Search

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. [ABSTRACT FROM AUTHOR]

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 bail bearings. If we consider the case of a beating 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. [ABSTRACT FROM AUTHOR]

Rolling elements bearings (REBs) is one of the most sensitive components and the common failure unit in mechanical equipment. Bearings failure prognostics, which aims to achieve an effective way to handle the increasing requirements for higher reliability and in the same time reduce unnecessary costs, has been an area of extensive research. The accurate prediction of bearings Remaining Useful Life (RUL) is indispensable for safe and lifetime-optimized operations. To monitor this vital component and planning repair work, a new intelligent method based on Wavelet Packet Decomposition (WPD) and deep learning networks is proposed in this paper. Firstly, features extraction from WPD used as input data. Secondly, these selected features are fed into deep Convolutional Neural Networks (CNNs) to construct the Health Indicator (HI). This study focuses on analysing the relationships such as correlations between the HI and temperature. We develop a solution for the Connectiomics contest dataset of bearings under different operating conditions and severity of defects. The performance of the proposed method is verified by four bearing data sets collected from experimental setup called "PRONOSTIA". The results show that the health indicator obtains fairly high monotonicity and correlation values and it is beneficial to bearing life prediction. In addition, it is experimentally demonstrated that the proposed method is able to achieve better performance than a traditional neural network based method. [ABSTRACT FROM AUTHOR]

Among the methods which make it possible to establish a diagnosis on the operating condition of a mechanism, the vibratory techniques seem very promising and are booming. The signals collected by accelerometers are the result of a mixture of various sources each of which corresponds to operation of a component. The diagnosis and monitoring of each component require the determination of the contribution of each source in the signal collected. The objective of this work is to use and optimize the techniques applied to the inverse problems with the aim of quantifying the contribution of each source in the obtained mixture, more particularly, the sources which are characteristic of a damaged element. However, the inverse problems are generally unstable. These instabilities are often related to the errors of measurement or the parasites contained in the signals and require methods of stabilization. This paper proposes a methodology, based on the restitution of the sources. Thus, the methodology ensures the detection and the localization of a defect of a component by the optimization of the position of a limited number of sensors. Two approaches are then proposed: the numerical approach which employs an updated numerical model of the studied structure and the experimental approach based on the correlation between the position of the sensors and the modal parameters of this same structure. The experimental validation of this methodology is carried out on a casing made up of two bearings. [Copyright &y& Elsevier]