A method and system for detecting heating performance failure of a graphite heater

Abstract

The application discloses a kind of graphite heater heating performance fault detection method and system, it is related to heating system performance safety detection related field, this method includes: in graphite heater middle deployment detection array, trigger based on first-order sensing acquisition obtains first detection signal group, graphite heating geometric structure is coaxially arranged spiral channel, using modular heating unit;Determine first heating test period, execute periodic physical end test and based on analog detector heating simulation test, mutual test determines first performance coefficient;Determine second pulse test condition, execute power pulsation sequence test based on non-stable direct current output and analog test based on analog detector in physical end, mutual test determines second performance coefficient;Determine comprehensive performance coefficient and carry out test platform visualization.The technical problem that the precision of existing graphite resistance type heating process performance safety detection is limited is solved, and the technical effect of improving the accuracy of heater performance safety evaluation is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of heating system performance safety detection, in particular to a graphite heater heating performance fault detection method and system. BACKGROUND

[0002] In many industrial and scientific scenes involving graphite resistance heating, such as the fields of semiconductor manufacturing, material synthesis, etc., the performance safety of the graphite resistance heating process is directly related to the quality of the product, the production efficiency, and the service life of the equipment, and is a key factor to ensure the stable and reliable operation of the entire process flow. At present, it mainly relies on contact temperature sensors and infrared thermal imaging technology for detection. However, these methods have the disadvantages of limited detection coverage and difficulty in capturing transient changes, especially in complex geometric structures and power pulsation conditions, making it difficult to accurately evaluate the performance safety of the graphite resistance heating process.

[0003] At the present stage, there is a technical problem of limited precision in the heating performance fault detection of a graphite heater in the related art. SUMMARY

[0004] The present application provides a graphite heater heating performance fault detection method and system, which arranges a detection array in a graphite heater with a modular heating unit and a coaxial layout spiral channel structure, acquires a first detection signal group through first-order sensing collection, sets a first heating test period, takes the first detection signal group as the initial state, synchronously performs periodic physical end test and heating simulation test based on the simulation detector, takes the temperature rise response and the temperature field uneven response as the guide, determines the first performance coefficient through mutual verification of the two, determines the second pulse test condition, performs power pulsation sequence test based on non-steady direct current output at the physical end, simultaneously performs simulation test based on the simulation detector, determines the second performance coefficient through mutual verification again, calculates the comprehensive performance coefficient according to the first performance coefficient and the second performance coefficient, and performs visual display on the test platform, etc. Technical means, solve the technical problem of limited precision in the existing graphite heater heating performance fault detection, and achieve the technical effect of improving the precision of the heater performance safety evaluation.

[0005] The application provides a heating performance fault detection method of a graphite heater, comprising: deploying a detection array in the graphite heater, triggering first detection signal groups based on first-order sensing collection, wherein the graphite heating geometry is a coaxial spiral channel, and a modular heating unit is used; determining a first heating test period, taking the first detection signal groups as an initial state, performing periodic physical end test, and mutual verification with heating simulation test based on an analog detector to determine a first performance coefficient, wherein temperature rise and fall response and temperature field uneven response are used as test guidance; determining a second pulse test condition, performing power pulsation sequence test based on non-steady direct current output at the physical end, and mutual verification with simulation test based on the analog detector to determine a second performance coefficient; and determining a comprehensive performance coefficient according to the first performance coefficient and the second performance coefficient, and performing test platform visualization.

[0006] In possible implementation manners, the following processing is performed: the deployment mode of the detection array at least includes that a micro temperature sensing array is deployed on the surface of the graphite heater, high-temperature strain gauges are deployed at key mechanical bearing points and connection positions of the graphite piece, and an acoustic emission sensing array is deployed around the furnace body of the graphite heater; the detection array is activated to perform same-frequency sensing to determine the first detection signal groups under the triggering of a detection instruction.

[0007] In possible implementation manners, before the heating simulation test based on the analog detector, the following processing is performed: a lightweight twin of the graphite heater is performed, state elements are introduced, and a state space is built, wherein the state elements at least include a temperature field, a stress wave frequency field and a gradient vector; an action space is introduced, wherein the power ratio of the modular heating unit corresponding to the top, the side and the bottom is determined; a constraint condition is determined, wherein the constraint condition includes a reward condition based on high temperature uniformity and low energy consumption operation, and a punishment condition based on temperature gradient exceeding the standard and energy consumption exceeding the limit; and the analog detector is constructed according to the state space, the action space and the constraint condition, wherein the analog detector is embedded in the test platform.

[0008] In possible implementation manners, the following processing is performed in the heating simulation test based on the analog detector: the state space is initialized according to the first detection signal groups, wherein the first detection signal groups are test data of an initial cycle node based on physical test; for the first heating test period, the analog detector is used to perform temperature rise and fall response simulation and regulation simulation based on temperature field unevenness to determine a first simulation data chain.

[0009] In a possible implementation, the mutual test determines a first performance coefficient, and performs the following processing: determining a first test data chain by performing physical testing based on a first heating test cycle and continuous sensing based on a detection array, and uploading to a test platform; mapping the first simulation data chain and the first test data chain, performing difference calculation and weighting calculation according to a data processing plug-in of the test platform, to determine the first performance coefficient, wherein a temperature rise and fall response is used for first weighting, a temperature unevenness response is used for second weighting, a layer weight is determined, and a sensing dimension is used to determine a second layer weight.

[0010] In a possible implementation, a power pulsation sequence test based on a non-steady direct current output is performed at a physical end, and the following processing is performed: determining a second pulse test condition, wherein the second pulse test condition is a high-frequency and small-amplitude power pulsation sequence; according to the second pulse test condition, injecting a power pulsation into a graphite heater, performing heat pulsation-based acoustic response sensing, and determining a second test data chain; and returning the second test data chain to the test platform.

[0011] In a possible implementation, the mutual test determines a second performance coefficient, and performs the following processing: performing linear conversion on the second test data chain according to a data processing plug-in of the test platform, to determine a first linear relationship, wherein the first linear relationship represents a mutual relationship between a power pulsation sequence and an acoustic response sequence; performing linear conversion on a second simulation data chain to determine a second linear relationship, wherein the second simulation data chain is generated based on a simulation tester; comparing the first linear relationship and the second linear relationship, and determining the second performance coefficient by solving a unit linear difference.

[0012] In a possible implementation, the following processing is performed: a first acoustic element of an acoustic signal has a first mathematical relationship with a second state element of a thermal field instantaneous state, wherein the first acoustic element at least includes a phase and an amplitude characteristic, and the second state element at least includes a heat capacity, a thermal resistance, and an air flow; and the first mathematical relationship is used to solve a unit linear difference.

[0013] In a possible implementation, the following processing is performed: integrating the first performance coefficient and the second performance coefficient, performing weighted summation, and taking the weighted summation as a comprehensive performance coefficient; and performing pop-up display of the comprehensive performance coefficient on a display interface of the test platform.

[0014] The application also provides a heating performance fault detection system of a graphite heater, comprising: a first-order sensing module, configured to deploy a detection array in the graphite heater, trigger a first detection signal group based on first-order sensing acquisition, wherein the graphite heating geometry is a coaxially-arranged spiral channel, and a modular heating unit is adopted; a first performance coefficient determination module, configured to determine a first heating test period, take the first detection signal group as an initial state, perform periodic physical end test and heating simulation test based on an analog detector, and determine a first performance coefficient through mutual verification, wherein temperature rise and fall response and temperature field uneven response are used as test guidance; a second performance coefficient determination module, configured to determine a second pulse test condition, perform power pulsation sequence test based on non-stable direct current output at a physical end, and determine a second performance coefficient through mutual verification with simulation test based on an analog detector; and a test platform visualization module, configured to determine a comprehensive performance coefficient according to the first performance coefficient and the second performance coefficient, and perform test platform visualization.

[0015] The application provides a heating performance fault detection method and system of a graphite heater. First, a detection array is deployed in the graphite heater to trigger a first detection signal group based on first-order sensing acquisition. The graphite heating geometry is a coaxially-arranged spiral channel, and a modular heating unit is adopted. Then, a first heating test period is determined, the first detection signal group is taken as an initial state, periodic physical end test and heating simulation test based on an analog detector are performed, and a first performance coefficient is determined through mutual verification, wherein temperature rise and fall response and temperature field uneven response are used as test guidance. Next, a second pulse test condition is determined, power pulsation sequence test based on non-stable direct current output at a physical end is performed, and a second performance coefficient is determined through mutual verification with simulation test based on an analog detector. Finally, a comprehensive performance coefficient is determined according to the first performance coefficient and the second performance coefficient, and test platform visualization is performed. The technical effect of improving the precision of heater performance safety evaluation is achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the application, the drawings of the embodiments of the application will be briefly introduced below. In the present application, a flowchart is used to illustrate the operations performed by the system according to the embodiments of the application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously according to needs. Meanwhile, other operations can be added to these processes, or a step or several steps can be removed from these processes.

[0017] Figure 1 The flowchart of the heating performance fault detection method of the graphite heater provided by the embodiments of the application.

[0018] Figure 2A structural schematic diagram of a graphite heater heating performance fault detection system provided by an embodiment of the application.

[0019] Legend: first-order sensing module 10, first performance coefficient determination module 20, second performance coefficient determination module 30, test platform visualization module 40. DETAILED DESCRIPTION

[0020] The above description is only a summary of the technical solutions of the application. In order to enable the technical means of the application to be more clearly understood, and to be implemented according to the content of the description, and in order to enable the above and other purposes, characteristics and advantages of the application to be more apparent and easy to understand, the following specific embodiments of the application are described.

[0021] In order to make the purposes, technical solutions and advantages of the application more clear, the following will further describe the application with reference to the drawings, and the described embodiments should not be regarded as limiting the application. All other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the application.

[0022] In the following description, "some embodiments" are described, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The term "first\second" is only to distinguish similar objects, and does not represent a specific order of the object. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the application belongs. The terms used herein are only for the purpose of describing the embodiments of the application.

[0023] The embodiments of the application provide a graphite heater heating performance fault detection method, as shown in the method, the method comprises the following steps. Figure 1

[0024] Step S100, deploying a detection array in the graphite heater, triggering a first detection signal group based on first-order sensing acquisition, wherein the graphite heating geometry is a coaxial spiral channel, and a modular heating unit is used.

[0025] ​Specifically, the structure of the graphite heater is as follows: the graphite heater is constructed in a manner of coaxial arrangement of multiple layers of graphite heating tubes, the axial spacing is optimized through calculation and experiment to ensure uniform distribution of the thermal field. The design of the spiral channel, the end heat conduction assembly, and the thermal insulation layer realizes directional conduction of the heat flow. The graphite heater is divided into multiple independent heating modules, each module having relatively independent functions and structures. For example, in the manufacturing process, multiple layers of graphite heating tubes are made using processing equipment; the end heat conduction assembly is made of a material with good heat conduction performance, such as copper or aluminum, and is tightly connected with the graphite heating tube, and a material with excellent thermal insulation performance, such as ceramic fiber, is used as a thermal insulation layer to reduce heat loss.

[0026] At key positions of the graphite heater, such as different layers of the spiral channel, connection positions of the modular heating units, and the heat flow directional conduction path, multiple types of sensors are installed to form a detection array. These sensors include but are not limited to temperature sensors for real-time monitoring of temperature changes, heat flow sensors for measuring heat flow density, pressure sensors for monitoring pressure changes that may occur during heating, etc. Through a data acquisition system, signals output by each sensor in the detection array are collected in real time at a preset sampling frequency, and after preliminary filtering, amplification, etc. of these signals, a first detection signal group is obtained.

[0027] For example, for weak electrical signals collected by temperature sensors, the signals are first amplified by a low-noise amplifier, then noise interference is removed by a band-pass filter, and finally the processed signals are transmitted to a data acquisition card for digital acquisition.

[0028] In one possible implementation, step S100 further includes step S110, and the deployment manner of the detection array at least includes: a micro temperature sensor array is deployed on the surface of the graphite heater, high-temperature strain gauges are deployed at key mechanical bearing points and connection positions of the graphite piece, and an acoustic emission sensor array is deployed around the furnace body of the graphite heater. Specifically, on the surface of the graphite heater, micro temperature sensors are uniformly distributed at a certain grid spacing. These micro temperature sensors are small in size and can be closely attached to the surface of the graphite heater to directly measure the temperature changes of the heater surface. For example, a thermocouple type micro temperature sensor is used, with its hot end in good contact with the surface of the graphite heater and its cold end connected to the data acquisition system through a wire. Since the temperature at different positions of the graphite heater may differ during operation, the surface temperature sensor array is used to monitor such temperature differences comprehensively to timely discover abnormal conditions such as local overheating or overcooling.

[0029] The structure of the graphite heater is analyzed to determine the key mechanical bearing points and connection positions. The key mechanical bearing points include the contact points between the supports supporting the graphite heating pipes and the heating pipes, etc., and the connection positions include the connection parts between the modular heating units, etc. High-temperature strain gauges are attached at these positions. The high-temperature strain gauges can work normally in a high-temperature environment and can produce resistance changes with the deformation of the graphite parts. For example, at the key mechanical bearing points, the high-temperature strain gauges are attached along the direction in which the maximum strain is likely to occur, ensuring that the strain at the position can be accurately measured. During the heating and cooling process of the graphite heater, stress and strain will occur at the key mechanical bearing points and connection positions due to thermal expansion and contraction and the weight of the graphite heater itself. The high-temperature strain gauges are used to monitor the strain changes at these positions in real time, and by analyzing the strain data, it can be determined whether the graphite parts are in a safe working state, avoiding the rupture or damage of the graphite parts due to excessive stress.

[0030] Around the furnace body of the graphite heater, acoustic emission sensors are installed at certain intervals to form a sensor array surrounding the furnace body. The acoustic emission sensors can receive acoustic emission signals generated inside the graphite heater due to micro-crack propagation, material phase transition, etc. For example, the acoustic emission sensors are installed on the surface of the furnace body by magnetic attraction or bolt fixation, ensuring good contact between the sensors and the furnace body and accurate reception of acoustic emission signals. During long-term use of the graphite heater, micro-cracks and other defects may occur inside. When these defects expand, acoustic emission signals will be generated. The acoustic emission sensor array is used to monitor these signals in real time, and by analyzing the characteristics of the acoustic emission signals, such as amplitude, frequency, duration, etc., it can be determined whether there is damage inside the graphite heater and the extent and location of the damage.

[0031] In step S120, the detection array is activated to perform same-frequency sensing to determine the first detection signal group triggered by the detection instruction. Specifically, the detection instruction can be manually triggered by a person or automatically triggered by a preset program. When the detection instruction is triggered, all sensors in the detection array start working at the same time to collect data at the same sampling frequency. For example, the sampling frequency is set to 1000 Hz, i.e., 1000 data points are collected per second. The data collected by each sensor is preliminarily processed and then integrated in a certain data format to form the first detection signal group. The first detection signal group contains temperature information on the surface of the graphite heater, strain information at key positions, and acoustic emission information around the furnace body.

[0032] This implementation forms a multi-dimensional detection system by deploying multiple types of sensors at different positions of the graphite heater. This comprehensive detection method can obtain various information of the graphite heater during operation, avoiding the limitations of single sensor detection. The same frequency sensing ensures that the data collected by different sensors are synchronized in time, making subsequent data analysis and processing more accurate. The technical effects of improving the comprehensiveness and accuracy of detection are achieved.

[0033] In step S200, a first heating test period is determined, and the first detection signal group is used as the initial state to perform periodic physical end testing and heating simulation testing based on the simulation detector, and the mutual verification determines the first performance coefficient, wherein the temperature rise and fall response and the temperature field uneven response are used as the test guide.

[0034] Specifically, according to the design parameters of the graphite heater, the use scene and the previous experience data, a heating test period is determined. For example, for some heating equipment that needs to be frequently started and stopped, the test period can be set to a shorter time interval; for the heating system that runs stably for a long time, the test period can be appropriately extended. In each heating test period, the graphite heater is subjected to actual physical testing according to the predetermined test procedure. The temperature rise and fall response refers to the characteristics and response speed of the temperature change with time of the graphite heater during the temperature rise or fall process, which reflects the thermal inertia size and temperature control precision of the heater. The temperature field uneven response refers to the degree of temperature distribution unevenness in the heating area of the graphite heater during operation and the change of such unevenness with time.

[0035] A model similar to the geometric structure and physical characteristics of the actual graphite heater is established by using computer simulation software, and the same test conditions such as temperature rise and fall curve, heating power, etc. are input, and the simulation detection signal is obtained by simulation calculation. For example, the finite element analysis software is used to perform thermal simulation analysis on the graphite heater to simulate the temperature field distribution under different test conditions.

[0036] The data obtained by the physical end testing is compared and analyzed with the simulation detection signal, and the deviation between the two is calculated, and the first performance coefficient is calculated according to the deviation, which reflects the performance of the graphite heater in the temperature rise and fall response and the temperature field uneven response.

[0037] In a possible implementation, before the heating simulation test based on the simulation detector, the construction of the simulation detector further includes the step S210 of performing lightweight twin of the graphite heater, introducing state elements, and building a state space, wherein the state elements at least include a temperature field, a stress wave frequency field, and a gradient vector. Specifically, a lightweight digital model of the actual graphite heater is created according to design drawings and physical parameters of the actual graphite heater by using computer-aided design (CAD) software and finite element analysis (FEA) tools. The lightweight twin is not to build a complex physical model exactly the same as the actual graphite heater, but to simplify the model under the premise of ensuring that the key characteristics and operation rules of the actual graphite heater can be accurately reflected. For example, some microscopic structural details that have little effect on the overall performance, such as small pores inside the graphite material, are ignored, and only key elements such as main heating components, connection structures, and overall shape are retained. In this way, the complexity of the model can be reduced, the consumption of computing resources can be reduced, and the efficiency of the simulation test can be improved.

[0038] Temperature is a key parameter in the operation process of the graphite heater, which directly affects the heating effect and the safety of the equipment. The temperature field describes the temperature distribution of each position inside the graphite heater, including the temperature values at different times and different spatial points. By introducing the temperature field as a state element, the temperature change inside the heater can be monitored in real time, and abnormal conditions such as local overheating or overcooling can be found in time. During the heating and cooling process of the graphite heater, stress waves will be generated due to thermal expansion and contraction and the effect of its own weight. The stress wave frequency field reflects the frequency distribution characteristics of stress waves at different positions. Different frequencies correspond to different types of stress states and potential defects, for example, high-frequency stress waves are related to the expansion of microcracks, and low-frequency stress waves are related to the deformation of the overall structure. By monitoring the stress wave frequency field, mechanical problems inside the heater can be found in time. The gradient vector includes the temperature gradient vector and the stress gradient vector. The temperature gradient vector describes the rate of change of temperature in space, reflecting the direction and intensity of heat transfer inside the heater. The stress gradient vector describes the rate of change of stress in space, reflecting the degree of non-uniformity of stress distribution inside the structure. The above-mentioned state elements such as the temperature field, the stress wave frequency field, and the gradient vector are integrated to build a multi-dimensional state space. Each point in the state space represents a specific state of the graphite heater at a certain time, containing information of all state elements. For example, a multi-dimensional vector can be used to represent a point in the state space, and each component of the vector corresponds to the value of the temperature field, the stress wave frequency field, and the gradient vector at different positions or directions.

[0039] Step S220 introduces an action space, which corresponds to the power ratio of the modular heating units at the top, sides, and bottom. Specifically, the graphite heater is designed as three modular heating units at the top, sides, and bottom, each with its heating power independently controllable. The action space refers to the set of all possible actions; in this application, the action is adjusting the power ratio of the top, sides, and bottom modular heating units. For example, the power ratio can be defined as ranging from 0 to 1, and the sum of the power ratios of the three units is 1. The action space can then be represented as a two-dimensional plane, where each point corresponds to a specific power ratio scheme.

[0040] Step S230: Determine the constraints, which include reward conditions based on high temperature uniformity and low energy consumption operation, and penalty conditions for exceeding temperature gradient limits and energy consumption limits. Specifically, during the operation of the graphite heater, it is desirable for the temperature distribution inside the heater to be as uniform as possible to ensure consistent heating effect. Therefore, when the simulation test results show that the temperature uniformity inside the heater reaches a certain standard, a corresponding reward is given. For example, a temperature uniformity index can be defined, and when this index exceeds a certain threshold, a certain positive reward score is given, the size of which can be proportional to the value of the temperature uniformity index. To reduce operating costs and energy consumption, low-energy-consumption operation is encouraged. When the energy consumption of the heater is lower than a certain preset value in the simulation test, a reward is given. For example, based on the difference between the actual energy consumption value and the preset value, a corresponding positive reward score is given, with a larger difference resulting in a higher reward score. Excessive temperature gradients may cause thermal stress inside the graphite heater, thereby affecting the service life and safety of the equipment. When the temperature gradient is found to exceed the set safety threshold in the simulation test, a penalty is given. For example, a negative penalty score is assigned based on the degree to which the temperature gradient exceeds a threshold; the greater the degree of exceedance, the higher the penalty score. If the heater's energy consumption exceeds a preset upper limit, it indicates that the operation method is not energy-efficient, and a penalty is imposed. The penalty score can be proportional to the degree to which the energy consumption exceeds the upper limit.

[0041] Step S240: Construct a simulation detector based on the state space, action space, and constraints, wherein the simulation detector is embedded within the test platform. Specifically, a reinforcement learning algorithm is used to construct the simulation detector by combining the state space, action space, and constraints. The simulation detector can select the optimal action from the action space based on the current state to maximize rewards and avoid penalties. The constructed simulation detector is integrated into the test platform, enabling it to interact and collaborate with other parts of the test platform. The test platform can provide a simulated operating environment for the graphite heater, data acquisition and processing functions, etc., while the simulation detector makes decisions and controls based on the data provided by the test platform.

[0042] In one possible implementation, the heating simulation test based on the simulated detector further includes step S250, which initializes the state space according to the first detection signal group, wherein the first detection signal group is test data from the initial cycle node of the physical test. Specifically, the first detection signal group is test data obtained from physical testing of the actual graphite heater at the initial cycle node based on the physical test. The state space is a mathematical space describing the operating state of the graphite heater, containing state elements such as temperature field, stress wave frequency field, and gradient vector. Initializing the state space means assigning initial values ​​to these state elements, so that the simulation test can start from the same initial state as the actual physical test. The data in the first detection signal group is mapped and transformed according to the definition of each state element in the state space. For example, the temperature value measured by the temperature sensor is mapped to the temperature field in the state space; the stress data measured by the stress sensor is processed and mapped to elements such as stress wave frequency field and gradient vector. In this way, the initial state of the state space in the simulation test is kept consistent with the actual initial state of the physical test, thereby ensuring that the initial state of the subsequent simulation test is consistent with the initial state of the physical test.

[0043] Step S260: For the first heating test cycle, based on the simulation detector, perform a heating / cooling response simulation and a temperature field non-uniformity control simulation within the cycle to determine the first simulation data chain. Specifically, within the first heating test cycle, the simulation detector simulates the heating / cooling process of the graphite heater according to a preset heating and cooling control strategy. For example, the simulation detector adjusts the heating power of the simulated heater according to the power ratio scheme of the top, side, and bottom modular heating units in the action space to achieve the heating process; when cooling is required, it achieves cooling by stopping heating or using other cooling methods. During the heating / cooling process, the simulation detector monitors the temperature field changes in the state space in real time. Based on the temperature field changes, the simulation detector automatically triggers a response according to a certain algorithm. For example, when the temperature rises too quickly or exceeds the preset safe temperature, the simulation detector automatically reduces the heating power or activates cooling measures. Through continuous adjustment and control, the ideal response of the heater during the heating / cooling process is simulated, and the state element data at each moment is recorded to form heating / cooling response simulation data.

[0044] Due to various factors, graphite heaters may experience temperature field non-uniformity during operation. A simulation detector monitors the temperature field distribution in the state space in real time. When non-uniformity is detected, an automatic control mechanism is triggered. Based on the degree and location of the non-uniformity, the simulation detector selects an appropriate power ratio from the action space, adjusting the power of the top, side, and bottom modular heating units to improve temperature field uniformity. For example, if the temperature in the top region is too high, the simulation detector will appropriately reduce the power of the top heating unit while potentially increasing the power of the side or bottom heating units to achieve a more even heat distribution. During the control process, the simulation detector continuously monitors changes in the temperature field until the required uniformity is achieved, recording the state element data throughout the entire control process to form control simulation data based on temperature field non-uniformity.

[0045] The simulation data of heating and cooling response and the simulation data of temperature field non-uniformity control are integrated and arranged in chronological order to form a complete first simulation data chain. This data chain reflects the adaptive control process and corresponding state changes of the graphite heater under ideal conditions during the first heating test cycle, after the simulation detector automatically triggers responses and makes decisions based on various conditions.

[0046] This implementation initializes the state space, ensuring that the initial state of the simulation test is consistent with the actual initial state of the physical test. This provides an accurate basis for subsequent simulations and tests, ensuring that the two are identical in their initial conditions and improving the consistency and comparability between simulation and physical tests.

[0047] In one possible implementation, mutual verification determines the first performance coefficient. Step S200 further includes step S270, which involves determining a first test data chain by performing physical testing based on a first heating test cycle and continuous sensing based on a detection array, and then uploading it to the test platform. Specifically, during the first heating test cycle, physical testing is performed, while continuous sensing is performed using a detection array deployed in the graphite heater. Various types of sensors in the detection array collect data in real time according to a preset sampling frequency. This data includes various information about the graphite heater during its operation during the first heating test cycle, such as surface temperature, strain at key locations, and acoustic emission around the furnace. After preliminary processing of this collected data, it is integrated according to a certain data format to form the first test data chain. The first test data chain reflects the state changes of the graphite heater during the first heating test cycle in the actual physical test. Then, the first test data chain is uploaded to the test platform.

[0048] Step S280: Based on the data processing plugin of the test platform, the first simulated data chain and the first test data chain are mapped, subtracted, and weighted to determine the first performance coefficient. The first weighting is based on the heating / cooling response, and the second weighting is based on the temperature non-uniformity response, thus determining one layer of weights. A second layer of weights is determined based on the sensing dimension. Specifically, the test platform has a data processing plugin that performs mapping, subtraction, and weighting calculations on the first simulated data chain and the first test data chain. The heating / cooling response reflects the thermal inertia and temperature control accuracy of the heater. Since the heating / cooling response is crucial for evaluating the performance of the graphite heater, it is given the first weight. The temperature non-uniformity response affects the heating effect and equipment safety, therefore it is given the second weight. Through the first and second weightings, a first layer of weights is determined to measure the relative importance of the heating / cooling response and the temperature non-uniformity response in performance evaluation. Data collected from different sensing dimensions in the detection array have different importance for evaluating the performance of the graphite heater; therefore, a second layer of weights is determined based on the sensing dimension. For example, temperature data is crucial for reflecting the thermal performance of the heater and can be given a high weight; acoustic emission data is mainly used to monitor internal damage, and its weight can vary depending on the actual situation. Combining the first and second layer weights, the difference results of the mapping between the first simulated data chain and the first test data chain are weighted and calculated to ultimately determine the first performance coefficient. Specifically, the difference between the corresponding data in the first simulated data chain and the first test data chain is calculated, separately calculating the data differences in heating / cooling response and temperature non-uniformity response. The data difference in heating / cooling response is multiplied by the first weight, and the data difference in temperature non-uniformity response is multiplied by the second weight to obtain the weighted difference between heating / cooling response and temperature non-uniformity response. Then, the results of each sensing dimension are weighted and summed according to the second layer weight to obtain the first performance coefficient. This coefficient reflects the degree of deviation between the actual and ideal performance of the graphite heater in terms of heating / cooling response and temperature field non-uniformity response, and is used to comprehensively evaluate the performance of the graphite heater during the first heating test cycle.

[0049] This implementation method, through mapping and subtraction, can accurately identify the differences between the first simulated data chain and the first test data chain, avoiding errors caused by relying solely on subjective judgment or simple comparison. Simultaneously, by employing a weighted calculation method, it considers the relative importance of different indicators and sensing dimensions in performance evaluation, enabling the first performance coefficient to more comprehensively and accurately reflect the actual performance of the graphite heater, thus improving the scientific rigor and reliability of the performance evaluation.

[0050] Step S300: Determine the second pulse test conditions, perform a power pulsation sequence test based on unstable DC output at the physical end, and cross-validate with the simulation test based on the analog detector to determine the second performance coefficient.

[0051] Specifically, based on the rated power, operating voltage, and current of the graphite heater, parameters such as pulsation frequency, pulsation amplitude, and pulsation time of the unstable DC output power pulsation sequence are determined. Unstable DC output refers to a DC power supply output that is not constant but varies with time according to a certain pattern, used to simulate unstable power input conditions that may occur in actual operation. The power pulsation sequence is a series of power output signals that vary according to a specific frequency, amplitude, and time pattern, used to test the performance of the graphite heater under unstable power conditions. An adjustable DC power supply is used to supply power to the graphite heater according to the set power pulsation sequence, while a detection array is used to collect real-time data on changes in physical quantities such as temperature of the graphite heater during the power pulsation process. For example, the output power of the DC power supply can be programmed to vary according to a preset pulsation sequence, while a data acquisition system records the signals output by the sensors in real time.

[0052] In computer simulation models, the same power pulsation sequence parameters as the physical terminal are input for simulation calculations to obtain simulated detection signals. For example, in finite element analysis software, boundary conditions that vary power over time are set to simulate the temperature field changes of a graphite heater during power pulsation.

[0053] The data obtained from the physical power pulsation sequence test are compared and analyzed with the simulated detection signal. The deviation between the two is calculated, and a second performance coefficient is obtained based on the deviation. This coefficient reflects the performance of the graphite heater under unstable power output conditions. For example, the second performance coefficient can be defined as the ratio of the actual temperature fluctuation range to the simulated temperature fluctuation range during power pulsation.

[0054] In one possible implementation, a power pulsation sequence test based on unstable DC output is performed at the physical end. Step S300 further includes step S310, determining the second pulse test conditions, wherein the second pulse test conditions are a high-frequency, low-amplitude power pulsation sequence. Specifically, this application aims to excite minute changes in the gas and structure inside the furnace through the power pulsation sequence, and then obtain relevant data through acoustic response. Therefore, the second pulse test conditions are set as a high-frequency, low-amplitude power pulsation sequence. High-frequency pulsation allows the power to change rapidly in a short time, generating more frequent thermal disturbances; low-amplitude pulsation ensures that the power change is within a controllable range, avoiding excessive impact on the graphite heater equipment, while effectively exciting the required acoustic response.

[0055] In step S320, according to the second pulse test conditions, a power pulsation is injected into the graphite heater, and acoustic response sensing based on thermal pulsation is performed to determine the second test data chain. Specifically, according to the second pulse test conditions determined in step S310, a set high-frequency, micro-amplitude power pulsation is injected into the graphite heater. When the power pulsation is injected, the graphite heater will experience temperature fluctuations due to power changes. This thermal pulsation will be transmitted to the gas and structure inside the furnace, causing them to undergo slight thermal expansion and contraction. Due to the stress changes caused by the thermal expansion and contraction of objects, infrasound and acoustic waves are excited. At this time, the acoustic emission sensor array pre-arranged on the furnace begins to work. This sensor array collects the excited infrasound and acoustic wave signals from all directions and multiple angles. These sensors perform preliminary processing on the collected analog signals, such as filtering and amplification, to improve the signal quality and recognizability. Then, the processed signals are converted into digital signals to form the second test data chain.

[0056] Step S330: The second test data link is transmitted back to the test platform. Specifically, the second test data link obtained in step S320 is transmitted back from the physical end to the test platform through a pre-designed data transmission channel. The data transmission channel can be wired or wireless, depending on the actual test environment and conditions.

[0057] In one possible implementation, after mutual verification to determine the second performance coefficient, step S300 further includes step S340, whereby, based on the data processing plugin of the test platform, a linear transformation is performed on the second test data chain to determine a first linear relationship. This first linear relationship characterizes the relationship between the power pulsation sequence and the acoustic response sequence. Specifically, the data processing plugin uses a specific algorithm to perform a linear transformation on the second test data chain. For example, a linear regression algorithm such as the least squares method can be used, with the power pulsation sequence as the independent variable and the acoustic response sequence as the dependent variable, to find the optimal linear relationship between the two by fitting data points. After calculation and analysis, the first linear relationship is finally determined. This relationship characterizes the correspondence between the power pulsation sequence and the acoustic response sequence under actual test conditions.

[0058] Step S350: Perform a linear transformation on the second simulated data chain to determine a second linear relationship, wherein the second simulated data chain is generated based on the simulation tester. Specifically, similar to the first simulated data chain, the second simulated data chain is generated based on the simulation tester. The simulation tester simulates power pulsations and the corresponding acoustic response process to generate the second simulated data chain. The data processing plugin of the test platform is also used to perform a linear transformation on the second simulated data chain. Similar to step S340, the same linear regression algorithm is used, with the simulated power pulsation sequence as the independent variable and the simulated acoustic response sequence as the dependent variable, for data fitting and analysis. Finally, the second linear relationship is determined, which reflects the relationship between the power pulsation sequence and the acoustic response sequence under ideal conditions.

[0059] Step S360: Verify the first linear relationship and the second linear relationship, and determine the second performance coefficient by solving for the unit linear difference. Specifically, compare and verify the first linear relationship obtained in step S340 and the second linear relationship obtained in step S350, and calculate the unit linear difference. The unit linear difference refers to the difference in the dependent variable under a unit change in the independent variable between the first and second linear relationships. By calculating this difference, the difference between the actual test and the simulated test is quantified. For example, if the first linear relationship and the second linear relationship are y1=a1x+b1 and y2=a2x+b2 respectively, then the unit linear difference can be expressed as the value of Δy=(a1−a2)x+(b1−b2) at x=1. Based on the solved unit linear difference, combined with the pre-set calculation model and rules, the second performance coefficient is determined. This performance coefficient comprehensively reflects the consistency and performance difference between the actual test system and the simulated system.

[0060] In one possible implementation, step S360 further includes step S361, whereby a first acoustic element of the acoustic signal has a first mathematical relationship with a second state element of the instantaneous state of the thermal field, wherein the first acoustic element includes at least phase and amplitude characteristics, and the second state element includes at least heat capacity, thermal resistance, and airflow. Specifically, the acoustic signal contains rich information, among which phase and amplitude characteristics are two key elements. Phase reflects the positional information of the acoustic signal on the time axis, determining the starting point and vibration rhythm of the sound wave; amplitude characteristics reflect the intensity of the acoustic signal and are closely related to the energy of the sound wave. In the test scenario where power pulsation induces acoustic response, changes in phase and amplitude can reflect the degree of influence of power pulsation on the acoustic signal. The instantaneous state of the thermal field is determined by multiple elements: heat capacity represents the ability of an object to store heat, affecting the rate of temperature change when the object is subjected to heat; thermal resistance reflects the degree of obstruction to heat transfer by the object, and the greater the thermal resistance, the more difficult the heat transfer; airflow plays a role in heat exchange and transmission in the thermal field, capable of carrying away or bringing heat, changing the distribution and state of the thermal field. These elements are interconnected and influence each other, jointly determining the instantaneous dynamic characteristics of the thermal field.

[0061] Through theoretical analysis and experimental research, a first mathematical relationship is established between the first acoustic elements of an acoustic signal (phase and amplitude characteristics) and the second state elements of the instantaneous thermal field (heat capacity, thermal resistance, and airflow). This mathematical relationship comprehensively considers various physical factors and interactions. For example, when sound waves propagate in a thermal field, heat capacity and thermal resistance affect the attenuation and propagation speed of the sound waves, and airflow causes refraction and scattering of the sound waves. These effects are quantified and described through a mathematical model.

[0062] Step S362: Using the first mathematical relation, solve for the unit linear difference. Specifically, when solving for the unit linear difference, the first mathematical relation is used to transform the change in acoustic response into a change in thermal field state elements. For example, when the power pulsation undergoes the same unit change in both actual and simulated tests, the change in acoustic response is obtained according to the first and second linear relations, respectively. These changes in acoustic response are then converted into changes in thermal field state elements using the first mathematical relation. Finally, the difference between the changes in thermal field state elements in the actual and simulated tests is calculated; this difference is the unit linear difference. In this way, the linear relationship that directly compares acoustic responses is transformed into a comparison of changes in thermal field state elements, thereby analyzing the root cause of the differences between actual and simulated tests.

[0063] Step S400: Determine the comprehensive performance coefficient based on the first performance coefficient and the second performance coefficient, and visualize the test platform.

[0064] Specifically, the comprehensive performance coefficient is calculated using a weighted average or other mathematical methods, based on the weighted allocation of the first and second performance coefficients. A graphical user interface (GUI) development tool is used to display the comprehensive performance coefficient and various data from the testing process in the form of charts and graphs on the testing platform interface. For example, a line graph can be used to show the temperature changes of the graphite heater under different test conditions, and a bar chart can be used to compare the values ​​of different performance coefficients.

[0065] In one possible implementation, step S400 further includes step S410, integrating the first performance coefficient and the second performance coefficient by performing a weighted summation to obtain the comprehensive performance coefficient. Specifically, based on the importance of the first and second performance coefficients in evaluating the overall performance of the test system, corresponding weights are assigned respectively. The first performance coefficient is multiplied by its corresponding weight, and the second performance coefficient is multiplied by its corresponding weight; then the two products are added together to obtain the comprehensive performance coefficient.

[0066] Step S420: On the display interface of the test platform, a pop-up window displays the comprehensive performance coefficient. Specifically, the display interface of the test platform is the window through which the user interacts with the test system and obtains information. It presents various data, results, and status information during the test process to the user in an intuitive and clear manner, facilitating the user's timely understanding of the test situation and enabling corresponding operations and decisions. The pop-up window is a way to highlight information, presenting the comprehensive performance coefficient result to the user in a prominent manner without interfering with the user's viewing of other information on the test platform. After the test system completes the calculation of the comprehensive performance coefficient, a window automatically pops up on the display interface, allowing the user to obtain this crucial information immediately without having to search through a complex interface, thus improving the efficiency and convenience of information acquisition.

[0067] This application embodiment employs a detection array arranged within a graphite heater featuring a modular heating unit and a coaxial spiral channel structure. A first detection signal group is acquired through first-order sensing. A first heating test cycle is set, and using the first detection signal group as the initial state, periodic physical-end testing and heating simulation testing based on a simulated detector are performed simultaneously. Guided by the heating / cooling response and temperature field non-uniformity response, a first performance coefficient is determined through mutual verification between the two. Second pulse test conditions are then determined. Power pulsation sequence testing based on unstable DC output is performed at the physical end, while simulation testing based on the simulated detector is conducted simultaneously. Mutual verification is performed again to determine the second performance coefficient. A comprehensive performance coefficient is calculated based on the first and second performance coefficients, and the results are visualized on a testing platform. These technical means solve the technical problem of limited accuracy in detecting heating performance faults in existing graphite heaters, achieving the technical effect of improving the accuracy of heater performance safety assessment.

[0068] In the above text, refer to Figure 1 A method for detecting heating performance faults in a graphite heater according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 A heating performance fault detection system for a graphite heater according to an embodiment of the present invention is described.

[0069] A graphite heater heating performance fault detection system according to an embodiment of the present invention addresses the technical problem of limited accuracy in existing graphite heater heating performance fault detection methods, thereby improving the accuracy of heater performance safety assessment. The graphite heater heating performance fault detection system includes: a first-order sensing module 10, a first performance coefficient determination module 20, a second performance coefficient determination module 30, and a test platform visualization module 40.

[0070] A first-order sensing module 10 is used to deploy a detection array in the graphite heater and trigger a first detection signal group acquired based on the first-order sensing. The graphite heating geometry is a coaxial spiral channel, and a modular heating unit is used. A first performance coefficient determination module 20 is used to determine a first heating test cycle. Taking the first detection signal group as the initial state, a periodic physical end test is performed, and a heating simulation test based on an analog detector is performed to cross-validate and determine the first performance coefficient. The heating and cooling response and the temperature field non-uniformity response are used as test guides. A second performance coefficient determination module 30 is used to determine the second pulse test conditions. A power pulsation sequence test based on unstable DC output is performed at the physical end, and a simulation test based on an analog detector is performed to cross-validate and determine the second performance coefficient. A test platform visualization module 40 is used to determine the comprehensive performance coefficient based on the first performance coefficient and the second performance coefficient and to visualize the test platform.

[0071] The specific configuration of the first-order sensing module 10 is described in detail below: As mentioned above, the first-order sensing module 10 may further include: a detection array deployment unit for deploying a detection array, wherein the deployment method of the detection array includes at least: a micro temperature sensing array deployed on the surface of the graphite heater, high-temperature strain gauges deployed at the key mechanical bearing points and connection positions of the graphite component, and an acoustic emission sensing array deployed around the furnace body of the graphite heater; and a synchronous sensing unit for activating the detection array to perform synchronous sensing upon triggering of a detection command, thereby determining the first detection signal group.

[0072] The detailed description of the specific configuration of the first performance coefficient determination module 20 is explained as follows: As mentioned above, before the heating simulation test based on the simulation detector, the construction of the simulation detector, the first performance coefficient determination module 20 may further include: a lightweight twin unit for lightweight twinning the graphite heater, introducing state elements, and building a state space, wherein the state elements include at least a temperature field, a stress wave frequency field, and a gradient vector; an action space introduction unit for introducing an action space, wherein the power ratio of the modular heating units corresponding to the top, sides, and bottom; a constraint condition determination unit for determining constraint conditions, wherein the constraint conditions include reward conditions based on high temperature uniformity and low energy consumption operation, and penalty conditions for exceeding temperature gradient limits and energy consumption limits; and a simulation detector construction unit for constructing a simulation detector based on the state space, action space, and constraint conditions, wherein the simulation detector is embedded in the test platform.

[0073] The heating simulation test based on the simulated detector, the first performance coefficient determination module 20 may further include: a state space initialization unit for initializing the state space according to the first detection signal group, wherein the first detection signal group is the test data of the initial cycle node based on physical testing; and a simulation unit for determining the first simulation data chain for the first heating test cycle by performing heating and cooling response simulation and temperature field non-uniformity control simulation within the cycle according to the simulated detector.

[0074] The mutual verification determines the first performance coefficient. The first performance coefficient determination module 20 may further include: a first test data chain determination unit, which is used to determine the first test data chain by performing physical testing based on the first heating test cycle and continuous sensing based on the detection array, and upload it to the test platform; and a first performance coefficient determination unit, which is used to perform mapping difference and weighting calculation on the first simulated data chain and the first test data chain according to the data processing plug-in of the test platform to determine the first performance coefficient, wherein the first weighting is performed based on the heating and cooling response, the second weighting is performed based on the temperature non-uniformity response, and a second weighting is determined based on the sensing dimension.

[0075] The detailed description of the specific configuration of the second performance coefficient determination module 30 is explained as follows: As mentioned above, the second performance coefficient determination module 30 may further include: a second pulse test condition determination unit for determining the second pulse test conditions, wherein the second pulse test conditions are a high-frequency, low-amplitude power pulsation sequence; an acoustic response sensing unit for injecting power pulsations into the graphite heater according to the second pulse test conditions, performing acoustic response sensing based on thermal pulsations, and determining the second test data link; and a data feedback unit for feeding back the second test data link to the test platform.

[0076] The mutual verification determines the second performance coefficient. The second performance coefficient determination module 30 may further include: a linear transformation unit for performing a linear transformation on the second test data chain according to the data processing plugin of the test platform to determine a first linear relationship, wherein the first linear relationship characterizes the relationship between the power pulsation sequence and the acoustic response sequence; performing a linear transformation on the second analog data chain to determine a second linear relationship, wherein the second analog data chain is generated based on the simulator; and a unit linear difference solving unit for calibrating the first linear relationship and the second linear relationship, and determining the second performance coefficient by solving the unit linear difference.

[0077] The unit linear difference solving unit may further include: a first mathematical relationship determining subunit for the first acoustic element of the acoustic signal having a first mathematical relationship with the second state element of the instantaneous state of the thermal field, wherein the first acoustic element includes at least phase and amplitude characteristics, and the second state element includes at least heat capacity, thermal resistance, and airflow; and a unit linear difference solving subunit for assisting the first mathematical relationship in performing unit linear difference solving.

[0078] The detailed description of the specific configuration of the test platform visualization module 40 is explained as follows: As mentioned above, the test platform visualization module 40 may further include: a weighted summation unit for integrating the first performance coefficient and the second performance coefficient, performing weighted summation, and obtaining a comprehensive performance coefficient; and a pop-up display unit for displaying the comprehensive performance coefficient in a pop-up window on the display interface of the test platform.

[0079] The graphite heater heating performance fault detection system provided in this embodiment of the invention can execute the graphite heater heating performance fault detection method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0080] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0081] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application. In some cases, the actions or steps described in this application can be performed in a different order than that shown in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for detecting heating performance faults in a graphite heater, characterized in that, The method includes: A detection array is deployed in a graphite heater to trigger the acquisition of the first detection signal group based on first-order sensing. The graphite heating geometry is a coaxial spiral channel, and a modular heating unit is used. The first heating test cycle is determined, and the first detection signal group is used as the initial state to perform periodic physical end tests and heating simulation tests based on the simulation detector. The first performance coefficient is determined by mutual verification, wherein the heating and cooling response and the temperature field non-uniformity response are used as the test guide. The second pulse test conditions are determined, and a power pulsation sequence test based on unstable DC output is performed at the physical end. This test is then cross-validated with a simulation test based on an analog detector to determine the second performance coefficient. Based on the first performance coefficient and the second performance coefficient, the comprehensive performance coefficient is determined and the test platform is visualized; Perform power ripple sequence tests based on unstable DC output at the physical end, including: The second pulse test conditions are determined, wherein the second pulse test conditions are a high-frequency, low-amplitude power pulsation sequence; According to the second pulse test conditions, power pulsation is injected into the graphite heater, acoustic response sensing based on thermal pulsation is performed, and the second test data chain is determined. The second test data link is sent back to the test platform; Mutual verification to determine the second performance coefficients includes: Based on the data processing plugin of the test platform, the second test data chain is linearly transformed to determine the first linear relationship, wherein the first linear relationship characterizes the relationship between the power pulsation sequence and the acoustic response sequence; A linear transformation is performed on the second simulated data chain to determine a second linear relationship, wherein the second simulated data chain is generated based on the simulation tester; By calibrating the first and second linear relationships, the second performance coefficient is determined by solving for the unit linear difference.

2. The method for detecting heating performance faults in a graphite heater as described in claim 1, characterized in that, The deployment of the detection array includes at least the following: a miniature temperature sensing array is deployed on the surface of the graphite heater; high-temperature strain gauges are deployed at the key mechanical load-bearing points and connection positions of the graphite component; and an acoustic emission sensing array is deployed around the furnace body of the graphite heater. Upon triggering of the detection command, the detection array is activated to perform synchronous sensing and determine the first detection signal group.

3. The method for detecting heating performance faults in a graphite heater as described in claim 1, characterized in that, Prior to the heating simulation test based on the simulation detector, the construction of the simulation detector includes: The graphite heater is subjected to lightweight twinning, state elements are introduced, and a state space is constructed, wherein the state elements include at least a temperature field, a stress wave frequency field, and a gradient vector. An action space is introduced, in which the power ratio of the modular heating units corresponding to the top, sides, and bottom is determined; Define the constraints, which include reward conditions based on high temperature uniformity and low energy consumption operation, and penalty conditions for exceeding temperature gradient limits and energy consumption limits. A simulation detector is constructed based on the state space, action space, and constraints, wherein the simulation detector is embedded in the test platform.

4. The method for detecting heating performance faults in a graphite heater as described in claim 3, characterized in that, Heating simulation tests based on analog detectors include: The state space is initialized according to the first detection signal group, wherein the first detection signal group is the test data of the initial periodic node based on physical testing; For the first heating test cycle, based on the simulation detector, the heating and cooling response simulation and the control simulation based on temperature field non-uniformity are performed within the cycle to determine the first simulation data chain.

5. The method for detecting heating performance faults in a graphite heater as described in claim 4, characterized in that, Mutual verification determines the first performance coefficient, including: By performing physical tests based on the first heating test cycle and continuous sensing based on the detection array, the first test data chain is determined and uploaded to the test platform; Based on the data processing plugin of the test platform, the first simulated data chain and the first test data chain are mapped, subtracted, and weighted to determine the first performance coefficient. The first weight is determined by the temperature rise and fall response, and the second weight is determined by the temperature non-uniformity response. The second layer of weight is determined by the sensing dimension.

6. The method for detecting heating performance faults in a graphite heater as described in claim 1, characterized in that, The first acoustic element of the acoustic signal has a first mathematical relationship with the second state element of the instantaneous state of the thermal field, wherein the first acoustic element includes at least phase and amplitude characteristics, and the second state element includes at least heat capacity, thermal resistance, and airflow. Using the first mathematical relationship as a reference, solve for the unit linear difference.

7. The method for detecting heating performance faults in a graphite heater as described in claim 1, characterized in that, The first performance coefficient and the second performance coefficient are integrated and weighted summation is performed to obtain the comprehensive performance coefficient. The overall performance coefficient is displayed in a pop-up window on the display interface of the test platform.

8. A heating performance fault detection system for a graphite heater, characterized in that, The system is used to implement the heating performance fault detection method for a graphite heater according to any one of claims 1-7, the system comprising: A first-order sensing module is used to deploy a detection array in a graphite heater to trigger the acquisition of a first detection signal group based on the first-order sensing. The graphite heating geometry is a coaxial spiral channel and a modular heating unit is used. The first performance coefficient determination module is used to determine the first heating test cycle, and to perform periodic physical end tests and heating simulation tests based on the simulation detector as the initial state, and to mutually verify and determine the first performance coefficient, wherein the heating and cooling response and the temperature field non-uniformity response are used as the test guide. The second performance coefficient determination module is used to determine the second pulse test conditions, perform a power pulsation sequence test based on unstable DC output at the physical end, and cross-validate with the simulation test based on the analog detector to determine the second performance coefficient. The test platform visualization module is used to determine the comprehensive performance coefficient based on the first performance coefficient and the second performance coefficient, and to visualize the test platform.

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