Calibration System and Method for a Sensing Machine

Abstract

A calibration system and method for calibrating an instrument includes at least one sensor, a processor, and a memory containing instructions that, when executed by the processor, cause the processor to perform the method, which includes obtaining a series of sensor measurements, identifying from the series a variation between changes in successive (or nearly successive) sensor measurements, estimating a stable point of the sensor measurements by identifying at least one sensor measurement from the series in which the variation in at least one previous sensor measurement is small compared to the variation, and adjusting a parameter of the instrument that represents a relationship between the sensor measurements and a known physical quantity based on the stable point.

Description

Technical Field

[0001] Cross - Reference to Related Applications This application is a non - provisional application of U.S. Application No. 63 / 035,318, filed on June 5, 2020, entitled "Gas Sensing Device Calibration System and Method", which is hereby incorporated by reference in its entirety, and claims all benefits including priority.

[0002] This disclosure generally relates to calibration systems and methods for sensing devices.

Background Art

[0003] Gas sensors should be calibrated regularly because they degrade in terms of signal output and drift over time. When performing this calibration, one often faces two important problems. First, the response time and signal output of the sensors vary depending on the environment in which they operate. That is, they are affected by factors such as temperature, humidity, radio frequency (RF), the presence of other gases, etc. Second, different service technicians may wait different amounts of time (and can be insufficient or excessive) to reach the output level required for calibration for the sensors.

[0004] There are two methods commonly used in sensor calibration. One common calibration method is to wait until the maximum output (or minimum output in the case of a reverse-polarity sensor) is obtained and then calibrate the sensor accordingly (i.e., adjust the gain). The problem with this is that it takes a long time. Considering that the signal approaches the maximum value asymptotically, there are many related problems with this waiting time. First, the technician may not wait long enough. In addition, the longer it takes to wait for the maximum output, the more toxic or flammable gas is consumed, which is expensive in terms of both labor and gas consumption. As a result, the toxic or flammable gas is released into the air for a long time, which can have an adverse effect on the environment. Another potential calibration method is to identify T80 or T90, which is defined as the time when the sensor's response to the gas is 80% or 90% of its final response, by exposing the sensor to the gas until it reaches 80% or 90% of the maximum output and estimating the required gain. As a result, this method is much faster than waiting until the maximum output is obtained, but the results are much less accurate and can lead to over-reporting and costly false alarms.

[0005] Calibration routines have also been developed based on an analysis of when the change in the measured value becomes small for several predefined thresholds. However, since both the speed and magnitude of the sensor response are affected by various factors, different thresholds are optimal for all environments (i.e., all possible permutations such as temperature, humidity, pressure, the presence of other gases, etc.), which is not practical. As a result, the use of changes related to several predefined thresholds will be lower than optimal in some situations and higher than optimal in other situations.

[0006] The output of the sensor is affected by temperature, humidity and other factors. In addition to the maximum (or minimum) expected output, the length of time required to reach them also varies depending on these factors. Based on the specifications provided by the sensor manufacturer, reference tables are frequently created for temperature or humidity. Generally, however, the manufacturer does not provide output data by changing the combination of temperature and humidity. It is possible to conduct research and collect data for a large number of combinations of temperature and humidity points, but this is very time-consuming and still subject to human error.

Summary of the Invention

Means for Solving the Problems

[0007] In some embodiments, a calibration system for calibrating an electrical device is provided. The calibration system includes at least one sensor, a processor, and a memory containing instructions that, when executed by the processor, cause the processor to: obtain a series of sensor readings; identify variations between changes in consecutive (substantially consecutive) 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 change in the sensor reading from at least one previous sensor reading is small compared to the variation; and adjust a parameter of the device representing the relationship between the sensor readings and a known physical quantity based on the stable point.

[0008] In some embodiments, another calibration system for calibrating an electrical device is provided. The calibration system includes at least one gas sensor, a processor, and a memory containing instructions, which, when executed by the processor, cause the processor to obtain a series of gas sensor measurements, identify the variation between changes in consecutive gas sensor measurements from the series of gas sensor measurements, identify at least one gas sensor measurement from the series of gas sensor measurements where the change in the sensor measurement from at least one previous gas sensor measurement is less than the variation, thereby estimating a stable point of the gas sensor measurements, and based on the stable point, adjust a parameter of the device representing the relationship between the sensor measurement and a known physical quantity.

[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 light and / or a sound sensor and / or a particle sensor. In these embodiments, as described above, the calibration system obtains a series of sensor measurements, identifies the variation between changes in consecutive sensor measurements from the series of sensor measurements, identifies at least one sensor measurement from the series of sensor measurements where the change in the sensor measurement from at least one previous sensor measurement is less than the variation, thereby estimating a stable point of the sensor measurements, and based on the stable point, adjusts a parameter of the device representing the relationship between the sensor measurement and a known physical quantity.

[0010] In some embodiments, a method for calibrating a device is provided. The method includes obtaining a series of sensor measurements, identifying the variation between changes in consecutive gas sensor measurements from the series of sensor measurements, identifying at least one sensor measurement from the series of sensor measurements where the change in the sensor measurement from at least one previous sensor measurement is less than the variation, thereby estimating a stable point of the sensor measurements, and based on the stable point, adjusting a parameter of the device representing the relationship between the sensor measurement and a known physical quantity.

[0011] In some embodiments, a method of calibrating a device is provided. The method includes obtaining a series of gas sensor measurements, identifying variations between changes in successive gas sensor measurements from the series of gas sensor measurements, estimating a stable point of the gas sensor measurements by identifying at least one sensor measurement from the series of gas sensor measurements where the change in the sensor measurement from at least one previous sensor measurement is small compared to the variations, and adjusting a parameter of the device that represents the relationship between the sensor measurement and a known physical quantity based on the stable point.

[0012] In various further aspects, the present disclosure provides corresponding systems and devices and logic structures such as sets of machine-executable code instructions for implementing such systems, devices, and methods.

[0013] In this regard, before describing at least one embodiment in detail, it should be understood that the embodiments are not limited in their application to the details of the component configurations and arrangements described below or shown in the drawings. It should also be understood that the expressions and terms used herein are for the purpose of description and should not be regarded as limiting.

[0014] Many further features and combinations thereof regarding the embodiments described herein will be apparent to those skilled in the art after reading the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Embodiments are described by way of example only with reference to the accompanying drawings.

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[0016] One objective of the present disclosure is · at high speed, ·Accurate and consistent, ·Automatically adjusted to drive gassing or for proper time application to other target phenomena, ·Collect and utilize data on factors that can affect measurements of gas (or other phenomena) such as temperature, humidity, vibration, pressure, etc., ·Require little computational resources, data storage, and / or power, It is to develop a method for calibrating sensing devices (e.g., gas sensors, temperature sensors, humidity sensors, pressure sensors, vibration sensors, motion sensors, light sensors, sound sensors, particle sensors, biosensors, and / or any sensors in electronic detection or sensing devices).

[0017] It is also worth noting that the smaller the requirements for data storage and power, the more feasible calibration is at the site where the phenomenon (e.g., gas) is detected.

[0018] In some embodiments, fluctuations inherent in the signal (e.g., small "random fluctuations") are used as a way for the sensor to self-evaluate when the output level reaches a value close enough to the maximum value (or minimum value in the case of a sensor that generates a decreasing signal) that the sensor can use to calculate the adjustments necessary for calibration. For example, by finding an output where the change in output (i.e., the slope of the signal) is close enough to 0 (i.e., "close" to 0) over a given sample set, an initial reference point (e.g., a signal where the gas concentration is 0) and a value "near" the maximum value at a point where the gas concentration is not 0 are estimated. As another example, the initial reference point and the value "near" the maximum value can be related to temperature (°C), measurement of relative humidity, vibration (rate of change of displacement per unit time), pressure (Pascals), frequency of the intensity of light or sound waves, number of particles, etc.

[0019] It should be understood that the "random" variations of signals are variations that generally exist in all electronic signals. They are sometimes referred to as electrical noise or harmonics. They generally represent "apparently" random variations that are somewhat repeatable or have a consistent amplitude or frequency superimposed on the signal (although this may not always be the case). In many cases, these variations are not random and are affected by electrical interference, fluctuating power, equipment degradation (e.g., inconsistencies in the distribution of electrolytes in an electrochemical sensor) and / or other factors, and in other cases, there may be no clear or detectable reason for the variations. One skilled in the art will understand that a finite impulse response (FIR) filter can be used to ensure that there are sufficient but not excessive variations around the trend of the signal and / or to normalize the variations of the signal.

[0020] It should be understood that the terms "near the maximum value" or "near 0" each include a measured value that is approximately close to the actual maximum measured value or 0 measured value. In some embodiments, the terms "near the maximum value" or "near 0" may each include the actual maximum value or the actual 0 measured value. It should also be understood that "near the maximum value" or "near the minimum value" may be related to the first or second difference in change (i.e., where the slope begins to increase, stabilize or reach an inflection point or other response pattern or range). It should be understood that the approximate limits (i.e., near the maximum value, near the minimum value, near 0) of the measured values of the gas (or other phenomenon) in the present disclosure may vary depending on the gas (or other phenomenon being measured). Throughout the present disclosure, references to "maximum value", "minimum value" and "0" should also be understood to include "near the maximum value", "near the minimum value" and "near 0".

[0021] In some embodiments, the output at any two known reference values is used by finding an output where the slope or change in slope of the signal is sufficiently close to 0 (or some other threshold) for the small "random" variations inherent in the signal. In some embodiments, calibration can be performed over a range different from the minimum and maximum values. For example, this approach can be applied to an oxygen sensor where the first reference is the background concentration and the second reference is 0% volume. This approach can also be applied when using the start point and inflection point of gas processing as reference points.

[0022] In another embodiment, it can have a standard deviation as a measure of the small random variations inherent in the signal, and to identify stability, points where the slope (or change in slope) is less than some standard deviations can be used.

[0023] In another embodiment, points (and associated outputs) where a "near stable" state is obtained are identified, and thus points where the slope is less than the standard deviation of the signal can be approximated. In one embodiment, the ratio of measurements where the first difference between any two output measurements is less than half of the maximum first difference observed in a previous series of measurements for a predefined latter series of measurements is continuously detected over a predefined minimum period. This is merely an example of a method for measuring stability using the small random variations inherent in the signal, and other algorithms or approaches can also be taken.

[0024] Figure 1 schematically shows an example of a calibration system 100 for calibrating a device according to some embodiments. The system 100 includes at least one sensor 102, a calibration unit 104, and a device 106. In some embodiments, the at least one sensor 102 can be one or more gas sensors, temperature sensors, humidity sensors, pressure sensors, vibration sensors, motion sensors, optical sensors, audio sensors, and / or particle sensors. Other components including one or more amplifiers can be added to the system 100. As will be described in more detail below, the calibration unit 104 receives sensor measurements from the sensor 102 and adjusts or calibrates the parameters of the device 106. For example, if the at least one sensor 102 is a gas sensor, the received sensor measurement can be the measurement of the gas sensor. In some embodiments, the sensor 102 and / or the calibration unit 104 can be components of the device 106. Other components including one or more amplifiers can be added to the system 100.

[0025] FIG. 2 shows, in a flowchart, an example of a method 200 for calibrating a device according to some embodiments. Method 200 may be performed by a calibration unit 104 or by a device 106 that includes logic performed by the calibration unit 104. Method 200 includes obtaining a series of sensor measurements (210). That is, the logic of calibration unit 104 receives measurements from sensor 102 and / or instructs a device or apparatus having sensor 102 to obtain measurements. Thereafter, small “random” variations between changes in successive sensor measurements from the series of sensor measurements may be identified (220). Thereafter, by identifying at least one sensor measurement from the series of sensor measurements, a stable point of the sensor measurements may be estimated where the change in the sensor measurement from at least one previous sensor measurement is small relative to the small “random” variations (230). In some embodiments, the stable point may include a point where the change in the sensor measurement is zero or close to zero. When a stable point is estimated (230), a parameter representing the relationship between the sensor measurement and a known physical quantity within the device (in some embodiments, the target value may represent the physical quantity) is adjusted (240). In some embodiments, this includes adjusting, in device 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 yet 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.

[0026] In some embodiments, the sensor measurements of FIG. 2 may be sensor 102, and the sensor measurements relate to a temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, a motion sensor, a light sensor, an audio sensor, and / or a particle sensor. The physical quantity in the relationship being adjusted is for the type of sensor 102. For example, in the case of a gas sensor, the physical quantity is the gas concentration level.

[0027] In some embodiments, system 100 includes a plurality of different types of sensors 102, and method 200 can be applied to those sensors. In such embodiments, each type of sensor 102 can obtain separate measurements and can be stored in different memory files. The steps of method 200 can be applied individually to those separate measurements independently of the other measurements. System 100 can be configured to calibrate one parameter related to one type of sensor at a time or different parameters for different sensors in a parallel (but separate) application of method 200.

[0028] The remaining method will be described for a gas sensor for ease of explanation. However, it should be understood that the following method can also be applied to different types of sensors with appropriate modifications. For example, the phenomenon being measured or the physical quantity being evaluated can be replaced with those applicable to different types of sensors. That is, references to a gas sensor or a measurement value or other measurement values related to a gas sensor can be replaced, as needed, with those applicable to different types of sensors (regardless of whether it is explicitly shown below).

[0029] FIG. 3 shows, in a flowchart, another example of a method 300 for calibrating a device, according to some embodiments. The method 300 can be performed by a calibration unit 104 or by the device 106 including the logic performed by the calibration unit 104. FIG. 3 shows high-level steps related to calibrating the gas sensing device 106 using small random variations inherent in the signal (described in more detail below). The method 300 includes collecting sample data in a buffer and receiving an input (400), optionally identifying a zero output level (500), waiting for a gas from a physical system (or other physical quantity) to be applied (600), and identifying a signal span (700). Optionally, the quality of the sensor output can be checked (310). When the span is identified (700) (or when the sensor output is checked (310)), the device is adjusted if the calibration passes (314). Otherwise (312), the calibration fails and the calibration mode ends (316). It should be understood that the calibration can pass or fail. In order to "pass", the result of the calibration attempt must meet a predetermined expected value. Otherwise, the calibration attempt is considered a "failure", meaning that the result of the calibration is not saved.

[0030] Figure 4 is a flowchart showing an example of a method 400 for collecting buffered data and receiving an input, according to some embodiments. The method 400 may be performed by a calibration unit 104 or a device 106 including logic performed by the calibration unit 104. The gas sensing device 106 is put into a calibration mode and starts receiving signals from the sensor 102 representing measurements of an analog-to-digital converter (ADC) (402). In some embodiments, the calibration mode involves having the same logic as that performed by the calibration unit 104 within the device 106. In other embodiments, the calibration mode may involve arranging a device including a calibration unit 104 that receives measurements from the gas sensor 102 associated with the device 106. The device 106 continues to receive data (402), and the data is passed to the calibration unit 104. The calibration unit 104 updates a reading buffer (406), which also propagates to a buffer of the first differences of the measurements. In some embodiments, a buffer of the running average of the first differences (RADi) is used. When the measurement buffer 410 and the RADi buffer 412 are full, the calibration unit 104 checks whether it has received the necessary gas (or other target phenomenon) information 414, which may include, but is not limited to, gas concentration (or other physical quantity), background gas (or other physical quantity) for calibration, temperature, humidity, and / or other factors known to affect (i.e., amplify, reduce, or otherwise excite) the signal. Note that in some embodiments, the measurement buffer and the RADi buffer may include one or more of the same or different buffers. When the buffers 410, 412 are full and the calibration unit 104 has received the necessary gas information 414, the calibration unit 104 moves to the next stage (in this example, stage 500 of looking for any 0 in FIG. 3 (or any initial starting point), but if several predetermined 0s are used, it may be stage 600 of waiting for the gas (or other target phenomenon or measurement) in FIG. 3).If calibration is performed by other hardware 108, the additional hardware 107 may need to receive gas measurement values for a predefined period or until the additional hardware 108 instructs the additional hardware 107 to stop receiving gas.

[0031] FIG. 5 shows, in flowchart form, an example of a method 500 for finding zero, according to some embodiments. The method 500 may be performed by a calibration unit 104 or a device 106 including logic performed by the calibration unit 104. The method 500 includes a (gas or other) sensing device 106 that is placed in a calibration mode and begins receiving a signal from a sensor 102 (402). The calibration unit 104 continues to receive data (402) and proceeds to identify the stability of the data 512 when the application of gas (or any other starting gas value or other target phenomenon) is zero. If unstable, the calibration unit 104 returns to receiving data (402) and continues to do so until a stable point is read (512). If stable, the calibration unit 104 counts consecutive stable observations (514) and proceeds to determine whether the consecutive stability is greater than a predefined threshold (516). If the threshold is exceeded, a zero signal is recorded (518), and the calibration unit 104 moves to the next stage (520); otherwise, the calibration unit 104 returns to the beginning of the process and receives the next observation (402). This threshold can be selected based on predictions from a theoretical model or experimentally. Increasing the threshold reduces the likelihood of finding stability if the signal is still moving, but increases the expected time until stability is found.

[0032] Figure 6 shows, in flowchart form, an example of a method 600 for waiting for a gas (or other target phenomenon) according to some embodiments. Method 600 can be performed by a calibration unit 104 or a device 106 that includes logic performed by calibration unit 104. Method 600 includes a (gas or other) sensing device 106 that is placed in a calibration mode and begins receiving a signal from sensor 102 (402). The device 100 continues to receive data (402) and proceeds to identify the stability of the data (512). If the data passes a stability extreme change, the calibration unit 104 recognizes that gas has been applied and moves to the next stage (514); otherwise, the calibration unit 104 returns to the beginning of the process and receives the next observation (402).

[0033] Figure 7 shows, in flowchart form, an example of a method for determining a span 700 according to some examples. Method 700 can be performed by a calibration unit 104 or a device 106 that includes logic performed by calibration unit 104. Method 700 includes a (gas or other) sensing device 106 that is placed in a calibration mode and begins receiving a signal from sensor 102 (402). The calibration unit 104 continues to receive data (402) and proceeds to identify the stability of the data when a gas (or other phenomenon) of a known concentration is applied (712). If unstable, the calibration unit 104 returns to receiving data (402) and continues until a stable point is read (712). If stable, the calibration unit 104 counts consecutive stable observations (714) and proceeds to determine if the consecutive stability is greater than another predefined threshold (716). If the threshold is exceeded, a span signal is recorded (718) and the calibration unit 104 moves to the next stage (720); otherwise, the calibration unit 104 returns to the beginning of the process and receives the next observation (402). This threshold can be selected based on predictions from a theoretical model or experimentally. Increasing the threshold reduces the likelihood of finding stability when the signal is moving but increases the expected time required to find stability.

[0034] FIG. 8 shows, in a flowchart, an example of a method 800 for determining stability according to some embodiments. The method 800 can be performed by a calibration unit 104 or a device 106 including logic performed by the calibration unit 104. The method 800 shows an embodiment of the detailed steps in determining stability to estimate span and gain adjustments. The gain is adjusted using an estimate of the small random variations inherent in the signal. In some embodiments, this estimate is the sample standard deviation. In other embodiments, the gain can be adjusted, for example, by using a computer simplification of the standard deviation estimate to minimize processing and power requirements on the sensor board. In any case, once the estimate is selected, the probabilistic characteristics of this estimate can be identified and analyzed using techniques familiar to those skilled in the art.

[0035] The method 800 presents this latter embodiment where twice the standard deviation of a set of samples is estimated using 1 / 2 of the difference between its maximum and minimum values. This method reduces the demand on hardware resources and allows for higher measurement accuracy compared to calculating the sample standard deviation of a set of samples. However, this approach to estimating the standard deviation of the sensing of a gas (or other phenomenon) can be easily extended to any estimator by using the small random variations inherent in the signal.

[0036] More specifically, this embodiment involves receiving (402) the ADC measurement value and storing it in the measurement value buffer (804). Thereafter, the measurement value is used to input (806) into the RADi buffer. Next, an estimated standard deviation calculated before N samples is obtained (810), where N is the size of the RADi buffer. This is used to construct an interval in which the measurement values are expected to be included when the signal is stable, and the number of entries in the RADi buffer included in this interval is counted (812). If it is found that the values of the two standard deviations are smaller than the resolution value (the smallest increment that the device can detect or report), the value of the standard deviation is rounded up so that the values of the two standard deviations become the minimum value of the resolution. More specifically, in this example, if the two standard deviations are smaller than one ADC count, the estimated value of the two standard deviations is rounded up to one ADC count. In one embodiment, the sensor measurement value is classified into one of three classes based on the ratio of the RADi buffer that falls within the interval constructed above. For the sake of brevity, these classes are called stable, unstable, and extreme. The extreme classification is expected when there is a sudden change in the signal, such as immediately after a gas (or other physical quantity) is applied to the sensor. The stable classification can be expected when sufficient time has been given for the system to reach equilibrium. The unstable classification can be expected during an intermediate period where the change in the signal is neither rapid enough to be considered extreme nor slow enough to be considered stable.

[0037] In some embodiments, \(N_s\) is defined such that the measured value is considered stable when the samples in the RADi buffer are included within at least \(N_s\) desired intervals 818. Similarly, \(N_e\) is defined such that the measured value is considered extreme when the samples in the RADi buffer are included within at most \(N_e\) desired intervals 816. If neither of these conditions is met or if additional samples are not extreme but there is an extreme sample among the last \(N\) samples 814, the measured value is considered unstable (820). The parameters \(N_e\) and \(N_s\) can be selected based on predictions from a theoretical model or experimentally. Decreasing \(N_e\) reduces the sensitivity to changes in the signal before it is determined that a change in concentration has been observed. Increasing \(N_s\) increases the specificity of the algorithm when determining when the signal has stabilized, resulting in a longer expected time to find the stabilization region. Finally, the stability classification of the samples is communicated to the device (822). Note that the estimate can alternatively be made (instead of range / 4) using the range or the standard deviation of the samples, etc.

[0038] To make it clearer, a general use case of gas sensing is described below. CO detection devices are often installed in parking garages. The typical alarm level is 25 PPM (operating the HVAC system to dissipate or exhaust the gas) And 75 PPM (generating audible and visual alarms to notify the occupants). In the initial calibration at the factory, the displayed value of 25 PPM can be associated with 2600 ADC counts. The signal output of the sensor often decreases at a rate of 2% per month. Therefore, if the device is calibrated in January, by June, when the device indicates 25 PPM of gas, it may only display 22 PPM. Therefore, the on-site service technician indicates to the transmitter to start calibration, exposes the sensor to 25 PPM of gas, and waits until the device finds a stable point. For example, when N_e is 3 and N_s is 32, in the initial stage of gas treatment, the slope can be steep, so there may be less than 3 samples in the RADi buffer that are close enough to 0, so the measured value is determined to be extreme and the sample is unstable. As gas treatment continues and the slope gradually begins to decrease, the number of samples in the RADi buffer that are close to 0 can exceed 3 but is less than the threshold of 32. Finally, since the measured value exceeding 32 is close enough to 0, the sample is considered stable and the corresponding ADC count is recorded. When the corresponding ADC count is 2400, the firmware updates the system memory to reflect that 25 PPM of gas is related to 2400 ADC counts. When the sensor encounters the gas that caused the new ADC count recorded in the memory next time, the sensor can activate the HVAC system.

[0039] For illustration, the above standard deviation estimator is compared with a more conventional sample standard deviation. The sample standard deviation of a series of observations {Xi(|i = 1...N) is

[0040] (External 1) Given by TIFF0007717732000001.tif23115, where

[0041] (External 2) It is TIFF0007717732000002.tif34153. Therefore, to calculate the sample standard deviation, 2N addition operations, N multiplication operations, N subtraction operations, two division operations, a square root operation, and a final multiplication by 2 are required, which can be simplified to a bit shift by 1. In some embodiments, it should be understood that a left shift multiplies and a right shift divides. Usually, an addition, subtraction, or bit shift operation takes 1 clock cycle, an integer multiplication takes 1 to 5, a division takes 10 to 40, and a square root takes 50 to 100. This means that for one calculation of the sample standard deviation, a maximum of 180 + 8N clock cycles or, for example, considering 32 points at a time, approximately 430 clock cycles are required. In contrast, the calculation of the estimator that is twice the standard deviation described above is performed via

[0042] (Outer 3) TIFF0007717732000003.tif24115, where max and min are the maximum and minimum observations recorded respectively. This only requires performing a subtraction and then dividing by 2, which can be simplified to a bit shift by 1. Therefore, only 2 clock cycles are required for one calculation of this measurement value.

[0043] This simplification also has advantages in terms of data loss due to rounding errors. If each measurement value is recorded with M-bit accuracy, an additional log2N bits are required for the calculation of the average value. Twice as many bits are required to square this value, and another log2N bits are required to sum them for calculating the variance. Combining these, for a measurement with M bits, a total of 2M + 3log2N available bits and an additional bit for the sign are required, meaning that otherwise information will be lost. According to an example where N is 32, if 32 bits are available for the calculation, only 8 bits are available for recording the measurement value. In addition, 16 bits of information are lost in the final square root operation. In contrast, estimating the standard deviation using the range means that only M + 2 bits need to be available, which means that in a 32-bit system, 30 bits can be used for recording the measurement value before data loss occurs.

[0044] Figures 9A and 9B show examples of sensor response versus time for conventional methods using T80 / 90 (Figure 9A) and maximum output (Figure 9B) in graphs 910 and 920. Figure 9C shows an example of sensor response versus time for the method described herein, according to some embodiments, in graph 930. As shown in graphs 910, 920, and 930, the point 935 at which the present disclosure determines the reading time is more accurate than 915 of the T80 / 90 method and faster than 925 of the maximum output method.

[0045] Figure 9D shows examples of sensor response versus time for all three methods of Figures 9A - 9C. Figure 9D shows measurements for comparable time frames leading up to T90 (915), "near maximum" of the present disclosure (935), and maximum value (925). In the case of near maximum 935 (of the present disclosure), the variation from the start of the period to near maximum is less than the variation between consecutive measurements within the range (see sub - graph 944). Conversely, looking at the measurements up to T90 (915), the variation between the start and end of a similar time range is greater than the variation of consecutive measurements within that time range (see sub - graph 942). The variation over the period is also positive but less than the variation between consecutive measurements near the maximum value (925) (see sub - graph 946). In both the case of near maximum 935 and maximum value 925 of the present disclosure, the response is still increasing as it asymptotically approaches its absolute maximum value, but the change over the period is less than the change within the period. Thus, the present disclosure provides a method of identifying a first point where the variation over a range is less than the variation within a range of consecutive or nearly consecutive samples by self - referencing historical consecutive variations.

[0046] The above example illustrates the concept of calibrating a detection device using self - referencing historical variations. One of ordinary skill in the art will understand the similarity to the complex radical methodologies used in calibrating the detection devices described herein.

[0047] The embodiments of the above disclosure 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 to any type of gas sensor. The above method can be easily extended to new types of sensors (including non-gas sensors) if one is familiar with the small random variations inherent in the signal patterns specific to that type of sensor.

[0048] Speed: As shown in a series of examples presented in Table 1 below, this calibration method typically returns outputs that are 96.3% - 98.6% of the output near the maximum value in less than 1 / 3 of the time (e.g., for the radical version, 67.0 seconds - 79.6 seconds for CO compared to 222.1 seconds - 235.8 seconds for the full max). Table 1 shows an example of the average statistical values by the calibration method.

[0049]

Table 1

[0050] Accuracy: As shown in Table 2 below, this calibration method returns more accurate outputs than other methods.

[0051]

Table 2

[0052] Generally, for this method, the coefficient of variation of the output response with respect to the maximum value is generally lower than the coefficient of variation (COV) of the output in the case of other methods. The coefficient of variation for the gain estimation in this fast version also produces a COV for the percentage of the maximum value, where the range is 0.6% for oxygen and 1.7% for carbon monoxide. (COV)

[0053] In addition, since the system is self-referential, it automatically adjusts the gas treatment time based on temperature and humidity. This is evident from the time vs. MAX (time to max), T80, T90, and COV for the present disclosure. The COV for time vs. MAX is the highest and most variable in the case of the present disclosure. This self-referential adjustment for temperature and humidity is an important driving force for the lower COV seen in the response of the present disclosure compared to other methods. In additional tests, including tests in more extreme temperature and humidity ranges, the COV for the maximum sensor output became disproportionately high compared to the "closest to maximum" sensor output resulting from the method described in the present disclosure.

[0054] Estimation using an approximation of the standard deviation produces results that are about as accurate as those generated using the sample standard deviation, but achieves it in only a fraction of the computation time.

[0055] In another embodiment, the same essential concept of using small random variations of a signal over a longer period for changes can be applied while the variations can be calculated in another way. For example, the following algorithm has been applied to the calibration of a NO2 sensor. It can find 97% of the maximum value within 77 seconds or within 24% of the time it can take to find the maximum value. This algorithm is self-referential and can still have all the same beneficial aspects of the approach described above in terms of self-adjusting the length of time required to find "near the maximum value". This algorithm can offer the advantage of being simpler to compute than the one above, but the one above found 97% of the maximum NO2 signal in only 11% of the time the gas was applied. Thus, the use of this concept can be adjusted based on whether minimizing the response speed or computational power during calibration is most important. For example, in the case of calibrating a gas sensor involving the release of a toxic and flammable expensive gas, speed may be more important.

[0056] Alternative algorithm applied to the calibration of the NO2 sensor:

[0057]

Number

[0058] (External 4) When TIFF0007717732000007.tif29153 < 0, gas treatment (or other phenomena) is started.

[0059] For all D1, ···, D6,

[0060] (External 5) When TIFF0007717732000008.tif25170 > 0, it has reached near the maximum value.

[0061]

Table 3

[0062] The algorithms described in this specification represent only two examples, and other embodiments of the principle of using signal variations in relation to signal changes over a certain period can also be developed.

[0063] As described above, embodiments of the teachings herein, but not limited to, can be applied to calibration methods for various types of sensors such as gas, temperature, humidity, vibration, pressure, motion, light, sound, particles, biosensors, etc. The target value for each type of sensor can be the measurement unit commonly used for the phenomenon detected by that sensor. For example, the target value for a gas sensor can be a 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 a relative humidity level. In the case of a vibration sensor, the target value is generally the rate of change of displacement per unit time level. In the case of a pressure sensor, the target value is generally in Pascal level. In the case of a motion sensor or a light sensor, the target value is frequency or luminous intensity. In the case of a sound sensor, the target value is the change in sound pressure level (SPL). In the case of particles, the target value is generally one millionth of the number of micrograms per cubic meter (μg / m3). In the case of microorganisms, the target value is generally measured in cell count or cell mass. Note that other measurement units may be used for each sensor, and those skilled in the art will understand which measurement unit is used in different situations.

[0064] 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.

[0065] FIG. 10 shows a schematic diagram of another example of a detection system 1000 according to some embodiments. As shown in FIG. 10, the detection system 1000 includes a device 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 can be stored in the memory 1032 as firmware and / or software. The memory 1032, the processor 1034, and the I / O unit 1036 can be included on a system-on-chip 1030.

[0066] The apparatus 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, all of which can be used to contribute to the estimation of gas concentration and / or gain adjustment for calibration. The apparatus 106 may also optionally include at least one amplifier 1010 to amplify the signal corresponding to the gas measurement so that small "random" variations can be better detected.

[0067] The system 1000 may optionally include additional sensors 1024 such as a gas sensor, a temperature sensor, a humidity sensor, a pressure sensor, a vibration sensor, or other types of sensors, all of which can be used to contribute to the estimation of the measurement of a phenomenon (e.g., gas concentration) and / or gain adjustment for calibration. The system 1000 may also optionally include an external device 1040 including a control or other system 1028 and its own memory 1042, processor 1044, and I / O unit 1046. The external device 1040 may include a smartphone, a tablet, a computer, or other computing devices that can communicate with the apparatus. For example, the external device 1040 may include logic corresponding to the calibration unit 104 such that the external device 1040 controls the calibration of the apparatus 106.

[0068] FIG. 11 is a schematic diagram of a computing device 1100 such as a server. As shown, the computing device includes at least one processor 1102, a memory 1104, at least one I / O interface 1106, and at least one network interface 1108.

[0069] The processor 1102 is, for example, an Intel or AMD x86 or x64, PowerPC, ARM processor, etc. The memory 1104 is, for example, a random access memory It may include an appropriate combination of computer memories located either internally or externally, such as a random access memory (RAM), a read-only memory (ROM), and a compact disc read-only memory (CDROM). Memory 1104 may store instructions corresponding to methods 200 - 800. Processor 1102 may execute the instructions.

[0070] Each I / O interface 1106 enables the computing device 1100 to interconnect with one or more input devices such as a keyboard, a mouse, a camera, a touch screen, and a microphone, or one or more output devices such as a display screen and a speaker.

[0071] Each network interface 1108 connects the computing device 1100 to a network (or multiple networks) capable of transmitting data, such as the Internet, Ethernet, a plain old telephone service (POTS) line, a public switched telephone network (PSTN), an integrated services digital network (ISDN), a digital subscriber line (DSL), a coaxial cable, an optical fiber, a satellite, a mobile, wireless (e.g., Wi-Fi, WiMAX), an SS7 signaling network, a landline, a local area network, a wide area network, etc., so that the computing device 1100 can communicate with other components, exchange data with other components, access and connect to network resources, provide services to applications, and execute other computing applications.

[0072] FIG. 12 shows a schematic diagram of a calibration unit 104, which is a combination of software and hardware components in a computing device 1200. The computing device 1200 may include one or more processing units 1202 and one or more computer-readable memories 1204 configured to store machine-readable instructions 1206 executable by the processing unit 1202 and to cause the processing unit 1202 to generate one or more outputs 1210 based on one or more inputs 1208. The input 1208 may include one or more signals representative of the inputs described in methods 200-800. The output 1210 may include one or more signals representative of the outputs described in methods 200-800.

[0073] The processing unit 1202 may include any suitable device configured to perform a series of steps by the computing device 1200 to perform a computer-implemented process such that the functions / operations specified in methods 200-800 are performed when the instructions 1206 are executed by the computing device 1200 or another programmable device. The processing unit 1202 may include, for example, any kind of general-purpose microprocessor or microcontroller, a digital signal processing (DSP) processor, an integrated circuit, a field programmable gate array (FPGA), a reconfigurable processor, other suitable programmed or programmable logic circuitry, or any combination thereof.

[0074] Memory 1204 may include any suitable known or other machine-readable storage medium. Memory 1204 may include, for example, but not limited to, non-transitory computer-readable storage media such as electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices or any suitable combination of the foregoing. Memory 1204 may include, for example, a suitable combination of any type of computer memory located either internally or externally to computing 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) and electrically erasable programmable read-only memory (EEPROM), ferroelectric RAM (FRAM (registered trademark)), etc. Memory 1204 may include any storage means (e.g., device) suitable for storing machine-readable instructions 1206 executable by processing unit 1202 in a retrievable manner.

[0075] This description provides exemplary embodiments of the subject matter of the invention. Each embodiment represents a single combination of elements of the invention, but the subject matter of the 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 invention is considered to include other remaining combinations of A, B, C, or D as well, even if not explicitly disclosed.

[0076] Embodiments of the apparatus, system, and method described in this application may be implemented in a combination of both hardware and software. These embodiments may 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 combinations thereof), and at least one communication interface.

[0077] The program code is applied to input data to perform the functions described in this application and generate output information. The output information is applied to one or more output devices. In some embodiments, the communication interface can be a network communication interface. In embodiments where elements can be combined, the communication interface can be a software communication interface such as for inter-process communication. In still other embodiments, there can be a combination of communication interfaces implemented as hardware, software, and combinations thereof.

[0078] Throughout the above discussion, numerous references are made to other systems formed from servers, services, interfaces, portals, platforms, or computing devices. It should be recognized that the use of such terms is considered to represent one or more computing devices having at least one processor configured to execute software instructions stored on a computer-readable tangible non-transitory medium. For example, a server can include one or more computers operating as a web server, a database server, or other types of computer servers in a manner that performs the described roles, responsibilities, or functions.

[0079] The technical solution of the embodiment can be in the form of a software product. The software product can be stored in a non-volatile or non-transitory storage medium that can be a compact disc read-only memory (CD-ROM), a USB flash drive, or a removable hard disk. The software product includes many instructions that enable a computer device (personal computer, server, or network device) to execute the method provided by the embodiment.

[0080] The embodiments described herein are implemented by physical computer hardware including computing devices, servers, receivers, transmitters, processors, memories, displays, and networks. The embodiments described in this application provide useful physical machines and arrangements of specially configured computer hardware.

[0081] Although the embodiments have been described in detail, it should be understood that various changes, substitutions, and modifications are possible in this specification.

[0082] 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 in the specification.

[0083] As will be understood, the examples described and illustrated above are intended as illustrative only.

Claims

1. A calibration system for calibrating a machine, the calibration system comprising: at least one sensor; a processor; a memory containing instructions; wherein, when the instructions are executed by the processor, the processor is caused to: obtain a series of sensor measurements; identify the variation between changes in consecutive sensor measurements from the series of sensor measurements; update a measurement buffer with the series of sensor measurements; populate a moving average of the first differences (RADi) buffer based on the data in the measurement buffer; estimate the standard deviation of the last N sample RADi measurements; obtain the standard deviation from the previous N sample RADi measurements of the last N sample RADi measurements; identify the interval within which stable measurements fall; identify the number of RADi buffer entries within the interval; estimate the stable points of the sensor measurements by identifying at least one sensor measurement from the series of sensor measurements where the change in the sensor measurement from a previous sensor measurement is small compared to the variation; adjust the parameters of the machine representing the relationship between the sensor measurements and a known physical quantity based on the stable points; A calibration system that performs the above.

2. The calibration system according to claim 1, wherein the stable points include the points where the change in the sensor measurements reaches near zero.

3. The calibration system according to claim 1, wherein the processor is configured to round up to the decomposition value when the standard deviation from the previous N sample RADi measurements of the last N sample RADi measurements is smaller than the decomposition value.

4. The processor is configured to: when the number is greater than a first threshold; when the number is greater than a second threshold; when there are no extreme RADi entries in the last N sample RADi measurements; determine that the last N sample RADi measurements are stable, the calibration system according to claim 1.

5. The processor is configured to: when the number is greater than a first threshold; when the number is greater than a second threshold; when there is at least one extreme RADi entry in the last N sample RADi measurements; determine that the last N sample RADi measurements are unstable, the calibration system according to claim 1.

6. The calibration system according to claim 1, wherein the processor is configured to determine that the last N RADi measurement values are extreme when the number is less than or equal to a first threshold value.

7. The calibration system according to claim 1, wherein the processor is configured to associate a physical quantity with the sensor measurement value at the stable point.

8. The calibration system according to claim 1, wherein the processor is configured to associate a target value with the sensor measurement value at the stable point.

9. A computer-implemented method for calibrating a device, the method comprising: obtaining a series of sensor measurement values; identifying a variation between changes in consecutive sensor measurement values from the series of sensor measurement values; updating a measurement value buffer with the series of sensor measurement values; populating a RADi buffer based on the data in the measurement value buffer; estimating a standard deviation of the last N sample RADi measurement values; obtaining a standard deviation from the previous N sample RADi measurement values of the last N RADi measurement values; identifying an interval within which stable measurement values fall; identifying the number of RADi buffer entries within the interval; estimating a stable point of the sensor measurement values by identifying at least one sensor measurement value from the series of sensor measurement values, wherein a change in the sensor measurement value from a previous sensor measurement value is small compared to the variation; adjusting a parameter of the device representing a relationship between the sensor measurement value and a known physical quantity based on the stable point; The method includes.

10. The method according to claim 9, wherein the stable point includes a point at which a change in the sensor measurement value reaches near zero.

11. The method according to claim 9, including rounding up to the decomposition value when the standard deviation from the previous N sample RADi measurement values of the last N sample RADi measurement values is smaller than the decomposition value.

12. When the number is greater than a first threshold value, When the number is greater than a second threshold value, When there is no extreme RADi entry in the last N sample RADi measurement values, The method according to claim 9, including determining that the last N sample RADi measurement values are stable.

13. When the number is greater than a first threshold value, When the number is greater than a second threshold value, If there is at least one extreme RADi entry in the last N sample RADi measurement values, The method according to claim 9, comprising determining that the last N sample RADi measurement values are unstable.

14. The method according to claim 9, comprising determining that the last N RADi measurement values are extreme when the number is less than or equal to a first threshold.

15. The method according to claim 9, comprising associating a physical quantity with the sensor measurement value at the stable point.

16. The method according to claim 9, comprising associating a target value with the sensor measurement value at the stable point.

17. A calibration subsystem for calibrating a device, the calibration subsystem comprising: A processor; A memory containing instructions; Including, When the instructions are executed by the processor, the instructions cause the processor to: Obtain a series of sensor measurement values from at least one sensor; Identify the variation between changes in consecutive sensor measurement values from the series of sensor measurement values; Update a measurement value buffer with the series of sensor measurement values; Populate a RADi buffer based on the data in the measurement value buffer; Estimate the standard deviation of the last N sample RADi measurement values; Obtain the standard deviation from the previous N sample RADi measurement values of the last N RADi measurement values; Identify the interval within which stable measurement values fall; Identify the number of RADi buffer entries within the interval; Estimate the stable point of the sensor measurement values by identifying at least one sensor measurement value from the series of sensor measurement values where the change in the sensor measurement value from the previous sensor measurement value is small compared to the variation; Adjust the parameters of the device representing the relationship between the sensor measurement value and a known physical quantity based on the stable point; A calibration subsystem that causes the above to be performed.

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