Z. Baniamerian,
Volume 5, Issue 1 (3-2015)
Abstract
Continuous radiation ovens are of widely used apparatuses in paint cure and coating industries. The most important issue that guarantee the quality of paint curing is suitable thermal condition. Designing of these ovens for curing paint on bodies of complex geometries has become a challenge for many years. In the present study a new designing approach is introduced and advised because of its acceptable capabilities as well as its high speed. This approach is based on cure window criterion and applies gradient optimization technique. The present work can be divided into two parts: first, geometric and thermal simulation of the curing body and second, preparing the design tool.Since a significant part of designing procedure usually devotes the iterations of optimization procedure, defining a proper objective function efficiently reduces the time consumed for designing procedure. Procedure of finding an appropriate objective function has been comprehensively discussed in the present article. In this regard a new approach, called Hybrid method, applying an objective function based on few number of elements on the curing body is introduced. That is more fast and capable relative to other methods addressed in this study. Capability of the proposed methods is then evaluated for a typical complicated geometry.
Z. Baniamerian,
Volume 6, Issue 1 (3-2016)
Abstract
<span style="line-height: 115%; font-size: 10pt; font-style: normal; mso-bidi-font-size: 12.0pt; mso-ascii-font-family: " times="" new="" roman";="" mso-hansi-font-family:="" "times="" mso-bidi-language:="" fa;"="">This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a least square support vector machine (LSSVM). Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared to select an appropriate wavelet for feature extraction. The fault diagnosis method consists of three steps, firstly the six different base wavelets are considered. Out of these six wavelets, the base wavelet is selected based on wavelet selection criterion to extract statistical features from wavelet coefficients of raw vibration signals. Based on wavelet selection criterion, Daubechies wavelet and Meyer are selected as the best base wavelet among the other wavelets considered from the Maximum Relative Energy and Maximum Energy to Shannon Entropy criteria respectively. Finally, the gearbox faults are classified using these statistical features as input to LSSVM technique. The optimal decomposition level of wavelet is selected based on the Maximum Energy to Shannon Entropy ratio criteria. In addition to this, Energy and Shannon Entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. Some kernel functions and multi kernel function as a new method are used with three strategies for multi classification of gearboxes. The results of fault classification demonstrate that the LSSVM identified the fault categories of gearbox more accurately with multi kernel and OAOT strategy.
