Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings

By R. Rubini, U. Meneghetti. 2001

361 citations recorded by the source corpus.

Abstract Fatigue faults on the surface of rolling bearing elements are some of the most frequent causes of malfunctions and breakages of rotating machines. In normal operating conditions this kind of damage can be revealed by classical vibration analyses, such as Spectral or Envelope ones. Furthermore, this last technique—by working in time domain—makes it possible to monitor the longitudinal dimension of the defect. In this paper, the limits of the mentioned methodologies are presented by showing their application to bearings affected by different pitting failures on the outer or inner race or a rolling element and subjected to a very low radial load. Results are compared with that obtained by an advanced signal processing method based on the evaluation of the wavelet transform. Effects of fault evolution are investigated.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/04fc08fbf59fa035527c3ffb4f55bf3c79711d72

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Jianbo Yu
2. N. Ouelaa
3. A. Djebala
4. N. Hamzaoui
5. Jay H. Lee
6. P. W. Tse
7. Mohammad Rafiee
8. Robert X. Gao

In Mechanical Engineering

1. Jianbo Yu
2. N. Ouelaa
3. A. Djebala
4. N. Hamzaoui
5. P. W. Tse
6. Mohammad Rafiee
7. Tugrul Özel
8. Robert X. Gao

In Mechanics of Materials

1. Jianbo Yu
2. N. Ouelaa
3. A. Djebala
4. N. Hamzaoui
5. Mohammad Rafiee

Citations in the corpus

• Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods (2007, background): “[10] reported, in the actual bearing working condition, due to the”
• Detection of rolling bearing defects using discrete wavelet analysis (2008, methodology): “By applying the Wavelet Multiresolution Analysis the bandwidth of the first detail (D1) must be [5000–10000] Hz, and [0–5000] Hz for the first approximation (A1), so they do not cover the resonance frequency, this is a serious problem.”
• Bearing performance degradation assessment using locality preserving projections (2011, methodology): “Wavelet transform has been widely applied to signal processing in bearing failure detection and diagnosis (Chow & Hai, 2004; Malhi & Gao, 2004; Rubini & Meneghetti, 2001).”
• Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison (2008, background): “In most situations, diagnostic methods, based on the analysis of the mechanical signals, have proved their effectiveness [8]–[10].”
• Application of mother wavelet functions for automatic gear and bearing fault diagnosis (2010, background): “The basic theory of CWT has been discussed in several papers (e.g. Peng & Chu, 2004; Rubini & Meneghetti, 2001; Staszewski & Tomlinson, 1994; Wang & McFadden, 1996).”
• A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm (2017, background): “Rubini and Meneghetti (2001) proposed the limits of the mentioned methodologies by showing their application to bearings affected by different pitting failures on the outer or inner race or a rolling element and subjected to a very low radial load.”