ABSTRACT
Condition monitoring systems are increasingly being employed in industrial applications to improve the availability of equipment to increase the overall equipment efficiency. Condition monitoring of gearboxes, a key element of rotating machines, ensures to continuously reduce and eliminate costs, unscheduled downtime and unexpected breakdowns. This study demonstrates a low-cost microcontroller-based non-contact data acquisition system for condition monitoring of rotating machinery. Experimental validation of the proposed system was carried out by performing examination tests on a gearbox test rig. A user-friendly graphical user interface was also developed which facilitates users to perform signal processing in both real-time and offline mode. The proposed system can perform most of the functions available in complex, stand-alone vibration analysers. The use of a general-purpose PC and standard programing language makes the system simple, economical and adaptable to a variety of problems. The tests show the developed system can perform properly as proposed.
ABSTRACT
Surface integrity has attracted the attention of researchers for improving the functional performance of engineering products. Improvement in surface finish, one of the important parameters in surface integrity, has been attempted by researchers through different processes. Grinding has been widely used for final machining of components requiring smooth surfaces coupled with precise tolerances. Proper selection of grinding wheel material and grade with grinding parameters can result in an improved surface finish and improved surface characteristics. The present work reports the study of the effect of grinding parameters on surface finish of EN8 steel. Experiments were performed on surface grinding and cylindrical grinding for optimization of grinding process parameters for improved surface finish. Grinding wheel speed, depth of cut, table feed, grinding wheel material and table travel speed for surface grinding operation, and work speed for cylindrical grinding operation were taken as the input parameters with four types of grinding wheels (Al2O3 of grades K and L, and white alumina of grades J and K). The surface roughness was taken as an output parameter for experimentation. The grinding wheel material and grade have been observed to be the most significant variables for both cylindrical grinding and surface grinding. Surface roughness in the case of surface grinding is better compared to that of cylindrical grinding, which can be attributed to vibrations produced in the cylindrical grinding attachment. Surface roughness (Ra) values of 0.757 µm in cylindrical grinding and 0.66 µm in surface grinding have been achieved.
ABSTRACT
Gearbox plays most essential role in the modern machinery for transmitting the required torque along with motion and contributes to wide range of applications. Any failure in gearbox components affects the productivity and efficiency of the system. Most machine breakdowns related to gears are a result of improper operating conditions and loading, hence lead to failure of the whole mechanism. Ensemble Empirical Mode Decomposition (EEMD) comprises advancement and valuable addition in Empirical Mode Decomposition (EMD) and has been widely used in fault detection of rotating machines. However, intrinsic mode functions (IMFs) produced by EEMD often carry the residual noise. Also, the produced IMFs are different in number due to addition of white Gaussian noise, which leads to final averaging problem. To alleviate these drawbacks, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was previously presented. This paper describes and presents the implementation of CEEMDAN for fault diagnosis of simulated local defects using sound signals in a fixed-axis gearbox. Statistical parameters are extracted from decomposed sound signals for different simulated faults. Results show the effectiveness of CEEMDAN over EEMD in order to obtain more accurate IMFs and fault severity.