AU2015261602B2 - A multi-sense environmental monitoring device and method - Google Patents

Abstract

Abstract An environmental monitoring device for detecting and warning users of unhealthy levels of a given substance is disclosed having more than one sensor for each substance to be detected. Each sensor for each substance detected may be positioned in more than one plane or surface on the device. The device may be capable of auto or self calibration. Methods for reading substance levels and auto calibrating are also disclosed. 2/n tt\\(j AN\ aA /h "A" . y \W~ \ \/L<

Description

1 2015261602 02 Aug 2017

A MULTI-SENSE ENVIRONMENTAL MONITOMNG DEVICE AND METHOD

Technical Field [0001] Embodiments of the present disclosure generally relate to environmental monitoring devices.

Background Art [0002] In this specification where a document, act or item ofknowledge is referred to or discussed, this reference 01- discussion is not an admission that tlie document, act or item of knowledge or any combination tliereofwas at the priority date publicly available, known to the public, part ofthe common general knowledge or known to be relevant to an attempt to solve any problem with which this specification is concerned.

[0003] In a numbei- of industrial work environments workers are at risk ofbeing exposed to a variety of hazardous environmental substances such as toxic or highly combustible gases, oxygen depleted environiuents, or radiation, etc. that pose a serious threat to worker safety. In order to keep workers safe, specialized enviromnental monitoring devices are used to alert workers of dangerous changes in their immediate environment.

[0004] Cun-ent practice involves using fixed point inoiiitoriiig devices t environment around where they are deployed or portable monitoring devices that are carried by tlie workers to monitor their immediate vicinity. Fixed point monitoring devices are typically used around potential hazard locations such as confined spaces to warn workers ofthe 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 (t^ically two to six) each monitoriirg a distinct substance.

[0005] Given that these environmeirtal luonitoring devices are life critical, i device fimctions properly and accurately. Current practice involves periodic bump testing and calibration ofmonitoring devices to guarantee proper fijnctioning. Bump tests involve exposing the monitoring device to a measured quairtity 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 ofthe sensors so it reads the quantity of gas 2 2015261602 02 Aug 2017 accurately. The pu^ose of calibration is to maintain the accuracy ofthe 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 ofmanufachrrers sell automated docking stations that autonratically 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 cumently practiced, does not take into account the achial state ofthe sensors or the environmental lironitoring 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 briirg the monitor to a central location, which typically is outside the work area, to perfonn the bump test and calibration, there is value in minimizing/optimizing this operation as much as possible without compromising safety.

[0009] Threshold limit values (TTV), 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 luore experience. Often these reductions are quite dramatic as in the case of tire recent (February 2010) reduction recoirrmended by the American Congress of Governmental Industrial Hygienists (ACGIH) for H2S exposure. The ACGIH reduced the TLV for H2S fi٠om a time weighted average (TWA) oflOppm to 1 ppm TWA averaged over eight hours. The effect ofsuch reductions puts a premium on accuracy of measurements. Current practice ofa fixed calibration policy, such as calibrate every thirty days, may not be enough to guarantee the level of accuracy to nreet the more stringeirt emerging TLV'S. While a blanket redirction in the frequency ofthe calibration interval, i.e. from thirty days, will help to inrprove accuracy, it would add significant cost to the use and maintenance ofthe 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 mininrize the need for calibration and bump testing. While there certainly is value in this approach, the cost 3 2015261602 02 Aug 2017 of these emerging sensoi' ofteir preclude its widespread use, particularly in personal monitoring applications where a large number ofthese monitors need to be deployed.

[0011 ] For all the aforementioired reasons there is value in developing monitors that use cunent 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 [0012] In one general aspect, embodijnents of the pi'esent disclosure 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 01' surface ofthe device. The processing unit may auto or self calibrate the sensors. Anotlier embodiment relates to a network ofmonitoi'ing devices. Other embodiments pertain to methods of monitoring a substance with a monitoring device liaving at least two sensors for that substance and auto or self calibrating tlie sensors.

[0013] According to one aspect ofthe disclosure, tliere is provided a monitoring device for monitoi'ing substances, the monitoring device comprising: a plurality of sensors, each of at least: two sensors ofthe plurality of sensors being configured to detect a same substance separately from all other sensors ofthe monitoring device and to generate a conesponding output signal in response to a detection oftlie same substance; a processing unit operably coupled to the plurality ofsensoi's, the processing unit being configured to: receive each ofthe output signals from the plurality of sensors, determine a detection signal for the same substance based on tlie output signals, and generate a calibration action responsive to a plurality ofthe output signals deviating by a threshold amount, the calibration action comprising ofperfonning self-calibration by adjusting a gain of one ofthe at least two sensors to decrease a variance in gain amongst the at least two sensors for the same substances; and 4 2015261602 02 Aug 2017 a display operably coupled to the processing unit, the display being configured to sliow a detection conditioir for the same substance in accordance with the detection signal.

[0014] According to a second aspect of the disclosure, there is provided nrethod for monitoring a substance using a nronitoring device operably coupled with a plurality of sensors, wherein each of the plurality of sensors is configured to detect the same substairce, the method comprising the steps ofi by a processor: detecting a concentration of the substance based on output signals from each ofthe plurality of sensors comprising concentration infonnation associated with detection ofthe same substance; determining a detection signal for said same substance based on the output signals; calculating a display reading ofthe substance, the display reading being determined based oir an aggregate ofthe output signals; perfonning selfcalibration responsive to an output signal of one sensor ofthe plurality of sensors deviating by a threshold amount compared to the other sensors ofthe plurality of sensors detecting the same substance, wherein the self calibration occurs by adjusting a gain of one ofthe at least two sensors to decease a variance in gain among the at least two sensors for the same substance; comparing the display reading to a threshold limit; and actuating an alarjn in response to the display reading being greater than or less than the threshold limit.

[0015] According to a firrther aspect ofthe disclosure, there is provided a monitoring device for monitoring substances, the monitoring device comprising: at least two sensors configured to detect a same substance and to each generate a conesponding output signal in response to a detection ofthe same substance; and a processing unit operably coupled to the plurality of sensors, the processing unit being configured to: receive each ofthe output signals from the plurality of sensors. 5 2015261602 02 Aug 2017 determine a weight ofeaclt oftlre plurality of sensors configured to indicate a reliability of each ofthe plurality ofseirsors, wherein each weight is detenuined based on at least one of span reserve, historical calibration performance, and historic bump test perfonnance aird determine an aggregate substance concentration readiirg by aggregating the output signals from the plui'ality ofsensors biased toward output sigirals fi٠om sensors indicated as being more reliable based on the weights.

[0016] Tlrose aird other details, objects, and advantages ofthe present disclosui'e will become better understood or apparent from the followiirg description and drawiirgs showing embodiments thereof.

Brief Description of Drawings [0017] Tlte accompairying drawiirgs illustrate examples ofenrbodiments ofthe disclosure. In such drawings: [0018] Fibres ΙΑ, IB and 1C illustrate monitoring devices having rivo sensors that detect tire same substance aird positioned on different planes or surfaces ofthe device, and Figure ID slrows a monitoring device lraving three sensors according to various embodiments ofthe present disclosure; [0019] Figure 2 shows a block diagram illustrating a few ofthe compoirents of tire monitoring device according to various embodiments oftlre present disclosure; [0020] Figure 3 illustrates a flowchart of an exairrple AI logic according to various embodiments ofthe present disclosure; and [0021] Figure 4Α illusti'ates a monitoi'iirg device with the plurality ofsensors lroused in multiple Irousings and conirected to a central processing unit and Figure 4Β illustrates a network ofirroiritoring devices according to various embodiirrents ofthe present disclosui'e.

Description of Representative Embodiments [0022] Various embodiirrents ofthe preseirt disclosure pertain to a monitoriirg device and metlrods used for environmeirtal monitoriirg ofsubstairces, such as, foi' exairrple and without limitation, gases, liquids, nuclear radiation, etc. 6 2015261602 02 Aug 2017 [0023] In an embodiment, as illustrated in Figres 1A-C, the monitoring device 90 has at least two sensors, 200a and 200b, which detect the same substance. The sensors may be positioned in more than one plane or surface ofthe 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 alann 203, shown in Figres 1 c and ID, such as, for example and without limitation, audio, visual, or vibration; and a housing 104. Tlie monitoring device 90 may have a user panic button 106, shown in Figures 1Α and IB, that allows the user to trigger an alann mechanism. In an example, as slrown in Figres 1A and IB, sensor 200a and 200b are on opposite sides ofthe device 90. In another example, as showir in Figre 1C, sensor 200a is on the front of tire device 90 and sensor 200b on the top. In yet anotlrer example, as shown in Figre ID, the device 90 has three sensors, 200a-c, sensing tire same substance and positioned in different plaires or surfaces ofthe device 90. The position of the sensors 200 in different and multiple planes geatly reduces tire likelihood ofmore than one sensor failing, for example by being clogged by debris from the device 90 being dropped. The monitoring device 90 may have irrore than one sensor 200 for each substance to be detected, i.e. the device 90 may detect irrore thair one substance. The sensors 200 for each substance may be positioned on more than one plane or surface ofthe device 90. For example, the device 90 may have two sensors 200a and 200b for H2S posi.tioned on different surfaces or planes, e.g. one on the top and one on the side, oftlre device 90 and two sensors 200c and 200d for oxygen positioned on different surfaces or planes oftlre device 90, e.g. one on top and one on the side.

[0024] In anotlrer embodiment the monitoring device 90, as shown in Figre 2, has a plurality of sensors 200a-n that detect the same substance. One benefit ofusing more than one sensor 200 for each substance to be detected is reduction in the frequency ofbunrp testing and calibration ofthe monitoring devices. As an example, in practice monitoring device types typically used for gas detection have been found to fail at a rate of0.3% a day based on field airalysis 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.

[0025] In further embodiments, tire monitoring device 90, as shown in Figre 2, has a processing unit 201; a plurality of sensors 200a-n tlrat sense the same substance, suclr as, for example and without limitation, a gas; a display 202; an alann 203 tlrat would generate an alann, for exairrple and witlrout limitation, an audio, visual, aird/or vibratory alarm; and a memory 204 to store, for example and without limitation, historic sensor and calibration/bump 7 2015261602 02 Aug 2017 test data. The processing unit 201 interfaces with the sensors 200a-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 ofthe sensor 200a-n readings. The achial reading may be based on artificial intelligence (AI) logic. The AI logic mechanism takes into account, for example and without limitation, the readings ftom the plurality of sensors 200a-n, historic sensor performance data in the memory 204, span reserve ofthe sensor 200, gain ofthe sensor 200, temperahire, etc., to detennine the acfiral reading. In another example, as an alternative to the displayed actual reading being the nraximum ofthe aggregate ofthe n sensors 200a-n, the displayed actual reading may be calculated as follows, where R denotes the displayed reading and Rj denotes the reading sensed by sensor i:

Then, the processing unit may display possible actions that need to be taken based on the acfiral readiirg derived, for example and witlrout limitation, activate the alarm, request calibration by user, indicate on the display tliat the sensors are not firnctioning properly, indicate the current reading of gas or otlier substance in the environment, auto calibrate sensors that are out of calibi'ation, etc.

[0026] One example ofthe artificial intelligence logic method would be for fire greater readings oftlre two seirsors 200a and 200b or the greater readings of a multitude of sensors 200a-n to be compared with a threshold airrount, and if the sensor reading crosses the threshold amount, an alarjn meclranism would be generated. Another example ofAI logic entails biasing the comparison between the sensor readings and the threshold amount by weights firat are assigned based on the current relialrility ofthe sensors 200a-n, i.e. a weiglrted average. These weights can be learned, for example and without limitation, ftom historic calibration and bump test perfonnance. Standard machine learning, AI, and statistical techniques can be used for the learning puj^oses. As an example, reliability ofthe sensor 200 may be gauged fi-om the span reserve or alternatively the gain ofthe 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 concenftation reading (or displayed reading) towards the more reliable sensors 200a-n. Consider R to denote the displayed reading, R, to denote the reading sensed by sensor I, and w,· to denote the weight associated by sensor i: 2015261602 02 Aug 2017 η where the weight Wi (0 < w >1) is proportional to span reading of sensor i or inversely proportional to the gain Gi. Alternatively, Wi can be derived from historical data analysis of the relationship between the gain Wi and span reserve or gain G;-. Historical data ofbump tests arrd calibration tests perfomred in the field, for example and without limitation, can be used to derive this data.

[0027] In addition, as illustrated in Figure 3, if the difference in readings between any two or more sensors 200 is greater than some threshold value tc, which could be detemrined in absolute terms or relative percentage terms and may vary by substance, then the nronitoring device 90 would generate an alann 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 tlran some higher threshold value tf, the monitoring device 190 would generate an alann and or indicate on the display 202 a message indicating a sensor failure.

[0028] In some circumstances, for example and without limitation, in the case of an oxygen sensor, the minimum reading ofa nrultitude of sensors 200a-n maybe used to trigger an alarm to indicate a deficient environmerrt.

[0029] In another embodiment, the moiritoring device 90 may have an orientation sensor, sucli 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 tlran air gases, for example, would affect sensors in a lower position more than on a higher position and lighter thair 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. aird used to adjust readings from multiple positions on tire device 90 to give the most accurate reading at tire desired location, suclr as the breathing area ofa user or a specific location in a defined space using the enviromnental moiritoring device 90 as a personnel protection device.

[0030] Another embodiirrent pertaiirs to a network 500 having the plurality of sensors 200a-n that detect a siirgle substance lroused in separate enclosures, placed in the vicinity of one anotlrer, e.g. from iirches to feet depending on the ai'ea to be monitored, and communicate with one another directly and/or the ceirtral processing uirit through a wireless or wired conirection. 9 2015261602 02 Aug 2017

See Figures 4Α and 4Β. Each of the housings 104 may have a separate processing unit 201, memory 204, and AI processing logic, as shown in Figure 4Β. Alternatively, or in combination, sensor units would share a central pi'ocessing unit 201 aird memory 204, as shown in Figure 4Α.

[0031] Based oir the plm'ality of sensor readings 200a-n, tire pi'ocessing unit, using standard AI and macliine learning techniques, etc., will adjust the gain ofthe sensors 200a-n to nratclr closer to the majority of sensors 200a-n for eaclr substance, i.e. minimize variance among the sensors. The variance may be, for example and without linritation, a statistical variance, otlier variance nretrics such as Euclidean distance, or calculated froirr the average, weiglrted average, irrean, median, etc. readings ofthe sensors. This would allow auto or self calibration of outlying sensors 200a-n without the use ofcalibi'ation gas usiirg a nranual irrethod or a docking station.

In an exanrple, ifn sensors 200a-n sensing a particular gas, suclr as H2S, are considered and R,-is tlie reading that represents tire coircentration of H2S seirsed by sensor i and M is the median value oftlre readiirg among tire n sensors, their the gain, given by G/, of each sensor can be adjusted so tlrat the reading R, moves towai'ds the median value by a small amount given by weight w(0 < w >1). For eaclr sensoi' i in (l,n):

Perfonning such gain adjustmeirt whenever tire monitoring device 90 is exposed to a substance in the field, for example, as part of day- to-day operatioir will reduce the fi-equency of calibrations required, thus saving money botlr directly from the reduction in calibration consumption, such as gas, and also costs involved in taking time away to perform tire calibration. Current monitoriirg devices that use a single gas sensor for detecting each gas type require a more fi-equent calibration schedule, thereby incurring significant costs.

[0032] Wile presently preferred embodiments ofthe disclosure have been showir and described, it is to be understood that the detailed embodiirrents and Figures are presented for elucidation and not limitation. Tire disclosure may be otherwise varied, modified or changed within the scope ofthe disclosure as defined in the appended claims.

EXAMPLE

[0033] The following discussion illustrates a non-limiting exairrple of embodiments ofthe present disclosure. 10 2015261602 02 Aug 2017 [0034] 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 tire device. The gas monitor is designed to monitor one of the following gases:

Measuring Gas Ranges Symbol Range Increments Carbon Monoxide CO 0-1,500 1 ppm Hydrogen Sulfide h2s 0-500 ppm 0.1 ppm Oxygen ح0 0-30% of volume 0.1./. Nitrogen Dioxide ؛NO 0-150 ppm 0.1 ppm Sulfur Dioxide S.2 0-150 ppm 0.1 ppm [0035] The sensors are placed oir two separate planes ofthe monitoring device, for example as depicted in Figures 1A-C. The gas concentration ofthe reading is calculated in the following liranner: 4 Sensor Reading^ ٢د Sensor Reading!1؛ reading = 4 g 2 g [0036] If the reading is higher (or lower iir tire case of oxygen) than a user defined alanrr tlrreshold, then an audio and visual alarm is geirerated.

[0037] Further, if reading > 0.5 * abs(alarm Threshold-normal Reading) and if ٦١ ٠ <abs( seirsor Re ading^- sensor Re adingT) لآ٠يم max(sensor Re adingl Sensor Re adingT ) then an auto calibrate firnction based on gain as described below is performed. The auto calibration may be done, based on a user defined setting in tire irronitoring device, without firrther input from the user ofthe monitoring device, andor the user will be inforjned that the gas irronitor has detected an anomaly and requests perjuission to auto calibrate.

[0038] If abs(sensor Re adingl - sensor Re adingT ) max(sensor Re adingT, Sensor Re adingT. ) then a irressage is displayed to the user to calibrate the gas monitor imirrediately using a calibratioir gas. 2015261602 02 Aug 2017 11 [0039] Gain ofeacli of the sensors is modified as follows in the auto or self calibration process: (إ*ئ.ا٠٠دد.-- - min(sensor Re adingk,sensor Re odingT) [0040] The word ‘comprising’ and fonns ofthe word ‘comprising’ as used in this description and in the claims does not limit the iirvention claimed to exclude any vai'iants or additions.

[0041] Modifications and improvements to tire invention will be readily apparent to tlrose skilled in tire art. Such irrodifications aird inrprovemeirts ai'e intended to be withiir the scope of this invention.

Claims (12)

1. Claims.

   1. A monitoring device for monitoring substances, the monitoring device comprising: a plurality of sensors, each of at least two sensors of the plurality of sensors being configured to detect a same substance separately from all other sensors of the monitoring device and to generate a corresponding output signal in response to a detection of the same substance; a processing unit operably coupled to the plurality of sensors, the processing unit being configured to: receive each of the output signals from the plurality of sensors, determine a detection signal for the same substance based on the output signals, and generate a calibration action responsive to a plurality of the output signals deviating by a threshold amount, the calibration action comprising of performing selfcalibration by adjusting a gain of one of the at least two sensors to decrease a variance in gain amongst the at least two sensors for the same substances; and a display operably coupled to the processing unit, the display being configured to show a detection condition for the same substance in accordance with the detection signal.
2. 2. The monitoring device of claim 1, wherein the processing unit is configured to determine a display reading based on a respective concentration detected by each of the plurality of sensors.
3. 3. The monitoring device of either one of claims 1 or 2, wherein self-calibration occurs by adjusting a gain of a sensor by taking into account a prior gain for that sensor and a comparison of a maximum and a minimum output signal of the at least two sensors to minimize the variance in gain among the sensors for the substance.
4. 4. The monitoring device of any one of claims 1 to 3, further comprising a user interface configured to provide control signals to said processing unit, the user interface comprising at least one of a button, key, or touch screen
5. 5. The monitoring device of any one of claims 1 to 4, wherein the self-calibration occurs by adjusting a gain of one of the at least two sensors according to the relationship:
6. 6. The monitoring device of any one of claims 1 to 5, wherein the processing unit is configured to determine a difference between output signals from the at least two sensors detecting the same substance and to generate a sensor fail signal responsive to the difference being outside of a threshold amount.
7. 7. A method for monitoring a substance using a monitoring device operably coupled with a plurality of sensors, wherein each of the plurality of sensors is configured to detect the same substance, the method comprising the steps of, by a processor: detecting a concentration of the substance based on output signals from each of the plurality of sensors comprising concentration information associated with detection of the same substance; determining a detection signal for said same substance based on the output signals; calculating a display reading of the substance, the display reading being determined based on an aggregate of the output signals; performing self-calibration responsive to an output signal of one sensor of the plurality of sensors deviating by a threshold amount compared to the other sensors of the plurality of sensors detecting the same substance, wherein the self-calibration occurs by adjusting a gain of one of the at least two sensors to decease a variance in gain among the at least two sensors for the same substance; comparing the display reading to a threshold limit; and actuating an alarm in response to the display reading being greater than or less than the threshold limit.
8. 8. The method of claim 7, further comprising determining a gain of a majority of sensors of the plurality of sensors detecting the same substance, wherein self-calibration occurs by adjusting a gain of a deviating sensor to correspond with the gain of the majority of sensors.
9. 9. The method of either one of claims 7 or 8, wherein the detection signal is determined based at least partially on at least one of historic sensor data, span reserve of the respective sensors, gain of the respective sensors, or ambient temperature.
10. 10. The method of any one of claims 7 to 9, further comprising generating the display reading R according to the relationship:

    wherein Wi is a value greater than 0 and less than or equal to one representing a weight of sensor i, Ri is a substance concentration reading sensed by sensor i, and n is a number of sensors of the plurality of sensors sensing the same substance.
11. 11. The method of claim 10, wherein w; is proportional to a span reserve of sensor i.
12. 12. The method of claim 10, wherein w, is inversely proportional to a gain of sensor i.