Abnormal monitoring method for heating system of carbon brush forming machine

Abstract

The present invention discloses a method for monitoring the abnormality of a heating system of a carbon brush forming machine, which relates to the technical field of abnormality monitoring of carbon brush forming machines. The method specifically includes the following steps: obtaining the heating control behavior correlation information of the heating system within the voltage fluctuation action time window, and based on the information, evaluating the degree of heating power output deviation of the heating system under the condition of frequent power supply voltage fluctuations, and dividing it into stable deviation degree, fluctuating deviation degree and uncontrolled deviation degree; based on the classification result of the degree of heating power output deviation, determining the abnormal risk level of temperature control distortion in the heating system; and executing corresponding heating control processing measures according to the determined abnormal risk level. The present invention accurately evaluates the risk of temperature control distortion by real-time monitoring of power supply voltage fluctuations and heating power deviations, realizes dynamic adjustment, improves the stability of the heating system, and ensures product quality consistency.

Description

Technical Field

[0001] The present invention relates to the technical field of abnormality monitoring of carbon brush forming machines, and in particular to an abnormality monitoring method for a heating system of a carbon brush forming machine. Background Art

[0002] A carbon brush forming machine is a type of industrial equipment specifically designed for manufacturing motor carbon brushes. Its primary function is to heat and press raw materials such as carbon powder and resin into finished carbon brushes with specific dimensions and performance requirements. This equipment plays a central role in the carbon brush production process. To achieve thermosetting of the raw materials, carbon brush forming machines are typically equipped with a heating system. This system uses devices such as electric heating plates and temperature control modules to precisely control the temperature of the mold or pressing chamber, ensuring temperature uniformity and heat treatment quality during the forming process. Carbon brush forming is extremely sensitive to temperature conditions. Any abnormalities in the heating system, such as overheating, sensor failure, temperature drift, or heating failure, can cause cracking, substandard hardness, abnormal deformation, and even equipment damage or production interruption. Therefore, real-time monitoring of heating system anomalies is crucial. By incorporating intelligent sensors and data acquisition and analysis algorithms, key parameters during the heating process can be continuously monitored to detect and warn of anomalies. This not only effectively ensures equipment operational stability and product consistency, but also improves production automation and safety. This technology is a key support for the development of modern intelligent manufacturing towards high quality and low failure rates.

[0003] Existing abnormality monitoring technology for the heating system of carbon brush forming machines mainly relies on temperature sensors (such as thermocouples and thermistors) installed at key positions of the heating module or mold to collect real-time temperature data of the heating zone, and perform logical judgment and threshold comparison on the collected data through a PLC controller or embedded system to achieve preliminary identification of abnormal conditions. The monitoring process usually includes four key links: first, the data acquisition link, in which the temperature of each key part of the heating system is obtained in real time through temperature sensors arranged at multiple points; second, the data transmission link, in which the collected temperature signal is transmitted to the central control unit via the signal conditioning module; third, the data analysis and judgment link, in which the control system performs algorithm analysis based on the set normal temperature range, temperature rise rate and other parameters to determine whether there are problems such as abnormal temperature fluctuations, sensor failure, heating timeout, etc.; fourth, the response execution link, once an abnormal state is identified, the system will automatically execute the corresponding control strategy, such as activating the alarm, cutting off the heating power supply, suspending equipment operation, etc., to prevent production accidents.

[0004] The existing technology has the following deficiencies:

[0005] During the heating operation of a carbon brush forming machine, if the factory power supply voltage fluctuates frequently, especially during shift handovers with frequent load switching, unstable heating power output can occur. Because the heating system's power control relies on real-time voltage, voltage fluctuations can directly cause the heating power to deviate from the preset output level, resulting in unstable execution of temperature control commands. Although the temperature value reported by the temperature sensor may not appear abnormal, the actual heating rate and heating uniformity are severely disrupted, and heat distribution within the mold is unbalanced. Existing anomaly monitoring technologies for carbon brush forming machine heating systems cannot determine whether the heating system is at risk of temperature control distortion based on the degree of heating power output deviation under frequent power supply voltage fluctuations. This is because their monitoring logic relies solely on static data from the temperature sensor to determine compliance, without establishing an analytical correlation between heating control behavior and external power supply status. As a result, the temperature control system is judged to be operating normally even when it is in a distorted state, causing temperature distribution anomalies to go undetected. This can lead to uneven mold heating, insufficient heat curing of carbon powder, or overheating and carbonization, resulting in quality issues such as uneven carbon brush product density, resistance fluctuations, and reduced mechanical strength. In severe cases, irreversible damage can also occur to the heating element and mold.

[0006] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0007] The purpose of the present invention is to provide a method for monitoring abnormalities in a heating system of a carbon brush forming machine, so as to solve the problems in the above-mentioned background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a method for monitoring abnormalities in a heating system of a carbon brush forming machine, specifically comprising the following steps:

[0009] Collect the power supply voltage data of the carbon brush forming machine during the heating operation, and determine whether the power supply voltage is in a state of frequent fluctuation based on the preset judgment rules;

[0010] If the power supply voltage is in a state of frequent fluctuation, the continuous time period during which the voltage fluctuation occurs is extracted and defined as the voltage fluctuation action time window;

[0011] Acquire heating control behavior information of the heating system within the voltage fluctuation time window, and based on this information, evaluate the degree of heating power output deviation of the heating system when the power supply voltage fluctuates frequently, and classify the deviation into stable deviation, fluctuating deviation, and uncontrolled deviation.

[0012] Based on the classification results of the degree of heating power output deviation, the abnormal risk level of temperature control distortion in the heating system is determined;

[0013] Execute corresponding heating control measures according to the determined abnormal risk level;

[0014] The data and control response information generated during the monitoring process are archived, and the processing flow for analyzing the associated information of heating control behavior is updated based on the archiving results to adapt to the monitoring needs under different working conditions.

[0015] Preferably, extracting the continuous time period in which voltage fluctuations continue to occur includes: when the power supply voltage is judged to be in a frequently fluctuating state, marking the starting moment when it is first judged to be in a frequently fluctuating state, and continuously judging whether the power supply voltage remains in a frequently fluctuating state in subsequent sampling cycles, until the judgment results in several consecutive sampling cycles are all in a non-frequently fluctuating state, determining the end moment, and using the time period between the start moment and the end moment as the voltage fluctuation action time window.

[0016] Preferably, within the voltage fluctuation action time window, the heating control behavior correlation information of the heating system is obtained, and based on the information, the heating power output deviation degree of the heating system under the condition of frequent power supply voltage fluctuation is evaluated, and the deviation degree is divided into a stable deviation degree, a fluctuating deviation degree, and an uncontrolled deviation degree, which specifically includes the following steps:

[0017] Acquire heating control behavior correlation information of the heating system within the voltage fluctuation action time window and preprocess it;

[0018] Extracting response fluctuation information and heating control behavior deviation information from the preprocessed heating control behavior correlation information, and analyzing them to generate a dynamic response fluctuation coefficient and a heating control deviation index, respectively;

[0019] A heating power output offset evaluation model is constructed based on the generated dynamic response fluctuation coefficient and heating control deviation index, and an offset evaluation coefficient is generated through weighted summation;

[0020] Determine the pre-set offset evaluation coefficient threshold range, and compare it with the generated offset evaluation coefficient after determination. According to the comparison results, evaluate the degree of offset of the heating power output of the heating system under the condition of frequent power supply voltage fluctuations, and divide it into stable offset degree, fluctuating offset degree and out-of-control offset degree.

[0021] Preferably, the logic for obtaining the dynamic response fluctuation coefficient is as follows: extract the response fluctuation information from the pre-processed heating control behavior correlation information, specifically including the heating power output value and power supply voltage change of the heating system at different times within the voltage fluctuation action time window, and use the function and To express, For time point, Indicates that within the voltage fluctuation time window The heating power output value of the heating system at all times, Indicates that within the voltage fluctuation time window The change in power supply voltage at the moment, the time period is defined as ;

[0022] Determine the factors affecting the heating system response by changes in heating power and power supply voltage through historical experimental regression analysis and , is the factor affecting the heating system response to heating power changes, is the influence factor of power supply voltage change on heating system response; calculate the dynamic response fluctuation coefficient, the specific calculation formula is as follows:

[0023] Where, is the dynamic response fluctuation coefficient, Indicates the rate of change of the heating power output value. Preferably, the logic for obtaining the heating control deviation index is as follows: extract the heating control behavior deviation information from the pre-processed heating control behavior association information, specifically including the target heating power setting value and the change amplitude of the power supply voltage at different times within the voltage fluctuation action time window, and use the function and To express, For time point, Indicates that within the voltage fluctuation time window The target heating power setting value at the moment, Indicates that within the voltage fluctuation time window The change range of the power supply voltage at the moment, the time period is defined as ; Determine the influencing factors of heating power deviation on heating system control by fitting historical experimental data ;

[0024] Calculate the heating control deviation index. The specific calculation formula is as follows: Where, is the heating control deviation index, Indicates the rate of change of the power supply voltage. Indicates that within the voltage fluctuation time window The heating power output value of the heating system at all times.

[0025] Preferably, the dynamic response fluctuation coefficient generated and heating control deviation index A heating power output offset evaluation model is constructed, and the offset evaluation coefficient is generated by weighted summation. The specific calculation formula is as follows: Where, is the offset evaluation coefficient, and Dynamic response fluctuation coefficient and heating control deviation index The non-zero weight coefficient of .

[0026] Preferably, a preset offset evaluation coefficient threshold interval is determined , and after determination, the generated offset evaluation coefficient A comparison was conducted, and based on the comparison results, the degree of heating power output deviation of the heating system under the condition of frequent power supply voltage fluctuations was evaluated. The deviation was divided into stable deviation degree, fluctuating deviation degree, and uncontrolled deviation degree. The specific comparison analysis is as follows:

[0027] like ,The degree of heating power output offset of the heating system under the condition of frequent power voltage fluctuations is a stable offset degree;

[0028] like ,The degree of heating power output offset of the heating system under the condition of frequent power voltage fluctuations is the fluctuating ,degree;

[0029] like , the degree of deviation of the heating power output of the heating system under the condition of frequent power supply voltage fluctuations is an out-of-control type deviation degree.

[0030] Preferably, based on the classification result of the degree of heating power output deviation, the abnormal risk level of temperature control distortion in the heating system is determined, specifically:

[0031] If the degree of deviation of the heating power output is stable, the abnormal risk level of temperature control distortion in the heating system is no risk;

[0032] If the degree of heating power output deviation is an out-of-control deviation, the abnormal risk level of temperature control distortion in the heating system is severe risk;

[0033] If the degree of heating power output deviation is a fluctuating deviation degree, several subsequently generated deviation evaluation coefficients are continuously obtained and analyzed, and the abnormal risk level of temperature control distortion in the heating system is determined based on the analysis results.

[0034] Preferably, if the heating power output deviation is a fluctuating deviation, several subsequent deviation evaluation coefficients are continuously obtained and recalibrated as , It is the number of several offset evaluation coefficients generated subsequently. , is a positive integer;

[0035] Calculate several subsequent generated offset evaluation coefficients Average value , according to the formula: ;

[0036] Calculate several subsequent generated offset evaluation coefficients Standard deviation , according to the formula: ;

[0037] Determine the preset average value threshold of several preset offset evaluation coefficients and preset standard deviation thresholds , and after determination, respectively with several calculated offset evaluation coefficients Average value and standard deviation Perform a comparison and determine the abnormal risk level of temperature control distortion in the heating system based on the comparison results. The specific comparison analysis is as follows:

[0038] like and , the heating system has a high risk level of abnormal temperature control distortion; if and , the abnormal risk level of temperature control distortion in the heating system is medium risk; if and , the abnormal risk level of temperature control distortion in the heating system is medium risk; if and , the abnormal risk level of temperature control distortion in the heating system is low risk.

[0039] Preferably, corresponding heating control measures are executed according to the determined abnormal risk level, specifically:

[0040] If the abnormal risk level of temperature control distortion in the heating system is no risk, the specific heating control treatment measures to be implemented are: continue to operate normally according to the predetermined heating program without any control intervention;

[0041] If the abnormal risk level of temperature control distortion in the heating system is low, the specific heating control treatment measures to be implemented are: continue heating, maintain the existing control parameters, and continuously monitor the status of the heating system;

[0042] If the heating system has a medium risk of temperature control distortion, the specific heating control measures to be implemented are: extend the heating time, adjust the heating power output to ensure a uniform heating process, and if the heating system experiences persistent fluctuations, immediately intervene manually and adjust the control strategy to avoid further temperature control distortion.

[0043] If the heating system has a high risk level of temperature control distortion, the specific heating control measures to be implemented are: immediately suspend the heating process, check the working status of the heating system, find out the cause of the temperature control distortion, and adjust and maintain the equipment to restore the normal operation of the heating system;

[0044] If the abnormal risk level of temperature control distortion in the heating system is serious, the specific heating control processing measures to be implemented are: immediately terminate the heating task, cut off the power supply of the heating system, record the abnormal information, conduct a comprehensive inspection and repair, and ensure that the heating system is restored to a safe working state.

[0045] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0046] 1. The present invention can promptly identify and respond to temperature control distortion by accurately monitoring and dynamically evaluating the operating status of the heating system under conditions of frequent power supply voltage fluctuations. By collecting power supply voltage and heating power data in real time, and combining the dynamic response fluctuation coefficient and heating control deviation index, this method can accurately assess the degree of heating power output deviation, ensuring that the heating system maintains efficient and stable operation under the influence of power supply voltage fluctuations. This approach can effectively avoid problems such as uneven temperature distribution, insufficient heating, or overheating, thereby improving the temperature control accuracy of the carbon brush forming process and ensuring the consistency and reliability of product quality.

[0047] 2. By incorporating a dynamic assessment model, the present invention automatically optimizes and adjusts the heating system based on real-time changes in heating power offset. In the event of power fluctuations, the system automatically determines the risk level by comparing the offset assessment coefficient with a preset threshold. Based on this determination, appropriate control measures are taken, such as extending the heating time, adjusting power output, or pausing the heating process. This dynamic monitoring and adaptive adjustment mechanism enables the heating system to adapt to varying operating conditions in real time, avoiding the limitations of traditional temperature control systems that rely solely on static data judgment, thereby significantly improving the system's flexibility and anti-interference capabilities.

[0048] 3. The present invention can accurately identify temperature control distortion that may occur in the heating system due to power supply voltage fluctuations and provide targeted treatment measures. By providing real-time early warnings and taking countermeasures such as pausing heating and adjusting heating power, timely intervention can be made before temperature control distortion occurs to prevent overheating or damage to the heating elements and molds. In addition, the system can archive data and optimize monitoring processes to further improve monitoring accuracy and ensure efficient operation under different operating conditions. This technical solution not only enhances the automation and intelligence level of the production process, but also significantly reduces losses caused by equipment failures and production interruptions, ensuring the continuous and stable operation of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction to the drawings required for use in the embodiments will be given below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0050] Figure 1 The figure is a flow chart of the abnormality monitoring method of the heating system of the carbon brush forming machine of the present invention. DETAILED DESCRIPTION

[0051] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art.

[0052] The present invention provides Figure 1 The abnormality monitoring method of the heating system of the carbon brush forming machine shown in the figure specifically includes the following steps:

[0053] Collecting power supply voltage data of the carbon brush forming machine during heating operation, and determining whether the power supply voltage is in a state of frequent fluctuation according to a preset judgment rule. The preset judgment rule includes comparing any one of the fluctuation amplitude, fluctuation frequency, and change rate of the power supply voltage with a corresponding threshold value to determine whether the power supply voltage is in a state of frequent fluctuation. If any one of the items is greater than the corresponding threshold value, it is considered that the power supply voltage is in a state of frequent fluctuation;

[0054] To achieve real-time acquisition of the power supply voltage during the heating operation of the carbon brush forming machine, a voltage detection device can be placed at the power supply input. For example, a high-precision sampling resistor combined with an analog front-end (AFE) module or an isolated voltage sensor can be used to sample the instantaneous voltage of the AC or DC power supply. After analog-to-digital conversion, the sampled signal is input into the data acquisition unit, which continuously records the voltage change value through a set sampling period and uploads the data stream in real time to the analysis module of the control host. The software layer controls the sampling frequency through scheduling logic and establishes a data cache structure with timestamps to ensure that the voltage data within each heating cycle is fully covered. Voltage acquisition not only includes the average voltage value but also retains key node information such as peaks, troughs, and zero-crossing times, providing a data foundation for subsequent dynamic analysis.

[0055] To determine whether the power supply voltage is in a state of frequent fluctuations, a set of judgment rules can be preset at the software layer. The rules include analysis logic for three types of parameters: voltage fluctuation amplitude, fluctuation frequency, and change rate. In the specific implementation, first, based on the collected voltage sequence data, the voltage fluctuation amplitude (the difference between the maximum and minimum values), fluctuation frequency (the number of times it exceeds the normal range per unit time), and change rate (the average or maximum value of the voltage slope per unit time) are calculated within a fixed time window. The above three indicators are compared with the thresholds set in the system respectively, and the conditional trigger judgment logic is used to determine whether the rule of "any indicator exceeds the limit" is met. Once any of the three items exceeds its corresponding threshold, the current voltage state can be marked as "frequent fluctuations" in the control software and pushed to the subsequent processing flow for response analysis.

[0056] This method is used to dynamically determine whether the power supply voltage is in a state of frequent fluctuations, which can effectively make up for the defect of existing abnormality monitoring methods in their weak ability to identify power supply disturbance factors. Traditional technologies usually rely solely on the feedback results of temperature sensors to judge the heating control state, while ignoring the potential impact of power supply voltage fluctuations on the accuracy of heating power control. This method sets clear voltage fluctuation analysis indicators and combines them with a quantitative threshold judgment mechanism to achieve early prediction of power offset problems caused by voltage instability during the heating process, thereby identifying in advance that the heating system is in a disturbed state before the temperature control anomaly manifests as abnormal temperature data. This judgment logic based on voltage behavior feature modeling can be automatically implemented through software algorithms, with high real-time performance, high versatility and scalability, effectively improving the intelligence level and engineering adaptability of heating system anomaly monitoring.

[0057] If the power supply voltage is in a state of frequent fluctuation, the continuous time period during which the voltage fluctuation occurs is extracted and defined as the voltage fluctuation action time window;

[0058] In this embodiment, extracting the continuous time period in which voltage fluctuations continue to occur includes: when the power supply voltage is judged to be in a frequently fluctuating state, marking the starting moment when it is first judged to be in a frequently fluctuating state, and continuously judging whether the power supply voltage remains in a frequently fluctuating state in subsequent sampling cycles, until the judgment results in several consecutive sampling cycles are all in a non-frequently fluctuating state, determining the end moment, and using the time period between the start moment and the end moment as the voltage fluctuation action time window.

[0059] To extract continuous time periods of persistent voltage fluctuations, the data acquisition unit must first collect real-time data on the power supply voltage, sampling the power supply voltage regularly using a set sampling cycle. The software analyzes the power supply voltage's fluctuations after each sampling cycle and marks the state by determining whether it is experiencing frequent fluctuations. Once the power supply voltage is determined to be experiencing frequent fluctuations, the software records that moment as the start of the time window. Subsequently, the software will continue to determine whether the power supply voltage remains in this state during each new sampling cycle. If the power supply voltage continues to fluctuate frequently for multiple consecutive cycles, the software will continue recording that time period until it detects that the power supply voltage is no longer fluctuating frequently, at which point the software will end recording for that time period. This is done to avoid erroneous state switching due to occasional voltage fluctuations and to accurately determine the duration of frequent voltage fluctuations, thereby improving monitoring stability and accuracy.

[0060] The specific value of "several" sampling cycles can be set through the parameter configuration in the software. Generally, this parameter value is reasonably set based on the stability requirements of the system, the voltage fluctuation characteristics during the heating process, and the required response time. By setting a minimum continuous fluctuation cycle threshold, for example, setting it to 3 times, 5 times or 10 times, the software will determine whether the power supply voltage continues to fluctuate frequently at the end of each sampling cycle. When the number of sampling cycles that continuously meet this state reaches the preset value, it is judged that the time period for the continuous voltage fluctuation has ended. The specific value of "several" sampling cycles can be adjusted according to the fluctuation frequency of the equipment in different working environments to adapt to the power supply voltage fluctuation characteristics of different production processes. The setting of this parameter can be optimized through production line debugging, system testing or historical data analysis to ensure that it can respond sensitively to frequent fluctuations while avoiding misjudging occasional interference.

[0061] Acquire heating control behavior information of the heating system within the voltage fluctuation time window, and based on this information, evaluate the degree of heating power output deviation of the heating system when the power supply voltage fluctuates frequently, and classify the deviation into stable deviation, fluctuating deviation, and uncontrolled deviation.

[0062] In this embodiment, within the voltage fluctuation time window, information related to the heating control behavior of the heating system is obtained, and based on this information, the degree of deviation of the heating power output of the heating system under the condition of frequent power supply voltage fluctuations is evaluated, and the deviation is divided into stable deviation degree, fluctuating deviation degree, and uncontrolled deviation degree. Specifically, the following steps are included:

[0063] Acquire heating control behavior correlation information of the heating system within the voltage fluctuation action time window and preprocess it;

[0064] To obtain information related to the heating control behavior of the heating system, it is first necessary to collect real-time data related to the heating control process through the data acquisition system. This data includes but is not limited to heating power output values, heating power control signals, and voltage fluctuation data. Within the time window of voltage fluctuation, the system will collect this data in real time through a timed sampling mechanism and store it in the data cache. The frequency of data collection can be adjusted according to actual needs, and is usually collected once per sampling cycle to ensure the timeliness and accuracy of the data. By synchronously processing this data, the software monitors the relationship between the heating power output and the control signal, as well as the impact of voltage fluctuations in real time, thereby obtaining information related to the heating control behavior of the heating system. The acquired data can be matched with the state changes of the heating control signal through timestamps to form a data stream containing multiple features for subsequent analysis.

[0065] Preprocessing of heating control behavior correlation information aims to improve data quality, reduce noise impact, and enhance the accuracy of subsequent analysis. In actual production processes, collected signal data may contain noise, outliers, or irregular fluctuations, which can impede accurate assessment of system status. Therefore, after data acquisition, the data must first be denoised. For example, a low-pass filter or sliding average filter is used to remove high-frequency noise while preserving the signal's primary trend. Next, the data is normalized to convert data from different sources to the same scale range to ensure that data from different sources are not unequally impacted by magnitude differences during calculations. For example, the numerical ranges of power output and voltage fluctuations may vary significantly, so these data must be normalized to maintain the same dimension. Finally, if missing values ​​exist in the data, interpolation methods (such as linear or polynomial interpolation) can be used to fill them in to ensure data continuity and integrity. All of these preprocessing steps are automatically performed by the software and can be adjusted using custom algorithms to ensure that the preprocessed data has a higher signal-to-noise ratio, providing a more accurate basis for subsequent dynamic analysis and offset assessment.

[0066] Extracting response fluctuation information and heating control behavior deviation information from the preprocessed heating control behavior correlation information, and analyzing them to generate a dynamic response fluctuation coefficient and a heating control deviation index, respectively;

[0067] Extracting response fluctuation information and heating control behavior deviation information can be accomplished through data analysis and signal processing algorithms within the software. First, by performing time series analysis on preprocessed heating control behavior-related information (such as heating power output and control signals), the software can extract response fluctuation information—that is, the system's response to voltage fluctuations. This process is accomplished by calculating the dynamic relationship between heating power output and voltage fluctuations. Typically, a moving window approach is used to locally process the data, analyzing the correlation between heating power changes and voltage changes within each time period. Indicators such as their rate of change and amplitude are then calculated to obtain response fluctuation information. For heating control behavior deviation information, the software compares the actual heating power output with the preset target value and calculates the deviation between them to extract control behavior deviation information. Specifically, the software calculates the power error (i.e., the difference between the target and actual values) at each moment and the temporal trend of this error to obtain heating control behavior deviation data. This extracted information serves as the basis for subsequent analysis, evaluation, and modeling, ensuring accurate representation of the heating system's response fluctuations and control deviations.

[0068] A heating power output offset evaluation model is constructed based on the generated dynamic response fluctuation coefficient and heating control deviation index, and an offset evaluation coefficient is generated through weighted summation;

[0069] Determine the pre-set offset evaluation coefficient threshold range, and compare it with the generated offset evaluation coefficient after determination. According to the comparison results, evaluate the degree of offset of the heating power output of the heating system under the condition of frequent power supply voltage fluctuations, and divide it into stable offset degree, fluctuating offset degree and out-of-control offset degree.

[0070] Determining the pre-set threshold range for the excursion assessment coefficient can be achieved through historical data analysis and statistical modeling. First, the software can utilize historical system operating data under different operating conditions to collect excursion assessment coefficient values. Statistical analysis methods (such as mean, standard deviation, and quantile analysis) can then be used to determine the normal range for the excursion coefficient. Specifically, the software can calculate the mean and standard deviation of the excursion assessment coefficient from historical data. Based on these statistical indicators, a reasonable threshold range can be set. For example, the normal excursion coefficient range can be set to ±2 standard deviations of the mean. This ensures that the coefficient falls within this range under most normal operating conditions. Furthermore, the software can combine process requirements and equipment operating tolerances, and further optimize the range through expert experience or machine learning models to determine an excursion assessment coefficient threshold range that is suitable for different equipment and operating conditions. As the system continues to operate, the threshold range can be further optimized through an adaptive adjustment mechanism to ensure high accuracy and reliability under various operating conditions.

[0071] In this embodiment, the logic for obtaining the dynamic response fluctuation coefficient is as follows: extract the response fluctuation information from the pre-processed heating control behavior correlation information, specifically including the heating power output value and power supply voltage change of the heating system at different times within the voltage fluctuation action time window, and use the function and To express, For time point, Indicates that within the voltage fluctuation time window The heating power output value of the heating system at all times, Indicates that within the voltage fluctuation time window The change in power supply voltage at the moment, the time period is defined as Real-time acquisition of "heating power output" and "power supply voltage change" data can be achieved through an integrated data acquisition system and sensors. Specifically, high-precision power sensors and voltage sensors must first be installed in the heating system of the carbon brush forming machine. The power sensor is responsible for measuring the power output of the heating system in real time. This value is typically converted by the sensor into power data via current and voltage signals. This data is then digitally processed and sent to the central control unit for storage and analysis. The voltage sensor is installed at the power input to measure power supply voltage fluctuations in real time. The data from these sensors can be controlled by sampling frequency, allowing data to be collected at a rate that suits actual needs, such as per second or per millisecond.

[0072] This acquired data is transmitted to the central processing unit via a real-time data acquisition system (such as a PLC or embedded controller). The data is timestamped to form a time-series data stream. The software system synchronously stores the heating power output and voltage fluctuation data during each sampling period. Based on real-time data updates and analysis, the heating power output and voltage changes are recorded as a time series within the voltage fluctuation time window. On the software side, the data is monitored and analyzed by a real-time monitoring system to ensure accurate recording of the heating system's power output and voltage fluctuations during each sampling period, providing accurate data support for subsequent calculations and evaluations. This method enables real-time and accurate acquisition of heating power output and power supply voltage change data, providing a strong basis for subsequent dynamic analysis and control decisions.

[0073] Determine the factors affecting the heating system response by changes in heating power and power supply voltage through historical experimental regression analysis and , is the factor affecting the heating system response to heating power changes, is the influence factor of power supply voltage change on the response of heating system;

[0074] Determine the factors that affect the response of the heating system due to changes in heating power and supply voltage and This can be achieved through historical experimental regression analysis. First, by conducting multiple experiments under different working conditions, historical data of the heating system under power supply voltage fluctuations and power changes are collected, including voltage fluctuation amplitude, heating power output value and corresponding heating temperature change data. After preprocessing (such as denoising, standardization, etc.), these data are used as input for regression analysis. In the software, linear regression or nonlinear regression algorithms are used to fit these historical data, and the influencing factors are determined by solving the coefficients in the regression equation. and Specifically, the impact factor Represents the degree of influence of heating power change on the response of the heating system, that is, the contribution of the heating power change rate to the temperature or power output change; and the impact factor It represents the degree to which power supply voltage variations affect the heating system's response. Specifically, it represents the relationship between the rate of change of voltage fluctuations and the fluctuations in heating system power. The regression analysis results provide specific values ​​for these two factors. These values ​​quantify the impact of power supply voltage and heating power variations on system response, providing a basis for subsequent calculation of the Dynamic Response Fluctuation Coefficient (DRFC). By establishing a regression model, the software can automatically update and optimize the values ​​of these two factors based on historical data, enabling real-time dynamic assessment and adjustment.

[0075] Calculate the dynamic response fluctuation coefficient. The specific calculation formula is as follows: Where, is the dynamic response fluctuation coefficient, Indicates the rate of change of the heating power output value.

[0076] The dynamic response fluctuation coefficient The calculation formula uses a weighted exponential operation to quantify the response of the heating system under voltage fluctuations. First, the rate of change of the heating power output value is used in the formula , which indicates the instantaneous reaction speed of the heating system to voltage fluctuations. When the power change rate is large, it means that the heating system responds more violently to voltage fluctuations, so the weight of this change is set to , which is the influence factor of heating power change on system response. Secondly, use the voltage change , and through logarithmic operation Perform the transformation. This processing can balance the impact of voltage fluctuations and avoid the over-amplification effect when the voltage fluctuation amplitude is large, thereby more stably reflecting the impact of voltage fluctuations on the heating system. The purpose of using logarithmic transformation is to convert the nonlinear change of voltage into a controllable linear measurement to ensure the smoothness and stability of the calculation. Finally, the exponential operation exp is used to deal with the combined effect between the power change rate and the voltage change. This processing method can enhance the impact of voltage fluctuations on the system response, and as the fluctuation intensifies, the response impact gradually becomes apparent. In this way, the final dynamic response fluctuation coefficient It provides a comprehensive indicator that can reflect the response fluctuation degree of the heating system under voltage fluctuation and the stability of the system, helping to accurately assess the risk of control distortion.

[0077] Dynamic response fluctuation coefficient The size of directly reflects the response intensity of the heating system to the power supply voltage fluctuation, and therefore has a close relationship with "evaluating the degree of deviation of the heating power output of the heating system under the condition of frequent power supply voltage fluctuation". When the value of is large, it means that the rate of change of heating power (i.e., the instantaneous change of power output) is more severe than the impact of voltage fluctuations, and the system is too sensitive to voltage fluctuations. This usually indicates that the temperature control accuracy of the heating system is low and the power output deviates greatly from the target value. Therefore, when the power supply voltage fluctuates frequently, a high value of This means that the system's heating power output deviation is high and the control system has a greater risk of distortion. When the value of is small, it indicates that the heating system responds more smoothly to voltage fluctuations, the deviation of power output is small, and the system can better maintain a stable heating process, indicating that the control stability of the system is good. The level of can serve as an important basis for judging whether the system has the risk of temperature control distortion, helping to assess the operating risk of the heating system under frequent power supply voltage fluctuations, and then taking appropriate control measures to ensure product quality and equipment safety.

[0078] In this embodiment, the logic for obtaining the heating control deviation index is as follows:

[0079] The heating control behavior deviation information is extracted from the pre-processed heating control behavior correlation information, specifically including the target heating power setting value and the change amplitude of the power supply voltage at different times within the voltage fluctuation time window, and the function is used to calculate the deviation of the heating control behavior according to the time series. and To express, For time point, Indicates that within the voltage fluctuation time window The target heating power setting value at the moment, Indicates that within the voltage fluctuation time window The change range of the power supply voltage at the moment, the time period is defined as ;

[0080] Real-time acquisition of two types of data, "target heating power setpoint" and "power supply voltage fluctuation range," can be achieved through an integrated control system and sensor monitoring. First, the target heating power setpoint can be obtained in real time through the heating system's control signal. The control system dynamically adjusts the target heating power based on set production parameters and process requirements. The software system obtains this setpoint in real time by reading the control signal. This data is typically transmitted by an industrial automation system (such as a PLC or embedded system), ensuring high real-time performance. Second, the power supply voltage fluctuation range can be acquired using a voltage sensor installed at the power input. The voltage sensor monitors power supply voltage fluctuations in real time and transmits the data through a data acquisition unit. The software system records the power supply voltage values ​​in real time through periodic sampling and calculates the voltage fluctuation range, which is the difference between the maximum and minimum voltage values. The entire data acquisition process is real-time and automated. All data is controlled through sampling cycles and synchronized processing to ensure accurate reflection of the target heating power setpoint and power supply voltage fluctuations. The data is promptly transmitted to the central processing unit and stored in time series for subsequent analysis and calculation, thus supporting dynamic monitoring of the heating system.

[0081] Determining the influencing factors of heating power deviation on heating system control by fitting historical experimental data ,This factor is used to quantify the impact of the deviation between the target heating power and the actual heating power on the control stability of the heating system;

[0082] Determine the influencing factors of heating power deviation on heating system control by fitting historical experimental data This can be achieved through regression analysis and data fitting methods. First, it is necessary to collect the operating data of the heating system in multiple experiments, including the target heating power setting value, the actual heating power output value, and the temperature response of the system. Through the software, the system inputs these experimental data into the regression analysis model, in which the deviation between the target heating power and the actual power and the response data of the control system will be used as input variables. Using algorithms such as nonlinear regression or least squares fitting, the system obtains the influencing factors through the fitting process. , which quantifies the degree of influence of the deviation between target power and actual power on the control performance of the heating system. This factor represents the sensitivity of the system to heating power deviations. Larger values ​​indicate a more dramatic system response to power deviations and poorer control system stability. Smaller values ​​indicate a weaker response to power deviations and a more stable control system. By fitting historical experimental data, the software continuously optimizes and adjusts this factor, ensuring precise control of the system under varying operating conditions, ultimately achieving efficient and stable heating control.

[0083] Calculate the heating control deviation index. The specific calculation formula is as follows: Where, is the heating control deviation index, Indicates the rate of change of the power supply voltage. Indicates that within the voltage fluctuation time window The heating power output value of the heating system at the moment. The heating control deviation index The calculation formula of is designed to comprehensively measure the response behavior of the heating system to power supply voltage fluctuations and control signal deviations. First, the formula Indicates the rate of change of the power supply voltage change amplitude. This term reflects the dynamic rate of change of voltage fluctuation. During the operation of the heating system, the fluctuation of the power supply voltage not only affects the output of heating power, but may also cause delay or instability in the system response. Therefore, this rate of change is a key factor in evaluating the response speed of the control system. Secondly, the formula is calculated by logarithmic operation. To deal with the deviation between the heating power set value and the actual heating power output value. The logarithmic operation is used to compress the impact of power deviation on system stability and avoid excessive response caused by extreme deviation values. By operating on the square of the deviation, the effect of larger deviations is further amplified, so that the system can respond more sensitively to changes in larger deviations. Then, the exponential operation exp is used to strengthen the interaction between voltage changes and power deviations to ensure that larger changes will have a significant impact on the final control behavior. Finally, all these operations within the entire time window are integrated into a weighted integral value, which represents the stability and control distortion of the system under power supply voltage fluctuations and power deviations. Through this comprehensive calculation method, It provides a dynamic and comprehensive control evaluation index that can accurately reflect the operating stability of the system under different working conditions. The size of directly reflects the degree of response of the heating system to power supply voltage fluctuations and control signal deviations, and is therefore closely related to "evaluating the degree of heating power output deviation of the heating system under frequent power supply voltage fluctuations". When the value of is large, it means that the output deviation of the heating power is large, that is, the gap between the target heating power and the actual heating power is large, and the system responds more violently to the power supply voltage fluctuation, resulting in unstable heating power output. This usually means that there is a high risk of control distortion in the system, and the temperature control system cannot effectively maintain a constant output, which may eventually lead to instability in the heating process and affect product quality. On the contrary, if The smaller the value of , the smaller the deviation of the heating power output. The system responds more smoothly to the fluctuation of the power supply voltage and can maintain a more stable heating state, indicating that the system control is more accurate and stable. The size of can be used as an important indicator to judge whether there is temperature control distortion in the heating system and whether the heating power output deviates from the target value, thereby effectively evaluating the degree of heating power output deviation of the heating system when the power supply voltage fluctuates frequently.

[0084] In this embodiment, the generated dynamic response fluctuation coefficient and heating control deviation index A heating power output offset evaluation model is constructed, and the offset evaluation coefficient is generated by weighted summation. The specific calculation formula is as follows: Where, is the offset evaluation coefficient, and Dynamic response fluctuation coefficient and heating control deviation index The non-zero weight coefficient of To achieve this weighted summation, the offset evaluation coefficients are generated First, we need to generate the dynamic response fluctuation coefficient and heating control deviation index The calculation results are weighted summed. In the software implementation process, the system will calculate the weighted sum based on the real-time calculation results. and , according to the preset weight coefficient and Specifically, and are two non-zero weight coefficients that determine and The sum of these two coefficients is always 1 (i.e. ), can be optimized based on experimental data, historical operating data or system requirements. For example, if the system is more sensitive to voltage fluctuations, it may be Assigning higher weights (i.e. larger), and The weight of In practical applications, the weight coefficient is usually optimized and adjusted through historical experimental analysis or regression algorithm to ensure that the offset evaluation coefficient can accurately reflect the overall stability and offset degree of the heating system under voltage fluctuations. It is a comprehensive indicator that can provide a basis for subsequent control decisions.

[0085] In this embodiment, the preset offset evaluation coefficient threshold interval is determined , and after determination, the generated offset evaluation coefficient A comparison was conducted, and based on the comparison results, the degree of heating power output deviation of the heating system under the condition of frequent power supply voltage fluctuations was evaluated. The deviation was divided into stable deviation degree, fluctuating deviation degree, and uncontrolled deviation degree. The specific comparison analysis is as follows:

[0086] like ,The degree of heating power output offset of the heating system under the condition of frequent power voltage fluctuations is a stable offset degree;

[0087] This means that even with frequent power supply voltage fluctuations, the heating system's heating power output remains stable, with minimal deviation from the target value. The system's temperature control accuracy is high, and its response is quick and accurate. The system's response to voltage fluctuations is effective and stable, ensuring uniform heating during the carbon brush molding process and effectively safeguarding product quality. The impact on the production process is that the system can maintain normal operation without the need for intervention, ensuring the stability and efficiency of the production line.

[0088] like ,The degree of heating power output offset of the heating system under the condition of frequent power voltage fluctuations is the fluctuating ,degree;

[0089] This indicates that power supply voltage fluctuations have a certain impact on the heating system's power output, causing the power output to fluctuate within a certain range. However, the deviations do not exceed the system's tolerance, and the control system can still maintain a certain degree of stability. Although the heating power output fluctuates, the system's adjustment capabilities are still able to adapt to these fluctuations, and the overall temperature control effect is not seriously distorted. The impact of this is that the production process may be slightly unstable, but it will not affect the final product quality. The system can compensate by extending the heating time or taking other adjustments to ensure the ultimate heating quality.

[0090] like , the degree of deviation of the heating power output of the heating system under the condition of frequent power supply voltage fluctuations is an out-of-control type deviation degree.

[0091] This situation means that under the condition of frequent power supply voltage fluctuations, the system's heating power output has seriously deviated from the target value, and the power output is highly unstable. The control system cannot effectively adjust it, resulting in temperature control distortion. At this time, the system's heating process may become severely uneven, and some areas may overheat or fail to reach the predetermined temperature, directly affecting the molding quality of the carbon brushes. It may cause problems such as uneven density and unstable resistance in the product, and may even cause damage to the equipment itself. At this point, the production process must be intervened immediately, and it may be necessary to suspend production, adjust the heating process, or implement other control measures to avoid large amounts of waste and equipment failures and ensure safe and stable production operations.

[0092] Based on the classification results of the degree of heating power output deviation, the abnormal risk level of temperature control distortion in the heating system is determined. Among them, the degree of stable deviation corresponds to no risk, the degree of uncontrolled deviation corresponds to severe risk, and the degree of fluctuating deviation is determined by further analyzing the comprehensive evaluation coefficient of multiple cycles to determine the specific risk level;

[0093] In this embodiment, based on the classification results of the degree of heating power output deviation, the abnormal risk level of temperature control distortion in the heating system is determined, specifically:

[0094] If the heating power output deviation is stable, the heating system has a temperature control distortion risk level of zero. This indicates that the heating system can maintain stable operation despite frequent power supply voltage fluctuations, the deviation between the power output and the target value is small, and the system's temperature control accuracy is high. Therefore, the corresponding temperature control distortion risk level is zero.

[0095] If the heating power output deviation reaches the level of runaway deviation, the heating system has a severe risk of temperature control distortion. This indicates that due to frequent power supply voltage fluctuations, the power output of the heating system deviates significantly from the target value, and the temperature control system cannot effectively adjust, resulting in a loss of control during the heating process. Therefore, the corresponding temperature control distortion risk level is severe.

[0096] If the degree of heating power output offset is a fluctuating offset, continuously obtain and analyze several subsequently generated offset assessment coefficients, and judge the abnormal risk level of temperature control distortion in the heating system based on the analysis results; this situation indicates that the power output of the heating system fluctuates within a certain range. Although it has not reached a state of out-of-control, there is still a certain degree of instability. At this time, it is necessary to further analyze the offset assessment coefficients of multiple cycles to determine the specific temperature control distortion risk level, and classify the risk level into low risk, medium risk or high risk based on the analysis results.

[0097] In this embodiment, if the heating power output deviation is a fluctuating deviation, several subsequent deviation evaluation coefficients are continuously obtained and recalibrated as , It is the number of several offset evaluation coefficients generated subsequently. , is a positive integer;

[0098] To continuously obtain the offset evaluation coefficients generated subsequently, a real-time data acquisition system and data processing algorithm can be used. First, the software system needs to regularly collect relevant heating power output data and power supply voltage data from the heating system through a timed sampling mechanism, and calculate the offset evaluation coefficients ( After each sampling, the system will dynamically generate a new offset evaluation coefficient based on the change in the degree of heating power output offset and store it in the data cache. The system can set the sampling period (such as every second, every minute, etc.) to control the frequency of data acquisition to ensure that new data can be continuously acquired within the time window of voltage fluctuation. Each collected data will be marked with a timestamp and a corresponding Value (i.e. This data is transmitted to the central processing unit via the real-time monitoring module. During analysis, the software automatically updates and records the deviation assessment coefficient for each cycle and stores it in a database for subsequent calculations and analysis. This ensures continuous, real-time acquisition and updating of the deviation assessment coefficient, providing accurate data support for subsequent analysis and risk assessment.

[0099] Calculate several subsequent generated offset evaluation coefficients Average value , according to the formula: ; Calculate several subsequent offset evaluation coefficients Standard deviation , according to the formula:

[0100] Determine the preset average value threshold of several preset offset evaluation coefficients and preset standard deviation thresholds , and after determination, respectively with several calculated offset evaluation coefficients Average value and standard deviation Perform a comparison and determine the abnormal risk level of temperature control distortion in the heating system based on the comparison results. The specific comparison analysis is as follows:

[0101] like and ,The abnormal risk level of temperature control distortion in the heating system is high;

[0102] This situation indicates that both the mean and standard deviation of the heating system's offset evaluation coefficient exceed preset thresholds. This indicates that, when the system experiences frequent power supply voltage fluctuations, the deviation in heating power output is large, and the fluctuation range of the deviation is relatively wide over multiple cycles, indicating significant control distortion. In this case, the system is unable to effectively regulate the heating power, resulting in a high risk of temperature control distortion. The impact is that temperature fluctuations during the heating process may lead to inconsistent product quality, such as uneven density and resistance fluctuations, potentially resulting in a large number of substandard products and even damage to the equipment itself. Immediate intervention is required, such as adjusting the control strategy or suspending production, to avoid serious consequences.

[0103] like and ,The abnormal risk level of temperature control distortion in the heating system is medium risk;

[0104] This situation indicates that the average deviation of the heating power output is small, but over multiple cycles, the system fluctuates significantly, with the standard deviation exceeding the preset value. This means that while the system as a whole does not have significant offset, the frequent fluctuations in the power supply voltage lead to large fluctuations in the heating power and poor control stability. This risk is considered medium and may affect the uniformity of the heating process, resulting in insufficient heating or overheating in certain areas, which in turn may affect product performance or quality. While the risk is less severe than in a high-risk situation, appropriate adjustments, such as optimizing the control algorithm or increasing monitoring frequency, are still necessary to ensure production process stability.

[0105] like and ,The abnormal risk level of temperature control distortion in the heating system is medium risk;

[0106] This indicates a large excursion in the heating power output, but the fluctuation range is small, and the standard deviation is below the preset value. This indicates that although the system's heating power output deviates significantly from the target value, the fluctuations in these deviations are relatively stable and do not fluctuate significantly. In this case, the system may be in a relatively stable but suboptimal state, with temperature control distortion present. However, due to the small fluctuations, the risk level is medium. While this does not result in a serious quality issue, optimization measures, such as adjusting control parameters or performing performance tuning, are still necessary to avoid potential issues during long-term operation.

[0107] like and , the abnormal risk level of temperature control distortion in the heating system is low risk.

[0108] This indicates that the mean and standard deviation of the heating system's offset evaluation coefficient are both below the preset thresholds. This means that the system is able to effectively control heating power output despite frequent power supply voltage fluctuations, with minimal deviation and fluctuation range, resulting in high temperature control accuracy. In this case, the system can effectively adapt to voltage fluctuations and maintain heating process stability, with a low risk of temperature control distortion. The impact on the production process is stable system operation, maintaining consistent product quality, and continuing normal operation with little to no intervention. Furthermore, the equipment is within a safe range and can efficiently and stably complete its tasks.

[0109] Determining the preset mean and standard deviation thresholds for the offset assessment coefficient can be achieved through historical data analysis and statistical modeling. First, the software collects historical experimental data or long-term operating data to analyze the offset assessment coefficient of the heating system under different power supply voltage fluctuations. Based on this data, the software uses statistical analysis methods (such as mean, standard deviation, and quantile analysis) to calculate the mean and standard deviation of the historical data. These calculation results provide the basis for determining the preset thresholds. Typically, the preset mean threshold can be set as the mean of the offset assessment coefficients in the historical data, while the preset standard deviation threshold can be set as the standard deviation of the offset assessment coefficients in the historical data. A reasonable safety factor or tolerance range can be established based on experience or system requirements. For example, the standard deviation threshold can be set within the range of ±2 standard deviations of the mean to cover most normal fluctuations. To ensure the adaptability of the thresholds, the software can periodically adaptively adjust these thresholds and update the calculation results based on the latest data, thereby improving the system's adaptability and accuracy. In this way, the preset mean and standard deviation thresholds can effectively reflect the stability of the system under normal operation and provide a reliable benchmark for subsequent risk assessments.

[0110] Execute corresponding heating control measures according to the determined abnormal risk level;

[0111] In this embodiment, corresponding heating control measures are executed according to the determined abnormal risk level, specifically:

[0112] If the abnormal risk level of temperature control distortion in the heating system is no risk, the specific heating control treatment measures to be implemented are: continue to operate normally according to the predetermined heating program without any control intervention;

[0113] When the heating system's temperature control distortion risk is rated as zero, the system is operating stably with no significant power deviations. At this point, the software can monitor the system's operating data (such as heating power setpoints, actual output values, and temperature data) in real time and continuously compare them to confirm compliance with pre-set standards. In software-controlled industrial automation systems, this data can automatically determine the risk level. If the system is considered zero risk, it will continue the heating program without any intervention. For example, the software can check real-time data against a pre-set tolerance range. If the deviation falls within this range, the system determines the system is safe and allows the equipment to continue operating without intervention. This approach reduces unnecessary control interventions, reduces operational complexity, and improves production efficiency.

[0114] If the heating system has a low risk level for temperature control distortion, the specific heating control measures to be implemented are: continue heating, maintain existing control parameters, and continuously monitor the status of the heating system to prevent fluctuations;

[0115] In low-risk scenarios, while the system's temperature control accuracy is high, there are certain fluctuations. In these situations, the software can continue heating and maintain existing control parameters, while enabling the real-time monitoring module to collect data and perform status checks on the system to monitor system stability. For example, the software regularly collects data from temperature sensors and power output and compares it with target setpoints to ensure that the temperature control system remains within an acceptable range. If deviations exceed the set tolerance, the system can automatically adjust the heating power output or adjust the temperature control parameters to restore normal operation. This approach is designed to ensure stable system operation and prevent production efficiency reductions caused by excessive intervention or frequent adjustments.

[0116] If the heating system has a medium risk of temperature control distortion, the specific heating control measures to be implemented are: extend the heating time, adjust the heating power output to ensure a uniform heating process, and if the heating system experiences persistent fluctuations, immediately intervene manually and adjust the control strategy to avoid further temperature control distortion.

[0117] In medium-risk situations, the software first compensates for system fluctuations by extending the heating time or automatically adjusting the heating power. By continuously monitoring temperature changes during the heating process, the software automatically calculates the required extension time and power adjustment. If the system fluctuates persistently, the software triggers an alarm and recommends manual intervention. During manual intervention, the software provides real-time data analysis to help the operator understand the effectiveness of the current control strategy and adjust control parameters, such as increasing heating power or changing the heating time. Through a data feedback loop, the software continuously optimizes the control strategy to ensure that temperature control distortion does not worsen and achieve stable system operation.

[0118] If the heating system has a high risk level of temperature control distortion, the specific heating control measures to be implemented are: immediately suspend the heating process, check the working status of the heating system, find out the cause of the temperature control distortion, and adjust and maintain the equipment to restore the normal operation of the heating system;

[0119] When the risk level is high, the software automatically pauses the heating process to prevent unstable temperatures from seriously impacting product quality. After the heating process is paused, the software system automatically checks various heating system parameters (such as temperature, heating power, and sensor data) through the diagnostic module to identify potential causes of temperature control distortion. Based on a predefined troubleshooting process, the software can automatically or manually trigger system checks to diagnose faults in the temperature control module, power supply voltage, heating elements, and other components. Once the cause is identified, the system provides specific adjustment and maintenance recommendations and records fault information in real time through the software platform for maintenance personnel's reference. This measure is intended to prevent severe temperature control distortion from further deteriorating and ensure long-term stable operation of the equipment.

[0120] If the abnormal risk level of temperature control distortion in the heating system is serious, the specific heating control processing measures to be implemented are: immediately terminate the heating task, cut off the power supply of the heating system, record the abnormal information, conduct a comprehensive inspection and repair, and ensure that the heating system is restored to a safe working state.

[0121] In the event of a serious risk, the software automatically terminates the heating task and cuts off the power supply to prevent the heating system from losing control and further impacting production or damaging the equipment. If the software detects a severe level of temperature control distortion, it triggers an emergency stop command, immediately halting the heating process and disconnecting the power supply to ensure the safety of equipment and personnel. After terminating heating, the system automatically records abnormal information, including current temperature control data, power output, and equipment status, and generates a detailed fault report. This report is automatically transmitted to maintenance personnel for comprehensive inspection and repair through the equipment management system. This ensures that the system is restored to a safe operating state and prevents the recurrence of similar faults. This measure minimizes potential risks and ensures the safety of equipment and personnel through timely response and automatic recording.

[0122] The data and control response information generated during the monitoring process are archived, and the processing flow for analyzing the associated information of heating control behavior is updated based on the archiving results to adapt to the monitoring needs under different working conditions.

[0123] Data archiving and updating analytical processes based on archiving results can be accomplished through a database management system and data processing algorithms. First, the software utilizes a data acquisition system to continuously collect monitoring data during each heating cycle, including key parameters such as temperature, heating power, and offset evaluation coefficients. This data is transmitted and stored in a database in real time during collection, forming a historical data record. Each data point is accompanied by a timestamp and device status identifier to ensure accurate data traceability. The system archives this data in a predetermined format, ensuring data integrity and accessibility, providing a reliable foundation for subsequent analysis.

[0124] Based on the archived results, the software system will regularly analyze historical data and identify patterns and trends in heating control behavior through data mining and pattern recognition techniques. Specifically, the software will evaluate the performance of the heating system under different operating conditions and automatically adjust the processing flow to suit different monitoring needs. For example, if the system finds that the control deviation is large under certain operating conditions, the software can improve the stability of the system and the temperature control accuracy by dynamically adjusting the threshold, updating the control strategy, or optimizing the parameter settings during the heating process. This process is automated and can continuously optimize the monitoring and control processes as the production environment and the operating status of the heating system change, ensuring that the system can adapt to different operating conditions, improve production efficiency, and reduce equipment failure rates.

[0125] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0126] The above embodiments can be implemented in whole or in part via software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in the embodiments of this application are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0127] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0128] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0129] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0130] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0131] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0132] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for monitoring abnormalities in a heating system of a carbon brush forming machine, characterized in that: The specific steps include: Collect the power supply voltage data of the carbon brush forming machine during the heating operation, and determine whether the power supply voltage is in a state of frequent fluctuation based on the preset judgment rules; If the power supply voltage is in a state of frequent fluctuation, the continuous time period during which the voltage fluctuation occurs is extracted and defined as the voltage fluctuation action time window; Acquire heating control behavior information of the heating system within the voltage fluctuation time window, and based on this information, evaluate the degree of heating power output deviation of the heating system when the power supply voltage fluctuates frequently, and classify the deviation into stable deviation, fluctuating deviation, and uncontrolled deviation. The specific steps include: Acquire heating control behavior correlation information of the heating system within the voltage fluctuation action time window and preprocess it; Extracting response fluctuation information and heating control behavior deviation information from the preprocessed heating control behavior correlation information, and analyzing them to generate a dynamic response fluctuation coefficient and a heating control deviation index, respectively; The logic for obtaining the dynamic response fluctuation coefficient is as follows: Extract response fluctuation information from the preprocessed heating control behavior correlation information, specifically including the heating power output value and power supply voltage change of the heating system at different times within the voltage fluctuation action time window, and represent them respectively using functions JPS(t) and VBL(t) according to the time series, where t is the time point, JPS(t) represents the heating power output value of the heating system at time t within the voltage fluctuation action time window, and VBL(t) represents the power supply voltage change at time t within the voltage fluctuation action time window, and the time period is defined as [t1, t2]; The influence factors α and β of heating power change and power supply voltage change on heating system response are determined by historical experimental regression analysis. α is the influence factor of heating power change on heating system response. β is the factor affecting the response of the heating system due to the change of power supply voltage; Calculate the dynamic response fluctuation coefficient. The specific calculation formula is as follows: Where DRFC is the dynamic response fluctuation coefficient, Indicates the rate of change of heating power output value; The logic for obtaining the heating control deviation index is as follows: Extract the heating control behavior deviation information from the preprocessed heating control behavior correlation information, specifically including the target heating power setting value and the change amplitude of the power supply voltage at different times within the voltage fluctuation time window, and represent them using functions JPY(t) and VBF(t) respectively according to the time series. t is the time point, JPY(t) represents the target heating power setting value at time t within the voltage fluctuation time window, VBF(t) represents the change amplitude of the power supply voltage at time t within the voltage fluctuation time window, and the time period is defined as [t1, t2]; The influence factor γ of heating power deviation on heating system control is determined by fitting historical experimental data; Calculate the heating control deviation index. The specific calculation formula is as follows: Where HCDI is the heating control deviation index, It represents the rate of change of the power supply voltage change amplitude, and JPS(t) represents the heating power output value of the heating system at time t within the voltage fluctuation action time window; A heating power output offset evaluation model is constructed based on the generated dynamic response fluctuation coefficient and heating control deviation index, and an offset evaluation coefficient is generated through weighted summation; Determine a preset offset evaluation coefficient threshold interval, compare it with the generated offset evaluation coefficient after determination, and evaluate the degree of heating power output offset of the heating system under the condition of frequent power supply voltage fluctuations based on the comparison result, and classify it into stable offset degree, fluctuating offset degree and uncontrolled offset degree; Based on the classification results of the degree of heating power output deviation, the abnormal risk level of temperature control distortion in the heating system is determined; Execute corresponding heating control measures according to the determined abnormal risk level; The data and control response information generated during the monitoring process are archived, and the processing flow for analyzing the associated information of heating control behavior is updated based on the archiving results to adapt to the monitoring needs under different working conditions.

2. The abnormality monitoring method of the heating system of the carbon brush forming machine according to claim 1 is characterized in that: Extracting the continuous time period in which voltage fluctuations continue to occur includes: when the power supply voltage is judged to be in a frequently fluctuating state, marking the starting moment when it is first judged to be in a frequently fluctuating state, and continuously judging whether the power supply voltage remains in a frequently fluctuating state in subsequent sampling cycles, until the judgment results in several consecutive sampling cycles are all in a non-frequently fluctuating state, determining the end moment, and using the time period between the start moment and the end moment as the voltage fluctuation action time window.

3. The abnormality monitoring method of the heating system of the carbon brush forming machine according to claim 2 is characterized in that: A heating power output offset assessment model is constructed based on the generated dynamic response fluctuation coefficient DRFC and heating control deviation index HCDI. The offset assessment coefficient is generated by weighted summation. The specific calculation formula is as follows: OEC=ω1*DRFC+ω2*HCDI Where OEC is the offset evaluation coefficient, ω1 and ω2 are the non-zero weight coefficients of the dynamic response fluctuation coefficient DRFC and the heating control deviation index HCDI, respectively, and ω1+ω2=1.

4. The abnormality monitoring method of the heating system of the carbon brush forming machine according to claim 3 is characterized in that: Determine the preset offset evaluation coefficient threshold range [OEC min , OEC max ], and after determination, it is compared with the generated offset evaluation coefficient OEC. According to the comparison results, the heating power output offset degree of the heating system under the condition of frequent power supply voltage fluctuations is evaluated, and it is divided into stable offset degree, fluctuating offset degree and out-of-control offset degree. The specific comparison analysis is as follows: If OEC<OEC min ,The degree of heating power output offset of the heating system under the condition of frequent power voltage fluctuations is a stable offset degree; If OEC min ≤OEC≤OEC max ,The degree of heating power output offset of the heating system under the condition of frequent power voltage fluctuations is the fluctuating ,degree; If OEC>OEC max , the degree of deviation of the heating power output of the heating system under the condition of frequent power supply voltage fluctuations is an out-of-control type deviation degree.

5. The abnormality monitoring method of the heating system of the carbon brush forming machine according to claim 4 is characterized in that: Based on the classification results of the degree of heating power output deviation, the abnormal risk level of temperature control distortion in the heating system is determined, specifically: If the degree of deviation of the heating power output is stable, the abnormal risk level of temperature control distortion in the heating system is no risk; If the degree of heating power output deviation is an out-of-control deviation, the abnormal risk level of temperature control distortion in the heating system is severe risk; If the degree of heating power output deviation is a fluctuating deviation degree, several subsequently generated deviation evaluation coefficients are continuously obtained and analyzed, and the abnormal risk level of temperature control distortion in the heating system is determined based on the analysis results.

6. The abnormality monitoring method of the heating system of the carbon brush forming machine according to claim 5 is characterized in that: If the heating power output deviation is a fluctuating deviation, continue to obtain several subsequent deviation evaluation coefficients and recalibrate them to OEC m , m is the number of several offset evaluation coefficients generated subsequently, m = 1, 2, 3, ..., d, d is a positive integer; Calculate the subsequent generated offset evaluation coefficients OEC m The average OEC - , according to the formula: Calculate the subsequent generated offset evaluation coefficients OEC m Standard Deviation of OEC σ , according to the formula: Determine the preset average value threshold of several preset offset evaluation coefficients and preset standard deviation thresholds After determination, the calculated offset evaluation coefficients OEC are used. m The average OEC - and standard deviation OEC σ Perform a comparison and determine the abnormal risk level of temperature control distortion in the heating system based on the comparison results. The specific comparison analysis is as follows: like and The abnormal risk level of temperature control distortion in the heating system is high risk; like and The abnormal risk level of temperature control distortion in the heating system is medium risk; like and The abnormal risk level of temperature control distortion in the heating system is medium risk; like and The abnormal risk level of temperature control distortion in the heating system is low risk.

7. The abnormality monitoring method of the heating system of the carbon brush forming machine according to claim 6, characterized in that: According to the determined abnormal risk level, the corresponding heating control treatment measures are implemented, specifically: If the abnormal risk level of temperature control distortion in the heating system is no risk, the specific heating control treatment measures to be implemented are: continue to operate normally according to the predetermined heating program without any control intervention; If the abnormal risk level of temperature control distortion in the heating system is low, the specific heating control treatment measures to be implemented are: continue heating, maintain the existing control parameters, and continuously monitor the status of the heating system; If the heating system has a medium risk of temperature control distortion, the specific heating control measures to be implemented are: extend the heating time, adjust the heating power output to ensure a uniform heating process, and if the heating system experiences persistent fluctuations, immediately intervene manually and adjust the control strategy to avoid further temperature control distortion. If the heating system has a high risk level of temperature control distortion, the specific heating control measures to be implemented are: immediately suspend the heating process, check the working status of the heating system, find out the cause of the temperature control distortion, and adjust and maintain the equipment to restore the normal operation of the heating system; If the abnormal risk level of temperature control distortion in the heating system is serious, the specific heating control processing measures to be implemented are: immediately terminate the heating task, cut off the power supply of the heating system, record the abnormal information, conduct a comprehensive inspection and repair, and ensure that the heating system is restored to a safe working state.

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