We present in this paper the detection of a rolling defect in an asynchronous machine by analysis of the electric signals. For this purpose, we used a Wiener filter to decrease the dynamics of the 50 Hz and to increase the frequencies associated to the mechanical load. Thus, we could detect the presence of a ball defect. This result is corroborated by an envelope analysis of the vibratory signals. The suggested method exploits the cyclostationarity of electrical signals (voltage and current) via their cyclic statistics to resynchronize the signals according to the electrical cycle (50 Hz) in order to recover frequency fluctuations. We then estimate the Wiener filter which is highly adapted to our application in order to obtain a signal corresponding to the electrical part only, which allows extracting the mechanical part on the measured current. An experimental result, in the presence of fault in rolling element bearings, illustrates the high performance of the proposed method

The well-known gear tooth defects such as root cracks and flank spalls have been widely investigated in previous studies to model their effects on the time varying mesh stiffness (TVMS) and consequently the dynamic response of motor-gearbox systems. Nevertheless, the effect of assembly errors such as the center distance and the eccentricity has been less considered in past works. Determining the signature of these errors on the system response can help for their early detection and diagnostic to avoid overloading and failure of gears. An original geometric-based method combined with the potential energy method is proposed in this paper to accurately model the effect of these assembly errors on the TVMS of mating spur gear pairs. This is achieved by updating the line of action equation (LOA) at each meshing step using the actual coordinates of gear centers and employing a contact detection algorithm (CDA) to determine the actual contact points coordinates. An electrical model of a three-phase induction machine was then coupled with a dynamic model of a one-stage spur gear system to simulate the effect of assembly errors on the electromechanical response of the motor-gearbox system. The simulation results showed that the center distance error induces a reduction in the TVMS magnitude and the contact ratio, whereas the eccentricity error causes a double modulation of the TVMS magnitude and frequency. In addition, the results showed that assembly errors can be detected and diagnosed by analyzing the system vibration and the motor phase-current.

The condition monitoring of gearboxes is crucial to ensuring the reliability and efficiency of modern industrial machinery. The accurate estimation of Time-Varying Mesh Stiffness (TVMS) is a key aspect of modeling gear meshing behavior and generating vibration signals used for fault diagnosis. In this study, TVMS is calculated by using the Refined Finite Element Method (R-FEM), which captures detailed gear-body compliance and distributed load effects. The dynamic model of a two-stage gearbox is then used to generate vibration responses under both healthy and faulty conditions. A comprehensive parametric sensitivity analysis is conducted on critical gear modeling parameters, including tooth profile deviations, mesh convergence in contact zones, assembly tolerance-induced interaxial variations, load-dependent stiffness variations, and hub-radius effects. Experimental validation using a gearbox test bench confirms that the proposed methodology accurately reproduces fault-specific harmonic components. These results indicate that the hybrid FEM–dynamic modeling approach effectively balances accuracy and computational efficiency, thereby providing a robust framework for advanced fault detection and maintenance strategies in gear systems.

We present in this paper the detection of a rolling defect in an asynchronous machine by analysis of the electric signals. For this purpose, we used a Wiener filter to decrease the dynamics of the 50 Hz and to increase the frequencies associated to the mechanical load. Thus, we could detect the presence of a ball defect. This result is corroborated by an envelope analysis of the vibratory signals. The suggested method exploits the cyclostationarity of electrical signals (voltage and current) via their cyclic statistics to resynchronization the signals according to the electrical cycle (50 Hz) in order to recover frequency fluctuations. We then estimate the Wiener filter which is highly adapted to our application in order to obtain a signal corresponding to the electrical part only, which allows extracting the mechanical part on the measured current. An experimental result, in the presence of fault in rolling element bearings, illustrates the high performance of the proposed method.

This paper deals with diagnosis and monitoring of gears. Classic methods aim to detect faults in early stage. In this work we propose a method which enables us not just to detect a spall but also its width in order to determine the remaining life of the defective element. This method is based on higher order statistic (HOS) to identify the mechanical structure. We research the optimal filter which corresponds to the transmission path. It is supposed an ARMA (p, q) non minimal phase filter. We identified it by using maximum kurtosis property supposed to be adapted to the impulsive nature of the gear tooth spall effect. The experimental results that will be presented validate the performance of these filtering techniques in detecting spalls in gears.