A system and method in which (i) a flame scanner provides UV and/or IR wave period and amplitude information for a burner flame and (ii) a flame stabilization metering module determines whether the wave period and amplitude of the flame have reached warning and shutoff set points for the burner, thus indicating that the operation of the burner is unstable, or that a flame out event is likely, and, in which event, corrective actions are taken, or the burner is shut down, to restore stable operation and prevent a flame out from occurring.

A means of detecting the in-situ fuel-to-air-ratio (FAR) in a combustor or flame zone using a Fourier-based flame ionization probe is presented. The use of multiple excitation frequencies and its detection at certain frequencies or combinations of harmonics of those excitation frequencies, namely, the inter-modulation distortion, provides a novel means of extracting a high signal-to-noise ratio (SNR) FAR measurement in a combustor.

A means of detecting the in-situ fuel-to-air-ratio (FAR) in a combustor or flame zone using a Fourier-based flame ionization probe is presented. The use of multiple excitation frequencies and its detection at certain frequencies or combinations of harmonics of those excitation frequencies, namely, the inter-modulation distortion, provides a novel means of extracting a high signal-to-noise ratio (SNR) FAR measurement in a combustor.

A flame analytics system that may incorporate a burner, one or more sensors at the burner, a historical database connected to the one or more sensors, a model training module connected to the historical database, and a runtime algorithm module connected to the one or more sensors and the model training module. The runtime algorithm may compare realtime data from the one or more sensors and historical data from the model training module in accordance with a machine learning algorithm. The system may further incorporate a fault detection module connected to the runtime algorithm module, a fault diagnostics module connected to the fault detection module, and an enunciator connected to the fault detection module. The one or more sensors may also include having video or acoustic sensitivity of combustion in the burner.

A flame analytics system that may incorporate a burner, one or more sensors at the burner, a historical database connected to the one or more sensors, a model training module connected to the historical database, and a runtime algorithm module connected to the one or more sensors and the model training module. The runtime algorithm may compare realtime data from the one or more sensors and historical data from the model training module in accordance with a machine learning algorithm. The system may further incorporate a fault detection module connected to the runtime algorithm module, a fault diagnostics module connected to the fault detection module, and an enunciator connected to the fault detection module. The one or more sensors may also include having video or acoustic sensitivity of combustion in the burner.

A flame analytics system that may incorporate a burner, one or more sensors at the burner, a historical database connected to the one or more sensors, a model training module connected to the historical database, and a runtime algorithm module connected to the one or more sensors and the model training module. The runtime algorithm may compare realtime data from the one or more sensors and historical data from the model training module in accordance with a machine learning algorithm. The system may further incorporate a fault detection module connected to the runtime algorithm module, a fault diagnostics module connected to the fault detection module, and an enunciator connected to the fault detection module. The one or more sensors may also include having video or acoustic sensitivity of combustion in the burner.

A flame analytics system that may incorporate a burner, one or more sensors at the burner, a historical database connected to the one or more sensors, a model training module connected to the historical database, and a runtime algorithm module connected to the one or more sensors and the model training module. The runtime algorithm may compare realtime data from the one or more sensors and historical data from the model training module in accordance with a machine learning algorithm. The system may further incorporate a fault detection module connected to the runtime algorithm module, a fault diagnostics module connected to the fault detection module, and an enunciator connected to the fault detection module. The one or more sensors may also include having video or acoustic sensitivity of combustion in the burner.