Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition

doi:10.1016/S0003-682X(97)00018-2

Applied Acoustics

Volume 53, Issues 1–3, January–March 1998, Pages 211-226

https://doi.org/10.1016/S0003-682X(97)00018-2 Get rights and content

Abstract

This paper presents a study on the application of sound pressure and vibration signals to detect the presence of defects in a rolling element bearing using a statistical analysis method. The well established statistical parameters such as the crest factor and the distribution of moments including kurtosis and skew are utilised in the study. In addition, other statistical parameters derived from the beta distribution function are also used. A comparison study on the performance of the different types of parameter used is also performed. The statistical analysis is used because of its simplicity and quick computation. A computer program has been developed using the C⁺⁺ language to perform the calculations of all the statistical parameters required in this study. A statistical analysis method is most suitable with random signals where other signal analysis methods based on the assumptions of deterministic signals are not applicable. The effect of shaft speed on the performance of the statistical method is also studied. Results from the study show that the statistical parameters are affected by the shaft speed due to the sensitivity of the bearing housing components to a longitudinal vibration that excites the fixing ring which holds the test bearing in its position. Under ideal conditions, the statistical method can be used to identify the different types of defect present in the bearing. In addition, the results also reveal that there is no significant advantages in using the beta function parameters when compared to using kurtosis and the crest factor for detecting and identifying defects in rolling element bearings from both sound and vibration signals.

References (12)

A.E. Diniz et al.
Correlating tool life, tool wear and surface roughness by monitoring acoustic emission in finish turning
Wear
(1992)
M. Trujillo
Inspection of micro-tools at high rotational speeds
International Journal of Machine Tools Manufacture
(1994)
E. Kannatay-Asibu
A study of tool wear using statistical analysis of metalcutting acoustic emission
Wear
(1982)
A. Daadbin et al.
Different vibration monitoring techniques and their application to rolling element bearings
International Journal of Mechanical Engineering Education
(1991)
H.R. Martin
Detection of gear damage by statistical vibration analysis
R.A. Cooper et al.

There are more references available in the full text version of this article.

Cited by (289)

The diagnosis of satellite flywheel bearing cage fault based on two-step clustering of multiple acoustic parameters
2022, Measurement: Journal of the International Measurement Confederation
As the critical component of satellite flywheels, bearings should be tested and selected on the ground before the flight. Aiming at the condition assessment requirements of flywheel bearing cage in the ground test, this paper proposes a non-contact diagnosis method based on the acoustic signal. First, the acoustic signals of the flywheel bearing cage under normal, rubbing, and uneven lubrication conditions are acquired, and it is found that the traditional envelope demodulation method is difficult to distinguish these states effectively. Therefore, an idea based on multi-parameter clustering fusion diagnosis is proposed. Then, the limitations of the direct clustering method to identify and classify the types of cage faults are revealed. Finally, a new diagnosis and identification method based on two-step clustering is proposed to improve the diagnosis capability. The test results show that cage temporary instability and rubbing fault can be diagnosed and identified effectively by the proposed method.
A novel vibro-acoustic fault diagnosis method of rolling bearings via entropy-weighted nuisance attribute projection and orthogonal locality preserving projections under various operating conditions
2022, Applied Acoustics
Accurate fault pattern recognition under various operating conditions is a huge challenge for vibro-acoustic fault diagnosis of rolling bearings. Traditional feature fusion methods are difficult to extract sensitive features related to faults under various operating conditions. To solve the problem, a novel feature fusion method is proposed in this paper based on entropy-weighted nuisance attribute projection (EWNAP) and orthogonal locality preserving projections (OLPP). Specifically, the method mainly includes three steps: feature extraction, alleviating interference of operating condition, and fusion feature. First, features are extracted from the acquired sound signals. Second, by introducing the covariance matrix of the acquired signals and fuzzy entropy to improve the weighted matrix of nuisance attribute projection (NAP), the EWNAP is proposed to reduce the impact of operating conditions in these extracted features. In the end, OLPP is adopted to fuse features and obtain sensitive features. The clustering performance of the proposed method is quantitatively described by an evaluation index, and for fault pattern recognition, the vectors as samples, which are composed of features extracted via EWNAP-OLPP, are fed into the back propagation (BP) neural network. The analysis on a fault case of rolling bearings shows that the proposed method is robust for vibro-acoustic fault diagnosis of rolling bearings under various operating conditions and superior to traditional methods.
Ranking and selection of optimum alternatives based on linear scale transformation for cylindrical roller bearing
2022, Engineering Failure Analysis
Citation Excerpt :
They extended their research and offered various statistical moments to monitor bearing faults [5]. Their research shows the effectiveness of skewness compared to kurtosis for initially corrected signals. [6] extracted the well-known statistical attributes, namely, kurtosis, skewness, and crest factor, along with attributes acquired from beta distribution to recognize the existence of a fault in the bearing.
Bearing is the most precautious element in rotating machinery. Defects produced in any part of the bearing may lead to a hazardous environment that increases the noise and vibration. It can be diminished with appropriate loading combinations. The selection of optimum loading conditions requires developing an algorithm. Multiple Attribute Decision Making (MADM) techniques are utilized to pick up a unique and ideal solution or ranking of choices from the available alternatives. The MADM techniques have not been used for bearing fault diagnosis so far. This paper offers a novel methodology for the Ranking and Selection of Optimum Alternative based on Linear Scale Transformation (RSOA-LST), having combined benefit and cost criteria that rank the choices and determine the percentage influence of various alternatives. This methodology has been performed with several experimental signals of cylindrical roller bearing, acquired from twenty-four bearing conditions having several speeds and loading combinations. The normalization of attributes (statistical features) considering combined benefit-cost criteria, mean of various parameters (speeds, attributes, and bearing conditions), and Percentage Influencing (PI) are intended simultaneously to obtain the ranking of alternatives (loading conditions). Local Defect Analysis (LDA) and Global Defect Analysis (GDA) are carried out in this research. In LDA, the ranking of alternatives based on the PI for each defect and the ranking of bearings based on fluctuation in PI of various alternatives are attained. In GDA, the sequence of alternatives is obtained globally. The effectiveness of the suggested methodology has been verified over other MADM techniques. The outcomes state that the suggested technique effectively bifurcates the influence of alternatives locally as well as globally over other techniques.
Printing smart coating of piezoelectric composite for application in condition monitoring of bearings
2022, Materials and Design
This paper reports on a novel technique of a bearing load monitoring based on the use of smart sensor coating. For easier process and integration, screen printing is carried out to achieve multilayered thin film deposited on an outer bearing or a simple flat steel substrate. The formulation of piezoelectric ink is relied on the development of a UV curable barium titanate/polyurethane acrylate (BaTiO₃/PUA) composite. A new design is proposed to enhance the sensitivity of the smart coating, which consists of three stacked layers: piezocomposite layer, dielectric layer (if needed), and conductive layer including electrodes and conductive tracks (CT). On one hand, the dimensions of the coating electrodes are revealed to be critical to the sensing performance. On the other hand, the CT has small impact on the piezoelectric measurement. As a result, no dielectric treatment is required between the composite and the conductive layers, leading to simplified printing process. Full characterizations of dielectric and mechanical properties, together with direct sensing measurement through electromechanical coupling are investigated on the home-made structure. Analytical and finite element models are developed to predict the mechanical properties of the tested substrate as well as the sensor sensitivity under different applied loads. Experiments are conducted on a 4-point bending (4 PB) setup, allowing to validate the analytical and numerical solutions. Good agreement between the model-predicted sensor outputs and the empirical measurements are observed, confirming high reliability of the proposed approach. It is eventually pointed out that the piezoelectric smart coating has higher sensitivity and easier integration than classical piezoresistive technology. Indeed, the printed sensor is capable to provide a direct voltage signal instead of a traditional strain gage where signal conditioning is needed. Accordingly, piezoelectric sensing together with printing technology could offer an efficient method for on-line and in situ monitoring of bearing.
A novel signal denoising method using stationary wavelet transform and particle swarm optimization with application to rolling element bearing fault diagnosis
2022, Materials Today: Proceedings
Citation Excerpt :
The same methodology was also tested successfully to diagnose the impacts generated on a signal collected from a spalled outer race bearing of a gearbox. Heng et al. [10] carried out a statistical analysis of sound pressure and vibration signal to detect defective rolling element bearing. Wavelet Transform (WT) was preliminarily developed by Malat [11], where it is seen that WT is superior to FFT for non-stationary signals.
In this paper, a novel signal denoising method based on Stationary Wavelet Transform and Particle Swarm Optimization Algorithm (SWT-PSO) is proposed. The threshold values for wavelet denoising are selected using the meta-heuristic algorithm of particle swarm optimization such that the denoised signal has maximum weighted kurtosis. In many faulty rotating machinery elements such as rolling element bearings and gears, impact-type vibration signals are produced. Such impact type vibration signals have a high value of kurtosis. The proposed SWT-PSO scheme can be used for the enhancement of fault signature in rotating machinery by increasing the kurtosis of such signals. The proposed algorithm has been applied on simulated as well as experimental defective rolling element bearing signal. Vibration signals from rolling element bearing with inner race and outer race defects are acquired by accelerometers on a Machinery Fault Simulator (MFS) setup. Then the proposed methodology is applied successfully to denoise the signals at various noise levels. Further, Spectrum envelope of the denoised vibration signal is used to obtain the characteristic defect frequencies. The developed methodology is finally compared with widely used Minimum Entropy Deconvolution (MED) algorithm. It has been demonstrated that the proposed algorithm has better capability of extracting the impulsive characteristic as well as preserving the signal geometrical structure than compared to MED.
Chatter Detection in Simulated Machining Data: A Simple Refined Approach to Vibration Data
2024, Research Square

View all citing articles on Scopus

View full text

Statistical analysis of sound and vibration signals for monitoring rolling element bearing condition

Abstract

Wear

International Journal of Machine Tools Manufacture

A study of tool wear using statistical analysis of metalcutting acoustic emission

Wear

Different vibration monitoring techniques and their application to rolling element bearings

International Journal of Mechanical Engineering Education

Detection of gear damage by statistical vibration analysis