least square support vector machine) based on hybrid KICA-GDA (kernel independent component analysis-generalized discriminate analysis) is presented in this study. A new method named sensitive subband feature set design (SSFD) based on wavelet packet is also presented; using proposed variance differential spectrum method, the sensitive subbands are selected. Firstly, independent features are obtained by KICA; the feature redundancy is reduced. Secondly, feature dimension is reduced by GDA. Finally, the projected feature is classified by LSSVM. The whole paper aims to classify the feature vectors extracted from the time series and magnitude of spectral analysis and to discriminate the state of the rolling element bearings by virtue of multiclass LSSVM. Experimental results from two different fault-seeded bearing tests show good performance of the proposed method.

Diagnosis of Machinery Fault Status using Transient Vibration Signal Parameters

2002 ◽  
Vol 8 (3) ◽  
pp. 321-335 ◽  
Author(s):  
Zhidong Chen ◽  
Chris K. Mechefske
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearings ◽  
Machine Condition ◽  
Transient Vibration ◽  
Vibration Signals ◽  
Model Based ◽  
Machine Condition Monitoring ◽  
Signal Parameters ◽  
Rolling Element ◽  
Efficient Machine

This paper reports the results of an investigation in which a Prony model based method is developed. The method shows potential for analysing transient vibration signals. An example is included that shows how the procedure was employed to analyse the transient vibration signals created from faulty low speed rolling element bearings. Spectral plots generated by applying the procedure to very short data samples, as well as trending parameters based on these spectral estimations and Prony parameters, are presented. An equation was also derived to quantitatively determine the fault status. It is shown that application of the Prony model based method has the potential to be an effective as well as efficient machine condition monitoring and diagnostic tool where short duration transient vibration signals are being generated.

The Effect of Inlet Grooves and Swirl on the Dynamics of Long Annular Seals in Centrifugal Pumps

1993 ◽  
Author(s):  
Mohamed Ismail ◽  
R. David Brown ◽  
David France
Keyword(s):  
Rolling Element Bearings ◽  
Rigid Rotor ◽  
Centrifugal Pumps ◽  
Test Rig ◽  
Singular Value Decomposition Method ◽  
Forcing Function ◽  
Rolling Element ◽  
The Time Domain ◽  
Value Decomposition ◽  
Annular Seals

Abstract This paper describes additional results from a continuing research program which aims to identify the dynamics of long annular seals in centrifugal pumps. A seal test rig designed to experimentally identify dynamic coefficients using a least-squares technique based on the singular value decomposition method. The analysis is carried out in the time domain using a multifrequency forcing function. The experimental method relies on the forced excitation of a flexibly supported stator by two hydraulic shakers. A rigid rotor supported in rolling element bearings runs through the stator. The only physical connection between shaft and stator is a pair of annular gaps filled with pressurised water discharged axially. The experimental coefficients obtained from the tests are compared with theoretical values.

Compound fault diagnosis of rolling element bearings using multipoint sparsity–multipoint optimal minimum entropy deconvolution adjustment and adaptive resonance-based signal sparse decomposition

2020 ◽  
pp. 107754632093819
Author(s):  
Ji Fan ◽  
Yongsheng Qi ◽  
Xuejin Gao ◽  
Yongting Li ◽  
Lin Wang
Keyword(s):  
Search Algorithm ◽  
Harmonic Signal ◽  
Vibration Signal ◽  
Cuckoo Search ◽  
Permutation Entropy ◽  
Rolling Element Bearings ◽  
Minimum Entropy ◽  
Vibration Signals ◽  
Sparse Decomposition ◽  
Rolling Element

The rolling element bearings used in rotating machinery generally include multiple coexisting defects. However, individual defect–induced signals of bearings simultaneously arising from multiple defects are difficult to extract from measured vibration signals because the impulse-like fault signals are very weak, and the vibration signal is commonly affected by the transmission path and various sources of interference. This issue is addressed in this study by proposing a new compound fault feature extraction scheme. Vibration signals are first preprocessed using resonance-based signal sparse decomposition to obtain the low-resonance component of the signal, which contains the information related to the transient fault–induced impulse signals, and reduce the interference of discrete harmonic signal components and noise. The objective used for adaptively selecting the optimal resonance-based signal sparse decomposition parameters adopts the ratio of permutation entropy to the frequency domain kurtosis, as a new comprehensive index, and the optimization is conducted using the cuckoo search algorithm. Subsequently, we apply multipoint sparsity to the low-resonance component to automatically determine the possible number of impulse signals and their periods according to the peak multipoint sparsity values. This enables the targeted extraction and isolation of fault-induced impulse signal features by multipoint optimal minimum entropy deconvolution adjustment. Finally, the envelope spectrum of the filtered signal is used to identify the individual faults. The effectiveness of the proposed scheme is verified by its application to both simulated and experimental compound bearing fault vibration signals with strong interference, and its advantages are confirmed by comparisons of the results with those of an existing state-of-the-art method.

A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults

2003 ◽  
Vol 125 (3) ◽  
pp. 282-289 ◽  
Author(s):  
J. Antoni ◽  
R. B. Randall
Keyword(s):  
Spectral Characteristics ◽  
Vibration Signal ◽  
Spectral Signature ◽  
Rolling Element Bearings ◽  
Envelope Analysis ◽  
Analysis Technique ◽  
Proposed Model ◽  
Rolling Element ◽  
Excitation Force ◽  
Localized Faults

This paper addresses the stochastic modeling of the vibration signal produced by localized faults in rolling element bearings and its use for diagnostic purposes. The aim is essentially to provide a better understanding of the recognized “envelope analysis” technique as classically used in the diagnostics of rolling element bearings, and incidentally give theoretical proofs for the specific features of envelope spectra as obtained from experimental data. The proposed model may also prove useful for simulation purposes. First, the excitation force generated by a defect is modeled as a random point process and its spectral signature is derived analytically. Then its transmission through the bearing is investigated in detail in order to find the spectral characteristics of the resulting vibration signal. The analysis finally gives sound justification for “squared” envelope analysis and the type of spectral indicators that should be used with it.

Rolling Bearing Localized Defect Evaluation by Multiscale Signature via Empirical Mode Decomposition

2012 ◽  
Vol 134 (6) ◽  
Author(s):  
Qingbo He ◽  
Peng Li ◽  
Fanrang Kong
Keyword(s):  
Health Information ◽  
Empirical Mode Decomposition ◽  
Multiple Scales ◽  
Regression Line ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Element Bearing ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Rolling Element

Measured vibration signals from rolling element bearings with defects are generally nonstationary, and are multiscale in nature owing to contributions from events with different localization in time and frequency. This paper presents a novel approach to characterize the multiscale signature via empirical mode decomposition (EMD) for rolling bearing localized defect evaluation. Vibration signal measured from a rolling element bearing is first adaptively decomposed by the EMD to achieve a series of usable intrinsic mode functions (IMFs) carrying the bearing health information at multiple scales. Then the localized defect-induced IMF is selected from all the IMFs based on a variance regression approach. The multiscale signature, called multiscale slope feature, is finally estimated from the regression line fitted over logarithmic variances of the IMFs excluding the defect IMF. The presented feature reveals the pattern of energy transfer among multiple scales due to localized defects, representing an inherent self-similar signature of the bearing health information that is embedded on multiple analyzed scales. Experimental results verify the performance of the proposed multiscale feature, and further discussions are provided.

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