Olivier Cousinard

Advanced detection of rolling bearing spalling from de-noising vibratory signals

The aim of this article is to show the interest of spectral subtraction for the improvement of the sensitivity of scalar indicators (crest factor, kurtosis) within the application of conditional maintenance by vibratory analysis on ball bearings. The case of a bearing in good conditions of use is considered; the distribution of amplitudes in the signal is of Gaussian kind. When the bearing is damaged, the appearance of spallings comes to disturb this signal, modifying this distribution. This modification is due to the presence of periodical impulses produced each time a rolling element meets a discontinuity on its way. Nevertheless, the presence of background noise induced by random impulse excitations can have an influence on the values of these temporal indicators. The de-noising of these signals by spectral subtraction in different frequency bands allows to improve the sensitivity of these indicators and to increase the reliability of the diagnosis.

On the extraction of rules in the identification of bearing defects in rotating machinery using decision tree

A methodology for the extraction of expert rules in the identification of bearing defects in rotating machinery is presented. Data sets are collected from signals measured by piezoelectric accelerometer fixed on bearings of an experimental set-up. Temporal and frequential analyses are then conducted to determine statistical parameters (crest factor (CF), kurtosis, root mean square) and spectrums (Fast Fourier Transform, envelope spectrum). The decision tree is then constructed by applying C4.5 algorithm on the dataset, and thus expert rules are established. The efficiency and applicability of expert rules over rules resulting from human experiments in rotating machinery maintenance is shown throughout the present study.

Early Detection of Rolling Bearing Defect by Demodulation of Vibration Signal Using Adapted Wavelet

Vibratory analysis allows us to interpret the fundamental conditions of rotating machines. This interpretation is useful in the diagnosis of defects. Many studies implement advanced processing tools for mechanical detection of defects in individual components. Among these processes, wavelet demodulation is a powerful tool for signal processing. This technique requires the use of a traditional wavelet, such as a Morlet wavelet, which is defined by two parameters: Decrease and frequency. However, this determination is hard to do. Moreover, the processing required is very expensive in computing time, which prevents instantaneous follow-up. This paper suggests a new form of wavelet, which is adapted to shock response, and a methodology for its use in which the parameters are determined automatically.

Low speed bearings fault detection and size estimation using instantaneous angular speed

The fault diagnosis and prognosis of low speed machines remains a difficult problem despite remarkable advances in the conditional monitoring domain. The Rolling-element bearing is a vital part of these machines and its failure is one of the main causes of machine breakdown. In order to have an efficient maintenance strategy, fault diagnosis of a bearing and time estimation of its remaining useful life is needed. However, conventional vibration analysis at very low speeds generally fails to detect vibrations issued from a faulty bearing due to its low energy, high and variable loading conditions and to the noisy environment generated by other mechanical components of low speed machines such as gearing systems. In this work, instantaneous angular speed (IAS)-based fault diagnosis is introduced in order to compensate for the shortcoming of conventional monitoring techniques since it is strictly synchronized to shaft rotation and much less dependent on the transfer path between the defect and the sensor contrary to vibration and acoustic emission analysis. At very low speeds and in the case of a seeded spall on the bearing’s race, the shaft IAS reveals the shaft dynamical behavior when the rolling element passes into the spall. It is proven that this behavior is different when entering the spall than when exiting. The determination of entrance and exit moments allows a precise fault size estimation which is a critical step for bearing prognosis. The proposed fault size estimation method is tested on different seeded spall widths at different low speeds. The results gave a satisfactory fault width estimation and show that IAS measurement is a promising tool for the health monitoring of very low speed machines.

Comparison of denoising methods for the early detection of fatigue bearing defects by vibratory analysis

The aim of this article is to show the interest of three major denoising methods for the improvement of the sensitivity of scalar indicators (crest factor, kurtosis) within the application of conditional maintenance by vibratory analysis on ball bearings. The case of a bearing in good condition of use is considered. The distribution of amplitudes in the vibratory signal is of the Gaussian kind. When the bearing is damaged, the appearance of spalling comes to disturb this signal, modifying this distribution. This modification is due to the presence of periodical impulses produced each time a rolling element meets a discontinuity on its way. Nevertheless, the presence of background noise induced by random impulse excitations can have an influence on the values of these temporal indicators. The denoising of these signals allows to improve the sensitivity of these indicators and to increase the reliability of the diagnosis.

Low Speed Bearings’ Instantaneous Angular Speed Behavior

This work describes shaft’s Instantaneous Angular Speed (IAS) behavior in the presence of a spall on bearing’s race at low speeds. It is shown that fault signature becomes more apparent in IAS signal when speed decreases and load increases. EEMD processing of the signal is implemented to overcome the shortcoming of IAS sensitivity at low loading conditions. IAS behaviour while passing through the spall perform three different variation stages: Speed increase (entrance moment) caused by the instantaneous loss of contact pressure, a speed sharp decrease when the rolling element hits the spall edge and finally speed recuperation phase (exit moment) and system’s dynamical response to impact. It is considered that the number of angular samples between the entrance and exit moments defines the fault size. Different fault sizes at different speeds (<60 rpm) were tested and its estimation was satisfactory which is the first step for a successful bearing prognosis.