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The design of a novel backstepping controller for using with active tuned mass damper (ATMD) system is investigated in this study. The main aim of this study is to design the controller for the ATMD system to reduce earthquake-induced vibrations in multistory buildings. Obtaining a generalizable control design for such systems is another important aim of this study. To reach this aim, the designed controller is based on the assumption that the system parameters are completely uncertain. The parametric uncertainty is coped with via adaptive compensation rules proposed in accordance with available system states. Effects of the control force on the displacement of the controller mass and the last floor of a multistory building are considered together. Owing to this approach, a control operation is provided in which zero convergence of the displacement of the mass of ATMD and all floors of the building can be guaranteed. Using Lyapunov-based arguments, theoretically, it has been proven that the designed controller can maintain its stability of the structure and controller mass while achieving this main control objective. The efficiency of the designed controller in terms of reaching the mentioned control objective is observed via numerical simulations. In these simulations, the designed controller is used in conjunction with an ATMD system placed on a multistory building model, and it has been shown that the controller designed in such structures can be used effectively for damping the earthquake-induced vibrations of these types of structures.

Especially, with the advancement of technology, mechanical parts need to be designed to be lighter and more durable. In the industry, gears are the most common power transmission equipment, and for this equipment, increasing durability and rigidity has an essential importance. During power transmission, undesired vibration and noise arise in gear systems. In addition to gear design, gearbox housing design is also essential to reduce the radiation of undesired structure-borne noise and vibration. In order to reduce noise and vibration levels, some modifications are frequently used on gearbox housings. In this study, three different gearbox housing designs (basic, cross and cellular) are formed and analysed by using ANSYS® software. The design alternatives for housings have been formed inside of the structure as different quantities of longitudinal and transverse stiffeners. In addition, all the external dimensions and the mass of these three housing designs are equal in order to observe just vibration and noise reduction. Fast Fourier Transform (FFT), statistical properties of vibration signals and sound levels of the gearbox have used for comparisons to determine which gearbox have better vibration and sound levels.

This study presents the severity detection of pitting faults on worm gearbox through the assessment of fault features extracted from the gearbox vibration data. Fault severity assessment on worm gearbox is conducted by the developed condition monitoring instrument with observing not only traditional but also multidisciplinary features. It is well known that the sliding motion between the worm gear and wheel gear causes difficulties about fault detection on worm gearboxes. Therefore, continuous monitoring and observation of different types of fault features are very important, especially for worm gearboxes. Therefore, in this study, time-domain statistics, the features of evaluated vibration analysis method and Poincaré plot are examined for fault severity detection on worm gearbox. The most reliable features for fault detection on worm gearbox are determined via the parallel coordinate plot. The abnormality detection during worm gearbox operation with the developed system is performed successfully by means of a decision tree.

Mechanical power transmission systems are an indispensable part of the industrial process. The most complex equipment of these processes is the gear systems. Among the gear systems the worm gearboxes are used in various applications, especially those that need high transmission ratios in one reduction stage. However, worm wheel manifests defects easily because it is made of soft material, in comparison with the worm. The stress on each tooth surface may increase because of overload, shock load, cyclic load change, gear misalignment, etc. This often causes pitting faults in worm gearboxes. This paper focuses on the detection of localized pitting damages in a worm gearbox by a vibration visualization method and artificial neural networks (ANNs). For this purpose, the vibration signals are converted into an image to display and detect pitting defects on the worm wheel tooth surface. In addition, statistical parameters of vibration signals in the time and frequency domains are used as an input to ANN for multi-class recognition. Later, the results obtained from ANN are compared for both axial and radial vibration. It is found that the ANN can classify with high accuracy for any sample of the vibration data obtained from the radial direction according to fault severity levels.