Temperature control system based on intelligent manufacturing of wind power blade mold

Abstract

The invention relates to the technical field of temperature control, and discloses a temperature control system based on intelligent manufacturing of a wind power blade mold, and the system comprises a temperature sensor which is used for detecting the real-time temperature of each part of the mold; the heating system is connected with the temperature sensor and is used for heating the mold; the cooling system is connected with the temperature sensor and is used for cooling the mold; the controller is provided with a temperature adjusting module, a data processing module, a feedback control module, a fault detection module and a self-adaptive tuning module; the temperature adjusting module is connected with the heating system and the cooling system and used for adjusting the heating system and the cooling system according to data detected by the temperature sensor. By integrating a PID control algorithm and an intelligent optimization function in the self-adaptive tuning module, control parameters can be dynamically adjusted according to real-time production conditions, and it is ensured that the temperature adjusting system can maintain a high-precision control effect in various complex production environments.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature control, and in particular to a temperature control system based on intelligent manufacturing of wind turbine blade moulds. Background Art

[0002] In the manufacturing process of wind turbine blades, precise control of mold temperature plays a vital role. This is because the mold temperature directly affects the quality, production efficiency and service life of the blades. If the mold temperature is too high or too low, it will cause deformation, cracks or other quality problems in the blades, thus affecting their performance and service life. Therefore, it is crucial to ensure that the mold temperature is within the optimal range.

[0003] Traditional temperature control systems usually use PID control algorithms with fixed parameters. Although they can achieve temperature regulation under certain conditions, the system's response often lags when faced with different mold shapes, material properties, and changes in the external environment, resulting in insufficient temperature control accuracy.

[0004] The temperature control system in the prior art usually relies on a single or a small number of sensors for monitoring, and the fault detection function is relatively limited, making it difficult to detect sensor failure or system abnormality in a timely manner.

[0005] In view of the shortcomings of these existing technologies, a temperature control system based on intelligent manufacturing of wind turbine blade molds is proposed. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a temperature control system based on the intelligent manufacturing of wind turbine blade molds, which solves the problem that traditional temperature control systems usually adopt a PID control algorithm with fixed parameters. Although temperature regulation can be achieved under certain conditions, when faced with different mold shapes, material properties and external environmental changes, the system's response is often delayed, resulting in insufficient temperature control accuracy.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A temperature control system based on intelligent manufacturing of wind turbine blade molds, comprising:

[0008] Temperature sensor, used to detect the real-time temperature of each part of the mold;

[0009] A heating system, connected to the temperature sensor, for heating the mold;

[0010] A cooling system, connected to the temperature sensor, for cooling the mold;

[0011] A controller having a temperature adjustment module, a data processing module, a feedback control module, a fault detection module and an adaptive tuning module;

[0012] The temperature adjustment module is connected to the heating system and the cooling system, and is used to adjust the heating system and the cooling system according to the data detected by the temperature sensor;

[0013] The data processing module is connected to the temperature sensor and the temperature adjustment module, and is used to perform real-time analysis and processing on the detected temperature data, including filtering, data fusion and trend prediction of the temperature data;

[0014] a feedback control module, connected to the data processing module and the temperature adjustment module, for generating a control signal based on the processed temperature data to optimize the heating and cooling process;

[0015] A fault detection module, connected to the controller and the temperature sensor, is used to detect the operating status of the temperature sensor, the heating system and the cooling system, and identify potential faults;

[0016] The adaptive tuning module is connected to the temperature adjustment module and the feedback control module and is used to dynamically adjust the parameters of the control system according to real-time operation data.

[0017] Preferably, the temperature sensor comprises a distributed thermocouple array and an infrared temperature sensor combination, which is used for performing contact and non-contact temperature detection simultaneously.

[0018] Preferably, the heating system comprises an electric heating device, an oil heating device and a hot air circulation device, and the hot air circulation device is used to evenly distribute the heat generated by heating.

[0019] Preferably, the cooling system comprises a water cooling device, an air cooling device and a spray cooling device, and the spray cooling device is used to accurately control the cooling of a local area.

[0020] Preferably, the temperature adjustment module comprises:

[0021] A temperature control unit for setting and adjusting a target temperature range;

[0022] A temperature balancing unit, used to balance temperature differences between multiple temperature-controlled zones;

[0023] A dynamic adjustment unit is used to adjust the temperature regulation strategy in real time when environmental changes are detected.

[0024] Preferably, the data processing module includes:

[0025] A data filtering unit, used to eliminate noise in the temperature signal;

[0026] A data fusion unit, used for fusing data from multiple temperature sensors to generate an overall temperature distribution map;

[0027] Trend prediction unit, used to predict future temperature changes based on historical data.

[0028] Preferably, the feedback control module includes:

[0029] Adaptive feedback unit, used to dynamically adjust the control strategy according to real-time temperature changes;

[0030] Model prediction control unit, used to predict future temperature changes based on the mathematical model of the system and perform advance control;

[0031] Fault-tolerant unit, used to maintain the basic functions of the system in the event of failure of some sensors or actuators.

[0032] Preferably, the fault detection module includes:

[0033] A sensor fault detection unit, used to detect the working state of the temperature sensor;

[0034] A heating system fault detection unit, used to detect the operating status of the heating system;

[0035] A cooling system fault detection unit, used to detect the operating status of the cooling system;

[0036] The diagnostic unit is used to analyze the cause of the fault and generate alarm information based on the fault detection result.

[0037] Preferably, the adaptive tuning module comprises:

[0038] A parameter adjustment unit, used to dynamically adjust control parameters based on real-time operating data;

[0039] The model adaptive unit is used to automatically adjust the mathematical model of the system according to the changes in production conditions; the intelligent optimization unit is used to optimize the control strategy according to the historical operation data and the current conditions. The intelligent optimization unit uses a genetic algorithm to perform global optimization of the multi-parameter control strategy. The parameter adjustment unit may involve gain adjustment in the control system, which can be described by a proportional-integral-differential (PID) control algorithm. The output u(t) of the PID controller is given by the following formula:

[0040]

[0041] Where: u(t) is the control signal e(t) is the error, i.e. the difference between the target value and the actual value K p is the proportional gain, K i is the integral gain, K d is the differential gain. The adaptive tuning module can adjust K p , K i and K d value to dynamically adapt to system changes.

[0042] Preferably, the data filtering unit adopts a Kalman filter algorithm to enhance the accuracy of the signal. The overall formula of the Kalman filter can be expressed as:

[0043]

[0044] in:

[0045] · is the predicted state estimate (based on the previous state) is the final state estimate at time k K k is the Kalman gain matrix that adjusts the difference between measurements and predictions for precise control of the temperature regulation process, ensuring that the temperature remains within the set range.

[0046] The present invention provides a temperature control system based on intelligent manufacturing of wind turbine blade molds. It has the following beneficial effects:

[0047] 1. The present invention integrates the PID control algorithm and intelligent optimization function in the adaptive tuning module, which can dynamically adjust the control parameters according to the real-time production conditions, ensuring that the temperature control system can maintain high-precision control effects in various complex production environments. The adaptive tuning module can not only adjust the proportional, integral and differential gains in real time, but also globally optimize the multi-parameter control strategy through genetic algorithms, so that the temperature control system can quickly adapt to and maintain the best working state when processing different materials and mold structures.

[0048] 2. The fault detection module of the present invention realizes real-time monitoring of temperature sensors, heating systems and cooling systems through a multi-level monitoring mechanism, and can quickly identify and diagnose potential faults. Combined with the fault-tolerant unit, the system can still maintain basic functions in the event of failure of some sensors or execution units, avoiding interruption of the production process. This multiple redundant design and automatic diagnosis capability improves the stability and reliability of the system and reduces the production risks caused by equipment failure.

[0049] 3. The present invention uses the Kalman filter algorithm in the data processing module to perform efficient real-time filtering on the temperature signal, eliminate noise, and ensure the accuracy of the temperature data. At the same time, the data fusion unit can integrate the data from multiple temperature sensors to generate a global temperature distribution map, providing a reliable basis for temperature regulation. Combined with the function of the trend prediction unit, the system can predict future temperature change trends based on historical data, thereby adjusting the temperature control strategy in advance to ensure that the temperature is always kept within the set range throughout the manufacturing process, effectively improving the continuity of the production process and the consistency of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1is a flow chart of the present invention;

[0051] Figure 2 It is a flow chart of the temperature adjustment module of the present invention;

[0052] Figure 3 It is a flow chart of the data processing module of the present invention;

[0053] Figure 4 It is a flow chart of the feedback control module of the present invention;

[0054] Figure 5 This is a flow chart of the fault detection module of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the specification 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.

[0056] Example:

[0057] Please see attached Figure 1 - Attachment Figure 5 The embodiment of the present invention provides a temperature control system based on intelligent manufacturing of wind turbine blade molds, including:

[0058] Temperature sensor, used to detect the real-time temperature of each part of the mold;

[0059] A heating system, connected to the temperature sensor, for heating the mold;

[0060] A cooling system, connected to the temperature sensor, for cooling the mold;

[0061] A controller having a temperature adjustment module, a data processing module, a feedback control module, a fault detection module and an adaptive tuning module;

[0062] The temperature adjustment module is connected to the heating system and the cooling system, and is used to adjust the heating system and the cooling system according to the data detected by the temperature sensor;

[0063] The data processing module is connected to the temperature sensor and the temperature adjustment module, and is used to perform real-time analysis and processing on the detected temperature data, including filtering, data fusion and trend prediction of the temperature data;

[0064] a feedback control module, connected to the data processing module and the temperature adjustment module, for generating a control signal based on the processed temperature data to optimize the heating and cooling process;

[0065] A fault detection module, connected to the controller and the temperature sensor, is used to detect the operating status of the temperature sensor, the heating system and the cooling system, and identify potential faults;

[0066] The adaptive tuning module is connected to the temperature adjustment module and the feedback control module and is used to dynamically adjust the parameters of the control system according to real-time operation data.

[0067] Specifically, a temperature sensor for detecting the real-time temperature of each part of the mold; a heating system connected to the temperature sensor and used to heat the mold according to the detected temperature data; a cooling system connected to the temperature sensor and used to cool the mold; and a controller, the controller includes multiple modules: a temperature adjustment module, a data processing module, a feedback control module, a fault detection module and an adaptive tuning module. The temperature adjustment module is connected to the heating system and the cooling system, and is used to adjust the heating and cooling operations according to the data detected by the temperature sensor to maintain the mold temperature within a set range; the data processing module is connected to the temperature sensor and the temperature adjustment module, and is used to perform real-time analysis and processing on the detected temperature data, including filtering of the temperature data, data fusion and trend prediction based on historical data; the feedback control module is connected to the data processing module and the temperature adjustment module, and is used to generate a control signal based on the processed temperature data, so as to optimize the execution of the heating and cooling process; the fault detection module is connected to the controller and the temperature sensor, and is used to monitor the operating status of the temperature sensor, the heating system and the cooling system, and identify and report potential fault conditions; the adaptive tuning module is connected to the temperature adjustment module and the feedback control module, and is used to dynamically adjust the control parameters according to the real-time operating data of the system to improve the system response speed and control accuracy.

[0068] The temperature sensor comprises a distributed thermocouple array and an infrared temperature sensor combination, and is used for simultaneously performing contact and non-contact temperature detection.

[0069] The heating system comprises an electric heating device, an oil heating device and a hot air circulation device, and the hot air circulation device is used for evenly distributing the heat generated by heating.

[0070] The cooling system includes a water cooling device, an air cooling device and a spray cooling device, and the spray cooling device is used to accurately control the cooling of a local area.

[0071] Specifically, the temperature sensor composed of a distributed thermocouple array and an infrared temperature sensor can perform contact and non-contact temperature detection at the same time to ensure comprehensive monitoring of the temperature of various parts of the mold; the heating system is composed of an electric heating device, an oil heating device and a hot air circulation device, and the hot air circulation device is used to evenly distribute the heat generated by the heating device, thereby ensuring the uniformity of the temperature inside the mold; the cooling system includes a water cooling device, an air cooling device and a spray cooling device, wherein the spray cooling device is used to precisely control the cooling of a local area of ​​the mold to ensure the accuracy and effect of temperature regulation of the mold in different areas, thereby improving the stability of the entire manufacturing process and product quality.

[0072] The temperature regulating module comprises:

[0073] A temperature control unit for setting and adjusting a target temperature range;

[0074] A temperature balancing unit, used to balance temperature differences between multiple temperature-controlled zones;

[0075] A dynamic adjustment unit is used to adjust the temperature regulation strategy in real time when environmental changes are detected.

[0076] Specifically, the temperature control unit is used to set and adjust the target temperature range of each area of ​​the mold to ensure that each area meets the expected process temperature requirements; the temperature balancing unit is responsible for balancing the temperature differences between multiple temperature control areas, and by adjusting the heating or cooling intensity of each area, the temperature distribution of the entire mold is more uniform, avoiding stress concentration or inconsistent material properties caused by excessive local temperature differences; the dynamic adjustment unit is used to adjust the temperature regulation strategy in real time when environmental changes (such as changes in external temperature and humidity) are detected, and dynamically respond to these changes to maintain the stability and consistency of the mold temperature, ensuring the efficiency of the production process and the quality of the final product.

[0077] The data processing module comprises:

[0078] A data filtering unit, used to eliminate noise in the temperature signal;

[0079] A data fusion unit, used for fusing data from multiple temperature sensors to generate an overall temperature distribution map;

[0080] Trend prediction unit, used to predict future temperature changes based on historical data.

[0081] Specifically, the data filtering unit is used to eliminate noise in the temperature signal to ensure that the acquired temperature data is accurate and reliable, so as to avoid measurement errors caused by signal interference; the data fusion unit is used to fuse the data from multiple temperature sensors, and generate a comprehensive overall temperature distribution map by integrating the detection information of different sensors to accurately reflect the temperature conditions of various areas of the mold; the trend prediction unit is based on historical temperature data and uses a prediction algorithm to predict future temperature change trends, thereby providing a forward-looking adjustment basis for temperature regulation, ensuring that the system can respond to temperature changes in advance and maintain the stability of the mold manufacturing process.

[0082] The feedback control module comprises:

[0083] Adaptive feedback unit, used to dynamically adjust the control strategy according to real-time temperature changes;

[0084] Model prediction control unit, used to predict future temperature changes based on the mathematical model of the system and perform advance control;

[0085] Fault-tolerant unit, used to maintain the basic functions of the system in the event of failure of some sensors or actuators.

[0086] Specifically, the adaptive feedback unit is used to dynamically adjust the control strategy according to real-time temperature changes. It can respond quickly and adjust the heating or cooling measures when temperature fluctuations are detected to ensure that the temperature is maintained within the set range; the model prediction control unit predicts future temperature change trends in advance based on the system's mathematical model, and adjusts temperature control measures in advance according to the prediction results, thereby avoiding temperature changes beyond the control range and improving the system's predictability and response speed; the fault-tolerant unit is used to ensure that the system can still maintain basic functional operations through redundant design and backup strategies in the event of failure of some sensors or execution units, thereby avoiding interruptions to the production process and degradation of product quality.

[0087] The fault detection module comprises:

[0088] A sensor fault detection unit, used to detect the working state of the temperature sensor;

[0089] A heating system fault detection unit, used to detect the operating status of the heating system;

[0090] A cooling system fault detection unit, used to detect the operating status of the cooling system;

[0091] The diagnostic unit is used to analyze the cause of the fault and generate alarm information based on the fault detection result.

[0092] Specifically, the sensor fault detection unit is used to monitor the working status of the temperature sensor in real time and detect whether the sensor has problems such as abnormal signals or functional failure; the heating system fault detection unit is used to monitor the operating status of the heating system, including checking the power output and temperature rise of the heating element to determine whether the heating system is working normally; the cooling system fault detection unit is responsible for detecting the operating status of the cooling system to ensure the cooling efficiency and working stability of the cooling device; the diagnosis unit conducts a comprehensive analysis based on the fault detection results of the above units, identifies the specific cause of the fault, and generates corresponding alarm information so that maintenance or repair measures can be taken in time to ensure the continuous and stable operation of the system.

[0093] The adaptive tuning module comprises:

[0094] A parameter adjustment unit, used to dynamically adjust control parameters based on real-time operating data;

[0095] The model adaptive unit is used to automatically adjust the mathematical model of the system according to the changes in production conditions; the intelligent optimization unit is used to optimize the control strategy according to the historical operation data and the current conditions. The intelligent optimization unit uses a genetic algorithm to perform global optimization of the multi-parameter control strategy. The parameter adjustment unit may involve gain adjustment in the control system, which can be described by a proportional-integral-differential (PID) control algorithm. The output u(t) of the PID controller is given by the following formula:

[0096]

[0097] Where: u(t) is the control signal e(t) is the error, i.e. the difference between the target value and the actual value K p is the proportional gain, K i is the integral gain, K d is the differential gain. The adaptive tuning module can adjust K p , K i and K d value to dynamically adapt to system changes.

[0098] Specifically, the parameter adjustment unit is used to dynamically adjust the parameters of the control system based on real-time operating data, especially in the gain adjustment of the control system, which is achieved by using the proportional-integral-differential (PID) control algorithm. The output control signal of the PID controller is given by the following formula:

[0099]

[0100] Where: u(t) is the control signal e(t) is the error, i.e. the difference between the target value and the actual value K p is the proportional gain, K i is the integral gain, Kd is the differential gain. The adaptive tuning module can adjust K p , K i and K d The value of is used to dynamically adapt to system changes, ensuring the response speed and accuracy of the control system under different production conditions. The model adaptation unit is used to automatically adjust the mathematical model of the system according to changes in production conditions, so that it can more accurately reflect the current production environment. The intelligent optimization unit optimizes the control strategy based on historical operation data and current conditions, using genetic algorithms to perform global optimization of multi-parameter control strategies, thereby improving the performance and stability of the entire system.

[0101] The data filtering unit adopts the Kalman filter algorithm to enhance the accuracy of the signal. The overall formula of the Kalman filter can be expressed as:

[0102]

[0103] in:

[0104] · is the predicted state estimate (based on the previous state) is the final state estimate at time k K k is the Kalman gain matrix that adjusts the difference between measurements and predictions for precise control of the temperature regulation process, ensuring that the temperature remains within the set range.

[0105] Specifically, by applying the Kalman filter algorithm, the data filtering unit can accurately control the temperature adjustment process and eliminate noise and errors in the measurement data, thereby ensuring that the system can maintain the temperature within the set range and improving the stability of the entire manufacturing process and product quality.

[0106] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A temperature control system based on intelligent manufacturing of wind turbine blade molds, characterized in that: include: Temperature sensor, used to detect the real-time temperature of each part of the mold; A heating system, connected to the temperature sensor, for heating the mold; A cooling system, connected to the temperature sensor, for cooling the mold; A controller having a temperature adjustment module, a data processing module, a feedback control module, a fault detection module and an adaptive tuning module; The temperature adjustment module is connected to the heating system and the cooling system, and is used to adjust the heating system and the cooling system according to the data detected by the temperature sensor; The data processing module is connected to the temperature sensor and the temperature adjustment module and is used to perform real-time analysis and processing on the detected temperature data, including filtering, data fusion and trend prediction of the temperature data; a feedback control module, connected to the data processing module and the temperature adjustment module, for generating a control signal based on the processed temperature data to optimize the heating and cooling process; A fault detection module, connected to the controller and the temperature sensor, is used to detect the operating status of the temperature sensor, the heating system and the cooling system, and identify potential faults; The adaptive tuning module is connected to the temperature adjustment module and the feedback control module and is used to dynamically adjust the parameters of the control system according to real-time operation data.

2. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The temperature sensor comprises a distributed thermocouple array and an infrared temperature sensor combination, and is used for simultaneously performing contact and non-contact temperature detection.

3. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The heating system comprises an electric heating device, an oil heating device and a hot air circulation device, and the hot air circulation device is used for evenly distributing the heat generated by heating.

4. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The cooling system includes a water cooling device, an air cooling device and a spray cooling device, and the spray cooling device is used to accurately control the cooling of a local area.

5. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The temperature regulating module comprises: A temperature control unit for setting and adjusting a target temperature range; A temperature balancing unit, used to balance temperature differences between multiple temperature-controlled zones; A dynamic adjustment unit is used to adjust the temperature regulation strategy in real time when environmental changes are detected.

6. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The data processing module comprises: A data filtering unit, used to eliminate noise in the temperature signal; A data fusion unit, used for fusing data from multiple temperature sensors to generate an overall temperature distribution map; Trend prediction unit, used to predict future temperature changes based on historical data.

7. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The feedback control module comprises: Adaptive feedback unit, used to dynamically adjust the control strategy according to real-time temperature changes; Model prediction control unit, used to predict future temperature changes based on the mathematical model of the system and perform advance control; Fault-tolerant unit, used to maintain the basic functions of the system in the event of failure of some sensors or actuators.

8. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The fault detection module comprises: A sensor fault detection unit, used to detect the working state of the temperature sensor; A heating system fault detection unit, used to detect the operating status of the heating system; A cooling system fault detection unit, used to detect the operating status of the cooling system; The diagnostic unit is used to analyze the cause of the fault and generate alarm information based on the fault detection result.

9. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The adaptive tuning module comprises: A parameter adjustment unit, used to dynamically adjust control parameters based on real-time operating data; The model adaptive unit is used to automatically adjust the mathematical model of the system according to the changes in production conditions; the intelligent optimization unit is used to optimize the control strategy according to the historical operation data and the current conditions. The intelligent optimization unit uses a genetic algorithm to perform global optimization of the multi-parameter control strategy. The parameter adjustment unit may involve gain adjustment in the control system, which can be described by a proportional-integral-differential (PID) control algorithm. The output u(t) of the PID controller is given by the following formula: Where: u(t) is the control signal e(t) is the error, i.e. the difference between the target value and the actual value K p is the proportional gain, K i is the integral gain, K d is the differential gain. The adaptive tuning module can adjust K p , K i and K d value to dynamically adapt to system changes.

10. The temperature control system based on intelligent manufacturing of wind turbine blade molds according to claim 1 is characterized in that: The data filtering unit adopts the Kalman filter algorithm to enhance the accuracy of the signal. The overall formula of the Kalman filter can be expressed as: in: is the predicted state estimate (based on the previous state) is the final state estimate at time k K k is the Kalman gain matrix that adjusts the difference between measurements and predictions for precise control of the temperature regulation process, ensuring that the temperature remains within the set range.

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