Effect of inner stiffeners on vibration and noise levels of gearbox housing without changing the mass

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

Experimental investigation of vibration attenuation on a cantilever beam using air-jet pulses with the particle swarm optimized quasi bang-bang controller

This study deals with the vibration reduction of a cantilever beam using air-jet thruster actuators controlled by the particle swarm optimized quasi bang-bang controller. In this study, the finite element model of a cantilever beam with the lumped mass of actuators is formed for the numerical simulations. Furthermore, the first-order plus dead time transfer function of the air-jet thruster actuator is found between the inlet pressure and the thrust. The quasi bang-bang control is proposed to suppress vibrations on the beam with impulsive air-jet pulses. The optimal location of the actuators and control parameters are determined with parametric study and particle swarm optimization, respectively. The performance of the control method is measured with the experiments of initial displacement, the presence of tip masses, and external disturbances. According to all results obtained in this study, it has been observed that the air-jet pulses successfully and rapidly attenuate vibration on the cantilever beam with the quasi bang-bang controller

Position Control of Flexible Manipulator using PSO-tuned PID Controller

Flexible manipulators have been tried to use due to their advantages such as the requirement of low drive power because of low weight, low power consumption, higher load capacity, high-speed operation, small actuators and low production costs. These advantages of flexible manipulators are due to their structural flexibility in joints or links. However, the flexibility causes vibrations in the end effector both on the move and after the force that causes the movement is removed. It takes some time to dampen these vibrations. In this study, particle swarm optimization (PSO)-tuned PID controller is used to control the position of the flexible manipulator directly and indirectly to reduce the vibration of the manipulator. The fitness function selected for the PSO includes parameters related to both the desired time response and vibration. Thus, the optimal PID parameters found with PSO control the position of the flexible manipulator and ensure low vibration

Smart condition monitoring of worm gearbox

© 2016, German Acoustical Society (DEGA). All rights reserved.Worm gearboxes are commonly used in many various fields of industrial applications such as escalators, presses, conveyors etc. However, heavy industries face important problems about this type of gears due to undetected failures. The vibration signal of a gearbox carries the signature of the fault. Hence, vibration measurement and graphical representation plays an important role for analysis physical conditions of gearboxes. Although there are many options for vibration measurement systems, cost effective and portable microcontrollers based monitoring system is a good option. In this study, smart monitoring system for worm gearboxes is investigated. Data acquisition system, various vibration analysis techniques, fault diagnosis and visualization are developed via Arm Cortex M4 microcontroller. It is shown that by analyzing the vibration signal using signal processing algorithms including time synchronous average (TSA), Fast Fourier Transform (FFT) and statistical metrics; early fault detection of the worm gearbox is possible. From the experimental results, the most suitable indicator for fault diagnosis of worm gearboxes is determined

Pitting detection in a worm gearbox using artificial neural networks

© 2016, German Acoustical Society (DEGA). All rights reserved.Diagnosis of worm gear faults using vibration analysis is difficult, for this reason; there have been quite little publications, although worm gears are used significant machines in assorted industrial fields. Whenever a defect occurs in a worm system (e.g. pitting, abrasive wear), the performances of the gears deteriorate. Therefore, transmission of motion and power cannot be transferred as demanded. As a result, occurrence of fatal defects becomes inevitable. This paper focuses upon the early detection of localized pitting damages in a worm gearbox using artificial neural networks (ANN) and vibration analysis. Worm gear vibrations are acquired from an experimental rig utilizing a 1/15 worm gearbox. Statistical parameters of vibration signals in the frequency domains are used as an input to classifier ANN for multi-class recognition

Fault severity detection of a worm gearbox based on several feature extraction methods through a developed condition monitoring system

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 Poincare 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

Classification of pitting fault levels in a worm gearbox using vibration visualization and ANN

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