System and method for calibrating a sensing device

Abstract

A calibration system and method for calibrating an instrument are provided. The system includes at least one sensor, a processor, and a memory containing instructions configured such that when the instructions are executed by the processor, the processor executes the method. The method includes obtaining a series of sensor readings, determining a variation between changes in consecutive (or substantially consecutive) sensor readings from the series of sensor readings, estimating a stable point of the sensor readings by identifying at least one gas sensor reading from the series of sensor readings where an increase in the sensor reading and a decrease in the sensor reading are substantially offset and the slope of the trend line approaches zero, and adjusting a parameter of the instrument representing the relationship between the sensor readings and a known physical quantity based on the stable point.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application claims the benefit of U.S. Provisional Patent Application No. 63 / 348,793, filed on June 3, 2022, the entire contents of which are incorporated herein by reference.

[0002] The present disclosure generally relates to systems and methods for calibrating sensing devices.

Background Art

[0003] Gas sensors need to be calibrated regularly because they deteriorate over time in terms of signal output and drift. When performing this calibration, several problems are often encountered. First, the response time and signal output of the sensor vary depending on the environment in which the sensor operates. That is, those response time and signal output are affected by factors such as temperature, humidity, radio frequency interference (RFI), and the presence of other gases. Second, considering that the sensor signal asymptotically approaches the maximum output, the waiting time for the service technician until the sensor reaches the output level required for calibration may vary (the waiting time may be insufficient or too long). Third, the speed to the output and the maximum output is different for each sensor, even for the same model from the same manufacturer.

[0004] Several methods can be used for sensor calibration. One common calibration method is to wait until the maximum output (or minimum output in the case of a sensor with reverse polarity) is achieved and then calibrate the sensor accordingly (i.e., adjust the gain). Considering that the signal asymptotically approaches the maximum output, there are numerous problems associated with this waiting period. First, the technician may not wait long enough. Additionally, the longer the waiting time until the maximum output, the more toxic or flammable gas is consumed, resulting in costs in terms of both labor and gas consumption, and the potential for the toxic or flammable gas to be released into the air for an extended period, which can have an adverse impact on the environment. Another potential calibration method is to determine T80 or T90, which are defined as the times at which the sensor's response to the gas reaches 80% or 90% of the final response, respectively. The sensor is exposed to the gas until it reaches 80% or 90% of the maximum output, and the necessary gain is estimated. As a result, this method is much faster than waiting until the maximum output is reached, but the accuracy of the result is much lower and can lead to over-reporting and costly false alarms. Furthermore, the sensor output decreases over time, and T80 and T90 also change over time, so the sensitivity of the sensor is unclear in this procedure.

[0005] Calibration routines have been developed based on the analysis of when the change in the reading becomes small compared to a certain predefined threshold. However, since both the speed and magnitude of the sensor response are affected by various factors, different thresholds are optimal for each environment (i.e., all possible permutations of temperature, humidity, pressure, the presence of other gases, etc.), which is not practical. As a result, the use of a change relative to a certain predefined threshold can be lower than optimal in some situations and higher than optimal in other situations.

[0006] Since sensor output is affected by temperature, humidity, and other factors, the maximum (or minimum) expected output, and the time required to reach them, also vary depending on these factors. Based on the specifications provided by the sensor manufacturer, lookup tables for temperature or humidity are frequently created, but the manufacturer generally does not provide output data for various combinations of temperature and humidity (nor does it provide lookup tables for all potential influencing factors). It is possible to conduct investigations and collect data for various combinations of temperature and humidity points, but this is very time-consuming and still prone to human error.

Summary of the Invention

[0007] In some embodiments, a calibration system for calibrating an electronic device is provided. The calibration system includes at least a sensor, a processor, and a memory containing instructions that, when executed by the processor, cause the processor to obtain a series of sensor readings, determine the variation between consecutive (or substantially consecutive) sensor readings, and estimate the stable point of the sensor readings by identifying a "stable" region where the increase in sensor readings and the decrease in sensor readings are substantially offset and the slope of the trend line approaches zero. More precisely, the stable region occurs when the sum of the increases between consecutive or substantially consecutive readings is approximately equal to the absolute value of the sum of the decreases between consecutive readings. Similarly, the sum of the increases between consecutive readings is approximately 50% of the sum of the absolute values of the differences between consecutive readings. In this embodiment, the stable point of the gas sensor readings is estimated by identifying at least one gas sensor reading from a series of gas sensor readings where the sum of the increases between consecutive readings is equal to approximately 50% of the sum of the absolute values of the differences between consecutive readings, and based on the stable point, the parameters of the device representing the relationship between the sensor readings and the known physical quantity of the gas are adjusted accordingly.

[0008] In some embodiments, another calibration system for calibrating an electronic device is provided. The calibration system includes at least one gas sensor, a processor, and a memory containing instructions that, when executed by the processor, cause the processor to obtain a series of gas sensor readings, determine the variation between changes in consecutive or substantially consecutive gas sensor readings from the series of gas sensor readings, identify at least one gas sensor reading from the series of gas sensor readings where the sum of the increases between consecutive readings is equal to approximately 50% of the sum of the absolute values of the differences between consecutive readings, estimate a stable point of the gas sensor readings, and based on the stable point, adjust a parameter of the device that represents the relationship between the sensor readings and the physical quantity of a known gas. There is a tolerance range for this ratio (e.g., + / - 0.025). This tolerance range can be a function of various parameters such as the local mean and standard deviation of the variation, and / or any suitable tolerance range based on analysis of the data (e.g., empirically measured), selected based on the type of measurement target (e.g., gas, humidity, pressure, etc.), a function selected based on the type of measurement target (e.g., selected based on the severity of the gas type, settings, etc.).

[0009] In some embodiments, the calibration system includes a processor, a memory containing instructions, and a temperature sensor and / or a humidity sensor and / or a pressure sensor and / or a vibration sensor and / or a motion sensor and / or a light sensor and / or a sound sensor and / or a particulate matter sensor. In these embodiments, as described above, the calibration system obtains a series of sensor readings, determines the variation between changes in consecutive or substantially consecutive sensor readings from the series of sensor readings, and from the series of sensor readings, identifies at least one sensor reading (a value within a certain range or a value close enough to a value representing a stable signal (e.g., 0.5)) where the sum of the increases between consecutive readings is approximately 50% of the sum of the absolute values of the differences between consecutive readings from at least one previous sensor reading, thereby estimating a stable point of the sensor readings, and based on the stable point, adjusts the parameters of the instrument representing the relationship between the sensor readings and a known physical quantity.

[0010] In some embodiments, a method for calibrating an instrument is provided. The method includes obtaining a series of sensor readings, determining the variation between changes in consecutive or substantially consecutive sensor readings from the series of sensor readings, estimating a stable point of the sensor readings by identifying at least one sensor reading where the sum of the increases between consecutive readings is equal to approximately 50% of the sum of the absolute values of the differences between consecutive readings, and based on the stable point, adjusting the parameters of the instrument representing the relationship between the sensor readings and a known physical quantity.

[0011] In various further aspects, the present disclosure provides corresponding systems and devices, as well as logical structures such as machine-executable sets of coded instructions for implementing such systems, devices, and methods.

[0012] In this regard, before describing at least one embodiment in detail, it should be understood that the embodiments are not limited to the details of the structure and arrangement of the components described in the following description or shown in the drawings. Also, it should be understood that the expressions and terms used in this specification are for the purpose of description and should not be regarded as limiting.

[0013] Many further features and combinations thereof regarding the embodiments described herein will be apparent to those skilled in the art upon reading the present disclosure.

Brief Description of the Drawings

[0014] The embodiments will be described by way of example only with reference to the accompanying drawings.

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Embodiments for Carrying Out the Invention

[0015] One of the objectives of the present disclosure is to develop a method for calibrating the following types of sensing instruments (gas sensors, temperature sensors, humidity sensors, pressure sensors, vibration sensors, motion sensors, optical sensors, sound sensors, particle sensors, biosensors, and / or any sensors within electronic detection instruments or sensing instruments, etc.): High-speed, Accurate and consistent, Automatically adjust the application of gas release or other target phenomena at an appropriate time, Collect and utilize data on factors that may affect the reading of gas (or other phenomena), such as temperature, humidity, vibration, pressure, etc., Require little computational resources, data storage, and / or power.

[0016] It is also worth noting that the smaller the data storage and power requirements, the higher the possibility of performing calibration at the site where the phenomenon (such as gas) is detected.

[0017] Typically, sensor signals are approximately normally distributed around a trend line. As a result, when the standard deviation of the signal is less than or approximately equal to the standard deviation of the previous signal, the signal can be said to be stable. In previous teachings, a method for calibrating a sensing instrument (such as a gas sensor, temperature sensor, humidity sensor, pressure sensor, vibration sensor, motion sensor, optical sensor, sound sensor, particulate matter sensor, biosensor, and / or any sensor within an electronic detection or sensing instrument, etc.) called RADiCal was provided. In this method, an estimated value of the standard deviation of the variations inherent in the sensor signal is used to self-evaluate when the sensor output reaches a level close enough to the maximum value (or the minimum value if the signal is decreasing), and to calculate the adjustments required for calibration. More specifically, the standard deviation can be used as a measure of the small random variations inherent in the signal, and stability is measured when the calculated slope (or change in slope) of the signal is less than the number of standard deviations. This method is effective, but in this method, changes and updates may be required depending on changes in hardware and firmware such as the sampling frequency, etc., compared to the method provided in this specification, and the amount of calculation tends to be larger than the method proposed in this specification.

[0018] In some embodiments, a stability indicator with low computational cost is provided without being troubled by many issues related to the influence on the above sensor readings (such as variations due to temperature, humidity, pressure, variations between sensor elements, aging of the sensor, etc.).

[0019] Specifically, for a series of consecutive or approximately consecutive signal output readings S1, S2, ···, S n the following indicator is used:

Equation

[0020] The AMI is an indicator different from the Relative Strength Index (RSI). The Relative Strength Index measures the scale of price fluctuations by using the ratio of the average of price increases and the average of price decreases over the specified number of days as follows. RSI = 100 - (100 / (1 + RS)) Here, RS = ((Average of x days’ up closes / Average of x days’ down closes)).

[0021] It should be noted that there are important differences worthy of attention between the AMI and the RSI. For example, the behavior and purpose of using RSI data in the stock market are different from the purpose of using AMI in the calibration of financial products.

[0022] The stock market rises and falls like waves. There is no pre - predicted pattern in the curve (although there are positive changes predicted over a very long period and vibrations around the trend line, and even the slope of the trend line changes over time). The rising (“bullish”) market tends to last longer than the sluggish or falling (“bearish”) market, but there is no predetermined time for each. Furthermore, the stock market typically moves with momentum, so during the “hot” period, it continues to rise until some event or series of events causes a decline, in which case it continues to fall until another event occurs. Additionally, as technology changes and specific industries evolve and grow while others mature or decline, the underlying assets also grow at different rates. In summary, the RSI has a negative serial correlation around the trend line in the very long term, but a positive serial correlation around the trend line in the short term, and the fluctuations are neither random nor normally distributed.

[0023] In the calibration of the instrument, the general shape of the expected curve is known, and the vibrations around that shape are generally random and approximate a normal distribution with a mean of 0 and a substantially constant standard deviation. Since the variations are typically random and normally distributed around the trend line, the following can be inferred: When AMI is 1, the sensor is on the upward part of the curve (in the case of an increasing sensor); When AMI is 0, the sensor is on the downward part of the curve (in the case of a decreasing sensor); When AMI is close to 0.5, the increases and decreases are in a stable region due to random and normally distributed variations around the trend line.

[0024] In some embodiments, variations inherent in the signal (e.g., small "random" variations) are used as a self - evaluation method that can be used to calculate the adjustments necessary for calibration when the sensor reaches an output level close enough to its maximum value (or minimum value in the case of a sensor generating a decreasing signal). For example, the initial reference point (e.g., a signal with zero gas concentration) and the "near" maximum value at a non - zero gas concentration point are estimated by finding an output where AMI is close enough to a value (e.g., 0.5) representing a stable signal over a given sample set. As another example, the initial reference point and the "substantially" maximum value may be related to Celsius temperature, relative humidity measurement, vibration (rate of change of displacement per unit time), pressure (in Pascals), frequency of the intensity of light or sound waves, number of particles, etc.

[0025] It should be understood that the terms "approximate maximum" or "approximate zero" each include a reading that is approximately close to the actual maximum reading or zero reading, respectively. In some embodiments, the approximate maximum or approximate zero may each include the actual maximum reading or the actual zero reading. Also, it should be understood that "approximate maximum" or "approximate minimum" may relate to the first or second difference in change (i.e., where the slope begins to increase, stabilize, or reach an inflection point or other pattern or response range). Also, it should be understood that references to the approximate limits (i.e., approximate maximum, approximate minimum, approximate zero) of gas (or other phenomenon) readings in the present disclosure may vary depending on the different gas (or other phenomenon being measured). Also, throughout the present disclosure, it should be understood that references to the terms "maximum," "minimum," and "zero" include "near maximum," "near minimum," and "near zero."

[0026] In some embodiments, the output at any two known reference values is used by finding an output that is sufficiently close to a value representing a signal where the rate of the most recent signal change that was upward has stabilized. In some embodiments, calibration can be performed over different ranges for the minimum and maximum values. For example, this approach is applied to an oxygen sensor where the first reference is the background concentration and the second reference is 0% volume ratio.

[0027] In some embodiments, the target gas is already present in the ambient area and thus already affects the reading of the gas sensor. In this embodiment, the person calibrating the instrument uses another device (typically portable) to estimate the background gas, enters the value of the background gas at the start of the calibration routine, finds stability within this background gas environment, and then applies the gas to find stability at a known (typically higher) gas concentration.

[0028] In another embodiment, the point (and associated output) at which “substantially stable” is achieved can be approximated by identifying the point at which the rate of the most recent signal change, which was upward, becomes sufficiently close to a value representing a stable signal. In one embodiment, this point is found when the ratio of the sum of the absolute values of the positive first differences between two output readings separated by a predefined distance to the sum of the absolute values of all first differences between two output readings separated by the same predefined distance for which a predefined number of observations were made in the past is sufficiently close to a value representing a stable signal over a predefined minimum number of periods. Next, this is repeated such that all such readings are separated by at least a predefined number of samples. This is just one example of a method of measuring stability using variations inherent in the signal, and other algorithms or approaches can also be used.

[0029] FIG. 1 schematically shows an example of a calibration system 100 for calibrating an instrument, according to some embodiments. The system 100 includes at least one sensor 102, a calibration unit 104, and an instrument 106. In some embodiments, the at least one sensor 102 may be one or more gas sensors, temperature sensors, humidity sensors, pressure sensors, vibration sensors, motion sensors, optical sensors, sound sensors, and / or particle sensors. Other components including one or more amplifiers may be added to the system 100. As will be described in more detail below, the calibration unit 104 receives sensor readings from the sensor 102 and adjusts or calibrates the parameters of the instrument 106. For example, when the at least one sensor 102 is a gas sensor, the received sensor readings may be gas sensor readings. In some embodiments, the sensor 102 and / or the calibration unit 104 may be components of the instrument 106. Other components including one or more amplifiers may be added to the system 100.

[0030] Figure 2 shows, in flowchart form, an example of a method 200 for calibrating an instrument according to some embodiments. Method 200 may be performed by calibration unit 104 or by an instrument 106 that includes logic performed by calibration unit 104. Method 200 includes a step 210 of obtaining a series of sensor readings. That is, the logic of calibration unit 104 receives readings from sensor 102 and / or instructs a device or instrument having sensor 102 to obtain readings. Next, a variation between changes in consecutive sensor readings from the series of sensor readings may be determined (220). Next, by identifying at least one sensor reading from the series of sensor readings at which the total positive change in consecutive or substantially consecutive sensor readings over a period of time is approximately equal to the total negative change in consecutive or substantially consecutive sensor readings over the same period of time, a characteristic point such as a stable point of the sensor readings may be estimated (230). When a stable point is estimated (230), a parameter representing the relationship between the sensor readings and a known physical quantity within the instrument (in some embodiments, the target value may represent the physical quantity) is adjusted (240). In some embodiments, this includes adjusting, within instrument 106, the ratio of engineering measurement units to the known physical quantity. In some embodiments, this ratio may be adjusted in firmware. In other embodiments, this ratio may be adjusted by changing the physical gain of one or more amplifiers. In still other embodiments, this ratio may be adjusted by a combination of firmware and adjustment of the physical gain of one or more amplifiers. Other steps may be added to method 200.

[0031] In some embodiments, the sensor readings of FIG. 2 may be sensor 102, and the sensor readings relate to a temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, a motion sensor, a light sensor, a sound sensor, and / or a particle sensor. The physical quantity in the association being adjusted is relative to the type of sensor 102. For example, in the case of a gas sensor, the physical quantity is the gas concentration level.

[0032] In some embodiments, system 100 includes a plurality of different types of sensors 102, and method 200 may be applied to the plurality of different types of sensors 102. In such embodiments, each type of sensor 102 may obtain separate measurements and store them in different memory files. The steps of method 200 may be applied individually to those separate measurements independently of other measurements. System 100 may be configured to calibrate one parameter related to one type of sensor at a time, or method 200 may be applied in parallel (but separately) to calibrate different parameters of different sensors.

[0033] The remaining methods will be described with respect to gas sensors for simplicity of explanation. However, it should be understood that the following methods can also be applied to different types of sensors with appropriate modifications. For example, the phenomenon being measured and the physical quantity being evaluated can be replaced with those applicable to different types of sensors. That is, references to gas sensors, or measurements or other readings related to gas sensors, can be replaced with those applicable to different types of sensors with the necessary modifications (regardless of whether explicitly shown below).

[0034] Figure 3 shows, in flowchart form, another example of a method 300 for calibrating an instrument, according to some embodiments. Method 300 is performed by calibration unit 104 or by an instrument 106 that includes logic performed by calibration unit 104. Figure 3 shows the high-level steps (described in more detail below) necessary when calibrating gas sensing instrument 106 using variations inherent in the signal. Method 300 includes collecting sample data in a buffer and receiving an input 400, (optionally) determining a stable zero output level 500, waiting for a gas (or other external stimulus) from the physical system to be applied 600, and determining a stable signal span 700. Optionally, the quality of the sensor output can also be checked 310. After the span is determined 700 (or the sensor output is checked 310), if the calibration passes 312, the instrument is adjusted 314. Otherwise 312, the calibration fails and the calibration mode ends 316. It should be understood that the calibration can pass or fail. For a calibration attempt to "pass", the results need to meet a predetermined expected value. Otherwise, the calibration attempt is considered "failed" and the calibration results are not saved.

[0035] Figure 4 is a flowchart showing an example of a method 400 for collecting buffered data and receiving an input according to some embodiments. Method 400 may be performed by calibration unit 104 or by an instrument 106 including logic performed by calibration unit 104. Gas sensing instrument 106 is set to calibration mode and begins receiving signals from sensor 102 that represent readings of an analog-to-digital converter (ADC) (402). In some embodiments, the calibration mode includes logic similar to the logic performed by calibration unit 104 within instrument 106. In other embodiments, the calibration mode includes arranging a device that includes calibration unit 104 to receive readings from gas sensor 102 associated with instrument 106. Instrument 106 continues to receive data 402 passed to calibration unit 104. Calibration unit 104 updates read buffer 404, and read buffer 404 also propagates to a buffer of first differences between consecutive or substantially consecutive readings within readings 406. In some embodiments, the first difference of filtered substantially consecutive readings, or the first difference of consecutive or substantially consecutive average readings, is stored in the buffer. When differential buffer 408 is full, calibration unit 104 checks whether it has received the necessary gas (or other target phenomenon) information 410. This information includes, but is not limited to, gas concentration (or other physical quantity) for calibration, background gas (or other physical quantity), temperature, humidity, and / or other factors known to affect the signal (i.e., amplify, reduce, or excite the signal). Note that in some embodiments, the read buffer and the differential buffer may be composed of one or more of the same or different buffers.When the read buffer is full (408) and the calibration unit 104 has received the required gas information 410, the calibration unit 104 proceeds to the next stage (in this example, it is the zero stability detection stage 500 (or any arbitrary starting point) of the option in FIG. 3, but if a pre-determined zero is used, it may be the gas (or other target phenomenon or reading) waiting stage 600 in FIG. 3). When calibration is performed using other hardware 108, the additional hardware 107 needs to receive the gas measurement values for a pre-specified period or until the additional hardware 108 instructs the additional hardware 107 to stop receiving the gas.

[0036] Figure 5 shows, in flowchart form, an example of a method 500 for finding an initial "before gas generation" stable reading according to some embodiments. Note that parameters such as the thresholds discussed here may be different, for example, from those in a later stage of determining the final or "after gas generation" stable reading. These parameters can be selected based on predictions from theoretical models or by experiment. These parameters affect the possibility of finding stabilization when the signal is still moving and the expected time required to find stabilization. Changing the parameters to reduce the possibility of finding stabilization when the signal is still moving typically has the effect of increasing the expected time required for stabilization. Method 500 may be executed by calibration unit 104 or by an instrument 106 including logic executed by calibration unit 104. Method 500 includes a sensing instrument 106 that is set to a calibration mode and starts receiving signals from sensor 102 (402). Calibration unit 104 continues to receive data (402) and determines the grade of the observation at the applied first known gas concentration (or other target phenomenon) (512). This first concentration may be the gas concentration (or other target phenomenon) present in the environment during calibration. If unstable, calibration unit 104 returns to receiving data (402) and continues until a stable point is read (512). Optionally, (to increase the stability requirement), if stable, calibration unit 104 determines whether the number of consecutive stable observations is greater than a predefined threshold (514). Otherwise, step 514 can be skipped and proceed directly to 516. If the threshold is exceeded, it is determined whether this process needs to be repeated (516). If it needs to be repeated, the next observations for a predetermined number of times are ignored and the process is repeated again (518). If it does not need to be repeated, the stable signal is recorded (520) and calibration unit 104 moves to the next state 600. Otherwise, calibration unit 104 returns to the beginning of the process and receives the next observation 402.

[0037] Figure 6 shows, in flowchart form, an example of a method for detecting that a gas (or other target phenomenon) has been applied to sensor 600, according to some embodiments. Method 700 may be performed by calibration unit 104 or by an instrument 106 that includes logic performed by calibration unit 104. Method 600 includes a sensing instrument 106 (gas or other) that is set to a calibration mode and begins receiving signals from sensor 102 (402). The instrument 100 continues to receive data (402) and proceeds to determine the grade of the observation (612). Optionally, if the data passes an extreme change in value, the calibration unit 104 can proceed to determine (614) whether the number of consecutive similar observations is greater than a predefined threshold. Otherwise, it can proceed to step 616 or skip step 616 and the unit can proceed directly to 700. If the threshold is exceeded, it is determined (616) whether the process needs to be repeated. If it needs to be repeated, subsequent observations after a predetermined number are ignored and the process is repeated again (618). If it does not need to be repeated, the calibration unit recognizes that the gas has been applied and proceeds to the next state 700. Otherwise, the calibration unit 104 returns to the beginning of the process and receives the next observation 402.

[0038] Figure 7 is a flowchart showing an example of a method 700 for finding a final "after gas generation" stable reading according to some embodiments. Note that parameters such as the threshold discussed here may be different from, for example, the stage before finding the initial "before gas generation" stable reading. These parameters can be selected based on predictions from theoretical models or by experiments. These parameters affect the possibility of finding stabilization when the signal is still moving and the expected time required to find stabilization. Changing the parameters to reduce the possibility of finding stabilization when the signal is still moving typically has the effect of increasing the expected time required for stabilization. Method 700 may be executed by calibration unit 104 or by an instrument 106 including logic executed by calibration unit 104. Method 500 includes a sensing instrument 106 (gas or other) that is set to a calibration mode and starts receiving a signal from sensor 102 (402). Calibration unit 104 continues to receive data (402), applies a second known gas concentration (or other target phenomenon), and proceeds to determine the grade of observation (712). If unstable, calibration unit 104 returns to receiving data (402) and continues until a stable point is read (712). Optionally, if stable, calibration unit 104 determines whether the number of consecutive stable observations is greater than a predefined threshold to increase the certainty of stability (714). If the threshold is exceeded, it is determined whether this process needs to be repeated (716). If it needs to be repeated, the next predetermined number of observations are ignored and the process is repeated again (718). If it does not need to be repeated, a stable signal is recorded (720) and calibration unit 104 moves to the next state 310. Otherwise, calibration unit 104 returns to the beginning of the process and receives the next observation (402). Figure 8 is a flowchart showing an example of a method (800) for determining the grade of observation according to some embodiments.Method 800 may be performed by calibration unit 104 or by an instrument 106 that includes logic to be performed by calibration unit 104. Method 800 illustrates one embodiment of the detailed steps for determining a grade of observations for the purpose of estimating span and gain adjustments. The grade of observations is determined using a predetermined statistic calculated from small random variations inherent in the signal. In some embodiments, this statistic is the percentage of the most recent total movement that was upward. In some embodiments, the standard deviation can be used as an additional criterion for determining an acceptable level of stability, in addition to or instead of a predefined tolerance range. For example, as the sensor ages, the variation may not fall within two standard deviations 95% of the time without serial correlation. Therefore, calibration may fail using the standard deviation for the standard deviation estimate. Alternatively, the standard deviation can be held from the first calibration and compared to future calibrations. In this case, the signal is not sufficiently stable if the standard deviation of the signal changes by more than a predetermined amount. In any case, once the statistic is selected, the probability characteristics of this statistic can be determined and analyzed using techniques well known to those with knowledge in this field.

[0039] In this embodiment, the ADC read value 402 is received and stored in the read buffer 804. Next, the read value is used to populate the differential buffer 806 with data. Next, the AMI is estimated using the values recorded in the differential buffer. Note that the AMI is considered 0.5 (808) when all the values in the differential buffer are zero. In one embodiment, the sensor read value is classified into one of four grades based on the value of the AMI. For the sake of brevity, these grades will be referred to as extremely rising, extremely falling, stable, and unstable. Extreme grades are predicted when the signal changes rapidly, such as immediately after applying a gas (or other physical quantity) to the sensor. The stable grade is predicted when enough time has been given for the system to reach equilibrium. The unstable grade is predicted at an intermediate stage where the change in the signal is not rapid enough to be considered extreme and not slow enough to be considered stable.

[0040] In some embodiments, it is stipulated that the observed value is stable (820) when the AMI of the observed value is at most a predetermined threshold α away from 0.5. Similarly, it is stipulated that the observation is extreme (816, 818) when the AMI of the observation is at most another predetermined threshold β away from 1 or 0 depending on the direction of the signal. When neither of these conditions is met, the observation is considered unstable (822). The parameters α and β can be selected based on predictions from a theoretical model or experimentally. Reducing β decreases the sensitivity to changes in the signal before determining that a change in concentration has been observed. Reducing α improves the specificity of the algorithm for determining whether the signal is stable, resulting in a longer expected time to find the stabilization region. Note that the selection of α and β may vary depending on the various stages of the calibration process. For example, α may be different when determining the stable read value 500 before the initial "degassing" and when determining the final stable read value 700 after the "degassing". Finally, the grade of the sample is transmitted to the instrument 824.

[0041] For clarity, a general use case of gas sensing is described below. In parking lots, CO detection devices are frequently installed. General alarm levels may be 25 PPM (to operate the HVAC system to dissipate or exhaust the gas) and 100 PPM (to generate audible and visual alarms to warn the occupants). In the first factory calibration, a reading of 100 PPM is associated with 2800 ADC counts. The sensor signal output often decreases at a rate of 2% per month. Therefore, if the device is calibrated in January, by June, even if 100 PPM of gas is displayed, the device may only read 88 PPM. As a result, the field service technician instructs the transmitter to start calibration, exposes the sensor to 100 PPM of gas, and waits until the device has found a stable point and notifies them. For example, when α is 0.025 and β is 0.05, in the initial stage of gas release, since the slope of the signal is steep, the AMI of the signal is between 0.95 and 1 (or between 0 and 0.05 for a decreasing sensor). Therefore, the reading is determined to be extreme. Finally, as the gas release continues and the slope gradually decreases, the AMI is between 0.475 and 0.525, the signal is evaluated as stable, and the corresponding ADC count is recorded. If the corresponding ADC count is 2400, the firmware updates the system memory to reflect that 100 PPM of gas is associated with 2600 ADC counts. When the sensor encounters gas again and reaches the new ADC count recorded in the memory, the HVAC system is activated.

[0042] Figure 9 shows, in graph 940, an example of sensor response versus time according to some embodiments. A conventional method using T90 (915) and maximum output (925) is compared with an example of sensor response versus time (935) of the method described herein. The total upward variation 915 between the start and end in a similar time range of T90 does not match the total downward variation. In the case near the maximum value 935 of the present disclosure, the response is still increasing as it asymptotically approaches the absolute maximum value, but the change is small enough to be considered an approximation near the maximum value. Accordingly, the present disclosure provides a method of identifying an initial point where the variation within a range is smaller than the variation within a range of consecutive or substantially consecutive samples by self-referencing historical continuous variations.

[0043] The above example illustrates the concept of calibrating a detection instrument using self-referencing historical variations. One of ordinary skill in the art will understand the similarity to more complex methodologies used for calibration of the detection instruments described herein.

[0044] The embodiments of the disclosure described above provide many advantages that can be explained through the analysis of a series of examples. The following examples relate to CO, NO2, and oxygen, but more generally relate to any type of gas sensor. The above method can be easily extended to new types of sensors (including non-gas sensors) as long as it is familiar with the variations specific to the signal pattern specific to that type of sensor.

[0045] Speed: As shown in a series of examples presented in Table 1 below, this calibration method typically returns an output of 95.0% - 99.2% of the output close to the maximum output, less than 1 / 4 (e.g., 34.3 - 50.1 seconds for the AMI version of CO, compared to 194.1 - 220.9 seconds for the maximum output). Table 1 shows examples of average statistics by calibration method.

Table 1

[0046] Accuracy: As shown in Table 2 below, the coefficient of variation (COV) of AMI is less than 2% as a percentage of the maximum value, indicating that high accuracy can be obtained in a short time.

Table 2

[0047] In some embodiments, this alternative method of the RADiCal method described above provides similar advantages to RADiCal in terms of accuracy, self-reference, and speed, but is less affected by changes in firmware and hardware configurations and can reduce the computational load.

[0048] This method can also be used to identify other characteristic points such as the inflection point of a signal. This characteristic point is indicated by applying AMI to the first difference of the signal. There is a predicted relationship between the maximum signal and the inflection point. AMI provides a method for determining the inflection point.

[0049] As described above, embodiments of the teachings herein can be applied to the application of calibration methods to various types of sensors, including but not limited to gas, temperature, humidity, vibration, pressure, motion, light, sound, particle, biosensor, etc. The target value for each type of sensor may be the measurement unit typically used for the phenomenon detected by that sensor. For example, the target value of a gas sensor may be the gas concentration level. In the case of a temperature sensor, the target value is generally in degrees Celsius. In the case of a humidity sensor, the target value is generally the relative humidity level. In the case of a vibration sensor, the target value is generally the rate of change of displacement per unit time. In the case of a pressure sensor, the target value is generally in Pascals. In the case of a motion sensor or a light sensor, the target value is the frequency or the luminous intensity. In the case of a sound sensor, the target value is the change in sound pressure level (SPL). In the case of a particulate matter sensor, the target value is usually the number of micrograms per cubic meter (μg / m 3(parts per million). In the case of a microbial sensor, the target value is usually measured in terms of cell number or cell mass. Note that other units of measurement may be used for each sensor, and those skilled in the art will understand which unit of measurement to use in various situations.

[0050] A further embodiment of the present disclosure is that the correction coefficients for temperature and humidity can be derived from the shape of the curve itself.

[0051] FIG. 10 schematically shows another example of a detection system 1000 according to some embodiments. As shown in FIG. 10, the detection system 1000 includes an instrument 106 including a first sensor 102 (e.g., a gas sensor or other type of sensor), a memory 1032, a processor 1034, and an input / output (I / O) unit 1036. Logic corresponding to the calibration unit 104 may be stored in the memory 1032 as firmware and / or software. The memory 1032, the processor 1034, and the I / O unit 1036 may be included in a system-on-chip 1030.

[0052] The instrument 106 may optionally include additional sensors 1022 such as a gas sensor, a temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, or other types of sensors, and all of these sensors can be used for gain adjustment for the estimation and / or calibration of the gas concentration. The instrument 106 may optionally also include at least one amplifier 1010 that amplifies the signal corresponding to the gas reading value. Thereby, small "random" fluctuations can be detected more appropriately.

[0053] System 1000 may optionally include additional sensors 1024 such as gas sensors, temperature sensors, humidity sensors, pressure sensors, vibration sensors, or other types of sensors, all of which can be used for estimating and / or calibrating the measurement of phenomena (e.g., gas concentration) and for gain adjustment. System 1000 may optionally also include a control system or other system 1028, and an external device 1040, which includes its own memory 1042, processor 1044, and I / O unit 1046. The external device 1040 may include a smartphone, tablet, computer, or other computer device that can communicate with the instrument. For example, the external device 1040 may include logic corresponding to the calibration unit 104, and the external device 1040 may control the calibration of the instrument 106 in some cases.

[0054] FIG. 10 is a schematic diagram of a computer device 1100 such as a server. As shown, the computer device includes at least one processor 1102, a memory 1104, at least one I / O interface 1106, and at least one network interface 1108.

[0055] The processor 1102 can be an Intel or AMD x86 or x64, PowerPC, ARM processor, etc. The memory 1104 may include an appropriate combination of computer memories arranged internally or externally, such as, for example, random access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM), etc. The memory 1104 can store instructions corresponding to methods 200 - 800. The processor 1102 can execute the instructions.

[0056] Each I / O interface 1106 enables the computer device 1100 to interconnect with one or more input devices such as a keyboard, mouse, camera, touch screen, microphone, etc., or one or more output devices such as a display screen and speaker.

[0057] Each network interface 1108 enables the computer device 1100 to communicate with other components, exchange data with other components, access and connect to network resources, provide applications, and execute other computing applications by connecting to a network (or networks) capable of transmitting data, such as the Internet, Ethernet, Plain Old Telephone Service (POTS) lines, Public Switched Telephone Network (PSTN), Integrated Services Digital Network (ISDN), Digital Subscriber Line (DSL), coaxial cable, fiber optic, satellite, mobile, wireless (e.g., Wi-Fi, WiMAX), SS7 signaling network, landline, local area network, wide area network, etc.

[0058] FIG. 12 shows a schematic diagram of a calibration unit 104 as a combination of software components and hardware components within a computer device 1200. The computer device 1200 may include one or more processing units 1202 and one or more computer-readable memories 1204 that store machine-readable instructions 1206, the instructions being executable by the processing unit 1202, and the processing unit 1202 being configured to generate one or more outputs 1210 based on one or more inputs 1208. The inputs 1208 may include one or more signals representing the inputs described in methods 200 - 800. The outputs 1210 may include one or more signals representing the outputs described in methods 200 - 800.

[0059] The processing unit 1202 is composed of any suitable device configured such that a series of steps are executed by the computer device 1200 to implement a process implemented by a computer. Thereby, when the instruction 1206 is executed by the computer device 1200 or other programmable devices, the functions / operations specified in the methods 200-800 can be executed. The processing unit 1202 can be composed of, for example, any type of general-purpose microprocessor or microcontroller, digital signal processing (DSP) processor, integrated circuit, field programmable gate array (FPGA), reconfigurable processor, other appropriately programmed or programmable logic circuits, or any combination thereof.

[0060] The memory 1204 can be composed of any suitable known or other machine-readable storage medium. The memory 1204 can include, for example, non-transitory computer-readable storage media such as electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or apparatuses, or any suitable combination of the foregoing, but is not limited thereto. The memory 1204 can include, for example, a suitable combination of any type of computer memory disposed inside or outside the computer device 1200, such as random access memory (RAM), read-only memory (ROM), compact disc read-only memory (CDROM) (or flash memory), electro-optical memory, magneto-optical memory, erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or ferroelectric RAM (FRAM (registered trademark)). The memory 1204 can include any storage means (e.g., device) suitable for storing the machine-readable instruction 1206 executable by the processing unit 1202 in an acquirable manner.

[0061] This discussion presents exemplary embodiments of the subject matter of the present invention. Each embodiment represents a single combination of elements of the present invention, but the subject matter of the present invention is considered to include all possible combinations of the disclosed elements. Thus, if one embodiment includes elements A, B, and C and a second embodiment includes elements B and D, the subject matter of the present invention is considered to include other remaining combinations of A, B, C, or D, even if not explicitly disclosed.

[0062] Embodiments of the devices, systems, and methods described herein can be implemented in a combination of both hardware and software. These embodiments can be implemented on a programmable computer, each computer including at least one processor, a data storage system (including volatile memory or non-volatile memory or other data storage elements, or a combination thereof), and at least one communication interface.

[0063] Program code is applied to input data to perform the functions described herein and generate output information. The output information is applied to one or more output devices. In some embodiments, the communication interface may be a network communication interface. In embodiments where elements can be combined, the communication interface may be a software communication interface such as for inter-process communication. In still other embodiments, a combination of communication interfaces implemented as hardware, software, and combinations thereof may be used.

[0064] Throughout the foregoing discussion, numerous references are made to servers, services, interfaces, portals, platforms, or other systems formed from computer devices. It should be understood that the use of these terms is considered to represent one or more computer devices having at least one processor configured to execute software instructions stored on a tangible computer-readable non-transitory medium. For example, a server includes one or more computers that operate as a web server, a database server, or other types of computer servers in a manner that performs the described roles, responsibilities, or functions.

[0065] The technical solution of the embodiment may be in the form of a software product. The software product may be stored in a non-volatile or non-transitory storage medium such as a compact disc read-only memory (CDROM), a USB flash drive, or a removable hard disk. The software product includes a number of instructions that enable a computer device (a personal computer server or a network device) to execute the method provided by the embodiment.

[0066] The embodiments described herein are implemented by physical computer hardware including computer devices, servers, receivers, transmitters, processors, memories, displays, and networks. The embodiments described herein provide useful physical machines and specially configured computer hardware configurations.

[0067] Although the embodiments will be described in detail, it should be understood that various changes, substitutions, and modifications can be made herein.

[0068] Furthermore, the scope of the present application is not intended to be limited to the specific embodiments of the processes, machines, manufactures, compositions of matter, means, methods, and steps described herein.

[0069] As will be understood, the examples described and illustrated above are intended for illustration only.

Claims

1. A calibration system for calibrating an instrument, the calibration system comprising: at least one sensor; a processor; a memory containing instructions; and, when the instructions are executed by the processor, the processor is configured to: obtain a series of sensor readings; determine the variation between changes in consecutive sensor readings from the series of sensor readings; estimate a stable point of the sensor readings by identifying at least one sensor reading from the series of sensor readings where the sum of the increases between consecutive readings is approximately 50% of the sum of the absolute values of the differences between consecutive readings; and adjust a parameter within the instrument that represents the relationship between the sensor readings and a known physical quantity based on the stable point. A calibration system.

2. The calibration system according to claim 1, wherein the stable point includes a point where the change in the sensor readings reaches substantially zero.

3. The processor is configured to: update a reading buffer with the series of sensor readings; input data into a differential buffer based on the reading buffer data; estimate the AMI of the differential readings of the last N samples; and obtain the AMI from the differentials of the previous N samples.

4. The calibration system according to claim 3, wherein the processor is configured to consider the AMI as 0.5 when all of the previous N derivative reading values are less than the resolution value.

5. The calibration system according to claim 3, wherein the processor is configured to determine that the observation is stable when a related statistical estimate value is close enough to a value representing a stable signal.

6. The calibration system according to claim 3, wherein the processor is configured to determine that the observation is extreme or at an inflection point in an upward or downward direction when a related statistical estimate value is close enough to a value representing a signal that changes rapidly.

7. The calibration system according to claim 3, wherein the processor is configured to determine that the observation is unstable when the observation is neither stable nor extreme.

8. The calibration system according to claim 1, wherein the processor is configured to associate a physical quantity with a sensor output identified through a calibration process. **Claim 9** The calibration system according to claim 1, wherein the processor is configured to associate a target value with a sensor output identified through a calibration process. **Claim 10** A method for calibrating an instrument implemented on a computer, the method comprising: obtaining a series of sensor readings; determining a variation between changes in consecutive sensor readings from the series of sensor readings; estimating a stable point of the sensor readings by identifying at least one sensor reading from the series of sensor readings, wherein a ratio of a most recent total movement that was upward falls within a specific threshold; adjusting a parameter of the instrument representing a relationship between the sensor readings and a known physical quantity based on the stable point. Method. **Claim 11** The method according to claim 10, wherein the stable point includes a point at which a change in the sensor readings reaches substantially zero. **Claim 12** updating a reading buffer with the series of sensor readings; inputting data into a differential buffer based on the reading buffer data; estimating a correlation statistic based on the last N observations; determining an interval within which the correlation statistic of the stable readings falls; determining one or more intervals within which the correlation statistic of the extreme readings falls. **Claim 13** The method according to claim 12, wherein AMI is considered to be 0.5 when all of the readings of the previous N derivatives are less than the resolution value. **Claim 14** The method according to claim 12, comprising determining that the observation is stable when a relevant statistical estimate is close enough to a value representing a stable signal. **Claim 15** The method according to claim 12, comprising determining that the observation is extreme or a point of inflection in an upward or downward direction when a relevant statistical estimate is close enough to a value representing a rapidly changing signal. **Claim 16** The method according to claim 12, comprising determining that the observation is unstable when the observation is neither stable nor extreme. **Claim 17** The method according to claim 10, comprising the step of associating a value of a physical quantity with a sensor output identified through a calibration process.

18. The method according to claim 10, comprising the step of associating a target value with a sensor output identified through a calibration process.

19. A calibration subsystem for calibrating an instrument, the calibration subsystem comprising: a processor; a memory containing instructions, wherein when the instructions are executed by the processor, the processor is configured to: obtain a series of sensor readings from at least one sensor; determine a variation between changes in consecutive sensor readings from the series of sensor readings; estimate a stable point of the sensor readings by identifying at least one sensor reading from the series of sensor readings, the ratio of the most recent total movement that was upward being within a specific threshold; and adjust a parameter of the instrument representing the relationship between the sensor readings and a known physical quantity based on the stable point. A calibration subsystem.

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