Fabrice Bolaers

Improvement of the sensitivity of the scalar indicators (crest factor, kurtosis) using a de-noising method by spectral subtraction: application to the detection of defects in ball bearings

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, thus 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.

Early detection of fatigue damage on rolling element bearings using adapted wavelet

Among the advanced techniques of the predictive maintenance, the vibratory analysis proves to be very effective, in particular, for monitoring rotating components such as the bearings. Their damage creates cyclic efforts which are at the origin of the processing of vibratory measurements. This processing can be made by temporal methods, frequential methods, or by time-scale methods using the wavelets for 2 decades. The wavelet transform is a very effective processing, however, the difficulties of application and interpretation of the results slow down their employment. The determination of the parameters of the wavelets makes its use all the more difficult. Moreover, the use of these time-scale methods is very expensive in time computation. This paper proposes a wavelet adapted to the mechanical shock response of a structure with n degrees of freedom. In addition, we developed a procedure for analysis of signals by this wavelet which makes it possible to accelerate the process and to improve detection in the case of disturbed signals. This methodology is compared with the traditional time-scale methods and is implemented to detect defects of different sizes on outer rings and inner rings of ball bearings.

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.

Method of De-Noising By Spectral Subtraction Applied to the Detection of Rolling Bearings Defects

In this paper we aim to show the significance 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. If we consider the case of a bearing in good condition of use, the distribution of the amplitudes in the signal is Gaussian. When the bearing is damaged, the appearance of spallings disturbs this signal, modifying this distribution. This modification goes through 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 us to improve the sensitivity of these indicators and to increase the reliability of the diagnosis.

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.

High-resolution methods in vibratory analysis : application to ball bearing monitoring and production machine

Abstract This paper is concerned with the implementation of parametric spectrum analysis using a high-resolution technique for setting up a conditional maintenance program via vibration analysis on a forming press. To achieve this, the resolving power of signal-processing-based parametric techniques is illustrated using spectrum assessment computation. Processing of the experimental results enabled (i) various autoregressive (AR) spectrum analysis methods and especially Burg’s algorithm to be tested and (ii) conventional spectrum analysis techniques such as the correlogram to be compared with parametric methods in terms of detection level as well as for mechanical component fault monitoring, especially ball bearing defects. Among various possible models, the AR model was retained along with Burg’s algorithm and the Akaike information criterion. A detection and location methodology for faults likely to occur on rotating machinery was developed on the basis of the results that were obtained. The methodology, supplementing other analysis techniques, relies on the understanding of component spectrum behavior and various constraints, such as component access, spectral resolution of the industrial measuring device, and statistical properties of the power spectral density measurements of a random signal. The results show that parametric methods are particularly worthwhile in the early detection of component defects, especially when two characteristic frequencies are close to one another. However, the complexity of these techniques necessitates many precautions when they are implemented; consequently, they should not replace conventional methods, but supplement them.

Hand-arm vibration in cycling

Numerous workers are exposed to vibrations which can turn out to be fatal for the health. Athletes can be included in this population, in particular cyclists who are exposed to vibration due to the irregularity of the road. This nuisance depends of the duration of exposure and the range of vibrations. While the worker is mostly directly excited by a vibrating system, the cyclist is indirectly subjected to it. He undergoes the vibrations of an excited sub-structure which is the bicycle. So the bicycle plays the role of a vibration filter or amplifier. In this paper we propose to (i) study the transmission of vibrations to the cyclist after excitation on a paving road, (ii) calculate the limit time of exposure to this type of excitation rate by the use of the standard ISO 5349 and the European directive 2002/44/EC, and (iii) compare the weighting curve of the standard with a vibrations transmissibility curve obtained between the collarbone and the stem. For this particular case of an excited sub-structure, a weighting curve is proposed by considering the first modal frequency of the bicycle.

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.

Early Detection of Gear Failure by Vibration Analysis

Gear mechanisms are an important element in a variety of mechanical systems, such as industrial machinery and automotive. Health monitoring of rotating machines is important to avoid failure of the system in advance. Principally, this paper consists of two parts: in the first part, a gear dynamic model including localized tooth defect has been developed. The model consists of a spur gear pair, two inertias. The model incorporates the effects of time-varying mesh stiffness and damping, excitation due to gear errors. The results of a dynamic modeling of the gears transmission are calculated by using the Newmark integration scheme. The second part consists of signal processing of simulated and experimental signals using the wavelet transform. It is shown that the kurtosis of the vibration signal is a sensitive indicator of the existence of damage in the gear pair.

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.