@ARTICLE{Ahsan_Muhammad_Analysis_Online, author={Ahsan, Muhammad and Bismor, Dariusz and Fabis, Paweł}, journal={Archives of Acoustics}, howpublished={online}, year={Online first}, publisher={Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics}, abstract={Vehicle engine vibration signals acquired using MEMS sensors are crucial in the diagnosis of engine malfunctions, notably misfires due to unwanted signals and external noises in the recorded vibration dataset. In this study, the ADXL1002 accelerometer interfaced with the Beaglebone Black microcontroller is employed to capture vibration signals emitted by the vehicle engine across various operational states, including unloaded, loaded, and misfire conditions at 1500 RPMs, 2500 RPMs, and 3000 RPMs. In conjunction with the acquisition of this raw vibration data, frequency-domain signal processing techniques are employed to meticulously analyze and diagnose the distinct signatures of misfire occurrences across various engine speeds and loads. These techniques encompass the fast Fourier transform (FFT), envelope spectrum (ES), and empirical mode decomposition (EMD), each tailored to discern and characterize the nuanced vibration patterns associated with misfire events at different operational conditions.}, type={Article}, title={Analysis of the Vehicle Engine Misfires using Frequency-Domain Approaches at Various RPMs with ADXL1002 Accelerometer}, URL={http://czasopisma.pan.pl/Content/132836/PDF/10.24425aoa.2024.148813.pdf}, doi={10.24425/aoa.2024.148813}, keywords={ADXL1002 accelerometer, MEMS sensors, vehicle engine vibration, misfire condition, fast Fourier transform, envelope spectrum, empirical mode decomposition}, }