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In order to achieve the high capability of the ride comfort and regulating the tire slip ratio, a preview of a nonlinear semi-active vibration control suspension system using a magnetorheological (MR) fluid damper is integrated with traction control in this paper. A controlled semi-active suspension system, which consists of the system controller and damper controller, was used to develop ride comfort, while the traction controller is utilized to reduce a generated slip between the vehicle speed and rotational rate of the tire. Both Fractional-Order Filtered Proportional-Integral-Derivative (P¯IλDμ) and Fuzzy Logic connected either series or parallel with P¯IλDμ are designed as various methodologies of a system controller to generate optimal tracking of the desired damping force. The signum function method is modified as a damper controller to calculate an applied input voltage to the MR damper coil based on both preview signals and the desired damping force tracking.

Dynamic modeling of the gear vibration is a useful tool to study the vibration response of a geared system under various gear parameters and operating conditions. An improved understanding of vibration signal is required for early detection of incipient gear failure to achieve high reliability. However, the aim of this work is to make use of a 6-degree-of-freedom gear dynamic model including localized tooth defect for early detection of gear failure. The model consists of a gear pair, two shafts, two inertias representing load and prime mover and bearings. The model incorporates the effects of time-varying mesh stiffness and damping, backlash, excitation due to gear errors and modifications. The results indicate that the simulated signal shows that as the defect size increases the amplitude of the acceleration signal increases. The crest factor and kurtosis values of the simulated signal increase as the fault increases.

The early detection of incipient failure in a mechanical system is of great practical importance as it permits scheduled inspections without costly shutdowns and indicates the urgency and locations for repair before a system incurs catastrophic failure. However, in this work a new technique for processing vibration data to quantify the level of damage, cracks only, in a gear system. The technique consists of a nonlinear numerical optimization. The optimization uses a dynamic model of the gear mesh used in vehicle gearbox and forms an estimate of both time-varying and frequency-varying mesh stiffness that best corresponds to the given set of vibration data. The procedure developed in this study can be applied as a part of either an onboard machine health monitoring system or a health diagnostic system used in the regular maintenance.