Abstract:
A flame detector for industrial safety applications in hazardous locations, configured for radiant energy monitoring, quantification, and information transmission. The system has at least one optical sensor channel, each including an optical sensor configured to receive optical energy from a surveilled scene within a field of view at a hazardous location, the channel producing a signal providing a quantitative indication of the optical radiation energy received by the optical sensor within a sensor spectral bandwidth. A processor is responsive to the signal from the at least one optical sensor channel to provide a flame present indication of the presence of a flame, and a quantitative indication representing a magnitude of the optical radiation energy from the surveilled scene. An Artificial Neural Network may optionally be used to provide an output corresponding to a flame condition.

Description:
BACKGROUND 
       [0001]    Flame detectors for industrial safety in hazardous locations have one or more optical sensors for detecting electromagnetic radiation, including visible, infrared or ultraviolet, which is indicative of the presence of a flame. A flame detector may detect and measure infrared (IR) radiation, for example at around 4.3 microns, a wavelength that is characteristic of the spectral emission peak of carbon dioxide produced by burning hydrocarbons. The optical sensors used in single through multi-sensor flame detectors continuously monitor the total radiation incident from all sources of radiation in the spectral range being sensed within their field of view. The sources of radiation include both flame sources that are to be detected, and non-flame nuisance sources such as sunlight, reflections, arc welding, heat generating equipment and structures that are typical of an industrial setting. Though such radiometric information may be continuously monitored by the optical sensors, industrial flame detectors for safety applications are “go no-go” devices with a normal quiescent state followed by warning and alarm states when a fire is detected. 
         [0002]    Flame detectors may produce false alarms caused by the instrument&#39;s inability to distinguish between radiation emitted by flames and that emitted by other nuisance sources such as those listed above. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0003]    Features and advantages of the disclosure will readily be appreciated by persons skilled in the art from the following detailed description of exemplary embodiments thereof, as illustrated in the accompanying drawings, in which: 
           [0004]      FIG. 1  is a schematic block diagram of an exemplary embodiment of a flame detection system utilizing multiple optical sensors. 
           [0005]      FIG. 1A  illustrates an exemplary sensor housing structure suitable for the optical sensors of a multi-spectral flame detection system. 
           [0006]      FIG. 2  is an electronic block diagram of an exemplary embodiment of the analog front end of the multi-spectral flame detection system of  FIG. 1 . 
           [0007]      FIG. 3  is a functional flow diagram of an exemplary embodiment of signal processing functions of the flame detection system of  FIG. 1 . 
           [0008]      FIG. 4A  is an exemplary flow diagram of an exemplary embodiment of pre-processing functions utilized in the multi-spectral flame detection system illustrated in  FIG. 3 . 
           [0009]      FIG. 4B  is a flow diagram of an exemplary embodiment of radiant energy computation utilized in the multi-spectral flame detection system illustrated in  FIGS. 1-4A . 
           [0010]      FIG. 5  illustrates an exemplary embodiment of the ANN processing utilized in the multi-spectral flame detection system illustrated in  FIGS. 1-4 . 
           [0011]      FIG. 6  is a functional block diagram of another exemplary embodiment of signal processing functions of a flame detection system as in  FIG. 1 . 
           [0012]      FIG. 7  is an illustration of exemplary 0-20 mA analog current interfaces for evaluating detected conditions, both quantitatively and qualitatively. 
           [0013]      FIG. 8  is a functional block diagram of another exemplary embodiment of signal processing functions of a flame detection system as in  FIG. 1 . 
           [0014]      FIG. 9  is a functional block diagram of another exemplary embodiment of signal processing functions of a flame detection system as in  FIG. 1 . 
       
    
    
     DETAILED DESCRIPTION 
       [0015]    In the following detailed description and in the several figures of the drawing, like elements are identified with like reference numerals. The figures are not to scale, and relative feature sizes may be exaggerated for illustrative purposes. 
         [0016]      FIG. 1  illustrates a schematic block diagram of an exemplary embodiment of a multiple sensor flame detection system  1  comprising four optical sensors or sensing elements  2   a,    2   b,    2   c,    2   d.  In this exemplary embodiment, the optical sensors are for sensing energy in the infrared spectrum. In an exemplary embodiment, the analog signals generated by the sensors are conditioned by electronics  3   a,    3   b,    3   c,    3   d  and then converted into digital signals by the analog to digital converter (ADC)  4 . 
         [0017]    In the exemplary embodiment of  FIG. 1 , the multi-spectral flame detection system  1  includes an electronic controller or signal processor  6 , e.g., a digital signal processor (DSP), an ASIC or a microcomputer or microprocessor based system. In an exemplary embodiment, the controller  6  may comprise a DSP, although other devices or logic circuits may alternatively be deployed for other applications and embodiments. In an exemplary embodiment, the signal processor  6  also includes a dual universal asynchronous receiver transmitter (UART)  61  as a serial communication interface (SCI), a serial peripheral interface (SPI)  62 , an internal ADC  63  that may be used to monitor a temperature sensor  7 , an external memory interface (EMIF)  64  for an external memory (SRAM)  21 , and a non-volatile memory (NVM)  65  for on-chip data storage. Modbus  91  or HART  92  protocols may serve as interfaces for serial communication over UART  61 . Both protocols are well-known in process industries, along with others such as PROFIbus, Fieldbus and CANbus, for interfacing field instrumentation to a computer or a programmable logic controller (PLC). 
         [0018]    In an exemplary embodiment, signal processor  6  receives the digital detector signals  5  from the ADC  4  through the SPI  62 . In an exemplary embodiment, the signal processor  6  is connected to a plurality of other interfaces through the SPI  62 . These interfaces may include an external NVM  22 , an alarm relay  23 , a fault relay  24 , a display  25 , and an analog output  26 . 
         [0019]    In an exemplary embodiment, the analog output  26  may be a 0-20 mA output. In an exemplary embodiment, a first current level at the analog output  26 , for example 16 mA, may be indicative of a flame warning condition, a second current level at the analog output  26 , for example 20 mA, may be indicative of a flame alarm condition, a third current level may be indicative of normal operation, e.g., when no flame is present, and a fourth current level at the analog output  26 , for example 0 mA, may be indicative of a system fault, which could be caused by conditions such as electrical malfunction. In other embodiments, other current levels may be selected to represent various conditions. The analog output  26  can be used to trigger a fire suppression unit, in an exemplary embodiment. 
         [0020]    In an exemplary embodiment, the signal processor  6  is programmed to perform pre-processing and ANN processing, as discussed more fully below. 
         [0021]    In an exemplary embodiment, the plurality of detectors  2  comprises a plurality of spectral sensors, which may have different spectral ranges and which may be arranged in an array. In an exemplary embodiment, the plurality of detectors  2  comprises optical sensors sensitive to multiple wavelengths. At least one or more of detectors  2  may be capable of detecting optical radiation in spectral regions where flames emit strong optical radiation. For example, the sensors may detect radiation in the UV to IR spectral ranges. Exemplary sensors suitable for use in an exemplary flame detection system  1  include, by way of example only, silicon, silicon carbide, gallium phosphate, gallium nitride, and aluminum gallium nitride sensors, and photoelectric tube type sensors. Other exemplary sensors suitable for use in an exemplary flame detection system include IR sensors such as, for example, pyroelectric, lead sulfide (PbS), lead selenide (PbSe), and other quantum or thermal sensors. In an exemplary embodiment, a suitable UV sensor operates in the 200-260 nanometer region. In an exemplary embodiment, the photoelectric tube-type sensors and/or aluminum gallium nitride sensors each provide “solar blindness” or an immunity to sunlight. In an exemplary embodiment, a suitable IR sensor operates in the 4.3 micron region specific to hydrocarbon flames, and/or the 2.9 micron region specific to hydrogen flames. 
         [0022]    In an exemplary embodiment, the plurality of sensors  2  comprise, in addition to sensors chosen for their sensitivity to flame emissions (e.g., UV, 2.9 micron and 4.3 micron), one or more sensors sensitive to different wavelengths to help identify and distinguish flame radiation from non-flame radiation. These sensors, known as immunity sensors, are less sensitive to flame emissions, however, provide additional information on infrared background radiation. The immunity sensor or sensors detect wavelengths not associated with flames, and may be used to aid in discriminating between radiation from flames and non-flame sources. In an exemplary embodiment, an immunity sensor comprises, for example, a 2.2 micron wavelength detector. A sensor suitable for the purpose is described in U.S. Pat. No. 6,150,659. 
         [0023]    In the exemplary embodiment of  FIG. 1 , the flame detection system  1  comprises an array of four sensors  2   a - 2   d,  which incorporates spectral filters respectively sensitive to radiation at 4.9 micron ( 2   a ), 2.2 micron ( 2   b ), 4.3 micron ( 2   c ) and 4.45 micron ( 2   d ). In an exemplary embodiment, the filters were selected to have narrow operating bandwidths, e.g. on the order of 100 nanometers, so that the sensors are only responsive to radiation in the respective operating bandwidths, and block radiation outside of the operating bands. In an exemplary embodiment, the optical sensors  2  are packaged closely together as a cluster or combined within a single detector package. This configuration leads to a smaller, less expensive sensor housing structure, and also provides for a more unified optical field of view of the instrument. An exemplary detector housing structure suitable for the purpose is the housing for the detector LIM314, InfraTec GmbH, Dresden, Germany.  FIG. 1A  illustrates an exemplary sensor housing structure  20  suitable for use in housing the sensors  2   a - 2   d  in an integrated unit. 
         [0024]    Referring now to the four optical sensors  2   a,    2   b,    2   c,    2   d,  in an exemplary embodiment the four sensors continuously monitor the total radiation incident from all sources of radiation in the spectral range being sensed within their field of view. The instrument may be configured to provide the radiometric information computed by a particular infrared channel of interest, for example, channel  2   c  at 4.3 um could be monitored as a guide to flame intensity. Likewise, flame channel sensor outputs  2   c  and  2   d  may be combined as a guide to flame intensity. A number of algebraic combinations are possible with four optical sensor outputs, such as, total, average, weighted average, or subtractions. Such computations could be performed onboard the instrument or remotely by the user on a control room computer using data sent continually, periodically, on request, or triggered by an event. The radiant energy computation could be used to set a flame detection threshold as described via  FIG. 3 . The radiant heat output (RHO) of fires generated by burning various fuels is of great interest as it is a measure of the fire&#39;s destructive potential. The radiant energy monitored by the optical sensors is in proportion to the radiant energy (joules) or heat generated at the sensor specific wavelengths. The Health and Safety Executive (HSE), U.K., has established guidelines on the effects of fire radiation exposure to humans. To quote, “Escape is assumed at 5 kWm−2, but fatalities within minutes assumed at 12.5 kWm−2 and instantaneous death at 37.5 kWm−2.” Radiant heat output monitoring is thus important from the aspect of its severity and effects on personnel and equipment involved in an unfortunate incident. 
         [0025]      FIG. 2  is an exemplary electronic block diagram of the analog front end of the multi-spectral flame detection system. Each infrared (IR) sensor is provided with an independent, automatic gain control (AGO) in the electronic front end  3 . In an exemplary embodiment the variable gain control is provided under processor control by attenuating the electronic signal in the blocks marked  33   a,    33   b,    33   c,    33   d  for each optical sensor  2   a,    2   b,    2   c,    2   d.  A four channel digital to analog converter (DAC)  9  sends commands  9   a,    9   b,    9   c,    9   d  to the individual attenuators  33   a,    33   b,    33   c,    33   d.  In an exemplary embodiment, a 0 to 24 dB attenuation range is possible, leading to a gain control from 30 down to 1 in a continuous manner. The highest gain of 30, corresponding to 0 dB attenuation, is the electronic gain in the absence of IR, with lower gains (higher attenuation) kicking in as the IR intensity is increased. For very intense IR radiation, as produced by a large fire or a close up and intense modulated heat source, the AGC operates with minimum gain. In this manner, the AGC scheme provides for optimal performance in the detection of small, distant fires (gain=30) as well for the detection of large or close up fires (gain=1), with independent gain control for each IR sensor channel. The scheme of  FIG. 2  thus eliminates, except perhaps for the most severe cases, the saturation effects that can impact the performance of optical flame detectors. Input signal conditioning before the attenuator block is provided to each sensor channel by high pass filters  31   a,    31   b,    31   c,    31   d  and preamplifiers  32   a,    32   b,    32   c,    32   d,  while further signal conditioning is provided by output amplifiers  34   a,    34   b,    34   c,    34   d  and low pass filters  35   a,    35   b,    35   c,    35   d.  In the exemplary embodiment illustrated in  FIGS. 1 and 2 , the processing in the electronic front end  3  shown in  FIG. 2 , including the AGC, is performed externally to the DSP  6 . The output  5  from the ADC  4  is sequential data corresponding to the respective four sensors, i.e. in a time-multiplexed manner. 
         [0026]    The value of the total electronic gain provided by fixed gain  31 ,  32 ,  34 ,  35  and AGC gain  33  that is variable, in the electronic front end  3 , for each of the four IR sensor channels (a, b, c, d) is a continuous indication in inverse proportion to the IR radiation received by the IR sensors within their spectral and temporal bandwidth. 
         [0027]    The radiant energy received by each IR sensor may be calculated by the DSP  6  as follows 
         [0000]        En   i   =Kn*En   o /(Fixed Gain*Variable Gain) 
         [0000]    where En o  is the value sent to the ADC  4  from the electronic front end  3  as signals  3   a,    3   b,    3   c  and  3   d  respectively, and En i  is a measure of the radiant energy received by the IR sensors within their spectral and temporal bandwidth. In the above computation, the gain values are numerical and not decibel, and n represents the IR sensors  1  through  4  (or n in general). Kn is a calibration constant that relates the IR sensor signals to known radiometric sources such as a blackbody and aids in converting the measurement into radiometric units of measurement such as milliWatts. The computation of the measure of radiant energy En i  is performed continuously by DSP  6  and available for further computational analysis and processing as described below. 
         [0028]    The radiant energy measure En i  may be put to use, e.g., in an exemplary embodiment the values of the AGC plus the fixed gain of the two flame sensing channels (4.3 um and 4.45 um) could be used to combine and average Ec i  and Ed i  to output an estimation of the radiant heat generated by the fire that caused a flame detection event. Such information may be very useful as a record of the radiated intensity of the fire that caused the alarm, including “trending” information on RHO that captures the evolution of the fire from before alarms were triggered till such time as the fires were finally extinguished. It is also well known to those skilled at studying fires and flame radiation that no two fire events are identical: even when a standard pan fire is lit the RHO varies as the fire grows and decays, along with effects caused by wind, and fuel contaminants such as water. The need, therefore, is well established to link the detection of a flame with the growth through decay of the fire along with environmental factors; radiant energy measurements by the flame optical sensors themselves provide the relevant information at no additional cost. 
         [0029]      FIG. 3  is a simplified functional block diagram of an exemplary flame detection system  100 . The system includes a sensor data collection function  110 , which collects the analog conditioned sensor signals  3   a,    3   b,    3   c,  and  3   d  from the multiple optical sensors  2   a,    2   b,    2   c,  and  2   d  respectively, and converts the sensor signals into digital form  112  for processing by the digital signal processor. Processing algorithms  120  are then applied to the sensor data, including signal pre-processing  121 , ANN validation function  122 , radiant energy computation  123 , and post-processing  124 , leading to decision block  125 . In an exemplary embodiment, the radiant energy computation  123  from sensors  2   a,    2   b,    2   c,    2   d  is compared against a preset threshold  126 , while the post processed ANN provides a determination as to whether the optical signals are generated by a real flame event  125 . In an exemplary embodiment, the combination of the decision blocks  125  and  126  results in four combinations:
       Output state  127 A for combination (1) Yes to Flame Event &amp; (2) Radiant Energy≧Threshold   Output state  1278  for combination (1) No to Flame Event &amp; (2) Radiant Energy≧Threshold   Output state  127 C for combination (1) Yes to Flame Event &amp; (2) Radiant Energy&lt;Threshold   Output state  127 D for combination (1) No to Flame Event &amp; (2) Radiant Energy&lt;Threshold       
 
         [0034]    Output state  127 A corresponds to the case of flames being detected and one that exceeds the radiant energy threshold ( 126 ). The threshold value ( 126 ) may be considered a flame detection threshold; the user may choose to set a higher alarm threshold for alarm relay  23  in the output block  128 . Output state  127 A also includes the more general case of real flames detected in the presence of a false alarm (background noise), as the ANN is trained to classify such a situation as a real flame event. Output state  127 B corresponds to the situation where the large measured radiant energy has been diagnosed as not being emitted by a fire, but rather from a false alarm source. Output state  127 C corresponds to the detection of a real fire, but small enough in magnitude to produce radiant energy less than the threshold ( 126 ). Output state  127 C may be considered to represent a minor fire and to provide the user with a warning of an imminent larger fire. The user would typically not take corrective action, and would be advised to monitor the facility more closely. Output state  127 D corresponds to the situation where nothing much is happening; there is no evidence of a fire and the background radiant energy is at a value considered insignificant. 
         [0035]    The information from output states  127 A,  127 B,  127 C, and  127 D is continuously transmitted via output block  128  to the relays  23  and  24 , display  25 , analog output  26 , and one or more external communication interfaces such as Modbus  91  and HART  92 . Output block  128  may generate signals derived from or representing the processing algorithm outputs  127 A- 127 D and  129  may be programmed by the user to define what is sent to the various user interfaces, e.g., the display may indicate the radiant energy regardless of it being caused by a fire or a false alarm, or the display may indicate the radiant energy only when it is determined to be caused by a real fire. It is also possible for the user to configure output block  128  to directly show just the radiant energy measured and transmitted via  129  regardless of the status of the output states  127 A,  1278 ,  127 C, and  127 D; in this manner the effect of ANN processing and decision making can be bypassed temporarily or permanently, as required. The user may also set an alarm radiant energy threshold via output block  128  to activate alarm relay  23  that is higher than the minimum flame detection threshold used in decision block  126 . The user may also program the output block  128  with a user settable time delay to ensure that an ANN determined flame event lasts for certain duration before taking corrective action, via, for example, alarm relay  23 . 
         [0036]    In an exemplary embodiment, an objective of the pre-processing function  121  is to establish a correlation between the frequency and time domain of the optical signals. In an exemplary embodiment shown in  FIG. 4A , the pre-processing function  121  includes applying  211  a data windowing function and a Joint Time-Frequency Analysis (JTFA) function  212  independently to each digitized optical sensor signal. In an exemplary embodiment, data windowing function  211  involves applying one of a Hanning, Hamming, Parzen, rectangular, Gauss, exponential or other appropriate data windowing function. In an exemplary embodiment, the data window function  211  comprises a Hamming window function which is described by a cosine type function: 
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         [0000]    where N is number of sample points (e.g. 512) and n is between 1 and N. 
         [0037]    In an exemplary embodiment of the data preprocessing  121 , the Hamming window function  211  is applied to a raw input signal before applying a JTFA function  212 . This data windowing function alleviates spectral “leakage” of the signal and, thus, improves the accuracy of ANN classification. 
         [0038]    Referring again to  FIG. 4A , in an exemplary embodiment, JTFA  212  encompasses a Discrete Fourier Transform. The JTFA may also encompass a Short-Time Fourier Transform (STFT) with a shifting time window (also known as Gabor transform), or a Discrete Wavelet Transform (DWT). The output of the Fourier transform may be filtered to remove frequencies outside a frequency band of interest to IR flame detection, for example, frequencies greater than 20 Hz. The JTFA application is followed by a scaling operation  213 ; this normalizes the data by subtracting the mean and dividing by the standard deviation to effectively scale the inputs to the ANN  122  ( FIG. 3 ). In an exemplary embodiment, coefficients and algorithms used for the windowing function  211 , JTFA  212 , and the scaling function  213  are stored in non-volatile memory. In an exemplary embodiment, the coefficients may be stored in NVM  65  ( FIG. 1 ). 
         [0039]    Referring again to  FIG. 3  and  FIG. 4A , the data pre-processed by the windowing function and the JTFA operation is also fed into the block  123  for radiant energy computation. In an exemplary embodiment, the radiant energy is computed by summing over various frequency magnitudes computed by the Fourier Transform and normalized by a calibration factor. In another exemplary embodiment, the radiant energy is derived directly from the time domain computed signals En i  described earlier rather than summing frequency component magnitudes generated by the Fourier Transform described above. Each of Ea i , Eb i , Ec i  and Ed i  may be outputted, averaged and/or combined. The computed radiant energy may be compared against a threshold value in decision block  126  ( FIG. 3 ). 
         [0040]      FIG. 4B  illustrates the exemplary embodiment where, as described earlier, the values of the AGC plus the fixed gain of the two flame sensing channels ( 2   c  at 4.3 um and  2   d  at 4.45 um) may be used to compute, combine and average Ec i  and Ed i  to output an estimate of the radiant heat generated by the fire that caused a flame detection event. The preprocessed time domain signals En o  are sent from block  121  to radiant energy computation block  123 . Values of En i  are calculated as described above using fixed and variable (AGC) gain values in the compute block  200 . Values of Ea i  and Eb i  (representing radiant energy at the immunity sensor wavelengths of 4.9 um and 2.3 um) may be sent ( 40   a,    40   b ) to output block  202  directly while Ec i  and Ed i  representing radiant energy at the two flame sensing wavelengths ( 40   c,    40   d ) could be combined in  201  by averaging the values and then transmitted  41  via output block  202  to the decision block  126  for comparison with a previously stored threshold value. 
         [0041]      FIG. 5  illustrates a functional block diagram of an exemplary embodiment of ANN processing  122 . ANN processing  122  may comprise two-layer ANN processing. In an exemplary embodiment, ANN processing  122  includes receiving a plurality of pre-processed signals  10  (x 1 -x i ) generated by the optical sensors  2   a,    2   b,    2   c,  and  2   d  (corresponding to the data windowed  211 , JTFA processed  212 , and scaled  213  signals resulting from the pre-processing  121  shown in  FIG. 4A ), a hidden layer  12  and an output layer  13 . In other exemplary embodiments, ANN processing  122  may comprise a plurality of hidden layers  12 . The pre-processed signals  10  from  121  (x 1 -x i ) include the respective pre-processed signals from optical sensors  2   a,    2   b,    2   c,  and  2   d  in a fixed, serial order in the input layer of ANN processing  122 . The fixed order is the order generated by the ADC  4  ( FIGS. 1 and 2 ) to stream data out to the DSP. The same order is maintained by  121  in  FIG. 3 . 
         [0042]    In an exemplary embodiment, the hidden layer  12  includes a plurality of artificial neurons  14 , for example five neurons as shown in  FIG. 5 . The number of neurons  14 , known as hidden neurons, may depend on the non-linearity of classification achieved by the ANN processing  122  during the training. In an exemplary embodiment, the output layer  13  includes a plurality of targets  15  (or output neurons) corresponding to various conditions. The number of targets  15  may vary from one to multiple. The exemplary embodiment of  FIG. 5  employs one target neuron  15 , which outputs the event likelihood  18 ′ to decision processing  19 . 
         [0043]    In an exemplary embodiment, the NVM  65  ( FIG. 1 ) holds synaptic connection weights H ij    11  for the hidden layer  12  and synaptic connection weights O jk    17  for the output layer  13 . In an exemplary embodiment, the signal processor  5  sums the plurality of pre-processed signals  10  at neuron  14 , each multiplied by the corresponding synaptic connection weight H ij    11 . A non-linear activation (or squashing) function  16  is then applied to the resultant weighted sum z i  for each of the plurality of hidden neurons  14 . In an exemplary embodiment, shown in  FIG. 5 , the activation function  16  is a unipolar sigmoid function (s(z i )). In other embodiments, the activation function  16  can be a bipolar function or another appropriate activation function. In an exemplary embodiment, a bias B H  is also an input to the hidden layer  12 . In an exemplary embodiment, the bias B H  has the value of one. Referring again to  FIG. 5 , in an exemplary embodiment, the neuron outputs (s(z i )) are input to the output layer  15 ; a bias B O  is also an input to the output layer  15 . In an exemplary embodiment, the outputs (s(z i )) are each multiplied by a corresponding synaptic connection weight O jk    17  and the corresponding results are summed for output target  15  in the output layer  13 , resulting in a corresponding sum y j . 
         [0044]    Thus, as depicted in  FIG. 5 , the signal-processed inputs X i    10  are connected to hidden neurons  14 , and the connections between input and hidden layers are assigned weights H ij    11 . At every hidden neuron, the multiplication, summation and sigmoid function are applied in the following order. 
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         [0045]    The outputs of sigmoid function S(Z j ) from the hidden layer  12  are introduced to the output layer  13 . The connections between hidden and output layers are assigned weights O jk    17 . Now at every output neuron multiplication, in this exemplary embodiment, summation and sigmoid function are applied in the following order: 
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         [0046]    In an exemplary process of ANN training, the connection weights H ij  and O jk  are constantly optimized by the Back Propagation (BP) algorithm. In an exemplary embodiment, the BP algorithm is based on mean root-square error minimization using the conjugate-gradient (CG) descent method. The algorithm is applied using MATLAB, a tool for numerical computation and data analysis, to optimize the connection weights H ij  and O jk . These connection weights are then used in ANN validation, to compute the ANN outputs S(Y k ), which are used for final decision making. 
         [0047]    In an exemplary embodiment, an ANN may be trained by exposing the flame detector to a plurality of combinations of flame and false alarm sources. During training the output values are compared with the correct answer. At each iteration, the algorithm adjusts the weights of each connection H ij  and O jk  in order to minimize the output error. After repeating this process for a sufficiently large number of training cycles, the network converges to a state where the error is small. Multi-layered ANNs and ANN training using the BP algorithm to set synaptic connection weights are described, e.g. in Rumelhart, D. E., Hinton, G. E. &amp; Williams, R. J., Learning Representations by Back-Propagating Errors, (1986) Nature, 323, 533-536. It is shown that a multilayer network, containing one or two layers of hidden nodes, is required to handle non-linear decision boundaries. 
         [0048]    In an exemplary embodiment, the ANN training involves a set of robust indoor and outdoor site tests. Data collected from these tests is used for ANN training performed on a personal or workstation computer equipped with MATLAB or a similar numerical computing program. The data can be collected using the hardware shown in  FIG. 1 , suitably mounted on a portable platform. U.S. Pat. No. 7,202,794 B2, the entire contents of which are incorporated herein by this reference, provides numerous examples of indoor and outdoor tests used for data collection and ANN training for a multi-spectral IR flame detector. The connection weights H ij  and O jk  derived from such comprehensive ANN training can be loaded into the embedded software of prototype flame detectors for further validation through rigorous laboratory and field testing for consistent flame detection and rejection of false positives (via decision block  125 ,  FIG. 3 ), as well as accurate radiant energy computation (via  123 ,  FIG. 3 ). Subsequent to the successful validation, the connection weights H ij  and O jk  may be programmed into manufactured units. 
         [0049]    In an exemplary embodiment illustrated in  FIG. 5 , the ANN processing  122  outputs value  18 ′ that represent a percentage likelihood of a flame detected by the flame detection system. A threshold applied to the output sets the limit of the likelihood, above which a real flame condition is indicated. In an exemplary embodiment, neuron output  18 ′ value above 0.9 (on a scale of 0 to 1) indicates a strong likelihood of flame detection, whereas a smaller output indicates a strong likelihood of false alarm conditions. This analysis is conducted in ANN decision block  19 . 
         [0050]    Referring back to  FIG. 3 , post-processing  124  takes the output of the ANN  122  via the ANN decision block  19  ( FIG. 5 ) and performs a final post-processing that may include other criteria such as factory or user defined criteria. Post-processing  124  may include post-processing such as counting the number of times the neuron output  18 ′ exceeds a threshold value as defined by the ANN decision block  19 . For example, it may be desirable to have the neuron output  18 ′ exceed a threshold four times within a given time period, for example, one second, before the flame condition is output. This limits the likelihood of an isolated spurious input condition or transient to be interpreted as a flame condition thus causing a false alarm. In an exemplary embodiment, the threshold value may be set at 0.8 on a scale of 0 to 1. 
         [0051]    Referring to  FIG. 3 , the output of the post-processing  124  is processed by decision block  125 . In an exemplary embodiment, if decision block  125  determines that a flame has been detected, this decision is tied in with the output of threshold decision block  126  that compares the computed radiant energy versus a preset threshold. As described earlier, four output state combinations  127 A,  127 B,  127 C, and  127 D are possible for this exemplary embodiment. The outputs of these output states  127 A,  1278 ,  127 C, and  127 D are continuously transmitted via output block  128  to the relays  23  and  24 , display  25 , analog output  26 , and external communication interfaces such as Modbus  91  and HART  92 . Output block  128  may be programmed by the user to define what is sent to the various user interfaces, e.g., the display may indicate the radiant energy regardless of whether it is caused by a flame or false alarm, or it may indicate the radiant energy only when it is determined to be caused by a flame. The user may also set an alarm radiant energy threshold via output block  128  to activate alarm relay  23  that is higher than the minimum radiant energy threshold set for decision block  126 . The user may also program the output block  128  with a user settable time delay to ensure that an ANN determined flame event lasts for certain duration before taking corrective action via, for example, alarm relay  23 . 
         [0052]    Referring now to  FIG. 6 , a functional block diagram  150  of another exemplary embodiment of a flame detector is depicted. This embodiment is similar to that described above regarding  FIGS. 1-5 . However, in this exemplary embodiment, the signal processor  6  is programmed to implement processing algorithms  120 ′, in which the computed radiant energy  123  is not compared against a preset threshold as shown in block  126  of  FIG. 3 . Rather, the computed radiant energy  129  is sent directly to the output block  128 ′. At the same time, the post processed ANN provides a determination via decision block  125  as to whether the optical signals are generated by a real flame indicated by output state  130  or by a false alarm as shown by output state  131 , both in  FIG. 6 . The output block  128 ′ then informs the user of the presence (derived from or representing output state  130 ) and severity (derived from or representing signal  129 ) of a real flame via the output functions of the alarm relay  23 , display  25 , analog output  26 , and external communication interfaces such as Modbus  91  and HART  92 . If the computed radiant energy is shown to be created by a false alarm via output state  131  from decision block  125 , the output block  128 ′ can similarly inform the user of the false alarm event and its severity via display  25 , analog output  26 , and external communication interfaces such as Modbus  91  and HART  92 ; in the case of a false alarm event indicated by output state  131  the alarm relay  23  would, however, not be activated. 
         [0053]    Referring now to  FIG. 7 , examples are shown as  7   a,    7   b,    7   c  as to how the radiant energy estimation could be outputted on the 0 to 20 mA analog output.  7   a  shows a conventional flame detector analog output with discrete outputs at 4 mA (no event) 16 mA (warning) and 20 mA (alarm). No radiant energy or heat estimation is provided.  7   b  shows a flame detector with two analog outputs, the first analog output is the conventional flame detector analog (of  7   a ) while the second analog output maps the radiant energy or heat (RHO) estimation continuously onto the 4 to 20 mA scale. Due to the substantial variation in received intensity, e.g., from a small fire at 210 feet distance to a much larger fire at closer distance, the mapping of radiometric energy to analog output would likely be logarithmic.  7   c  shows a combined or hybrid analog output scale that eliminates the second analog output of  7   b  by indicating the radiometric estimated value continuously between 4 and 12 mA. The 12 mA value typically represents the maximum unsaturated value of the sensor channel output signals. This third combined or hybrid scheme has the disadvantage that once a flame has been detected the analog output jumps to 16 and then 20 mA; the radiometric information is not available on the analog output once a flame event is recorded. This scheme may be called “trending” as it monitors the radiant heat prior to entering the alarm mode, but not once the flame detector is in the alarm mode. 
         [0054]    In another embodiment, radiometric and flame detection status could be sent continuously to the user via serial communication such as Modbus  91  or HART  92  ( FIG. 1 ). This allows the radiometric information to be transmitted continuously including during a flame event without the need for a second analog output. 
         [0055]    Referring now to  FIG. 8 , a functional block diagram  100 ′ of another exemplary embodiment of a flame detector is depicted. This embodiment is similar to that described above regarding  FIGS. 1-3 . However, processing block  122 ′ of  FIG. 8  does not comprise an ANN as shown in  122  of  FIG. 3 . Rather expert based rules such as described in U.S. Pat. No. 6,150,659 may be used to decide on the presence or absence of a fire in decision block  125 . U.S. Pat. No. 6,150,659, the entire contents of which are incorporated herein by this reference, utilizes two infrared detectors, one for detecting radiation emitted by hydrocarbon fires and the second for distinguishing infrared radiation from other sources such as modulated sunlight, artificial as well as natural hot objects, illuminating light sources and arc welders. The first infrared detector is followed by two electronic circuits such that a fire is sensed by either of the two circuits depending on its size, one circuit being optimized for flicker frequencies present in a large fire while the second circuit checks for optical signals at flicker frequencies dominant in a small fire. The two electronic outputs along with the output of the second infrared detector are digitally processed and analyzed for spectral and temporal characteristics to distinguish the presence of a real fire from that of various false alarm sources. FIG. 2 and FIG. 3 of U.S. Pat. No. 6,150,659 are detailed flow diagrams of the algorithms used and along with the text describing these algorithms details the rules followed in this expert rules based flame detection system. In this approach a trained Artificial Neural Network is not used, instead the rules established by a human expert, i.e. predetermined rules, are followed to decide on the presence or absence of optical radiation emitted by flames. 
         [0056]    Referring now to  FIG. 9 , a functional block diagram  150 ′ of another exemplary embodiment of a flame detector is depicted. This embodiment is similar to that described above regarding  FIG. 8 . However, in this exemplary embodiment, the signal processor  6  is programmed to implement processing algorithms  120 ′, in which the computed radiant energy  123  is not compared against a preset threshold as shown in block  126  of  FIG. 8 . Rather, the computed radiant energy  129  is sent directly to the output block  128 ′. At the same time, the post processed signals  124 ′ provide a determination via decision block  125  as to whether the optical signals are generated by a real flame indicated by output state  130  or by a false alarm as shown by output state  131 , both in  FIG. 9 . The output block  128 ′ then informs the user of the presence (from output state  130 ) and severity (from signal  129 ) of a real flame via the output functions of the alarm relay  23 , display  25 , analog output  26 , and external communication interfaces such as Modbus  91  and HART  92 . If the computed radiant energy is shown to be created by a false alarm via output state  131  from decision block  125 , the output block  128 ′ can similarly inform the user of the false alarm event and its severity via display  25 , analog output  26 , and external communication interfaces such as Modbus  91  and HART  92 ; in the case of a false alarm event indicated by output state  131  the alarm relay  23  would, however, not be activated. 
         [0057]    Although the foregoing has been a description and illustration of specific embodiments of the invention, various modifications and changes thereto can be made by persons skilled in the art without departing from the scope and spirit of the invention.