Anomaly detection in thermal pulse combustors using symbolic time series analysis
نویسنده
چکیده
This paper presents symbolic time series analysis of observable process variables for anomaly detection in thermal pulse combustors. The anomaly detection method has been tested on the time series data of pressure oscillations, generated from a non-linear dynamic model of a generic thermal pulse combustor. Results are presented to exemplify early detection of combustion instability due to reduction of friction coefficient in the tailpipe, which eventually leads to flame extinction.
منابع مشابه
Thermal anomalies detection before earthquake using three filters (Fourier, Wavelet and Logarithmic Differential Filter), A Case Study of two Earthquakes in Iran
Earthquake is one of the most destructive natural phenomena which has human and financial losses. The existence of an efficient prediction system and early warning system will be useful for reducing effects of destroying earthquake. In this research, the soil temperature time-series data, obtained from three meteorological station, using three filters (Fourier, Wavelet and Logarithmic Different...
متن کاملModeling of the Combustion Oscillations and Soot Formation in Aerovalved Pulse Combustors
This paper describes the modifications and evolution of a thermal pulse combustionmodel for predicting the combustion oscillations of an aerovalved 250 kW pulse combustorincorporating a soot formation-combustion model. Validation of the model is carried out from theexperimental data of an aerovalved Helmholtz type pulse combustor, where a sinusoidal air inlet massflow coupled with pressure osci...
متن کاملSymbolic time series analysis for anomaly detection: A comparative evaluation
Recent literature has reported a novel method for anomaly detection in complex dynamical systems, which relies on symbolic time series analysis and is built upon the principles of automata theory and pattern recognition. This paper compares the performance of this symbolic-dynamics-based method with that of other existing pattern recognition techniques from the perspectives of early detection o...
متن کاملSymbolic dynamic analysis of complex systems for anomaly detection
This paper presents a novel concept of anomaly detection in complex dynamical systems using tools of Symbolic Dynamics, Finite State Automata, and Pattern Recognition, where time-series data of the observed variables on the fast time-scale are analyzed at slow time-scale epochs for early detection of (possible) anomalies. The concept of anomaly detection in dynamical systems is elucidated based...
متن کاملSymbolic time series analysis via wavelet-based partitioning
Symbolic time series analysis (STSA) of complex systems for anomaly detection has been recently introduced in literature. An important feature of the STSA method is extraction of relevant information, imbedded in the measured time series data, to generate symbol sequences. This paper presents a wavelet-based partitioning approach for symbol generation, instead of the currently practiced method ...
متن کامل