In this paper, railway-induced ground vibrations are analysed experimentally with the aim of addressing questions raising from the planning of a new RER network, available in the near future in order to alleviate pollution and the traffic jams. Free field ground vibrations are measured during the passing of InterCity and InterRegion trains. Various sites along the main line L161 Brussels-Luxembourg are chosen and investigated, with specific differences about the track and soil configurations. In order to compare these results with numerical ones, a deep dynamic characterisation of the track, the soil and the train is first performed. The analysis of horizontal and vertical ground vibrations measured during the passage of domestic trains (AM96, AM86, AM80, AM75, HLE27/M4 or M5) at various speeds (from 40 to 120 km.h-1) is then presented. The results show that the ground vibration amplitude depends on various factors: soil configuration, train type and speed, direction of measurement, track quality. In a particular case, a local defect (rail joint) induces large deformations of the soil.

Early damage detection on structures plays a very important role for ensuring safety and reliability. This paper provides an efficient method based on wavelet transforms in order to detect and localize damage on structures subjected to moving loads such as beams and bridges. A numerical model based on the experimental test-rig utilized in this study is developed by using a finite element commercial software. Different types of damage on the bridge of the numerical model are simulated and transient analyses are performed by incorporating a load which moves constantly along the beam nodes. Continuous wavelet transform diagrams using the vertical acceleration responses show that damage can be identified and localized even with significant percentages of noise. Nevertheless, the method is improved by filtering the signals, removing the border effects, and calculating the total wavelet energy of the beam from the coefficients along the selected range of scales. Thus, the accumulation of wavelet energy could indicate the presence of damage. Finally, laboratory experiments are conducted to validate this work and a good agreement between numerical and experimental results is obtained.

This paper introduces a dynamic classification method inspired by DBSCAN clustering method for machine condition monitoring in general and for bearings in particular. This method has been developed for two purposes; first to monitor the health condition of a bearing in real time and second to study the behavior of defected rolling element bearing. To fulfill those purposes, the temporal indicator RMS (Root Mean Square) has been chosen as an indicator of the bearing health condition; this indicator has been computed from signals extracted from an experimental bench by two piezoelectric sensors placed radially and axially. The decision upon the right classification method was taken after a comparative study between two classical of the clustering methods (K-means and Density Based Spatial Clustering of Applications with Noise DBSCAN), which led to the conclusion that DBSCAN is more adapted to vibratory signals. DBSCAN was re-adapted to follow any changing in bearings behavior.

In recent years, the adaptive decomposition methods have become the center of interest of researchers in many fields and especially in the vibration diagnosis of rotating machines. This paper compares the sensitivity of defect detection of two adaptive methods: local mean decomposition (L.M.D) and empirical mode decomposition (E.M.D). The efficiency of L.M.D and E.M.D methods for detecting defects is investigated for two cases, one from numerical signals and the other from signals recorded during a fatigue test on gear bench. The time descriptors Kurtosis, Thalaf and Thikat are applied on the signal and on its Hilbert transform. The results reveal that both techniques seem to be suitable and have good efficiency for the fault detection. From experimental signals, the comparative results reveal that both methods allow for monitoring wear, but that L.M.D is more sensitive to detect rapid changes of degradation than E.M.D method for the considered case. Consequently these features have potential to become powerful tools for the monitoring of rotating machinery.

Rotating machines monitoring in a non-stationary regime, characterized by the operating parameters (speed, load) variation, presents a particular challenge. The speed variation has an impact on the vibratory response given by the accelerometers and therefore masks any defect that may be detected by classical indicators (for example RMS value). To overcome this problem, a new indicator is proposed. For this, a signal accelerometer and a signal from an optical encoder are acquired simultaneously. An algorithm to estimate the instantaneous speed from the signal delivered by the optical encoder is applied. Then, each sample of the accelerometer signal is divided by its corresponding instantaneous speed sample. The RMS value is then applied to the resulting signal. A model simulation signal is used to test the proposed method. A test rig is performed to extract signals of different degradation states of thrust bearings in variable speed regime. The results show a correlation between the proposed RMS value and the thrust bearings state in variable regime.

In this paper, a subspace fitting method is proposed to update, in the time domain, the finite element model of a rotating machine. The procedure is achieved by minimizing an error norm, leading to the comparison between experimental and theoretical observability matrices. Experimental observability matrix is obtained through a MOESP subspace identification algorithm, by projecting the output signal onto some appropriate subspaces, resulting in a cancellation of input excitations and noises. The theoretical observability matrix is obtained from modal parameters of a finite element model of the structure. The minimization procedure is carried out through a Gauss-Newton algorithm. The method is applied to determine the foundation stiffness of an experimental rotating machine subject to a random noise.

The pulleys of an automotive front engine accessory drive are driven in rotation by a poly-V belt itself driven by the crankshaft pulley. This driving pulley is often used as a torsional vibration damper (TVD) for the crankshaft. Three elements compose the pulley: the hub, a rubber ring and an inertia steel ring with v-ribs on its outer diameter. Although the crankshaft torsional vibrations are dampen, they are transmitted to the belt transmission and therefore to the driven accessories. Hence, recent developments have conducted to add a decoupling function to these pulleys. The decoupling is realized by an other rubber ring. The TVD pulley components are designed in order to dampen crankshaft vibrations over a given frequency range, i.e. stiffness and damping characteristics are determined for the rubber ring. These expected characteristics have to be checked after manufacturing for product certification but also to give some real measured data input for simulation models. An experimental characterization method is presented and discussed for the determination of the stiffness and damping coefficients of the rubber rings used in such pulley. The test rig developed is temperature controlled in order to show the influence of the temperature.