Flame detection system

1. A flame detection system, comprising:

a plurality of discrete optical radiation sensors;

means for joint time-frequency signal pre-processing outputs from the plurality of discrete optical radiation sensors to provide pre-processed signals;

an Artificial Neural Network for processing the pre-processed signals and providing an output indicating a flame condition;

said flame condition comprising the presence of flame or the absence of flame; and

a fire alarm activated in response to an output indicating the presence of flame.

2. The system of claim 1 , wherein the flame condition further comprises a false alarm condition.

3. The system of claim 1 , wherein the plurality of optical radiation sensors comprises an array of discrete sensors.

4. The system of claim 3 , wherein the array of discrete sensors are mounted in a unitary housing structure.

5. The system of claim 1 , wherein the plurality of discrete optical radiation sensors comprises a 4.9 um sensor, a 2.2 um sensor, a 4.3 um sensor and a 4.45 um sensor.

6. The system of claim 1 , wherein the Artificial Neural Network comprises a two-layer Artificial Neural Network.

7. The system of claim 1 , wherein said pre-processing means establishes a correlation between frequency and time domain of the outputs from the discrete optical sensors.

8. The system of claim 7 , wherein said means for establishing a correlation comprises an electronic signal processor adapted to perform one of Discrete Fourier Transform, Short-Time Fourier Transform with a shifting time window or a Discrete Wavelet Transform.

9. The system of claim 1 , further comprising a temperature sensor for sensing a temperature of the system, and said Artificial Neural Network is further responsive to signals indicative of the sensed temperature to provide said output.

10. The system of claim 1 , further comprising a vibration sensor for sensing a vibration level experienced by the system, and said Artificial Neural Network is further responsive to signals indicative of the sensed vibration level to provide said output.

11. A flame detection system, comprising:

a plurality of discrete optical radiation sensors; and

an Artificial Neural Network for processing a plurality of signals indicative of outputs from the plurality of sensors and providing an output indicating a flame condition;

means for establishing a correlation between frequency and time domain of the outputs from the discrete optical sensors, wherein said means for establishing a correlation comprises an electronic signal processor adapted to perform one of Discrete Fourier Transform, Short-Time Fourier Transform with a shifting time window or a Discrete Wavelet Transform;

said flame condition comprising the presence of flame or the absence of flame; and

a flame suppression system activated in response to an output indicating the presence of flame.

12. The system of claim 11 , wherein the flame condition further comprises a false alarm condition.

13. The system of claim 11 , wherein the plurality of optical radiation sensors comprises an array of discrete sensors.

14. The system of claim 13 , wherein the array of discrete sensors are mounted in a unitary housing structure.

15. The system of claim 11 , wherein the plurality of discrete optical radiation sensors comprises a 4.9 um sensor, a 2.2 um sensor, a 4.3 um sensor and a 4.45 um sensor.

16. The system of claim 11 , wherein the Artificial Neural Network comprises a two-layer Artificial Neural Network.

17. The system of claim 11 , further comprising a temperature sensor for sensing a temperature of the system, and said Artificial Neural Network is further responsive to signals indicative of the sensed temperature to provide said output.

18. A flame detection system, comprising:

a plurality of discrete sensors for generating a plurality of respective sensor signals, said plurality of sensors including a set of optical radiation sensors responsive to flame emissions;

a digital signal processor including an Artificial Neural Network (ANN) for processing the sensor signals to provide an output corresponding to a detector flame condition, said flame condition including the presence of flame or the absence of flame, the digital signal processor further comprising a pre-processing means for processing the sensor signals to provide pre-processed signals for said ANN, wherein said pre-processing means comprises means for establishing a correlation between frequency and time domain of the signals, said means performing one of Discrete Fourier Transform, Short-Time Fourier Transform with a shifting time window or a Discrete Wavelet Transform; and

a flame suppression system activated by a detector flame condition corresponding to the presence of flame.

19. The system of claim 18 , wherein the flame condition comprises a false alarm condition.

20. The system of claim 18 , wherein the plurality of discrete sensors comprises an array of sensors mounted in a common housing structure.

21. The system of claim 20 , wherein the set of optical radiation sensors comprises a 4.9 um sensor, a 4.3 um sensor and a 4.45 um sensor.

22. The system of claim 18 , wherein the plurality of sensors further comprises an immunity sensor sensitive to radiation in an optical spectrum from ultraviolet to infrared.

23. The system of claim 22 , wherein said immunity sensor is sensitive to 2.2 micron wavelength radiation.

24. The system of claim 18 , wherein the plurality of sensors comprises a temperature sensor for generating a temperature sensor signal indicative of a temperature.

25. The system of claim 18 , wherein the Artificial Neural Network comprises a two-layer Artificial Neural Network.

26. The system of claim 25 , wherein the Artificial Neural Network comprises a hidden layer of artificial neurons which apply a set of hidden layer connection weights and a sigmoid function to said pre-processed signals to provide hidden layer output signals, and an output layer of output neurons which apply a set of output connection weights and a sigmoid function to said hidden layer output signals to provide flame neuron output values.

27. The system of claim 18 , further comprising a decision processor responsive to outputs from the ANN to determine a flame detection state based on said sensor signals.

28. The system of claim 27 , wherein the decision processor generates an alarm condition when a threshold limit is exceeded.

29. A method for detecting flames, comprising:

sensing optical radiation over a field of view with a plurality of discrete sensors and generating sensor signals indicative of the sensed radiation;

establishing a correlation between frequency and time domain of the sensor signals, wherein said establishing a correlation comprises performing one of Discrete Fourier Transform, Short-Time Fourier Transform with a shifting time window or a Discrete Wavelet Transform;

processing the sensor signals by a digital signal processor including an Artificial Neural Network (ANN) to provide detection outputs corresponding to a flame condition, said flame condition comprising the presence of flame or the absence of flame; and

activating a fire alarm in the event of a detection output corresponding to the presence of flame.

30. The method of claim 29 , wherein the flame condition comprises a false alarm condition.

31. The method of claim 29 , wherein the plurality of optical radiation sensors comprises a 4.9 um sensor, a 2.2 um sensor, a 4.3 um sensor and a 4.45 um sensor.

32. The method of claim 29 , wherein the artificial neural network comprises a two-layer Artificial Neural Network.

33. A flame detection system, comprising:

a plurality of discrete optical radiation sensors;

means for joint time-frequency signal pre-processing outputs from the plurality of discrete optical radiation sensors to provide pre-processed signals;

a digital signal processor for processing the pre-processed signals to detect a flame in a field of view surveilled by said plurality of discrete optical radiation sensors, and providing an output indicating a flame condition;

a fire alarm system activated in response to an output indicating that a flame has been detected in said field of view.

34. The system of claim 33 , wherein the flame condition comprises one of the presence of flame, the absence of flame and false alarm.

35. The system of claim 33 , wherein the flame condition is one of the presence and the absence of flame.

36. The system of claim 33 , wherein the plurality of optical radiation sensors comprises an array of discrete sensors.

37. The system of claim 33 , wherein the plurality of discrete optical radiation sensors comprises a 4.9 um sensor, a 2.2 um sensor, a 4.3 um sensor and a 4.45 um sensor.

38. The system of claim 33 , wherein the digital signal processor comprises an Artificial Neural Network.

39. The system of claim 33 , wherein said pre-processing means establishes a correlation between frequency and time domain of the outputs from the discrete optical sensors.

40. The system of claim 39 , wherein said pre-processing means is adapted to perform one of Discrete Fourier Transform, Short-Time Fourier Transform with a shifting time window or a Discrete Wavelet Transform.

41. The system of claim 1 , further comprising a flame suppression system activated in response to an output indicating the presence of flame.

42. The method of claim 29 , further comprising:

activating a flame suppression system in response to an output indicating the presence of flame.

43. The system of claim 33 , further comprising a flame suppression system activated in response to an output indicating that a flame has been detected within said field of view.