A method and system for online temperature monitoring of processing flow

Abstract

The invention relates to the technical field of machining process monitoring, and specifically discloses an online temperature monitoring method and system for a machining process, comprising: a temperature sensor, used for real-time monitoring of temperature data of a position point corresponding to each temperature sensor; an infrared image acquisition module, used for acquiring real-time infrared image information of PAN raw silk in each processing chamber; a temperature control data docking port, used for acquiring real-time temperature control data of each processing chamber; a circulating fan control data docking port, used for acquiring real-time circulating fan control data of each processing chamber; and a monitoring and early warning module, used for judging the temperature state of each reaction chamber according to the temperature data monitored by the temperature sensor and the real-time infrared image information, and performing abnormal analysis on the machining state according to the comparison between the judgment result and the real-time temperature control data and the circulating fan control data.

Description

Technical Field

[0001] The present invention relates to the technical field of machining process monitoring, and in particular to a machining process online temperature monitoring method and system. Background Art

[0002] Temperature monitoring is a common control indicator in the processing and production process. Deviations and abnormalities in temperature control will have a great impact on the quality of the product. For the processing of carbon fiber, it is necessary to pre-oxidize, low-temperature carbonize, high-temperature carbonize and graphitize the pre-treated polyacrylonitrile (PAN) fibers. In this process, it is necessary to accurately detect the ambient temperature state and product temperature state in each processing chamber, and then take timely measures when abnormalities occur to ensure product quality.

[0003] The temperature monitoring method mainly sets a temperature sensor to monitor the temperature in each processing chamber in real time, and then judges the accuracy of temperature control by comparing the temperature value with the set temperature. When the actual temperature in the processing chamber is significantly different from the controlled temperature, an early warning is issued, and the problems with the equipment are then dealt with.

[0004] Although the existing temperature monitoring method can judge the temperature of each processing chamber during the carbon fiber processing process, due to the sensitivity of PAN silk to temperature, the uniformity of the temperature distribution of each processing chamber has a great impact on the consistency of the product. At the same time, the existing temperature judgment method has poor sensitivity for abnormal judgment of temperature control. When there is a large difference between the actual temperature and the controlled temperature, it means that a large impact has been caused on the product, which will cause certain losses. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for online temperature monitoring of a processing flow to solve the following technical problems:

[0006] How to improve the comprehensiveness and sensitivity of temperature monitoring for each reaction chamber.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A processing flow online temperature monitoring system, the system comprising:

[0009] Temperature sensors: each processing chamber is provided with several groups of temperature sensors for real-time monitoring of the temperature data of the corresponding position points of each temperature sensor;

[0010] Infrared image acquisition module, used to collect real-time infrared image information of PAN raw fibers in each processing chamber;

[0011] Temperature control data docking port, used to obtain real-time temperature control data of each processing chamber;

[0012] Circulation fan control data docking port, used to obtain real-time circulation fan control data of each processing chamber;

[0013] The monitoring and early warning module is used to judge the temperature status of each reaction chamber based on the temperature data monitored by the temperature sensor and the real-time infrared image information, and to perform abnormal analysis on the processing status based on the comparison of the judgment result with the real-time temperature control data and circulating fan control data;

[0014] The feedback adjustment module is used to perform feedback adjustment on the temperature control data and the circulating fan control data according to the results of the abnormal analysis.

[0015] Furthermore, the process of determining the temperature state of each reaction chamber includes:

[0016] Obtaining the mean value of the ambient temperature in the reaction chamber and the ambient temperature uniformity coefficient according to the temperature data monitored by the temperature sensor;

[0017] Identify the location of the PAN raw silk in the infrared image information, and obtain the dot matrix temperature mean and dot matrix temperature uniformity coefficient in the identification area according to the preset points;

[0018] The temperature field mean value of the reaction chamber is determined according to the ambient temperature mean value and the lattice temperature mean value, and the temperature field uniformity coefficient of the reaction chamber is determined according to the ambient temperature uniformity coefficient and the lattice temperature uniformity coefficient; the temperature state of each reaction chamber is judged according to the temperature field mean value and the temperature field uniformity coefficient.

[0019] Furthermore, the process of obtaining the ambient temperature uniformity coefficient includes:

[0020] By formula:

[0021]

[0022] Calculate the ambient temperature uniformity coefficient s at the current time point ET (t);

[0023] The process of obtaining the lattice temperature uniformity coefficient includes:

[0024] By formula:

[0025]

[0026] Calculate the temperature uniformity coefficient s of the current time point PT (t);

[0027] The process of obtaining the temperature field average value includes:

[0028] By formula:

[0029]

[0030] Calculate the mean temperature T at the current time point field (t);

[0031] By formula:

[0032]

[0033] Calculate the temperature field uniformity coefficient s at the current time point field (t);

[0034] Where n is the number of temperature sensors in the current reaction chamber, i∈[1,n], ET i (t) is the real-time temperature obtained by the i-th sensor, Get the real-time temperature mean for all sensors, m is the number of points in the identification area, j∈[1,m], PT i (t) is the real-time temperature at the jth point, is the mean temperature of all lattices, μ is the thermal conductivity, γ is the adjustment coefficient, and γ>1.

[0035] Furthermore, the process of analyzing processing anomalies includes:

[0036] By formula:

[0037]

[0038] Calculate the temperature control difference T at the current time point Δ (t);

[0039] The temperature control difference T Δ (t) and the first preset difference interval [T1 Δ low, T1 Δ up] for comparison:

[0040] like Then the temperature of the current reaction chamber is warned;

[0041] If T Δ (t)∈[T1 Δ low, T1 Δ up], then T Δ (t) and the first preset difference interval [T2 Δ low, T2 Δ up] for comparison:

[0042] If T Δ (t)∈[T2 Δ low, T2 Δup], it is judged that the current reaction chamber temperature control state is normal;

[0043] like Then the current reaction chamber temperature control data is feedback-adjusted;

[0044] Where p(t) is the real-time power of the reaction chamber temperature control device, f T is the temperature diffusion function of the reaction chamber, and t1 is a preset fixed period.

[0045] Furthermore, the process of feedback-adjusting the current reaction chamber temperature control data includes:

[0046] Taking t1 as the cycle, the temperature control data of the next cycle is adjusted according to the temperature control difference of the previous cycle. The adjustment process includes:

[0047] By formula:

[0048]

[0049] Calculate and obtain the adjustment amount Δp1 of the reaction chamber temperature control device in the current cycle;

[0050] Among them, T Δ is the temperature control difference of the previous cycle, f T The inverse function of , W=1, otherwise, W=-1; μ is the incremental coefficient, and satisfies 1.1>μ>1.

[0051] Furthermore, the process of analyzing the processing anomaly also includes:

[0052] By formula:

[0053]

[0054] Calculate the temperature uniformity difference s at the current time point Δ (t);

[0055] The temperature uniformity difference d Δ (t) and the preset uniformity difference interval [d1 Δ , d2 Δ ] for comparison:

[0056] If Δ (t)<d1 Δ , it is judged that the current temperature uniformity of the reaction chamber is normal;

[0057] If d Δ (t)>s2 Δ , then the temperature uniformity warning of the current reaction chamber is issued;

[0058] If Δ (t)∈[s1 Δ , s2 Δ ], then take t1 as the period to determine whether the average power value of the circulating fan control in the period reaches the preset critical value:

[0059] If it is reached, a temperature uniformity warning will be issued for the current reaction chamber;

[0060] If it is not reached, feedback adjustment is performed on the current temperature uniformity of the reaction chamber;

[0061] Wherein, t1 is a preset fixed period.

[0062] Furthermore, the process of feedback-adjusting the current temperature uniformity of the reaction chamber includes:

[0063] By formula:

[0064]

[0065] Calculate and obtain the current cycle fan control power increase Δp2;

[0066] Among them, pm is the preset critical value, is the average power value of the fan control in the previous cycle, s Δ is the temperature uniformity difference of the previous cycle, and τ is the preset fixed coefficient.

[0067] The invention discloses an online temperature monitoring method for a processing flow, wherein the method adopts an online temperature monitoring system for a processing flow to perform temperature monitoring and control process.

[0068] Beneficial effects of the present invention:

[0069] (1) The present invention can accurately judge the overall temperature state in the processing chamber through the monitoring process of multiple temperature sensors and product surface temperature in each processing chamber, thereby improving the comprehensiveness of the temperature monitoring process, and combining the monitored data with real-time temperature control data and circulating fan control data for analysis, so as to make more timely judgments on existing temperature control anomalies, that is, improve the sensitivity of abnormal warning. In addition, the feedback adjustment module performs feedback adjustment on the temperature control data and circulating fan control data according to the results of the abnormal analysis, and then adaptively self-adjusts the current temperature state within the controllable error range, thereby ensuring the accuracy of temperature control. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The present invention will be further described below in conjunction with the accompanying drawings.

[0071] Figure 1 It is a logic block diagram of the online temperature monitoring system for the processing flow of the present invention. DETAILED DESCRIPTION

[0072] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0073] See also Figure 1 As shown, in one embodiment, an online temperature monitoring system for a processing flow is provided, the system comprising a temperature sensor, an infrared image acquisition module, a temperature control data docking port, a circulating fan control data docking port, a monitoring and early warning module, and a feedback adjustment module, wherein a plurality of groups of temperature sensors are arranged in each processing chamber for real-time monitoring of the temperature data of the position points corresponding to each temperature sensor; the infrared image acquisition module is used to collect the real-time infrared image information of the PAN raw silk in each processing chamber, so that through the monitoring process of the plurality of temperature sensors in each processing chamber and the surface temperature of the product, the overall temperature state in the processing chamber can be accurately judged, thereby improving the comprehensiveness of the temperature monitoring process, the temperature control data docking port is used to obtain the real-time temperature control data of each processing chamber; the circulating fan ... The docking port is used to obtain real-time circulating fan control data of each processing chamber. Therefore, the temperature state of each reaction chamber is judged by the monitoring and early warning module according to the temperature data monitored by the temperature sensor and the real-time infrared image information. The processing state is analyzed for abnormalities based on the comparison of the judgment result with the real-time temperature control data and circulating fan control data. In this process, the monitored data is combined with the real-time temperature control data and circulating fan control data for analysis, which can make more timely judgments on existing temperature control abnormalities, that is, improve the sensitivity of abnormal early warning. In addition, the feedback adjustment module performs feedback adjustment on the temperature control data and circulating fan control data according to the results of the abnormal analysis, and then adaptively self-adjusts the current temperature state within the controllable error range, thereby ensuring the accuracy of temperature control.

[0074] The process of judging the temperature state of each reaction chamber includes: obtaining the mean value of the ambient temperature in the reaction chamber and the ambient temperature uniformity coefficient according to the temperature data monitored by the temperature sensor; wherein the process of obtaining the ambient temperature uniformity coefficient includes: using the formula:

[0075]

[0076] Calculate the ambient temperature uniformity coefficient s at the current time point ET (t);

[0077] Where n is the number of temperature sensors in the current reaction chamber, i∈[1,n], ET i (t) is the real-time temperature obtained by the i-th sensor, The real-time temperature average is obtained for all sensors, so the ambient temperature uniformity coefficient can reflect the dispersion of temperature values ​​at different locations, and can be calculated through s ET (t) Determine the current temperature distribution state. If the temperature distribution is relatively uniform, then s ET (t) The value approaches 0; then the position of the PAN raw fiber in the infrared image information is identified. It should be noted that the above identification process is based on the machine learning training model. The specific machine learning training model is obtained by training the working state images of different processing chambers, which will not be further described here. Then, the dot matrix temperature mean and the dot matrix temperature uniformity coefficient in the identification area are obtained according to the preset points; the process of obtaining the dot matrix temperature uniformity coefficient includes: through the formula:

[0078]

[0079] Calculate the temperature uniformity coefficient s of the current time point PT (t); where m is the number of points in the recognition area, j∈[1,m], PT i (t) is the real-time temperature at the jth point, is the average temperature of all lattices. Therefore, the lattice temperature uniformity coefficient obtained by the above calculation can judge the temperature uniformity of the current temperature acting on the product, and then determine the average temperature field of the reaction chamber according to the average ambient temperature and the average lattice temperature. The formula is:

[0080]

[0081] Calculate the mean temperature T at the current time point field (t); where μ is the thermal conductivity, and the temperature state in the current reaction chamber can be judged more accurately by the average value of the temperature field; the temperature field uniformity coefficient of the reaction chamber is determined according to the ambient temperature uniformity coefficient and the lattice temperature uniformity coefficient; through the formula:

[0082]

[0083] Calculate the temperature field uniformity coefficient s at the current time point field (t); wherein γ is an adjustment coefficient and satisfies γ>1. Therefore, this embodiment comprehensively considers the product temperature and the spatial temperature in the reaction chamber to accurately judge the overall temperature status and overall distribution status of the temperature field mean value and the temperature field uniformity coefficient, and the process of processing abnormality analysis includes:

[0084] By formula:

[0085]

[0086] Calculate the temperature control difference T at the current time point Δ (t); where p(t) is the real-time power of the reaction chamber temperature control device, f T is the temperature diffusion function of the reaction chamber, which is obtained by fitting the reaction chamber test data, and t1 is a preset fixed period, which is selected and set according to empirical data. Therefore, the temperature control difference T Δ (t) and the first preset difference interval [T1 Δ low, T1 Δ up] to compare: [T1 Δ low, T1 Δ up] is set according to the error tolerance in the test data. This indicates that there is an abnormal temperature control problem in the differential process, and a temperature warning is issued for the current reaction chamber; if T Δ (t)∈[T1 Δ low, T1 Δ up], then T Δ (t) and the first preset difference interval [T2 Δ low, T2 Δ up] to compare: [T2 Δ low, T2 Δ up] is set according to the allowable error data under ideal conditions and satisfies [T2 Δ low, T2 Δ up]∈[T1 Δ low, T1 Δ up], so if T Δ (t)∈[T2 Δ low, T2 Δ up], the current reaction chamber temperature control state is judged to be normal; if Feedback adjustment is then performed on the current reaction chamber temperature control data. The process of feedback adjustment on the current reaction chamber temperature control data includes: taking t1 as a cycle, adjusting the temperature control data of the next cycle according to the temperature control difference of the previous cycle. The adjustment process includes:

[0087] By formula:

[0088]

[0089] Calculate and obtain the current cycle reaction chamber temperature control device adjustment amount Δp1; where T Δ is the temperature control difference of the previous cycle, f TThe inverse function of When W=1, otherwise, W=-1; μ is the incremental coefficient and satisfies 1.1>μ>1. It is set according to the test data fitting and is used to correct the obtained adjustment amount. Therefore, through the above process, on the basis of judging that the temperature control abnormality occurs, different processing strategies can be adopted when the cumulative difference mean is too large. On the one hand, potential risks can be judged in time. On the other hand, when it is judged that the temperature is within the controllable range, the normal operation of the current processing process can be guaranteed by feedback adjustment, thereby ensuring the quality of the prepared product.

[0090] In addition, the process of processing abnormality analysis also includes: through the formula:

[0091]

[0092] Calculate the temperature uniformity difference s at the current time point Δ (t); the temperature uniformity difference s Δ (t) and the preset uniformity difference interval [s1 Δ , s2 Δ ] for comparison, the preset uniformity difference interval is set according to the empirical data fitting, so if s Δ (t)<s1 Δ , it means that the current temperature distribution state is relatively uniform, and then it is judged that the current temperature uniformity state of the reaction chamber is normal; if s Δ (t)>s2 Δ , it means that the current temperature state is in an uncontrollable state, and then the temperature uniformity warning of the current reaction chamber is issued; if s Δ (t)∈[s1 Δ , s2 Δ ], then take t1 as the period to determine whether the average power of the circulating fan control in the period reaches the preset critical value: if it reaches it, the temperature uniformity warning of the current reaction chamber is issued; if it does not reach it, the temperature uniformity of the current reaction chamber is feedback-adjusted. The process of feedback-adjusting the temperature uniformity of the current reaction chamber includes:

[0093] By formula:

[0094]

[0095] Calculate and obtain the current cycle fan control power increase Δp2; where pm is the preset critical value, is the average power value of the fan control in the previous cycle, d Δis the temperature uniformity difference of the previous cycle, τ is the preset fixed coefficient, which is set according to the fitting of empirical data, and should be used to correct the obtained increase. It should be noted that when the circulating fan control power is large, it will have a certain impact on the PAN raw yarn. Therefore, the preset critical value is set to avoid the influence of large airflow on the PAN raw yarn, and then on the basis of ensuring the quality of the raw yarn, by increasing the cycle circulation fan control power, the operability of the temperature in the reaction chamber is adaptively adjusted to ensure the overall quality of the product.

[0096] In one embodiment, a method for online temperature monitoring of a processing flow is provided. The method uses an online temperature monitoring system for a processing flow to perform temperature monitoring and control processes.

[0097] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A processing flow online temperature monitoring system, characterized in that: The system comprises: Temperature sensors: each processing chamber is provided with several groups of temperature sensors for real-time monitoring of the temperature data of the corresponding position points of each temperature sensor; Infrared image acquisition module, used to collect real-time infrared image information of PAN raw fibers in each processing chamber; Temperature control data docking port, used to obtain real-time temperature control data of each processing chamber; Circulation fan control data docking port, used to obtain real-time circulation fan control data of each processing chamber; The monitoring and early warning module is used to judge the temperature status of each reaction chamber based on the temperature data monitored by the temperature sensor and the real-time infrared image information, and to perform abnormal analysis on the processing status based on the comparison of the judgment result with the real-time temperature control data and circulating fan control data; A feedback adjustment module is used to perform feedback adjustment on the temperature control data and the circulating fan control data according to the results of the abnormal analysis; The process of judging the temperature status of each reaction chamber includes: Obtaining the mean value of the ambient temperature in the reaction chamber and the ambient temperature uniformity coefficient according to the temperature data monitored by the temperature sensor; Identify the location of the PAN raw silk in the infrared image information, and obtain the dot matrix temperature mean and dot matrix temperature uniformity coefficient in the identification area according to the preset points; Determine the temperature field mean value of the reaction chamber according to the ambient temperature mean value and the lattice temperature mean value, determine the temperature field uniformity coefficient of the reaction chamber according to the ambient temperature uniformity coefficient and the lattice temperature uniformity coefficient; judge the temperature state of each reaction chamber according to the temperature field mean value and the temperature field uniformity coefficient; The process of obtaining the ambient temperature uniformity coefficient includes: By formula: Calculate the ambient temperature uniformity coefficient s at the current time point ET (t); The process of obtaining the lattice temperature uniformity coefficient includes: By formula: Calculate the temperature uniformity coefficient s of the current time point PT (t); The process of obtaining the temperature field average value includes: By formula: Calculate the mean temperature T at the current time point field (t); By formula: Calculate the temperature field uniformity coefficient s at the current time point field (t); Where n is the number of temperature sensors in the current reaction chamber, i∈[1,n], ET i (t) is the real-time temperature obtained by the i-th sensor, Get the real-time temperature mean for all sensors, m is the number of points in the identification area, j∈[1,m], PT i (t) is the real-time temperature at the jth point, is the mean temperature of all lattices, μ is the thermal conductivity, γ is the adjustment coefficient, and γ>

1.

2. The online temperature monitoring system for a processing flow according to claim 1, characterized in that: The process of processing abnormality analysis includes: By formula: Calculate the temperature control difference T at the current time point Δ (t); The temperature control difference T Δ (t) and the first preset difference interval [T1 Δ low, T1 Δ up] for comparison: like Then the temperature of the current reaction chamber is warned; If T Δ (t)∈[T1 Δ low, T1 Δ up], then T Δ (t) and the first preset difference interval [T2 Δ low, T2 Δ up] for comparison: If T Δ (t)∈[T2 Δ low, T2 Δ up], it is judged that the current reaction chamber temperature control state is normal; like Then the current reaction chamber temperature control data is feedback-adjusted; Where p(t) is the real-time power of the reaction chamber temperature control device, f T is the temperature diffusion function of the reaction chamber, and t1 is a preset fixed period.

3. The online temperature monitoring system for a processing flow according to claim 2 is characterized in that: The process of feedback adjustment of the current reaction chamber temperature control data includes: Taking t1 as the cycle, the temperature control data of the next cycle is adjusted according to the temperature control difference of the previous cycle. The adjustment process includes: By formula: Calculate and obtain the adjustment amount Δp1 of the reaction chamber temperature control device in the current cycle; Among them, T Δ is the temperature control difference of the previous cycle, f T The inverse function of , W=1, otherwise, W=-1; μ is the incremental coefficient, and satisfies 1.1>μ>1.

4. The online temperature monitoring system for a processing flow according to claim 1, characterized in that: The process of processing abnormality analysis also includes: By formula: Calculate the temperature uniformity difference s at the current time point Δ (t); The temperature uniformity difference s Δ (t) and the preset uniformity difference interval [s1 Δ , s2 Δ ] for comparison: If Δ (t)<s1 Δ , it is judged that the current temperature uniformity of the reaction chamber is normal; If Δ (t)>s2 Δ , then the temperature uniformity warning of the current reaction chamber is issued; If Δ (t)∈[s1 Δ , s2 Δ ], then take t1 as the period to determine whether the average power value of the circulating fan control in the period reaches the preset critical value: If it is reached, a temperature uniformity warning will be issued for the current reaction chamber; If it is not reached, feedback adjustment is performed on the current temperature uniformity of the reaction chamber; Wherein, t1 is a preset fixed period.

5. The online temperature monitoring system for a processing flow according to claim 4, characterized in that: The process of feedback adjustment of the current reaction chamber temperature uniformity includes: By formula: Calculate and obtain the current cycle fan control power increase Δp2; Among them, pm is the preset critical value, is the average power value of the fan control in the previous cycle, s Δ is the temperature uniformity difference of the previous cycle, and τ is the preset fixed coefficient.

6. A method for online temperature monitoring of a processing flow, characterized in that: The method adopts an online temperature monitoring system for a processing flow as described in any one of claims 1 to 5 to perform temperature monitoring and control processes.

Images