Abstract:
An ultrasonic gas leak detector is configured to discriminate the ultrasound generated by a pressurized gas leak into the atmosphere from false alarm ultrasound. An exemplary embodiment includes a sensor for detecting ultrasonic energy and providing sensor signals, and an electronic controller responsive to the sensor signals. In one exemplary embodiment, the electronic controller is configured to provide a threshold comparator function to compare a sensor signal value representative of sensed ultrasonic energy to a gas detection threshold value, and an Artificial Neural Network (ANN) function for processing signals derived from the digital sensor signals and applying ANN coefficients configured to discriminate false alarm sources from gas leaks. An output function generates detector outputs in dependence on the threshold comparator output and the ANN output.

Description:
BACKGROUND 
     Ultrasonic gas leak detectors measure the sound pressure waves generated by the turbulent flow when gas escapes from higher pressures to the ambient atmosphere. Such gas leak detectors are used as industrial safety devices to monitor the unwanted or unexpected release of combustible or toxic gases into the atmosphere. The leaks need to be identified quickly before they grow further in magnitude, to allow for timely remedial action to be taken. 
     Conventional ultrasonic gas leak detectors are threshold devices that cannot discriminate between the ultrasound created by other manmade or natural sources, such as machinery, electrical discharge, acoustic speakers or biological sources, from those produced by real gas leaks. A way to mitigate false alarms, avert nuisance trips, and avoid costly unwarranted process shutdowns with such ultrasonic gas leak detectors is to raise the alarm threshold level several decibels above the background ultrasonic level. Raising the alarm level has the drawback of reducing detection distance to the gas leak, thereby the total area of coverage, or of ignoring gas leaks until they build up in severity, often with catastrophic consequences. Another precaution against false alarms is via the use of lengthy time delays which result in undesirable delays to the remedial action in case of a dangerous gas leak, negating the benefit of the fast response time inherent with ultrasonic gas leak detectors. 
     Another drawback of conventional ultrasonic gas leak detectors that depend on thresholds and time delays for their functionality is the inability to effectively verify their performance in the field, and to conduct functional safety checks at proof test intervals. The conventional gas leak detectors are unable to differentiate between the sound emitted by a real gas release and a remote ultrasonic test source to be used for periodic system performance check. This is a major inconvenience to the industrial facility that leads to either the bypassing of critical proof testing or a significant operating cost burden. Conventional ultrasonic gas leak detectors provide maintenance personnel with no means to test the gas leak detector without the disruption caused by disabling alarms. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Features and advantages of the disclosure will readily be appreciated by persons skilled in the art from the following detailed description when read in conjunction with the drawing wherein: 
         FIG. 1  is a schematic block diagram of an exemplary embodiment of an ultrasonic gas leak detection system with false alarm discrimination. 
         FIG. 2  is a functional block diagram of features of the detection system of  FIG. 1 . 
         FIG. 3  is an exemplary flow diagram of the pre-processing functions utilized in the detection system of  FIG. 2 . 
         FIG. 4  illustrates an exemplary embodiment of artificial neural network (ANN) processing utilized in the detection system of  FIG. 2 . 
         FIG. 5  is a functional block diagram of another exemplary embodiment of an ultrasonic gas leak detection system with false alarm discrimination. 
     
    
    
     DETAILED DESCRIPTION 
     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. 
       FIG. 1  illustrates a schematic block diagram of an exemplary ultrasonic gas leak detection system  1  including an ultrasonic microphone  2  as a sensing element. In an exemplary embodiment, the ultrasonic microphone  2  may be a pre-polarized pressure microphone, such as manufactured by G.R.A.S. Sound and Vibration of Nolte, Denmark, Microtech Gefell GmbH of Gefell, Germany, or Bruel Kjaer of Naerum, Denmark. The ultrasonic region is defined as a frequency range beyond human hearing, starting at approximately 20 kHz in healthy, young human adults. Higher ultrasonic frequencies are attenuated more rapidly in air than lower frequencies, and the practical applications for an ultrasonic gas leak detection system are typically for frequencies less than 100 kHz. 
     In another exemplary embodiment, the ultrasonic microphone  2  may be a fiber optical microphone (FOM). An exemplary FOM suitable for the purpose is manufactured by Sennheiser Electronic GmbH of Wedemark, Germany. Another manufacturer of fiber optic microphones is Optoacoustics of Moshav Mazor, Israel. 
     In yet another exemplary embodiment, the ultrasonic microphone  2  may be a miniature microphone based on MEMS (Micro Electro Mechanical Systems) technology that can be operated well beyond the audible range of 15 kHz and into the ultrasonic frequency range out to 100 kHz. Such a MEMS microphone may be mounted on a printed circuit board (PCB) and housed in an environmentally robust mechanical enclosure that permits passage of ultrasonic sound energy to the sensing element. An exemplary MEMS microphone that may be used in such fashion is the SiSonic™ Surface Mount Microphone manufactured by Knowles Acoustics of Itasca, Ill. In an exemplary embodiment suitable for operation in a hazardous location, the MEMS microphone may be housed behind a flame arrestor. Such a flame arrestor prevents the transmission of ignited flames from within the microphone housing structure to the external environment while permitting acoustic energy to flow from the external environment to the microphone. Such a method of protection is known as explosion proof or flame proof. Some of the standards that are widely accepted by the industry and government regulatory bodies for explosion proof or flame proof designs are CSA C22.2 No. 30-M1986 from the Canadian Standards Association, FM 3600 and 3615 from Factory Mutual, and IEC 60079-0 and IEC 60079-1 from the International Electrotechnical Commission. Other protection methods may be applied for other environmental protection requirements such as ingress protection against sold objects, liquids, and mechanical impact as described in IEC 60529 from the International Electrotechnical Commission. 
     Regardless of the microphone type and protection concept utilized, the analog signal generated by the microphone  2  is converted into a digital signal by an analog to digital converter (ADC)  3 . In an exemplary embodiment, the ADC  3  provides a signal  4  with 12-bit signed integer resolution and a sampling rate of 200 kHz. 
     In an exemplary embodiment, the ultrasonic gas leak detection system  1  includes an electronic controller  5 , e.g., a digital signal processor (DSP), an ASIC or a microcomputer or microprocessor based system. In an exemplary embodiment, the signal processor  5  may comprise a DSP, although other devices or logic circuits may alternatively be employed for other applications and embodiments. In an exemplary embodiment, the signal processor  5  also comprises a dual universal asynchronous receiver transmitter (UART)  51  as a serial communication interface (SCI), a serial peripheral interface (SPI)  52 , an internal ADC  53 , an external memory interface (EMIF)  54  for an external memory (SRAM)  21 , and a non-volatile memory (NVM)  55  for on-chip data storage. Modbus  91  or HART  92  protocols may serve as interfaces for serial communication over UART  51 . Both protocols are well-known in process industries, along with others such as PROFIbus, Fieldbus and CANbus, for interfacing field instrumentation to the user&#39;s computer or programmable logic controller (PLC). 
     In an exemplary embodiment, signal processor  5  receives the digital detector signals  4  from the ADC  3  through the SPI  52 . In an exemplary embodiment, the signal processor  5  is connected to a plurality of other interfaces through the SPI  52 . These interfaces may include an external NVM  22 , a real-time clock  23 , an alarm relay  24 , a fault relay  25 , a display  26 , and an analog output  27 . 
     In an exemplary embodiment, the analog output  27  may produce an indicative current level between 0 and 20 milliamps (mA), which can be used to trigger a remedial action, such as, by way of example only, shutting down process equipment pursuant to an established facility protocol. A first current level at the analog output  27 , for example between 4 mA and 20 mA, may be indicative of a gas leak, a second current level at the analog output  27 , for example 4 mA, may be indicative of normal operation, e.g., when no gas leak is present, and a third current level at the analog output  27 , 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. 
     In an exemplary embodiment, ultrasonic gas leak detection system  1  may also include a temperature sensor  6  for providing a temperature signal  7 , indicative of an ambient temperature of the gas detector system for subsequent temperature compensation. The temperature detector  6  may be connected to the internal ADC  53  of the signal processor  5 , which converts the temperature signal  7  into a digital representation. 
     In an exemplary embodiment, the signal processor  5  is programmed to perform signal pre-processing and artificial neural network (ANN) processing, as discussed more fully below. 
       FIG. 2  is an exemplary functional block diagram  100  of an exemplary gas detection system. The system includes a sensor data collection function  110 , which collects the analog sensor signals  111  from the microphone sensor, 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 , sound pressure computation  123 , and post-processing  124  to determine the sensor state. In an exemplary embodiment, the computed sound pressure level (SPL) is compared against a preset threshold  126 , while the post processed ANN provides a determination as to whether the microphone signal is generated by a real gas leak  125 . In an exemplary embodiment, the combination of the decision blocks  125  and  126  result in four combinations:
         Output state  127 A for combination (1) Yes to Gas Leak &amp; (2) Yes to SPL&gt;threshold   Output state  127 B for combination (1) No to Gas Leak &amp; (2) Yes to SPL&gt;threshold   Output state  127 C for combination (1) Yes to Gas Leak &amp; (2) No to SPL&gt;threshold   Output state  127 D for combination (1) No to Gas Leak &amp; (2) No to SPL&gt;threshold       

     Output state  127 A corresponds to the case of a real gas leak and one that exceeds the SPL threshold ( 126 ). The threshold value ( 126 ) may be considered a gas detection threshold; the user may choose to set a higher alarm threshold for alarm relay  24  in the output block  128 . Output state  127 A also includes the more general case of a real gas leak in the presence of a false alarm (background noise) as the ANN is trained to classify such a situation as a real gas leak. Output state  127 B corresponds to the situation where the large measured SPL has been diagnosed as not being caused by a gas leak, but rather from a false alarm source. Output state  127 C corresponds to the detection of a real gas leak, but small enough in magnitude to produce an SPL less than the threshold ( 126 ). Output state  127 C may be considered to be a minor leak, or to provide a warning to the user of an imminent larger leak. The user would typically not take corrective action but is 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 gas leak and the background SPL is at a value considered insignificant. Output state  127 D would be typical of a quiet industrial environment such as a remote onshore wellhead. 
     The information from output states  127 A,  127 B,  127 C, and  127 D is continuously transmitted via output block  128  to the relays  24  and  25 , display  26 , analog output  27 , 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 SPL regardless of it being caused by a gas leak or a false alarm, or the display may indicate the SPL only when it is determined to be caused by a real gas leak. It is also possible for the user to configure output block  128  to directly show just the SPL measured and transmitted via  129  regardless of the status of the output states  127 A,  127 B,  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 SPL threshold via output block  128  to activate alarm relay  24  that is higher than the minimum gas 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 gas leak lasts for certain duration before taking corrective action, via, for example, alarm relay  24 . 
     The exemplary embodiment of ultrasonic gas leak detection system  1  described in  FIG. 1  and  FIG. 2  provides means for distinguishing the ultrasound generated by pressurized gas leaking into the atmosphere from the ultrasound generated by other mechanical, electrical discharge, acoustic or biological sources in the vicinity. The ultrasound from such other sources, classified as false alarms, may produce a large background ultrasound reading with prior art ultrasonic gas leak detectors: this high background results in the setting of elevated alarm levels, typically 6 decibels above the background ultrasound. Raising the alarm level has the drawback of reducing detection distance to the gas leak and thereby the total area of coverage, resulting in an area gas leak monitor behaving more like a point gas leak detector. Additionally, real gas leaks may be ignored until they build up in severity, often with catastrophic consequences. False alarm sources that produce transient or short lived ultrasound are also handled with prior art ultrasonic gas leak detectors via the use of time delays, which result in undesirable delays to the remedial action in case of a dangerous gas leak. A method for the reliable discrimination and quantification of gas leaks provides room for lowering the alarm level thereby extending the range of detection and area of coverage, as well as for reducing time delays to remedial action. Such a method may enable the ultrasonic gas leak detection system to provide one or more of the following benefits, (1) an area monitor, (2) a response time based on the speed of sound, and (3) an increase in overall process production due to the reduction of nuisance alarms. 
     In an exemplary embodiment, the analog signals from the microphone  2  are periodically converted to digital form by the ADC  3 . As shown in  FIG. 2 , pre-processing  121  is performed on the digitized sensor signals. In an exemplary embodiment, an objective of the pre-processing function  121  is to establish a correlation between frequency and time domain of the signal. In an exemplary embodiment shown in  FIG. 3 , the pre-processing function  121  includes applying  211  a data windowing function and applying  212  a Joint Time-Frequency Analysis (JTFA) function. 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: 
               W   Hm     =       1   2     ⁢     {     1.08   -     0.92   ⁢           ⁢     cos   ⁡     (       2   ⁢           ⁢   π   ⁢           ⁢   n       N   -   1       )           }             
where N is number of sample points (e.g. 512) and n is between 1 and N.
 
     In an exemplary embodiment of the data preprocessing  121 , the Hamming window function is applied  211  to a raw input signal before applying  212  a JTFA function. This data windowing function alleviates spectral “leakage” of the signal and thus improves the accuracy of ANN classification. 
     Referring again to  FIG. 3 , 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 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. 2 ). 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  55  ( FIG. 1 ). 
     Referring again to  FIG. 2  and  FIG. 3 , the pre-processed data after application of the windowing function and JTFA operation is also fed into the block  123  for sound pressure computation. The SPL is computed by summing over the magnitude of the intensities at the various ultrasonic frequencies utilized in the Fourier Transform and normalized by a calibration factor dependent on the microphone sensitivity and electronic gain. The ultrasonic SPL is expressed in decibels (dB), which is a logarithmic measure of the effective pressure of sound relative to a reference value. The commonly used “zero” reference sound pressure (0 dB) in air is 20 μPa RMS, historically derived from the threshold of human hearing. The typical values of ultrasonic SPL in a quiet industrial environment such as remote onshore wellheads may be between 40 dB and 58 dB, while the background ultrasonic SPL can be much higher in the presence of machinery in operation such as compressors, generators and coolers (fin-fans). 
     The computed SPL from computation  123  is compared against a threshold in decision block  126  ( FIG. 2 ). The SPL calibration factor for  123  and SPL threshold value for  126  may be stored in non-volatile memory NVM  55 . 
       FIG. 4  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  121  (x 1 -xi) (corresponding to the data windowed  211 , JTFA processed  212 , and scaled signals  213  shown in  FIG. 3 ), a hidden layer  12  and an output layer  13 . In other exemplary embodiments, ANN processing  122  may comprise a plurality of hidden layers  12 . 
     In an exemplary embodiment, the hidden layer  12  includes a plurality of artificial neurons  14 , for example five neurons as shown in  FIG. 4 . The number of neurons  14 , known as hidden neurons, may depend on the level of training and classification achieved by the ANN processing  122  during 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 be, for example, from one to four. The exemplary embodiment of  FIG. 4  employs one target neuron  15 , which outputs event likelihood  18 ′ to decision processing  19 . 
     In an exemplary embodiment, the NVM  55  ( 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. 4 , the activation function  16  is a unipolar sigmoid function (s(z i )). In other embodiments, the activation function  16  can be a bipolar activation function or other appropriate 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. 4 , in an exemplary embodiment, the neuron outputs (s(z i )) are input to the output layer  15 . In an exemplary embodiment, 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 . 
     Thus, as depicted in  FIG. 4 , 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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     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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     In an exemplary process of ANN training, the connection weights H ij  and O jk  are constantly optimized by Back Propagation (BP). In an exemplary embodiment, the BP algorithm applied is based on mean root square error minimization via 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. In an exemplary embodiment, an ANN may be trained by exposing the ultrasonic gas leak detector to a plurality of combinations of ultrasound generated by real gas leaks, false alarm sources comprising mechanical, electrical discharge, acoustic, or biological sources, and combinations of real gas leaks and false alarm sources. During training the output values are compared with the correct answer; the algorithm adjusts the weights of each connection H ij  and O jk  in order to reduce the value of the error function at each iteration. After repeating this process for a sufficiently large number of training cycles, the network usually 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. 
     In an exemplary embodiment, the training for the ANN employs a set of robust indoor, outdoor, and industrial 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. Alternately, a commercially available ultrasonic microphone and recorder, such as the Model D1000X ultrasound detector from Pettersson Electronik, Uppsala, Sweden, can be used for data collection. False alarm sources used for both indoor and outdoor collection include ultrasonic dog whistles that use piezoelectric transducers to generate high intensity, single frequency ultrasound; mechanical sources of ultrasound include metal grinders and sand blasters; electrical discharge sources of ultrasound include welding and corona discharge. Field data collected from industrial sites may include noise generated by compressors, generators, choke valves, separators, and coolers (fin-fans). Data collected from real gas leaks may include a plurality of gas types, pressures, orifice sizes and flow rates. Gases under consideration could include those of low molecular weight such as hydrogen and methane, as well as those of higher molecular weight such as carbon dioxide, ethylene and propane. It should be noted that many of the gases with higher molecular weight are in liquid state when under pressure; the ultrasound is generated when they became gaseous upon release to the atmosphere. Technical details of the experimental setup to generate such gas leaks are described, e.g. in Naranjo, E., Baliga, S., Neethling, G. A., &amp; Plummer, C. D., Safe Detection of Small to Large Gas Releases, (January 2011) Hydrocarbon Processing, 57-60. 
     In an exemplary embodiment, the training data may include over one hundred such files of ultrasound produced by a plurality of false alarm sources, real gas leaks and combinations of real gas leaks and false alarm sources. The connection weights H ij  and O jk  derived from such comprehensive ANN training can be loaded into the software of prototype ultrasonic gas leak detectors for further validation by rigorous laboratory and field testing for false alarm rejection and consistent gas leak detection (via decision block  125 ,  FIG. 2 ), and accurate SPL computation (via  123 ,  FIG. 2 ). Subsequent to the successful validation, the connection weights H ij  and O jk  may be programmed into manufactured units. 
     In an exemplary embodiment illustrated in  FIG. 4 , the ANN processing  122  outputs value  18 ′ that represent a percentage likelihood of a gas leak detected via ultrasonic gas leak detection. A threshold applied to the output, sets the limit of the likelihood, above which a gas leak 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 gas leak, whereas a smaller output indicates a strong likelihood of false alarm conditions. This analysis is conducted in ANN decision block  19 . 
     Referring back to  FIG. 2 , post-processing  124  takes the output of the ANN  122  via the ANN decision block  19  ( FIG. 4 ) 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 gas leak condition is output. This limits the likelihood of an isolated spurious input condition or transient to be interpreted as a gas leak 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. 
     Referring to  FIG. 2 , the output of the post-processing  124  is processed by decision block  125 . In an exemplary embodiment, if ANN decision block  125  determines that a gas leak has occurred this decision is tied in with the output of threshold decision block  126  that compares the computed SPL versus a preset gas detection 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,  127 B,  127 C, and  127 D are continuously transmitted via output block  128  to the relays  24  and  25 , display  26 , analog output  27 , 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 SPL regardless of whether it is caused by a gas leak or false alarm, or the display may indicate the SPL only when it is determined to be caused by a real gas leak. The user may also set an alarm SPL threshold via output block  128  to activate alarm relay  24  that is higher than the minimum gas 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 gas leak lasts for certain duration before taking corrective action via, for example, alarm relay  24 . 
     Referring now to  FIG. 5 , features of another exemplary embodiment of an ultrasonic gas leak detector are depicted, depicting a functional block diagram  150  of the gas leak detector. This embodiment is similar to that described above regarding  FIGS. 1-4 . However, in this exemplary embodiment, the signal processor  5  is programmed to implement processing algorithms  120 ′, in which the computed SPL from sound pressure computation  123  is not compared against a preset threshold as shown in block  126  of  FIG. 2 . Rather, the computed SPL  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 microphone signal is generated by a real gas leak indicated by output state  130  or by a false alarm as shown by output state  131 , both in  FIG. 5 . The output block  128 ′ then informs the user of the presence (from output state  130 ) and severity (in dB) (from signal  129 ) of a real gas leak via the output functions of the alarm relay  24 , display  26 , analog output  27 , and external communication interfaces such as Modbus  91  and HART  92 . If the computed SPL 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 (in dB) via display  26 , analog output  27 , 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  24  would, however, not be activated. 
     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.