JP2973218B2 - Compensation method for change in received light amount of photoelectric sensor - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

<< Industrial application field >> The present invention relates to a method for correcting a change in the amount of received light of a photoelectric sensor such as a photocoupler, and in particular, correcting a change in the amount of received light due to a change in luminous efficiency of a light emitting element, fogging of a lens surface, and contamination. And
The present invention relates to a method for correcting a change in the amount of received light for preventing a malfunction. << Conventional Technology >> A light-emitting device such as a light emitting diode (LED) and a light-receiving device such as a photodiode (PD) are used. Photoelectric sensors configured to detect movement and presence are well known and are used in a wide variety of fields. As a problem of the photoelectric sensor, the luminous efficiency of the light emitting element changes due to a temperature change, or the lens surfaces of the light emitting element and the light receiving element become fogged or dust adheres, and furthermore, due to vibration or the like. The amount of light sensed by the light receiving element, that is, the amount of received light may change due to a shift in the optical axis between the light emitting element and the light receiving element, which may change the magnitude of the light receiving signal input to the light receiving circuit. , Which can cause malfunctions. In view of the above problems, it has already been proposed to incorporate an APC circuit using a monitor photodiode separately from the light receiving element, thereby compensating for a change in the light emitting efficiency of the light emitting element due to a temperature change or the like. In addition, the amount of light received by the light receiving element is measured an arbitrary number of times, and the average value is obtained, and a correction value of the received light amount is found by comparing the average value with the previous or initial data. Feedback compensation has been proposed. 《Problems to be solved by the invention》 An APC circuit using a monitor photodiode is effective for temperature compensation of a light emitting element, but is not effective for changes in the amount of received light due to fogging and dirt on a lens surface. No response, no compensation for this. In the feedback control based on the average value of the received light amount, in addition to the temperature compensation of the light emitting element, a change in the received light amount due to fogging and dirt on the lens surface is compensated. If the received light amount change correction control is performed only by comparing the average value, it is not possible to sufficiently cope with a variation in the received light amount measurement value or a sudden one-off change, and thus an appropriate received light amount change correction is not necessarily performed. In addition, depending on the number of times of measurement for obtaining the average value, there is a possibility that the control system may oscillate due to variation in the measured value, and it may be difficult to perform stable correction. The present invention has been made by focusing on the above-described problems in the conventional light reception amount change correction, and comprehensively describes the change in the light reception amount due to the temperature characteristics of the light emitting element, dirt on the lens surface, and the like. The change in received light amount can be compensated accurately and reliably, and the control system does not oscillate due to the dispersion of the measured values.Stable correction is performed. Accordingly, it is an object of the present invention to provide a light-receiving-amount change correction method for performing a highly reliable light-receiving-amount change correction while minimizing an obstacle to correction for an original change. << Means for Solving the Problems >> In the present invention, the object as described above is to measure the amount of light received by a light receiving element of a photoelectric sensor, and to calculate an average value and a distribution of the measured amounts by a predetermined number of times for each predetermined number of times. The average value of the current measurement value is compared with the average value of the previous measurement value, and the distribution of the current measurement value is compared with the distribution of the previous measurement value. This is achieved by a method for correcting a change in the amount of received light of a photoelectric sensor, wherein a correction value of the amount of received light is determined to compensate. Further, in the received light amount change correction method according to the present invention, when the measured value of the received light amount of the light receiving element is significantly different from the previous measured value, the measured value is not allowed to participate in the calculation and distribution of the average value. Alternatively, the weight given to the calculation of the average value and the distribution frequency by the measurement value is reduced, and the measurement value is calculated with the increase in the number of times the measurement value that is significantly different from the previous measurement value is measured. And the weight given to the distribution frequency may be increased. Further, in the method for correcting a change in the amount of received light of the photoelectric sensor according to the present invention, when the distribution range of the measured values is expanded as compared with the previous case, the number of measurements for detecting the average value and the distribution is increased, and the distribution range is increased. The number of measurements for detecting the average value and the distribution may be reduced when the size is reduced as compared with the previous case. << Operation >> According to the method for correcting a change in the amount of received light of the photoelectric sensor according to the present invention, the corrected amount of received light is determined in consideration of the change in the distribution of the measured values in addition to the change in the average of the measured values of the received light. In addition, a change in the amount of received light due to the temperature characteristics of the light emitting element, contamination of the lens surface, and the like can be appropriately compensated. When the measured value of the received light amount is significantly different from the previous measured value, that is, when a sudden spontaneous change occurs, the average value and the distribution frequency of the measured value are updated according to the frequency of the change. Therefore, even if a sudden single change occurs, it is possible to minimize the obstacle to the correction for the original change, and to perform the highly reliable received light amount correction for this as well. Also, the number of measurements for calculating the average value is determined in accordance with the change in the distribution range of the measured values, so that the control system is prevented from oscillating due to the dispersion of the measured values, and stable correction is performed. Become. << Example >> Hereinafter, the present invention will be described in detail with reference to the accompanying drawings with reference to examples. FIG. 1 is a circuit block diagram of a photoelectric sensor and a light-receiving amount change control device used for implementing a method for correcting a change in photoelectric amount of a photoelectric sensor according to the present invention. In FIG. 1, reference numeral 1 denotes a photoelectric sensor, and the photoelectric sensor 1 includes a light projecting element 3 and a light receiving element 5. The light projecting element 3 is composed of a pulse modulation type LED or the like, and is driven by the light projecting circuit 7 to emit light. The light receiving element 5 is configured by a photodiode or the like, receives light from the light projecting element 3, converts the light into a current, and outputs a current signal. The current signal from the light receiving element 5 is given to the I / D converter 9 and is converted into a voltage value. Since this voltage signal is generally weak, it is amplified by the amplifier 11. The amplifier 11 is configured as a variable gain amplifier that can variably set a gain for gain adjustment for light amount variation correction. Since the light to which the light receiving element 5 responds is a pulse modulated wave from the light projecting element 3, the voltage signal from the variable gain setting unit 11 is supplied to the peak hold sample and hold circuit 13 and converted into a DC component. A / D converter 1
The signal is converted into a digital signal by 5 and input to the CPU 17. The voltage signal output from the variable gain amplifier 11 is provided to the signal processing circuit 19. The signal processing circuit 19 may be the same as a signal processing circuit in a general photoelectric sensor, and performs operation such as discriminating light incident or non-light incident by a comparator, removal of a noise component by a noise filter, and the like. It outputs a signal. This operation signal 19 is also input to the CPU 17. The CPU 17 may be of a general structure,
The correction of the change in the amount of received light of the photoelectric sensor 1 is performed according to an operation system and a program stored in the storage device. Note that the CPU 17 may be of a type incorporating the A / D converter 15 and the EEPROM 21. The CPU 17 outputs a gain setting signal to the variable gain setting device 11 for correcting a change in the amount of light received by the photoelectric sensor 1, outputs an alarm signal, and inputs a trigger signal. In order to correct the change in the amount of light received by the light receiving element 5, it is necessary to sequentially acquire the measured value of the amount of received light, and the measured value of the amount of received light serving as initial data for correcting the amount of received light, in other words, sensitivity correction. Therefore, a measured value of the amount of received light in a state that can serve as a reference is required. The reference state may be a light-on type in which the amount of light received is stable as shown in FIG. 2 due to the difference between the photoelectric sensor of the reflected light detection type and the transmitted light detection type, as shown in FIG. As shown in FIG. 3, there are two types, dark on, in which the amount of received light during light shielding is stable. Therefore, the state in which light enters stably is set to the on state, and the amount of received light in this on state is measured. To Strictly speaking, for example, the hardware configuration or software is used to capture the maximum amount of light received when the light is on and the minimum amount of light received when the dark is on. For example, a change in the amount of light during the transition from off to on or from on to off is not erroneously taken in as sensitivity correction data. If it is difficult to take in the timing of taking in the measured value of the received light amount unless an external gate is applied, a trigger signal may be input to the CPU 17 to apply the gate. In order to observe the change over time in the amount of light received by the light receiving element 5 of the photoelectric sensor 1, it is necessary to determine the difference in the change due to the variation in the measured values in addition to the change in the average value of the measured values for a predetermined number of times. An example of data processing required for this will be described below. First, since it is necessary to take in initial data to be used as a comparison reference, for example, immediately after setting of the photoelectric sensor 1 or after inputting any trigger, a received light amount measurement value is taken n times a predetermined number of times. That is, for example, a measured value of the amount of received light, such as flowing an article through the transport line until the photoelectric sensor 1 is turned on n times, is acquired. Then, the CPU 17 calculates the average value and the variation width of the measured values of the amount of received light by the n times, that is, the distribution range, and stores them in the internal memory of the CPU 17 as initial data. Next, the actual operation is started, and the received light amount measurement values are sequentially taken in n times in the same manner. From the values, the average value and the distribution range are calculated, and this is compared with the initial data, the received light amount correction value is determined in a manner described later, and the gain setting signal is sent to the variable gain amplifier 11 based on this. Output. Subsequently, the received light amount measurement values are successively taken over the next n times, the average value and the distribution range are calculated, and this is compared with the average value and the distribution range calculated one time ago. The calculation of the received light amount correction value is repeated. Next, a description will be given of a processing algorithm of a method for correcting a change in the amount of received light according to the present invention for correcting a change in the amount of received light of the photoelectric sensor in the first embodiment. If the amount of light received decreases due to contamination of the lens surfaces of the light projecting element 3 and the light receiving element 5 in order to correct the change in the amount of light received by the light receiving element 5, the gain of the variable gain amplifier 3 in FIG. Done. That is, the average value and the distribution range of the measured values of the amount of received light are input, and one of the outputs after data processing is a gain for signal amplification. In addition, when the distribution range of the measured value is larger than the initial data or the previous one, it is necessary to increase the number of measurements of the average value and the distribution range calculation in order to increase the reliability as data and prevent occurrence of oscillation phenomenon. A favorable situation appears. Conversely, if the distribution range of the measured values is smaller than before, it is better to increase the response speed at which the received light amount correction is performed by reducing the number of measurements for calculating the average value and the distribution range, and to increase the efficiency. There is also. The output after data processing in such a case is the number of measurements for calculating the average value and the distribution range. Here, as the input data, the average value and the variation of the measured amount of received light are described. However, in this data, for example, a foreign substance accidentally passes through the detection optical axis of the sensor for a moment and is illuminated. As in the case where the axis is blocked, there is a case where data to be originally excluded as data for calculating the average value is mixed. However, when the number of occurrences (the number of appearances) of these data also increases, it is necessary to separately prepare a processing algorithm for them in order to increase the reliability as the processing data. Hereinafter, this processing is referred to as exception processing. Therefore, there are three processing algorithms for correcting a change in the amount of received light, those relating to sensitivity correction, those relating to adjustment of the number of measurements for calculating an average value and a distribution range, and those relating to exception processing. The respective processing algorithms will be described below. ◎ Processing for Sensitivity Correction As shown in FIG. 4, if the variation of the received light amount measurement value over n times is a normal distribution, the relationship between the data of the current measurement value and the data of the previous measurement value is As shown in FIG. 5, there are a case where the distribution range of the current measurement value does not fall within the distribution range of the previous measurement value, and a case where it falls as shown in FIG. As shown in FIG. 5, when the distribution range of the current measurement value does not completely belong to that of the previous measurement, the average value of the current measurement value returns to the level of the average value of the previous measurement value. a, it is sufficient performed gain adjustment, equation at this time, G _n-1 the current gain, the gain of the corrected G _n, a _n-1 the average value of the previous measurement value, the current measurement Assuming that the average value of the values is _An , it is represented by the following equation. G _n = (A _n-1 / A _n ) G _n-1 (1) As shown in FIG. 6, the distribution range of the current measurement value completely belongs to the distribution range of the previous measurement value. In this case, even if the average value itself of the measured values changes, it is considered that the change is merely a variation within the allowable variation. Therefore, the received light amount correction value, that is, the gain correction amount is shown in FIG. It is made smaller than in such cases. For example, when the gain adjustment amount is set to approximately half that in the case as shown in FIG. 5, the calculation formula for calculating the gain is represented by the following expression. G _n = 0.5 (A _n-1 / A _n ) G _n-1 (2) Accordingly, the change in the distribution range of the measured amount of received light at the predetermined number of times is determined by the pattern shown in FIG. Alternatively, it is determined whether the pattern is as shown in FIG. 6, and the gain correction amount is set, whereby the light receiving amount correction is performed in a feedback manner. Further, if further correction is not possible due to the limit of the variable gain setting width of the variable gain setting device 11, the CPU 17 only needs to output an alarm signal. It becomes possible to have. ◎ Processing algorithm related to adjustment of the number of measurements for calculating the average value and distribution range For example, assume that the number of measurements can be variably set from a minimum of 20 to a maximum of 50, and this is set to 35 at the time of initial data capture It is assumed that If the distribution range of the current measurement value is the same as the previous one, do not change the measurement count of 35, and process so that the distribution count approaches the minimum of 20 as the distribution range approaches zero . Conversely, if the distribution range is large, for example, if the distribution range of the measured value is three times the previous one,
The number of measurements is set to be the maximum of 50 times, and the processing is performed so that the number of measurements increases as the distribution range increases. Equation for determination of the measurement number, R _n-1 the distribution range of the previous measurement value, indicated the distribution range of the current measurement value R _n, the number of measurements adjusted by n Tosureba following formula It is. If R _n <R _n-1 n = (R _n / R _n-1 ) 15 + 20 ... (3) If R _n ≧ R _n-1 n = (R _n / R _n-1 ) 7.5 + 27.5. (4) However, since the number of measurements n is only a positive integer, in the above formula, if n is a rational number, it is only necessary to round off the decimal point. In addition, when the variation in the distribution of the measured amount of received light exceeds a predetermined value, an alarm may be output, so that the user can be notified of a large change in the amount of received light of the photoelectric sensor 1 or the like. Become. ◎ An example of exception handling exception processing algorithm, where the measured value distribution range R _n of the current measured value exceeds three times the distribution range R _n-1 of the previous target of exception processing. This exception processing is ignored as the number of occurrences of the measurement value to be subjected to the exception processing as described above decreases, and as the number increases, the weight as correction data is increased. In this case, in detail, when the measured value of the amount of light received by the light receiving element 5 is significantly different from the previous measured value, the measured value is not allowed to participate in the calculation and distribution of the average value, or the measured value is The weight given to the calculation of the average value and the distribution frequency is reduced, and the measurement value is given to the calculation of the average value and the distribution frequency with an increase in the number of times that the measurement value greatly different from the previous measurement value is measured. Increase weight. Next, as the number of occurrences of the measurement value subject to exception processing increases, it is calculated with the value itself, and as the number of times decreases, it is processed smaller and processed. A calculation example of a method of changing the weight when the average value of the measurement values of the exception target is later added to the entire measurement data is described below. Note that, in this calculation example, during n measurements, k
It is assumed that there are three exceptional measurement values E.

_[E> A n-1 + _3R For _{n-1] Ae = [{E 1 + E} 2 + ... E k) / k - (A n-1 + 3R n-1)} (k / n)} + (A _n-1 + 3Rn _-1 ) (5) {E = Ae · k · (k / n) (6) {A _n = (ΣE + ΔM) / {k (k / n) + (nk)} (7) where ΔE is the sum of the measured values other than the exception target. At this time, the minimum value of the measured values within + R = Ae−R = 3R _n-1

[E <A _n-1 -3R _n-1 ] Ae = (A _n-1 -3R _n-1 )-[｛(A _n-1 -3R _n-1 )-(E ₁ + E ₂ + ... (E _k ) / k｝ (k / n)] (8) ΣE = Ae · k · (k / n) (9) ∴A _n = (ΣE + ΣM) / ｛k (k / n) + (n− k)｝ (10) At this time, the maximum value of the measured value within + R = 3R _n-1 −R = Ae (5) and (8) are the previous measured value distribution ranges of the measured value E of the exception target. By multiplying the average value of the deviation from by (k / a), the calculation is performed so as to approach the value as the number of appearances k of the measurement value of the exception target increases.
If it is desired to change the degree of weighting with respect to the number of appearances k of the measurement value E of the exception target, processing such as raising (k / n) to the power may be performed. Equations (6) and (9) are obtained by calculating the total sum of the measurement values E of the exception target, and by multiplying the number k of these values by (k / n), the measurement value E of the exception target is obtained. As the number k of data increases, the measured value approaches the actual measured value, and as the number k decreases, the probability of being ignored (disregarded) increases. Equations (7) and (10) represent the average of the weights of the entire measured value after processing the exceptional measured value E in the above processing. When a large number of exceptional measured values exceed a predetermined value, an alarm may be output, and this allows the user to be notified of changes in the external environment. Next, a case will be described in which the above-described process of correcting the change in the amount of received light is performed using fuzzy inference. By using the fuzzy inference, the processing algorithm system is simple and easy to understand, and it is possible to incorporate a process suitable for the situation in a general-purpose manner and to improve the responsiveness. A processing algorithm for light amount variation correction using fuzzy inference will be described separately for sensitivity correction, measurement frequency adjustment, and exceptional disposal, as in the above-described embodiment. ◎ Fuzzy inference algorithm for sensitivity correction An example of fuzzy inference rules for sensitivity correction (10)
To (15). At input requirements for this fuzzy inference, whether all of the measurement values of the average value A _n and the current light reception amount of the measured value of the light receiving amount is included all the variations set of previous measurement value (distribution range) The output is the gain change rate ΔG. In the fuzzy inference rules shown in Equations (11) and (15), the measured value of the received light amount this time exceeds the variation range of the measured value of the previous received light amount. The feedback control by gain adjustment is performed so that the value returns to the value. In the fuzzy inference rules shown in the equations (12) and (14), the measured value of the received light amount is the measured value of the previous received light amount. Therefore, the gain change rate is reduced and the feedback control is performed slightly. For example, in the case of the fuzzy inference rule of Expression (11),
This means that if the average value is considerably reduced and the current measurement data exceeds the range of the variation of the previous measurement data, the gain should be considerably increased. Figure 7 is an input membership function of the mean value A _n of received light amount measurement value, Figure 8 shows the output membership function of the gain change rate .DELTA.G. For example, 8 if at bit CPU performs processing, minimum value of the average value A _n of received light amount measurement value (NL) is 1 and the maximum value (PL) 256, and the hand when the change of the average value is almost no ( ZR) is the previous average value _An-1 . The membership function of the gain change rate ΔG is calculated as NL, ZR as the correction value and the change rate value for the current gain so as to surely return to the average value of the previous measurement value even if the average value of the measurement values changes. , PL value. ◎ A fuzzy inference algorithm for adjusting the number of measurements The example of the fuzzy inference rules for adjusting the number of measurements for calculating the average value and the distribution range of the measured values is shown in (16) to (1).
8) shown in equation. If R _n / R _n-1 = NL then Δn = NL ... (16) If R _n / R _n-1 = ZR then Δn = ZR ... (17) If R _n / R _n-1 = PL then Δn = PL ... (18) at the input requirements to the fuzzy inference rule is the ratio of the distribution range R _n of current measurement values on the distribution range R _n-1 of the previous measurement value (R _n / R _n-1), an output Is a variation Δn as an adjustment frequency of the number of times of measurement. This indicates how many times the number of measurements is increased or decreased from the current number of measurements. For example, in the case of the fuzzy inference rule shown in the expression (16), the instruction is “if the distribution range of the current measurement data is considerably smaller than that of the previous measurement, reduce the number of measurements considerably”. Equation (18) does not change the number of measurements because there is almost no change in the distribution range.
Since the change in the distribution range is quite large, it means that the number of measurements can be significantly increased. That is, the fuzzy inference rule is set so that the number of measurements is increased as the distribution range is increased. Figure 9 shows the input membership functions of the distribution range ratio R _n / R _n-1. The minimum value (NL) of the distribution range ratio is R _n =
It becomes 0 from 0, and becomes 1 when it hardly changes (ZR). The maximum value (PL) is set to, for example, a triple of the previous distribution range Rn _-1 . The maximum value may be set to an optimal value according to the application. FIG. 10 shows an output membership function indicating the degree of adjustment Δn of the number of measurements. Regarding the adjustment degree Δn,
In this case, NL = −15, ZR = 0, and PL = 15. ◎ Fuzzy inference algorithm for exception processing An example of a fuzzy inference rule for sensitivity correction in exception processing is shown in equations (19) to (23). The input requirement in this fuzzy inference is that the measured values considered as exceptions, for example, the average value Ae of the measured values exceeding three times the range of the distribution range of the previous measured value, and the appearance of the measured value of the exception target The number of times is k, and the output is the gain change rate ΔG ₂ . The gain change rate ΔG ₂ is obtained after the gain adjustment by the above-described sensitivity correction. Therefore, in this case, it is assumed that the gain change rate ΔG of the above-described sensitivity correction does not include the correction component based on the measurement value of the exception target. If Ae = PL and k = PL then ΔG 2 = NL ... (19) If Ae = PL and k = NL then ΔG 2 = ZR ... (20) If Ae = ZR then ΔG 2 = ZR ... (21) If Ae = NL and k = NL then ΔG ₂ = ZR ... (22) If Ae = NL and k = PL then ΔG ₂ = PL ... (23) In this fuzzy inference rule, the average value Ae of the measured values of the exception target is The gain is corrected so as to be smaller as the value is larger.
Is smaller, the magnitude of the correction is smaller. That is, as the number of appearances k of the measurement value of the exception target is smaller, the influence of the measurement value of the exception target is reduced.
For example, in the case of the fuzzy inference rule shown in Expression (19), "if the average value of the measured values of the exceptional targets is quite large and the number of appearances thereof is also quite large, lower the gain considerably." Command. This fuzzy inference enables reliable light quantity correction even if there is a sudden abnormal measurement value. FIG. 11 shows the input membership function of the average value Ae of the measurement values of the exception target, and FIG. 12 shows the number of appearances k of the measurement values of the exception target.
FIG. 13 shows the gain correction rate Δ
Shows each output membership function of G _2. In addition,
The input membership function shown in FIG. 11 and the output membership function shown in FIG. 14 are the same as the input membership function and the output membership function in the fuzzy inference algorithm regarding the sensitivity correction described above. . This is a case where the minimum value (NL) does not appear at all in the number of appearances k of the measurement value of the exception target, which is zero. The maximum value (PL) is the maximum number of measurements at that time, and the intermediate value is ZR. To change the weight for the number of times, the value of ZR may be manipulated. It should be noted that the fuzzy inference algorithm relating to the exception processing and the fuzzy inference algorithm relating to the sensitivity correction are combined, and the fuzzy inference includes, for example, `` There is a considerable change in the light amount, but the measurement value of the exception target includes a little,
Change the sensitivity slightly larger ". As described above, by setting the fuzzy inference rules and membership functions, a simple algorithm can be used.
As a result, a stable correction of a change in the amount of light is performed. << Effects of the Invention >> As understood from the above description, according to the method for correcting a change in the amount of received light of the photoelectric sensor according to the present invention, a change in the distribution of the measured values is considered in addition to the change in the average of the measured values of the received light. As a result, the received light amount correction value is determined, so that a change in the received light amount due to the temperature characteristics of the light emitting element, contamination of the lens surface, and the like can be appropriately compensated. Further, when the measured value of the received light amount is significantly different from the previous measured value, that is, when a sudden spontaneous change occurs, the average value and the distribution frequency of the measured value are updated according to the frequency of the sudden change. Even if a sudden one-off change occurs, the obstacle to the correction for the original change is reduced as much as possible, and the light amount correction with high reliability is performed for this as well. Also, the number of measurements for calculating the average value is determined in accordance with the change in the distribution range of the measured values, so that the control system is prevented from oscillating due to the dispersion of the measured values, and stable correction is performed. Become.

[Brief description of the drawings]

FIG. 1 is a block diagram showing an embodiment of a photoelectric sensor and a received light amount change correction control device used for implementing a method of correcting a received light amount change of a photoelectric sensor according to the present invention, and FIG. 2 is a block diagram of a light-on type photoelectric sensor. FIG. 3 is a graph showing a change in the amount of received light of a dark-on type photoelectric sensor, FIG. 4 is a graph showing an average value of measured values of the amount of received light a predetermined number of times, and a distribution range thereof. 5 and 6 are graphs respectively showing the difference between the distribution range of the present measurement value and the distribution range of the previous measurement value, and FIG. 7 is the average of the measurement values which is an input requirement in fuzzy inference regarding sensitivity correction. 8 is a graph showing an output membership function of a gain change rate as an output thereof, and FIG. 9 is an input requirement in fuzzy inference regarding adjustment of the number of measurements. Graph showing the input membership function of the distribution range of measured values, Fig. 10 is a graph showing the output membership function of the degree of adjustment of the number of measurements that is the output, and Fig. 11 is the input requirement in fuzzy inference regarding exception handling Is a graph showing the input membership function of the average value of the measured values of the exception target, and FIG. 12 is a graph showing the input membership function of the number of occurrences of the exceptional measured value, and FIG.
The figure is a graph showing the output membership function of the gain change rate which is the output. 1 ... photoelectric sensor 3 ... light emitting element 5 ... light receiving element 11 ... variable gain amplifier 17 ... CPU 19 ... signal processing circuit

────────────────────────────────────────────────── ─── Continued on the front page (72) Inventor Yasuhiro Tsutsumi Omron Co., Ltd. (10) Hanazono Dodocho, Ukyo-ku, Kyoto-shi, Kyoto (56) References JP-A-1-94283 (JP, A) JP-A-1- 286655 (JP, A) Japanese Utility Model 60-113573 (JP, U) JP-B 62-47262 (JP, B2) (58) Fields investigated (Int. Cl. ⁶ , DB name) G01S 7/48-7 / 51 G01S 17/00-17/95 H03K 17/78 G01V 8/10

Claims (3)

(57) [Claims] 1. A method for measuring the amount of light received by a light receiving element of a photoelectric sensor,
The average value and distribution based on the predetermined number of measurements are detected for each predetermined number of measurements, and the average value of the current measurement value and the average value of the previous measurement value are compared, and the distribution of the current measurement value and the previous measurement are compared. A method for correcting a change in the amount of received light of a photoelectric sensor, comprising comparing a distribution of values and a light-receiving amount correction value for compensating for a change in the amount of received light. 2. When the measured value of the amount of light received by the light receiving element is significantly different from the previous measured value, the measured value is not allowed to participate in the calculation and distribution of the average value, or the measured value is calculated based on the average value. The weight given to the calculation and the distribution frequency is reduced, and the weight given to the calculation of the average value and the distribution frequency by the measurement value is increased with the increase in the number of times that the measurement value greatly different from the previous measurement value is measured. 2. The method according to claim 1, wherein the received light amount change of the photoelectric sensor is corrected. 3. When the distribution range of the measurement value is expanded as compared with the previous time, the average value and the number of measurements for detecting the distribution are increased, and when the distribution range is reduced as compared with the previous time, the average value is reduced. 3. The method according to claim 1, wherein the number of measurements for detecting the distribution is reduced.