Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods

Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform

A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings

Ball bearings faults are one of the main causes of breakdown of rotating machines. Thus, detection and diagnosis of mechanical faults in ball bearings is very crucial for the reliable operation. This study is focused on fault diagnosis of ball bearings using artificial neural network (ANN) and support vector machine (SVM). A test rig of high speed rotor supported on rolling bearings is used. The vibration response are obtained and analyzed for the various defects of ball bearings. The specific defects are considered as crack in outer race, inner race with rough surface and corrosion pitting in balls. Statistical methods are used to extract features and to reduce the dimensionality of original vibration features. A comparative experimental study of the effectiveness of ANN and SVM is carried out. The results show that the machine learning algorithms mentioned above can be used for automated diagnosis of bearing faults. It is also observed that the severe (chaotic) vibrations occur under bearings with rough inner race surface and ball with corrosion pitting.

Fault diagnosis of ball bearings using machine learning methods

This paper presents a review of the state-of-the-art in sensors and signal processing methodologies used for tool condition monitoring (TCM) systems in industrial machining applications. The paper focuses on the technologies used in monitoring conventional cutting operations, including drilling, turning, end milling and face milling, and presents important findings related to each of these fields. Unlike existing reviews, which categorize results according to the methodology used, this paper presents results organized according to the type of machining operation carried out. By extensively reviewing and categorizing over one hundred important papers and articles, this paper is able to identify and comment on trends in TCM research, and to identify potential weaknesses in certain areas. The paper concludes with a list of recommendations for future research based on the trends and successful results observed, thus facilitating the cross-fertilization of ideas and techniques within the field of TCM research.

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State-of-the-art methods and results in tool condition monitoring: a review

Application of discrete wavelet transform for detection of ball bearing race faults

Detection and monitoring of cracks in a rotor-bearing system using wavelet transforms

Vibrations of cracked rotor system: transverse crack versus slant crack

Crack versus coupling misalignment in a transient rotor system

The transient response of a misaligned rotor—coupling—bearing system passing through the critical speed has been analysed by using the finite element method (FEM) for flexural vibrations. The coupling has been modelled in two ways: a frictionless joint and a joint with stiffness and damping. From the vibration analysis, the subcritical speeds at one-half, one-third and one-fourth the critical speed have been found when the misaligned rotor—coupling—bearing system passes through its critical speed. The continuous wavelet transform (CWT) has been used as a tool to extract the silent features from the time response of the rotor system. A parametric study has been carried out to investigate the transient response of this rotor system for different angular accelerations in different types of misalignment.

S. Prabhakar

Papers

Application of discrete wavelet transform for detection of ball bearing race faults

Detection and monitoring of cracks in a rotor-bearing system using wavelet transforms

Vibrations of cracked rotor system: transverse crack versus slant crack

Crack versus coupling misalignment in a transient rotor system

Vibration analysis of a misaligned rotor—coupling—bearing system passing through the critical speed: