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Multi-Spectral Infrared Flame Detector with Neural Network Technology

Multi-Spectral Infrared Flame Detector with Neural Network Technology. Introduction. Flame Detection Applications Existing Technologies Flame Detection Requirements Main Competitors Key Customer Concerns Neural Network Technology FlameGard 5 MSIR Flame Detector. Applications.

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Multi-Spectral Infrared Flame Detector with Neural Network Technology

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  1. Multi-Spectral Infrared Flame Detector with Neural Network Technology

  2. Introduction • Flame Detection Applications • Existing Technologies • Flame Detection Requirements • Main Competitors • Key Customer Concerns • Neural Network Technology • FlameGard 5 MSIR Flame Detector

  3. Applications Some of the most challenging industrial environments include: • Oil / Gas Production (Offshore / Onshore) • Oil / Gas Pipeline (Distribution / Production / Transmission) • Chemical Plants • Electric Power • Aviation / Aerospace

  4. Typical Industrial Flame Sources The most common potential ignition sources: • Alcohols • Chemicals • Diesel • Gasoline • Kerosene • Jet Fuels • LNG / LPG • n-Heptane • Solvents • Textiles

  5. Industrial Flame Detection Requirements • Flame Detector Performance Criteria • Detection Range • Field of View (FOV) • Response Time • Optical Integrity Check / Continuous Optical Path Monitoring (COPM) • Operating Temperature • Communication Capabilities • False Alarm Immunity • Detector must be immune to a multitude of IR signals, including: • Random motion • Modulation of heated surfaces • Hot air flow, arc welding • Reflection off water surfaces • Other related environmental nuisances which may result in false alarms

  6. Key Customer Concerns • False Alarm Immunity • Uninterrupted 24/7 operations without shut-down for false alarms • Eliminates the high cost of emergency response • Materials, Clean-up, Resources, Production Time • Field of View / Detector Range • Precision uniform sensing for protection and reliability with no blind spots • Wider field of view at greater distances requires fewer detectors and reduces costs (acquisition, installation, etc.) • Responsivity • Fast detection protects people; minimizes damage to equipment / facilities • Rugged Design • Must operate in extreme environments with reduced maintenance costs

  7. Recognizing False Alarm Issues • Optical flame detector manufacturers have attempted to resolve false alarm issues by using: • Multiple Sensors • UV, IR, Visual • Advanced signal processing techniques: • Correlation • Frequency Analysis • Periodicity Check • Ratios Analysis • Threshold Crossing

  8. Expert System vs. NNT System • Current optical flame detection systems • Utilize expert decision systems, a set of classification conditions defined by direct human analysis of experimental data • Generating accurate rules for classifying flames from false alarms is extremely difficult and time consuming • Neural-Network Technology (NNT) based optical flame detection systems • Utilize artificial neural networks (ANN), a pattern recognition mechanism derived from statistical analysis of experimental data • Development time and effort is shifted from expert to computer • Superior discrimination of flames from false alarms is provided by computer optimization to lower classification errors • The FlameGard 5 MSIR was exposed to over 450 flame and non-flame events as part of its training

  9. MSIR / NNT Processing Modes • Training Mode • A set of processing modules running on a computer, separate from the flame detector, learns patterns from input data and establishes coefficients of correlation to target flame or false-alarm condition • Detection Mode • A simple arithmetic computation uses input sensor data and constant correlation coefficients trained on archived sensor data to decide on presence or lack of flame • The quality of decision scheme is independent of its complexity and embedded system resources

  10. MSIR / NNT Processing System

  11. MSIR / NNT Waveforms Wave Forms for the Four Wavelengths ANN Output

  12. MSIR / NNT Flame & False AlarmTesting Apparatus

  13. MSIR / NNT Training Data Collection & Testing n-Heptane Flame Burning in 1 ft x 1 ft Pan Grade 87 Gasoline Burning in 1 ft x 1 ft Pan

  14. FlameGard 5 MSIR Flame Detector MSIR with NNT

  15. FlameGard 5 MSIR Mechanical Dimensions

  16. FlameGard 5 MSIR Connectivity

  17. Optical Housing Module FlameGard 5 MSIR Modules Base Housing Module

  18. FlameGard 5 MSIR Cross Section

  19. FlameGard 5 MSIR Mounting Accessories

  20. FlameGard 5 MSIR COPM – Every two minutes Green and Red LED Indicators

  21. FlameGard 5 MSIR ModulesElectro-Optical & Termination Modules

  22. FlameGard 5 MSIR Modules – Dip Switch Settings

  23. FlameGard 5 MSIR Modules – Wiring Terminals

  24. Arc Welding Immunity

  25. Field of View Comparison

  26. Field Of View with Sensitivities

  27. Detection Area Coverage FlameGard 5 UV/IR

  28. Detection Area CoverageFlameGard 5 MSIR

  29. Detection Area CoverageFlameGard 5 MSIR

  30. FlameGard 5 Test Lamp 35 Feet (10.7 meters)

  31. FlameGard 5 MSIR Features / Benefits

  32. FlameGard 5 MSIR Highlights • Sets a new industry benchmark • Superior false alarm immunity: Arc weld as close as 5-15 ft • Greater detection range: 230 ft max • Expanded field of view: 100 degrees @ 100 ft • Test Lamp: Test the unit from 35 ft • MTBF: 150,000 hrs • FM Certified to IEC 61508 (SIL 3 suitable) • High quality stainless steel package • MSIR/NNT provides highest levels of flame detection with respect to performance, safety, reliability and value • Approvals: FM, CSA, ULC, ATEX, CE Marking

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