Ion exchange furnace operation monitoring method and platform for self-focusing lens preparation

Abstract

The invention belongs to the field of ion exchange furnace control, and particularly discloses an ion exchange furnace operation monitoring method and platform for self-focusing lens preparation. According to the method, three statistical models including center thermocouple temperature monitoring, upper-layer thermocouple temperature monitoring and lower-layer thermocouple temperature monitoring are provided, and a combined monitoring strategy is provided for the three monitoring statistical models, so that the monitoring method can early warn system abnormity in the operation process of the ion exchange furnace; the quality and safety problems in the operation of the ion exchange furnace are alarmed in advance, so that workers can check and eliminate production hidden dangers in an early stage, the product quality is improved, and the stability and reliability of production are ensured.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of ion exchange furnace control, and particularly relates to an ion exchange furnace operation monitoring method and platform for self-focusing lens preparation. BACKGROUND

[0002] The self-focusing lens is an optical element, which can be applied to fiber coupling, fiber collimator, wavelength division multiplexer and other fiber communication equipment. Ion exchange is an important link in the production of self-focusing lenses. The temperature of the ion exchange furnace needs to be precisely controlled during ion exchange, otherwise the yield and product quality will be affected.

[0003] In the existing ion exchange temperature monitoring, a single thermocouple (such as a center thermocouple) is generally arranged at the center or top of the molten salt. Due to factors such as thermal convection difference of the molten salt at different positions and uneven distribution of the heating source, the temperature field actually presents a non-uniform distribution (especially the vertical gradient is significant). Single-point measurement cannot reflect the overall temperature state. When the actual temperature deviates from the detection value, the system cannot be adjusted in time, resulting in inconsistent ion exchange rate, which may cause abnormal refractive index distribution of the lens or bending of the rod. Therefore, there is an urgent need for a monitoring method capable of effectively detecting the temperature state of the ion exchange furnace. SUMMARY

[0004] In order to solve the problems of the prior art, the application aims to provide an ion exchange furnace operation monitoring method for self-focusing lens preparation, which can timely alarm the system temperature abnormality in the ion exchange furnace operation process, realize early warning of quality and safety problems in the ion exchange furnace operation, so that the staff can early troubleshoot and eliminate production hazards, thereby improving product quality and ensuring the stability and reliability of production.

[0005] In order to achieve the above purpose, the technical scheme adopted by the application is:

[0006] An ion exchange furnace operation monitoring method for self-focusing lens preparation, which is realized based on the following steps:

[0007] S1. Assuming that the production process is in a statistically stable state, the temperature characteristic value detected by the center thermocouple is a random variable and obeys a normal distribution, and a center thermocouple temperature monitoring model is established;

[0008] S2. Assuming that the production process is in a statistically stable state, the temperature detection characteristic value of the upper thermocouple and the temperature detection characteristic value of the lower thermocouple are both random variables obeying a normal distribution; and the upper thermocouple temperature monitoring model and the lower thermocouple temperature monitoring model are respectively established according to the construction method of the center thermocouple temperature monitoring model in step S1;

[0009] S3. Collecting the upper thermocouple temperature detection characteristic value, the center thermocouple temperature detection characteristic value and the lower thermocouple temperature detection characteristic value at fixed time intervals in the production process each time;

[0010] S4. Calculating the sample mean of the center thermocouple temperature, if the sample mean of the center thermocouple temperature does not exceed the control limit, the production process is in a state of statistical control; if the sample mean exceeds the control limit, it is judged that there is a system abnormal factor affecting the production process, and the production process is in a state of statistical out-of-control, and the alarm is given through the sound and light alarm system;

[0011] S5. Calculating the sample mean of the upper thermocouple temperature and the sample mean of the lower thermocouple temperature respectively; if the two sample means exceed the control limit, the system abnormal factors of the corresponding resistance wire related equipment are checked, and the hidden danger is timely eliminated; if the two sample means do not exceed the control limit, other system abnormal factors of the production equipment are checked, and the hidden danger is timely eliminated.

[0012] Further, the step S1 of establishing the center thermocouple temperature monitoring model specifically includes the following contents:

[0013] Suppose that the production process is in a state of statistical stability, the center thermocouple detection temperature characteristic value is a random variable, denoted as X; the random variable X obeys the normal distribution, then

[0014] X ~ N(μ,σ 2 )(1);

[0015] Wherein, N represents the normal distribution, μ is the mean of the temperature random variable X, and σ is the standard deviation of the random variable X;

[0016] The probability density function of X is:

[0017]

[0018] Wherein, x is a real number, then the probability is:

[0019] P{μ-3σ<X<μ+3σ}=99.73% (3);

[0020] Let the lower control limit and the upper control limit be LCL and UCL respectively, then:

[0021]

[0022] That is, if X obeys the normal distribution, the probability of the possible value of X exceeding the control limit is only 0.27%, which is a small probability random event;

[0023] Suppose that (X1,X2,…,X n ), 3≤n≤5 are samples from the random variable X, then the sample mean statistic X is:

[0024]

[0025] In the formula, X i Let X represent the i-th variable in the sample of random variable X;

[0026] Suppose we take m samples, where m ≥ 25, and each sample contains n observations; let the means of the m samples be respectively The overall sample mean is:

[0027]

[0028] In the formula, It is an estimator of μ; the range R of m samples i for:

[0029] R i =X max -X min , i = 1, 2, ... m (7);

[0030] In the formula, X max X is the maximum observation value of the i-th sample. min It is the minimum observation value of the i-th sample; the mean sample range for:

[0031]

[0032] The control limits for X are:

[0033]

[0034] in, To control the upper limit, express The centerline, To control the lower limit; A2 is a constant related to the sample size n; when n=3, A2=1.023; when n=4, A2=0.729; when n=5, A2=0.577.

[0035] Furthermore, the temperature sampling in step S3 specifically includes the following:

[0036] Under statistically stable conditions in the production process, n temperature detection characteristic values ​​of the central thermocouples are collected at fixed time intervals as sample observations, where 3 ≤ n ≤ 5; a total of m sample observations are collected, then (x i1 ,x i2 ,…,x in ), i = 1, 2, ..., m; where m ≥ 25, x ij Let j represent the j-th observation of the i-th sample collection, where j = 1, 2, ..., n.

[0037] Further, the real-time monitoring of the center thermocouple in step S4 specifically comprises the following contents:

[0038] S4-1. Calculate the upper control limit and the lower control limit:

[0039] Sample mean Calculation:

[0040]

[0041] Calculate the total mean according to formula (6)

[0042] Calculate the average sample range according to formula (8)

[0043] Calculate the upper control limit according to formula (9) And the lower control limit

[0044] S4-2. Determine the early warning:

[0045] After calculating the upper control limit and the lower control limit, n center thermocouple temperature detection characteristic values are collected at fixed time intervals during the production process, and the sample mean is calculated according to formula (10) If It does not exceed the control limit, the production process is in a state of statistical control; if It exceeds the control limit, that is, a small probability event occurs, it is judged that the production process is affected by a system abnormal factor and is in a state of statistical loss of control, and an audible and visual alarm system is used for alarm.

[0046] Correspondingly, the application also provides an ion exchange furnace operation monitoring platform for self-focusing lens preparation, which is realized based on the above-mentioned ion exchange furnace operation monitoring method, and comprises a temperature detection module, a data processing system and an audible and visual alarm system; the temperature detection module is used for real-time detection of the internal temperature of the ion exchange furnace, and comprises an upper thermocouple, a center thermocouple and a lower thermocouple; the data processing system is used for analyzing and processing the temperature data detected by the thermocouples; and the audible and visual alarm system is used for audible and visual alarm when there is a system abnormality in the production process.

[0047] Further, the center thermocouple is vertically installed in the molten salt in the furnace shell, and the upper thermocouple and the lower thermocouple are horizontally installed in the furnace shell to detect the heating temperature of the upper and lower electric furnace filaments for heating the molten salt.

[0048] The application has the beneficial effects that:

[0049] The application provides an ion exchange furnace operation monitoring method for self-focusing lens preparation, which provides three statistical models including center thermocouple temperature monitoring, upper layer thermocouple temperature monitoring and lower layer thermocouple temperature monitoring, and proposes a joint monitoring strategy for the three monitoring statistical models, so that the monitoring method can early warn system abnormalities in the ion exchange furnace operation process, realize early warning of quality and safety problems in the ion exchange furnace operation, enable workers to early check and eliminate production hazards, thereby improving product quality and ensuring production stability and reliability. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The figure is a joint monitoring strategy diagram of the three monitoring statistical models of the application. DETAILED DESCRIPTION

[0051] The principles and characteristics of the application will be described below in combination with the drawings, and the examples are only used to explain the application and not to limit the use range of the application.

[0052] In the specific implementation of the application, a center thermocouple is arranged in a vertical manner in the center position of the ion exchange furnace shell and inserted into the molten salt. An upper layer resistance wire and a lower layer resistance wire are arranged in the furnace shell for heating the molten salt, and an upper layer thermocouple and a lower layer thermocouple are installed for detecting the temperature of the upper layer resistance wire and the lower layer resistance wire, respectively.

[0053] In the production process, the ion exchange furnace temperature will change due to the influence of random factors and system factors. When the production process is only affected by a small random factor, the production process remains stable, and the furnace temperature is regarded as a statistical controlled state. When the production process is affected by system factors, the furnace temperature will have abnormal fluctuations, which is regarded as a statistical out-of-control state. The furnace temperature characteristics in the statistical stable state are random variables subject to a certain probability distribution, and the distribution can be estimated by statistical methods according to the observation data obtained in a long stable state. After the distribution is determined, the mathematical model of the furnace temperature characteristics is also determined. In order to test whether the subsequent production process is also in a controlled state, it is necessary to test whether the furnace temperature characteristics conform to this mathematical model. Therefore, the furnace temperature is detected in the production process, and the quality characteristics are calculated. If the numerical value conforms to the mathematical model, the production process is considered normal, otherwise, it is considered that there is a certain systematic change in the production process, or the process is out of control. At this time, the cause needs to be found out and eliminated to restore normal production and prevent the out-of-control state from developing.

[0054] Based on the above facilities, the ion exchange furnace operation state monitoring method provided by the application includes three temperature monitoring models: a center thermocouple temperature monitoring model, an upper layer thermocouple temperature monitoring model and a lower layer thermocouple temperature monitoring model, and a joint monitoring strategy is formed for the three monitoring models.

[0055] AsFigure 1 The specific embodiments of the present application are as follows:

[0056] An ion exchange furnace operation monitoring method for self-focusing lens preparation is realized based on the following steps:

[0057] S1. Assuming that the production process is in a statistically stable state, the central thermocouple detects the temperature characteristic value as a random variable and obeys the normal distribution, and a central thermocouple temperature monitoring model is established. Specifically, it includes the following contents:

[0058] Assuming that the production process is in a statistically stable state, the central thermocouple detects the temperature characteristic value as a random variable, denoted as X; the random variable X obeys the normal distribution, then

[0059] X~N(μ,σ 2 )(1);

[0060] Where N represents the normal distribution, μ is the mean of the temperature random variable X, and σ is the standard deviation of the random variable X;

[0061] The probability density function of X is:

[0062]

[0063] Where x is a real number, then the probability is:

[0064] P{μ-3σ<X<μ+3σ}=99.73% (3);

[0065] Let the control lower limit and the control upper limit be LCL and UCL, respectively, then:

[0066]

[0067] That is, if X obeys the normal distribution, the probability of the possible value of X exceeding the control limit is only 0.27%, which is a small probability random event;

[0068] Let (X1, X2, …, X n ), 3≤n≤5 be a sample from the random variable X, then the sample mean statistic is:

[0069]

[0070] In the formula, X i represents the i-th variable in the sample of the random variable X;

[0071] Let m samples be taken, m≥25, and each sample contains n observation values; let the mean values of the m samples be Then the total sample mean is:

[0072]

[0073] In the formula, It is an estimator of μ; the range R of m samples i for:

[0074] R i =X max -X min , i = 1, 2, ... m (7);

[0075] In the formula, X max X is the maximum observation value of the i-th sample. min It is the minimum observation value of the i-th sample;

[0076] Mean Sample Range for:

[0077]

[0078] The control limits are:

[0079]

[0080] in, To control the upper limit, express The centerline, To control the lower limit; A2 is a constant related to the sample size n;

[0081] When n = 3, A2 = 1.023; when n = 4, A2 = 0.729; when n = 5, A2 = 0.577.

[0082] S2. Assume the production process is in a statistically stable state, and the temperature detection characteristic values ​​of the upper thermocouple and the lower thermocouple are both random variables that follow a normal distribution; and establish the upper thermocouple temperature monitoring model and the lower thermocouple temperature monitoring model respectively according to the construction method of the central thermocouple temperature monitoring model in step S1.

[0083] S3. During the production process, temperature detection characteristic values ​​of the upper thermocouple, the center thermocouple, and the lower thermocouple are collected at fixed time intervals. Temperature sampling specifically includes the following:

[0084] Under statistically stable conditions in the production process, n (3≤n≤5) temperature detection characteristic values ​​of the central thermocouples are collected as sample observations at fixed time intervals; a total of m sample observations are collected, then (x i1 ,x i2 ,…,x in), i = 1, 2, …, m; where m > 25, x ij represents the jth observation of the ith sample, j = 1, 2, …, n.

[0085] S4. Calculate the sample mean of the center thermocouple temperature according to formula (10), if the sample mean of the center thermocouple temperature does not exceed the control limit, the production process is in a state of statistical control; if the sample mean exceeds the control limit, it is judged that the production process is affected by a system abnormal factor, and is in a state of statistical out-of-control, and an alarm is given through the sound and light alarm system. The real-time monitoring of the center thermocouple specifically includes the following contents:

[0086] S4-1. Calculate the upper control limit and the lower control limit:

[0087] Sample mean Calculation:

[0088]

[0089] Calculate the total mean according to formula (6)

[0090] Calculate the average sample range according to formula (8)

[0091] Calculate the upper control limit according to formula (9) and the lower control limit

[0092] S4-2. Judgment of early warning:

[0093] After calculating the upper control limit and the lower control limit, n center thermocouple temperature detection characteristic values are collected at fixed time intervals in the production process each time, and the sample mean is calculated according to formula (10) If does not exceed the control limit, the production process is in a state of statistical control; if exceeds the control limit, i.e. a small probability event occurs, it is judged that the production process is affected by a system abnormal factor, and is in a state of statistical out-of-control, and an alarm is given through the sound and light alarm system.

[0094] S5. Calculate the sample mean of the upper layer thermocouple temperature and the sample mean of the lower layer thermocouple temperature according to formula (10) respectively; if the two sample means exceed the control limit, the system abnormal factor of the corresponding resistance wire related equipment is checked, and the hidden danger is timely eliminated; if the two sample means do not exceed the control limit, other system abnormal factors of the production equipment are checked, and the hidden danger is timely eliminated.

[0095] Correspondingly, the application further provides an ion exchange furnace operation monitoring platform for self-focusing lens preparation, which is realized based on the ion exchange furnace operation monitoring method and comprises a temperature detection module, a data processing system and an audible and visual alarm system; the temperature detection module is used for detecting the internal temperature of the ion exchange furnace in real time and comprises an upper thermocouple, a center thermocouple and a lower thermocouple; the data processing system is used for analyzing and processing the temperature data detected by the thermocouples; and the audible and visual alarm system is used for audible and visual alarm when there is system abnormality in the production process.

[0096] The center thermocouple is vertically installed in the molten salt in the furnace shell and is used for detecting the temperature of the center molten salt; and the upper thermocouple and the lower thermocouple are horizontally installed in the furnace shell and are respectively used for detecting the heating temperature of the upper and lower electric furnace filaments for heating the molten salt.

[0097] Simulation experiment:

[0098] When the production process is in a statistically stable state, the characteristic value of the temperature detected by the center thermocouple is a random variable, denoted as X, and the random variable X obeys a normal distribution. A center thermocouple temperature monitoring model is established, and the specific implementation steps are as follows:

[0099] 1. Every 5 minutes, 3 center thermocouple temperature detection values are randomly collected, and the average of the 3 temperatures is calculated. A total of 75 center thermocouple temperature detection values are collected, as shown in the following table.

[0100] Sequence No. 1 2 3 4 5 6 7 8 9 10 Temperature 544 546 548 545 546 547 546 547 548 547 Sequence No. 11 12 13 14 15 16 17 18 19 20 Temperature 547 547 547 546 547 548 548 548 548 548 Sequence No. 21 22 23 24 25 26 27 28 29 30 Temperature 548 547 548 549 548 544 546 543 548 546 Sequence No. 31 32 33 34 35 36 37 38 39 40 Temperature 543 545 548 546 546 546 545 547 549 548 Sequence No. 41 42 43 44 45 46 47 48 49 50 Temperature 548 548 547 548 549 549 549 547 546 548 Sequence No. 51 52 53 54 55 56 57 58 59 60 Temperature 547 543 548 546 543 545 548 546 546 546 Sequence No. 61 62 63 64 65 66 67 68 69 70 Temperature 545 547 549 548 548 548 547 548 549 549 Sequence No. 71 72 73 74 75 Temperature Sequence No. Temperature 549 547 546 548 547

[0101] 2. The total sample mean is calculated according to formula (6) as follows:

[0102]

[0103] 3. The average sample range is calculated according to formula (8) as follows:

[0104]

[0105] 4. The upper control limit and the lower control limit

[0106]

[0107] At this time, n = 3 and A2 = 1.023.

[0108] In the production process, three center thermocouple temperature detection values are randomly sampled every 5 minutes, and the sample mean of the three temperatures is calculated, if the sample mean does not exceed the control limit, the production process is in a state of statistical control. If the sample mean exceeds the control limit, it is judged that there is a system abnormal factor affecting the production process, and the production process is in a state of statistical out-of-control, and the alarm is given through the sound and light alarm system.

[0109] The application provides three statistical models including center thermocouple temperature monitoring, upper thermocouple temperature monitoring and lower thermocouple temperature monitoring, and provides a joint monitoring strategy of the three monitoring statistical models, which can early warn system abnormalities in the running process of the ion exchange furnace, realize early warning of quality and safety problems in the running of the ion exchange furnace, enable workers to early check and eliminate production hazards, thereby improving product quality and ensuring the stability and reliability of production.

[0110] Obviously, the above-described embodiments are only part of the embodiments of the present application, rather than all the embodiments, and the preferred embodiments of the present application are given in the drawings, but do not limit the patent scope of the present application. The present application can be realized in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing specific embodiments, or equivalently replace some technical features therein. Any equivalent structure made by using the content of the specification and drawings, directly or indirectly applied to other related technical fields, is also within the patent protection scope of the present application.

Claims

1. A method for monitoring the operation of an ion exchange furnace for the production of self-focusing lenses, characterized in that: The method is realized based on the following steps: S1. Assuming that the production process is in a state of statistical stability, the temperature characteristic value detected by the center thermocouple is a random variable and obeys a normal distribution, and a center thermocouple temperature monitoring model is established; S2. Assuming that the production process is in a state of statistical stability, the temperature detection characteristic value of the upper thermocouple and the temperature detection characteristic value of the lower thermocouple are both random variables obeying a normal distribution; and the upper thermocouple temperature monitoring model and the lower thermocouple temperature monitoring model are respectively established according to the method for establishing the center thermocouple temperature monitoring model in step S1; S3. In the production process, the temperature detection characteristic value of the upper thermocouple, the temperature detection characteristic value of the center thermocouple and the temperature detection characteristic value of the lower thermocouple are collected at a fixed time interval each time; S4. The sample mean of the center thermocouple temperature is calculated, if the sample mean of the center thermocouple temperature does not exceed the control limit, the production process is in a state of statistical control; if the sample mean exceeds the control limit, it is judged that there is a system abnormal factor affecting the production process, and the production process is in a state of statistical out-of-control, and an audible and visual alarm system is alarmed; S5. The sample mean of the upper thermocouple temperature and the sample mean of the lower thermocouple temperature are respectively calculated; if the two sample means exceed the control limit, the system abnormal factor of the corresponding resistance wire related equipment is investigated, and the hidden danger is timely eliminated; if the two sample means do not exceed the control limit, other system abnormal factors of the production equipment are investigated, and the hidden danger is timely eliminated.

2. The ion exchange furnace operation monitoring method for self-focusing lens fabrication according to claim 1, characterized by: The establishment of the center thermocouple temperature monitoring model in step S1 specifically includes the following contents: Assuming that the production process is in a state of statistical stability, the temperature characteristic value detected by the center thermocouple is a random variable, denoted as X; the random variable X obeys a normal distribution, then X ~ N (μ, σ 2 )(1); Wherein, N represents a normal distribution, μ is the mean of the temperature random variable X, and σ is the standard deviation of the random variable X; The probability density function of X is: Wherein, x is a real number, then the probability is: P{μ-3σ<X<μ+3σ}=99.73% (3); Denote the lower control limit and the upper control limit as LCL and UCL, respectively, then: That is, if X obeys a normal distribution, the probability of the possible value of X exceeding the control limit is only 0.27%, which is a small probability random event; Let (X1, X2, …, Xn) be a sample from a random variable X, 3 ≤ n ≤ 5, then the sample mean statistic n is:​ wherein X i denotes the i-th variable in a sample of random variables X; Let take m samples, m ≥ 25, each sample contains n observations; Let the mean of the m samples respectively The total mean of the samples is: wherein is an estimate of μ; the range R of the m samples i is: R i = X max - X min , i = 1, 2,... m (7); where X max is the maximum observed value of the ith sample, X min is the minimum observed value of the ith sample; average sample range is: The control limits are: wherein is the upper control limit, CL X represents the center line, is the lower control limit; A2 is a constant related to the sample size n; when n = 3, A2 = 1.023; when n = 4, A2 = 0.729; when n = 5, A2 = 0.

577.

3. The ion exchange furnace operation monitoring method for self-focusing lens fabrication according to claim 2, wherein: The temperature sampling in step S3 specifically includes the following contents: Under the condition that the production process is in a state of statistical stability, n center thermocouple temperature detection characteristic values are collected as sample observation values at a fixed time interval each time, 3≤n≤5; The sample observation values are collected m times, (x i1 ,x i2 ,…,x in ), i = 1, 2, …, m; where m > 25, x ij denotes the jth observation of the ith collected sample, j = 1, 2,..., n.

4. The ion exchange furnace operation monitoring method for self-focusing lens fabrication according to claim 3, wherein: The center thermocouple real-time monitoring in step S4 specifically includes the following contents: S4-1. Calculate the control upper limit and the control lower limit: Sample mean Calculation: The total mean is calculated according to formula (6) The average sample range is calculated according to formula (8) The control upper limit is calculated according to formula (9) and the control lower limit S4-2. Judgment of early warning: After the upper and lower control limits are calculated, n characteristic values of the central thermocouple temperature are collected at fixed time intervals during the production process, and the sample mean is calculated according to formula (10) If If the control limit is not exceeded, the production process is in a state of statistical control; if If the control limit is exceeded, i.e. a small probability event occurs, it is judged that there is a systematic abnormal factor affecting the production process, which is in a state of statistical out-of-control, and an alarm is given through the sound and light alarm system.

5. An ion exchange furnace operation monitoring platform for self-focusing lens fabrication, the ion exchange furnace operation monitoring platform is implemented based on the ion exchange furnace operation monitoring method for self-focusing lens fabrication of claim 4, characterized in that: The temperature detection module is used for real-time detection of the internal temperature of the ion exchange furnace, including the upper thermocouple, the center thermocouple and the lower thermocouple; the data processing system is used for analyzing and processing the temperature data detected by the thermocouples; and the audible and visual alarm system is used for audible and visual alarm when there is a system abnormality in the production process.

6. The ion exchange furnace operation monitoring platform for selfoc lens fabrication of claim 5, wherein: The central thermocouple is vertically installed in the molten salt inside the furnace shell, and the upper and lower thermocouples are horizontally installed in the furnace shell to detect the heating temperature of the upper and lower electric furnace filaments for heating the molten salt, respectively.

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