2023/11/03 by Arkadiusz Syta, Jacek Czarnigowski, P. Jakliński +1 · 1 voice · 10 citations
Chemistry · Engineering · #Acoustics #Automotive engineering #Computer science #Cylinder #Engineering #Engineering Diagnostics and Reliability #Fault Detection and Control Systems #Mechanical engineering #Physics #Piston (optics) #Series (stratigraphy) #Spectroscopy and Chemometric Analyses #Vibration
paper · doi:10.1016/j.measurement.2023.113763
published in Measurement 223, 113763 (Elsevier BV)
openalex publication_date 2023/11/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/08
We suggest an approach for detecting and identifying ignition failure on a internal combustion engine used in aviation through the analysis of vibration time series. The research is carried out at the experimental stage, where time series of vibrations are collected from sensors installed in various parts of the facility at various rotational speeds and various operating conditions (no failure/failure of a selected piston). The time series were decomposed into periodic components centered around dominant frequencies. Data with greater dimensionality was statistically described using linear and non-linear indicators in short time windows, and labeled accordingly. Instead of examining the statistical significance of the characteristics of individual groups, machine learning classification methods were used, which allowed to distinguish the operating state of the engine (damaged/undamaged), and also to identify a specific unfired cylinder. The use of non-linear indicators allowed us to obtain 100% classification accuracy with a small number of samples.