Volume 62, Issue 10 will be published on 02 December 2020
Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
This study proposes a new diagnosis method for identifying the vibration data of the multi-level fault of gear. The diagnosis method is based on the feature representation of the vibration signal and optimized machine learning model. Firstly, a deep Auto-Encoder (AE) bottleneck network is constructed with the two hidden layers to extract the fault features of gear vibration data, named AE-BtF. In which, the AE-BtF use the basic unsupervised learning algorithm to reveal the significant characteristics in the complex data with the nonlinear, non-station properties. The obtained features can provide good discriminability for fault diagnosis task. Secondly, an optimal classifier model is formed to perform supervised fine-tuning and classification. This model is based on the least square support vector machine (LSSVM) classifier and chemical reaction optimization algorithm (CRO), named CRO-LSSVM. The meta-heuristics CRO algorithm is used to exploit the appropriate parameters for the LSSVM. Based on the vibration data of gear fault status, the proposed AE-BtF-CRO-LSSVM technique shows a good ability for identifying the gear fault accuracy. The diagnosis results have demonstrated that the AE-BtF based feature extraction in conjunction with the CRO-LSSVM classifier model can achieve higher accuracies than the other popular classifier models to 60%
Restricting the amount of air mass induced into the engine is the most convenient way, and is commonly used to limit engine performance in racing competitions. Student Formula Japan uses this method in its competition. Another regulation is that the engine capacity cannot exceed 710 cc, but the engine must still use a 20 mm restrictor in the air intake system. With the restrictor, it is necessary to adjust various engine geometry parameters to maximize the performance of the engine. In this study, these adjustments were made to the camshaft phasing configuration, and it increases engine power and torque of up to 3%. The peak power and torque are delivered at lower engine speeds of 5,000 - 8,800 RPM. It provides progressive torque delivery and makes the engine much more suitable for FSAE and Formula Student applications. Modification of advance timing causes the power and torque curve at low speed to be wider, but its peak power and torque to be lower. Modify the valve timing configuration is the easiest way to adjust the engine performance characteristics on the track. These findings contribute to adjusting the valve timing configuration into advanced timing