• Transactions of the Korean Society of Automotive Engineers
• /
• v.24 no.2
• /
• pp.144-151
• /
• 2016

This study effect of engine oils on regulated fuel economy and emissions including particulate matter (PM) to provide basic data for management of engine oil in vehicles. Three engine oils (Group III base oil, Group III genuine oil with additive package and synthetic oil with poly alpha olefins (PAOs)) were used in one gasoline, one LPG(liquefied petroleum gas) and two diesel vehicles. In the case of diesel vehicles, one is a diesel vehicle without DPF (diesel particulate filter) other is a diesel vehicle with DPF. In this study, the US EPA emission test cycle FTP-75, representing city driving, was used. HORIBA, PIERBURG, and AVL gas analyzers were used to measure the fuel economy and regulated emissions such as CO, NOx, and THC. The number of PM was measured using a PPS (pegasor particle sensor). And, the shape of PMs was analyzed by SEM (scanning electron microscope). The effects of oil type on fuel economy, exhaust gas, and PM were not significant because engine oil consumption by evaporation and combustion in the cylinder is very tiny. Fuel and vehicle type were dominant factors in fuel economy and emissions. HC emission from gasoline vehicles was higher than that from other vehicles and NOx emission from diesel vehicles was higher than that from other vehicles. The number of PM was not affected by the engine oil, but by the driving pattern and fuel. The shapes of the PM, sampled from each vehicle using any test engine oil, were similar.

• The KIPS Transactions:PartB
• /
• v.18B no.3
• /
• pp.147-156
• /
• 2011

This paper proposes a 3-stage (preprocessing, feature extraction, and classification) fault detection and classification algorithm for induction motors. In the first stage, a low-pass filter is used to remove noise components in the fault signal. In the second stage, a discrete cosine transform (DCT) and a statistical method are used to extract features of the fault signal. Finally, a back propagation neural network (BPNN) method is applied to classify the fault signal. To evaluate the performance of the proposed algorithm, we used one second long normal/abnormal vibration signals of an induction motor sampled at 8kHz. Experimental results showed that the proposed algorithm achieves about 100% accuracy in fault classification, and it provides 50% improved accuracy when compared to the existing fault detection algorithm using a cross-covariance method. In a real-world data acquisition environment, unnecessary noise components are usually included to the real signal. Thus, we conducted an additional simulation to evaluate how well the proposed algorithm classifies the fault signals in a circumstance where a white Gaussian noise is inserted into the fault signals. The simulation results showed that the proposed algorithm achieves over 98% accuracy in fault classification. Moreover, we developed a testbed system including a TI's DSP (digital signal processor) to implement and verify the functionality of the proposed algorithm.