02309nam a22001697a 4500 NUST 621 Mustafa, Atta ul 119691 Acoustic Emission of Engine Valve Train / Atta ul Mustafa Islamabad : SMME- NUST; 2023. 55p. ; Soft Copy 30cm. This study presents the use of acoustic emission (AE) analysis in combination with signal processing and LabVIEW software for monitoring 4 different types of oils in different conditions. AE signals were collected using a free filed microphone 4188 with good frequency range placed near the valve train, and the signals were conditioned using a signal conditioner to amplify the signal. The amplified signals were then processed using Fast Fourier Transform (FFT) analysis with National Instruments DAQ cards and LabVIEW software to extract features related to valve train behavior i.e. engine sound, frictional torque etc. The results showed that AE analysis using LabVIEW and signal processing techniques can effectively detect and characterize valve train behavior under various operating conditions. Then investigates the effect of different engine oils on the behavior of engine valve trains using acoustic emission (AE) analysis and FFT analysis with LabVIEW software. Four different types of engine oils of the same grades were tested on a single-cylinder four-stroke engine with four valves. AE signals were collected using a microphone and amplified using a signal conditioner, and then analyzed using FFT analysis with National Instruments DAQ cards and LabVIEW software to extract peak values related to valve train behavior. The results show that the different types of engine oils have a significant effect on the peak values of the AE signals, which is indicative of valve train behavior and from this behavior the frictional torque can be find out[1]. The study demonstrates the potential of AE analysis and FFT analysis with LabVIEW software as a valuable tool for evaluating the performance of engine oils on engine valve train behavior. MS Mechanical Engineering 119495 Supervisor : Prof. Dr. Riaz Ahmed Mufti 119679 http://10.250.8.41:8080/xmlui/handle/123456789/34903 ddc THE 607321 607321 0 0 0 0 SMME SMME EB 2023-12-12 0 621 SMME-TH-879 2023-12-12 2023-12-12 THE