Patent Document

CROSS-REFERENCE TO RELATED APPLICATION 
       [0001]    This application is a continuation of U.S. application Ser. No. 13/168,577 entitled “Multi-Sense Environmental Monitoring Device and Method,” filed Jun. 24, 2011, which claims benefit of priority to U.S. Provisional Patent Application No. 61/358,729 filed on Jun. 25, 2010 entitled “Multi-Sense Environmental Monitoring Device and Method,” the entire contents of which are hereby incorporated by reference in their entireties. 
     
    
     FIELD OF THE INVENTION 
       [0002]    Embodiments of the present invention generally relate to environmental monitoring devices. 
       BACKGROUND OF THE INVENTION 
       [0003]    In a number of industrial work environments workers are at risk of being exposed to a variety of hazardous environmental substances such as toxic or highly combustible gases, oxygen depleted environments, or radiation, etc. that pose a serious threat to worker safety. In order to keep workers safe, specialized environmental monitoring devices are used to alert workers of dangerous changes in their immediate environment. 
         [0004]    Current practice involves using fixed point monitoring devices that monitor the environment around where they are deployed or portable monitoring devices that are carried by the workers to monitor their immediate vicinity. Fixed point monitoring devices are typically used around potential hazard locations such as confined spaces to warn workers of the environment before they enter. Portable monitoring devices are often used for personal protection. These monitoring devices may have a single sensor to monitor one specific substance or multiple sensors (typically two to six) each monitoring a distinct substance. 
         [0005]    Given that these environmental monitoring devices are life critical, it is important the device functions properly and accurately. Current practice involves periodic bump testing and calibration of monitoring devices to guarantee proper functioning. Bump tests involve exposing the monitoring device to a measured quantity of gas and verifying that the device responds as designed, i.e., it senses the gas and goes into alarm. Calibration involves exposing the device to a measured quantity of gas and adjusting the gain of the sensors so it reads the quantity of gas accurately. The purpose of calibration is to maintain the accuracy of the monitoring device over time. 
         [0006]    Current best practice followed by leading manufacturers of environmental monitors recommends bump testing the monitoring device before every days work and calibrating the device once at least every thirty days. While a number of manufacturers sell automated docking stations that automatically perform calibration and bump testing when a monitoring device is docked, there are still a number of disadvantages to the current practice. 
         [0007]    A fixed bump and calibration policy, such as currently practiced, does not take into account the actual state of the sensors or the environmental monitoring device. Such a fixed policy (bump test every day and calibrate every thirty days) by its very nature is a compromise that is too stringent in many cases and too liberal in many others. 
         [0008]    Given that the docking operation requires the user to bring the monitor to a central location, which typically is outside the work area, to perform the bump test and calibration, there is value in minimizing/optimizing this operation as much as possible without compromising safety. 
         [0009]    Threshold limit values (TLV), namely the maximum exposure of a hazardous substance repeatedly over time which causes no adverse health effects in most people is constantly being reduced by regulatory authorities as scientific understanding and evidence grows and we accumulate more experience. Often these reductions are quite dramatic as in the case of the recent (February 2010) reduction recommended by the American Congress of Governmental Industrial Hygienists (ACGIH) for H2S exposure. The ACGIH reduced the TLV for H2S from a time weighted average (TWA) of 10 ppm to 1 ppm TWA averaged over eight hours. The effect of such reductions puts a premium on accuracy of measurements. Current practice of a fixed calibration policy, such as calibrate every thirty days, may not be enough to guarantee the level of accuracy to meet the more stringent emerging TLV&#39;s. While a blanket reduction in the frequency of the calibration interval, i.e., from thirty days, will help to improve accuracy, it would add significant cost to the use and maintenance of the environmental monitoring devices. 
         [0010]    One solution to this problem, pursued by some, is to use newer and more advanced technology sensors with a higher degree of accuracy and tolerance to drift that minimize the need for calibration and bump testing. While there certainly is value in this approach, the cost of these emerging sensor often preclude its widespread use, particularly in personal monitoring applications where a large number of these monitors need to be deployed. 
         [0011]    For all the aforementioned reasons there is value in developing monitors that use current low cost sensor technologies while still meeting emerging TLV regulations and allow for a more adaptive calibration/bump policy that takes into account the state of the sensors and monitoring devices. 
       SUMMARY OF THE INVENTION 
       [0012]    In one general aspect, embodiments of the present invention generally pertain to a monitoring device having at least two sensors for each substance to be detected, a display, a processing unit, and an alarm. The sensors may be positioned on more than one plane or surface of the device. The processing unit may auto or self calibrate the sensors. Another embodiment relates to a network of monitoring devices. Other embodiments pertain to methods of monitoring a substance with a monitoring device having at least two sensors for that substance and auto or self calibrating the sensors. 
         [0013]    Those and other details, objects, and advantages of the present invention will become better understood or apparent from the following description and drawings showing embodiments thereof. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]    The accompanying drawings illustrate examples of embodiments of the invention. In such drawings: 
           [0015]      FIGS. 1A ,  1 B and  1 C illustrate monitoring devices having two sensors that detect the same substance and positioned on different planes or surfaces of the device, and  FIG. 1D  shows a monitoring device having three sensors according to various embodiments of the present invention; 
           [0016]      FIG. 2  shows a block diagram illustrating a few of the components of the monitoring device according to various embodiments of the present invention; 
           [0017]      FIG. 3  illustrates a flowchart of an example AI logic according to various embodiments of the present invention; and 
           [0018]      FIG. 4A  illustrates a monitoring device with the plurality of sensors housed in multiple housings and connected to a central processing unit and  FIG. 4B  illustrates a network of monitoring devices according to various embodiments of the present invention. 
       
    
    
     DETAILED DESCRIPTION 
       [0019]    Various embodiments of the present invention pertain to a monitoring device and methods used for environmental monitoring of substances, such as, for example and without limitation, gases, liquids, nuclear radiation, etc. 
         [0020]    In an embodiment, as illustrated in  FIGS. 1A-C , the monitoring device  90  has at least two sensors,  200   a  and  200   b,  which detect the same substance. The sensors may be positioned in more than one plane or surface of the device  90 . The device  90  also has a display  202 ; a user interface  102 , such as, for example and without limitation, at least one key or key pad, button, or touch screen, for control and data entry; an alarm  203 , shown in  FIGS. 1C and 1D , such as, for example and without limitation, audio, visual, or vibration; and a housing  104 . The monitoring device  90  may have a user panic button  106 , shown in  FIGS. 1A and 1B , that allows the user to trigger an alarm mechanism. In an example, as shown in  FIGS. 1A and 1B , sensor  200   a  and  200   b  are on opposite sides of the device  90 . In another example, as shown in  FIG. 1C , sensor  200   a  is on the front of the device  90  and sensor  200   b  on the top. In yet another example, as shown in  FIG. 1D , the device  90  has three sensors,  200   a - c,  sensing the same substance and positioned in different planes or surfaces of the device  90 . The position of the sensors  200  in different and multiple planes greatly reduces the likelihood of more than one sensor failing, for example by being clogged by debris from the device  90  being dropped. The monitoring device  90  may have more than one sensor  200  for each substance to be detected, i.e., the device  90  may detect more than one substance. The sensors  200  for each substance may be positioned on more than one plane or surface of the device  90 . For example, the device  90  may have two sensors  200   a  and  200   b  for H2S positioned on different surfaces or planes, e.g., one on the top and one on the side, of the device  90  and two sensors  200   c  and  200   d  for oxygen positioned on different surfaces or planes of the device  90 , e.g., one on top and one on the side. 
         [0021]    In another embodiment the monitoring device  90 , as shown in  FIG. 2 , has a plurality of sensors  200   a - n  that detect the same substance. One benefit of using more than one sensor  200  for each substance to be detected is reduction in the frequency of bump testing and calibration of the monitoring devices. As an example, in practice monitoring device types typically used for gas detection have been found to fail at a rate of 0.3% a day based on field analysis data and thus daily bump tests have been mandated; however, equivalent safety may be gained with two sensors by bump testing every week, thereby reducing bump testing by seven fold. 
         [0022]    In further embodiments, the monitoring device  90 , as shown in  FIG. 2 , has a processing unit  201 ; a plurality of sensors  200   a - n  that sense the same substance, such as, for example and without limitation, a gas; a display  202 ; an alarm  203  that would generate an alarm, for example and without limitation, an audio, visual, and/or vibratory alarm; and a memory  204  to store, for example and without limitation, historic sensor and calibration/bump test data. The processing unit  201  interfaces with the sensors  200   a - n  and determines the actual reading to be displayed. The actual reading may be, for example and without limitation, the maximum, minimum, arithmetic, mean, median, or mode of the sensor  200   a - n  readings. The actual reading may be based on artificial intelligence (AI) logic. The AI logic mechanism takes into account, for example and without limitation, the readings from the plurality of sensors  200   a - n,  historic sensor performance data in the memory  204 , span reserve of the sensor  200 , gain of the sensor  200 , temperature, etc., to determine the actual reading. In another example, as an alternative to the displayed actual reading being the maximum of the aggregate of the n sensors  200   a - n,  the displayed actual reading may be calculated as follows, where R denotes the displayed reading and R i  denotes the reading sensed by sensor i: 
         [0000]    
       
         
           
             R 
             = 
             
               
                 
                   
                     
                       ∑ 
                       
                         i 
                         = 
                         0 
                       
                       n 
                     
                      
                     
                       R 
                       i 
                       k 
                     
                   
                   n 
                 
                 k 
               
               . 
             
           
         
       
     
         [0000]    Then, the processing unit may display possible actions that need to be taken based on the actual reading derived, for example and without limitation, activate the alarm, request calibration by user, indicate on the display that the sensors are not functioning properly, indicate the current reading of gas or other substance in the environment, auto calibrate sensors that are out of calibration, etc. 
         [0023]    One example of the artificial intelligence logic method would be for the greater readings of the two sensors  200   a  and  200   b  or the greater readings of a multitude of sensors  200   a - n  to be compared with a threshold amount, and if the sensor reading crosses the threshold amount, an alarm mechanism would be generated. Another example of AI logic entails biasing the comparison between the sensor readings and the threshold amount by weights that are assigned based on the current reliability of the sensors  200   a - n,  i.e., a weighted average. These weights can be learned, for example and without limitation, from historic calibration and bump test performance. Standard machine learning, AI, and statistical techniques can be used for the learning purposes. As an example, reliability of the sensor  200  may be gauged from the span reserve or alternatively the gain of the sensor  200 . The higher the gain or lower the span reserve, then the sensor  200  may be deemed less reliable. Weights may be assigned appropriately to bias the aggregate substance concentration reading (or displayed reading) towards the more reliable sensors  200   a - n.  Consider R to denote the displayed reading, R i  to denote the reading sensed by sensor I, and w i  to denote the weight associated by sensor i: 
         [0000]    
       
         
           
             R 
             = 
             
               
                 
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                     = 
                     1 
                   
                   n 
                 
                  
                 
                   
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                     i 
                   
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         [0000]    where the weight w i  (0&lt;w&gt;1) is proportional to span reading of sensor i or inversely proportional to the gain G. Alternatively, w i  can be derived from historical data analysis of the relationship between the gain w i  and span reserve or gain G i . Historical data of bump tests and calibration tests performed in the field, for example and without limitation, can be used to derive this data. 
         [0024]    In addition, as illustrated in  FIG. 3 , if the difference in readings between any two or more sensors  200  is greater than some threshold value t c , which could be determined in absolute terms or relative percentage terms and may vary by substance, then the monitoring device  90  would generate an alarm or visual indication in the display  202  requesting a calibration by docking on a docking station or manually be performed on the device  90 . Further, if the difference in readings is greater than some higher threshold value t f , the monitoring device  190  would generate an alarm and or indicate on the display  202  a message indicating a sensor failure. 
         [0025]    In some circumstances, for example and without limitation, in the case of an oxygen sensor, the minimum reading of a multitude of sensors  200   a - n  may be used to trigger an alarm to indicate a deficient environment. 
         [0026]    In another embodiment, the monitoring device  90  may have an orientation sensor, such as, for example and without limitation, an accelerometer, that would allow the artificial intelligence logic to factor in relative sensor orientation to account for the fact that heavier than air gases, for example, would affect sensors in a lower position more than on a higher position and lighter than air sensors would. The degree of adjustment to the reading based on orientation can be learned, for example and without limitation, from the calibration data, field testing, distance between sensors, etc. and used to adjust readings from multiple positions on the device  90  to give the most accurate reading at the desired location, such as the breathing area of a user or a specific location in a defined space using the environmental monitoring device  90  as a personnel protection device. 
         [0027]    Another embodiment pertains to a network  500  having the plurality of sensors  200   a - n  that detect a single substance housed in separate enclosures, placed in the vicinity of one another, e.g., from inches to feet depending on the area to be monitored, and communicate with one another directly and/or the central processing unit through a wireless or wired connection. See  FIGS. 4A and 4B . Each of the housings  104  may have a separate processing unit  201 , memory  204 , and AI processing logic, as shown in  FIG. 4B . Alternatively, or in combination, sensor units would share a central processing unit  201  and memory  204 , as shown in  FIG. 4A . 
         [0028]    Based on the plurality of sensor readings  200   a - n,  the processing unit, using standard AI and machine learning techniques, etc., will adjust the gain of the sensors  200   a - n  to match closer to the majority of sensors  200   a - n  for each substance, i.e., minimize variance among the sensors. The variance may be, for example and without limitation, a statistical variance, other variance metrics such as Euclidean distance, or calculated from the average, weighted average, mean, median, etc. readings of the sensors. This would allow auto or self calibration of outlying sensors  200   a - n  without the use of calibration gas using a manual method or a docking station. In an example, if n sensors  200   a - n  sensing a particular gas, such as H2S, are considered and R i  is the reading that represents the concentration of H2S sensed by sensor i and M is the median value of the reading among the n sensors, then the gain, given by G i , of each sensor can be adjusted so that the reading R i  moves towards the median value by a small amount given by weight w(0&lt;w&gt;1). For each sensor i in (1,n): 
         [0000]    
       
         
           
             
               G 
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         [0000]    Performing such gain adjustment whenever the monitoring device  90  is exposed to a substance in the field, for example, as part of day-to-day operation will reduce the frequency of calibrations required, thus saving money both directly from the reduction in calibration consumption, such as gas, and also costs involved in taking time away to perform the calibration. Current monitoring devices that use a single gas sensor for detecting each gas type require a more frequent calibration schedule, thereby incurring significant costs. 
         [0029]    While presently preferred embodiments of the invention have been shown and described, it is to be understood that the detailed embodiments and Figures are presented for elucidation and not limitation. The invention may be otherwise varied, modified or changed within the scope of the invention as defined in the appended claims. 
       EXAMPLE 
       [0030]    The following discussion illustrates a non-limiting example of embodiments of the present invention. 
         [0031]    A single gas monitor that is used as a small portable device worn on the person and used primarily as personal protection equipment may be used to detect the gases within the breathing zone of the bearer of the device. The gas monitor is designed to monitor one of the following gases: 
         [0000]    
       
         
               
               
               
               
               
             
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 Gas 
                 Symbol 
                 Range 
                 Increments 
               
               
                   
                   
               
             
             
               
                   
               
             
          
           
               
                 Measuring 
                 Carbon Monoxide 
                 CO 
                 0-1,500 
                   1 ppm 
               
               
                 Ranges: 
                 Hydrogen Sulfide 
                 H 2 S 
                 0-500 ppm 
                 0.1 ppm 
               
               
                   
                 Oxygen 
                 O 2   
                 0-30% of volume 
                 0.1% 
               
               
                   
                 Nitrogen Dioxide 
                 NO 2   
                 0-150 ppm 
                 0.1 ppm 
               
               
                   
                 Sulfur Dioxide 
                 SO 2   
                 0-150 ppm 
                 0.1 ppm 
               
               
                   
               
             
          
         
       
     
         [0032]    The sensors are placed on two separate planes of the monitoring device, for example as depicted in  FIGS. 1A-C . The gas concentration of the reading is calculated in the following manner: 
         [0000]    
       
         
           
             reading 
             = 
             
               
                 
                   
                     SensorReading 
                      
                     
                         
                     
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         [0033]    If the reading is higher (or lower in the case of oxygen) than a user defined alarm threshold, then an audio and visual alarm is generated. 
         [0034]    Further, if reading&gt;0.5*abs(alarmThreshold−normalReading) and if 
         [0000]    
       
         
           
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         [0000]    then an auto calibrate function based on gain as described below is performed. The auto calibration may be done, based on a user defined setting in the monitoring device, without further input from the user of the monitoring device, and/or the user will be informed that the gas monitor has detected an anomaly and requests permission to auto calibrate. 
         [0035]    If 
         [0000]    
       
         
           
             
               
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         [0000]    then a message is displayed to the user to calibrate the gas monitor immediately using a calibration gas. 
         [0036]    Gain of each of the sensors is modified as follows in the auto or self calibration process: 
         [0000]    
       
         
           
             
               sensorGain 
               new 
             
             = 
             
               
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                 old 
               
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Technology Category: 3