Calcining furnace shoveling plate state monitoring and early warning method based on infrared sensor

Abstract

The invention discloses a calcining furnace shoveling plate state monitoring and early warning method based on an infrared sensor, which comprises the following steps: (1) real-time data acquisition: acquiring temperature data of a plurality of parts in real time through temperature sensors mounted on the plurality of parts of a rotary calcining furnace; optionally (2) signal processing and analog-to-digital conversion: each sensor converts an analog temperature signal monitored by the sensor into a voltage signal, and after amplification and analog-to-digital conversion, the voltage signal is converted into a digital signal; (3) data cleaning and de-noising: removing abnormal data through data cleaning and de-noising; (4) calculating a root mean square (RMS) value; and (5) comparing with a threshold value, and judging whether to be normal or abnormal. The temperature change of the shoveling plate in each area of the rotary furnace can be monitored in real time through non-contact detection, potential problems of the shoveling plate can be found in time, efficient early warning is improved, and safe production is guaranteed.

Description

Technical Field

[0001] This invention relates to industrial automation monitoring technology, particularly a method for monitoring and early warning of the status of the lifting plates in a rotary calcining furnace based on infrared sensors. Specifically, it relates to a technical solution that involves installing infrared sensors on the outside of the rotary furnace cylinder to monitor temperature data related to the lifting plates in real time, and processing and analyzing the data to achieve early warning of anomalies and status monitoring, thereby preventing equipment damage or safety accidents caused by the detachment or damage of the lifting plates. Background Technology

[0002] Rotary calcining furnaces are widely used in metallurgy, chemical industry, and other sectors, primarily for the calcination and heat treatment of materials. Under the high temperature and intense movement of the furnace body, the lifting plates, as crucial working components, endure immense mechanical loads and high-temperature environments. The lifting plates may detach, become damaged, or deform due to prolonged friction, temperature changes, or mechanical fatigue, leading to production interruptions and even endangering equipment and personnel safety. If a detached lifting plate falls into subsequent processing equipment, the consequences could be disastrous. In this field, regarding lifting plates, CN212639971U discloses a lifting plate arrangement structure for a heavy ash self-returning alkali steam calcining furnace, and CN216245479U discloses a detachable lifting plate device for a rotary calcining furnace. Most existing technologies focus on improvements to the structure and arrangement of the lifting plates themselves, with little attention paid to monitoring the working status of the lifting plates.

[0003] In existing technologies, traditional methods for detecting the status of reverse engineering boards mostly rely on periodic shutdowns for maintenance or contact-based sensor detection. These methods either suffer from delays in repairing and replacing boards in abnormal conditions, making it difficult to detect problems promptly, resulting in high maintenance costs and long cycles, or are affected by environmental factors, where prolonged contact may cause sensor wear and malfunction, again potentially leading to delayed problem detection. Therefore, a non-contact, reliable, and high-temperature-adaptable reverse engineering status monitoring technology is needed. Summary of the Invention

[0004] In view of the problems existing in the prior art, the present invention provides a method for monitoring and early warning of the plate-lifting status in a calcining furnace (especially a rotary calcining furnace) based on infrared sensors. By installing infrared sensors on the outside of the rotary furnace cylinder, temperature data related to the plate-lifting is collected in real time, and the working status of the plate-lifting is monitored through data processing and analysis. When an abnormality is detected, an alarm signal is immediately issued and emergency measures are taken, thereby ensuring the safe operation of the equipment.

[0005] The technical solution of the present invention includes the following main steps:

[0006] (1) Installation of infrared sensors and temperature data acquisition: Temperature sensors, such as infrared sensors, are installed on the outside of the rotary kiln cylinder. Preferably, temperature sensors (infrared sensors) are installed in one or more or all four key areas: the front end, middle end, middle rear end, and rear end. For example, Keyence HT-50K infrared sensors are used to collect temperature data from different areas. Each infrared sensor independently monitors the temperature change of the plate in its corresponding area.

[0007] Optional (2) Signal amplification and analog-to-digital conversion: The temperature signal collected by the infrared sensor is an analog signal. It is amplified by the amplification circuit and converted into a digital signal by the analog-to-digital converter (ADC) for subsequent data processing and analysis.

[0008] (3) Data processing and analysis: This may include filtering, denoising, outlier detection, and feature extraction of temperature data through real-time data acquisition and processing algorithms to remove abnormal data. Based on the set temperature threshold and fluctuation analysis, it can be determined whether the reverse engineering process has encountered any abnormalities. The working status of the reverse engineering process can be determined by calculating temperature fluctuation values ​​(such as RMS values).

[0009] (4) Calculate the root mean square (RMS) value

[0010]

[0011] Where N is the number of sampling points, T i Let be the temperature value at the i-th time point. It is the average of the temperature data;

[0012] (5) Compare with the threshold to determine whether it is normal or abnormal.

[0013] Normal state: When the RMS value is less than the set threshold and the temperature value is within the normal range, the reverse engineering is considered to be in normal working state; Abnormal state: If the RMS value is greater than the set threshold and the temperature value exceeds the set range, the reverse engineering is considered to have malfunctioned.

[0014] Furthermore, the system sets a temperature threshold. When the RMS value exceeds the set threshold and the temperature exceeds the predetermined range, an alarm mechanism is triggered. The alarm signal will alert personnel to check the abnormal area and take measures such as stopping the furnace operation for maintenance to prevent damage to the flip-flops or safety accidents.

[0015] The front end of the rotary kiln cylinder is the feed end, which is the part of the kiln closest to where the raw material enters. Its characteristic is that the temperature in this area is relatively low because the kiln is just beginning to heat up, and the material is not yet fully heated. This area mainly serves for preheating and initial calcination of the material.

[0016] The middle section of the rotary kiln cylinder is located in the center of the kiln, between the front and rear ends. Its characteristics are: the temperature in this area begins to rise, making it the main heating and reaction zone of the rotary kiln. Further calcination of the material typically occurs here, resulting in a relatively uniform and high temperature.

[0017] The rear section of the rotary kiln cylinder is the area between the middle of the kiln and the kiln outlet, located between the middle and rear ends. Its characteristics are: the temperature in this area is usually higher, indicating that the material has already reached a relatively high temperature, the calcination process in the kiln is complete, and it is close to the material discharge point.

[0018] The rear end of the rotary kiln cylinder is the outlet end, where the calcined material exits. Its characteristics include a typically higher temperature in the rear area, but slightly lower than the middle and rear sections. This area is primarily for material discharge; after final heating and calcination, the material proceeds to the next stage of the process. This division of areas helps to more accurately monitor the working status of the lifting plates and determine whether any abnormalities have occurred in different areas based on temperature changes, enabling more precise location of detached lifting plates for timely handling.

[0019] In this application, the temperature sensor can be an infrared temperature sensor known in the art, such as the Keyence HT-50K infrared sensor, which can detect temperatures from 0 to 1350°C and can detect distances of up to 3000 mm. It is suitable for high-temperature, high-pressure, and long-distance environments and has strong anti-interference capabilities.

[0020] Furthermore, signal processing and analog-to-digital conversion are performed as follows: After the original output voltage of the temperature sensor passes through the signal amplification circuit, the output voltage will increase, according to the amplification formula:

[0021] V amp =V out ·G

[0022] Among them, V amp V is the amplified voltage. out The original output voltage of the temperature sensor is the signal in the range of 0-1V. G is the amplification gain, which is set to a large value so that the output voltage reaches or approaches 5V. Here it is set to 5.

[0023] Modular-to-digital conversion formula:

[0024]

[0025] Among them, V ref The reference voltage is set to 5V, N is the effective number of bits of the ADC (16), and D is the digitized signal.

[0026] Furthermore, in step (3), outliers are detected and removed using the Z-score method:

[0027]

[0028] Where X is the observed value (the raw temperature value collected by the temperature sensor at a certain time), μ is the data mean (the average of all observed values), and σ is the standard deviation. When the Z value exceeds a set threshold (e.g., Z>2), the data point is considered an anomaly and is removed.

[0029] Furthermore, in step (4), the front-end RMS threshold is <5, the middle-end RMS threshold is <6, the middle-end RMS threshold is <7, and the back-end RMS threshold is <7.

[0030] Furthermore, T i =a·V out +b, V out Here, is the original output voltage of the temperature sensor, and a and b are sensor calibration constants: a = 10, b = 150, or... Where D is the digitized signal value obtained in step (2).

[0031] The infrared sensor-based calcining furnace plate-fed status monitoring and early warning method of the present invention can further include LSTM model prediction: the system also uses an LSTM model based on historical data to predict future temperature trends and achieve early warning. By training on temperature data, the LSTM model can identify potential temperature fluctuation trends, further improving the accuracy of the early warning.

[0032] Includes the following steps:

[0033] (1) Input temperature data within a certain time range in the past (e.g., within 30-120 minutes, such as 30 minutes or 60 minutes), with one data point every time interval (e.g., every minute);

[0034] (2) The LSTM layer receives and processes this time series data;

[0035] (3) The output of the LSTM model is the temperature prediction value for a future time period (e.g., 10 minutes), which will be used as the basis for judging the future temperature trend.

[0036] In this application, "optional" means that the steps specified thereafter may or may not be performed.

[0037] Advantages and effects of the present invention

[0038] (1) Real-time monitoring: It can monitor the temperature changes of the plate-scraping in each area of ​​the rotary kiln in real time and promptly detect potential problems in plate-scraping;

[0039] (2) Non-contact detection: The infrared sensor does not need to be in direct contact with the reading board, avoiding the wear problem of traditional temperature sensors and improving the reliability of long-term monitoring;

[0040] (3) High-efficiency early warning: Through the prediction and anomaly judgment mechanism of the LSTM model, the trend of temperature fluctuation can be detected in advance to avoid sudden equipment damage;

[0041] (4) Safety assurance: When the temperature is abnormal, the system can quickly trigger an alarm and take measures to avoid equipment failure leading to production stoppage or personal injury. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the overall structure of the rotary kiln cylinder.

[0043] Figure 2 This is a flowchart of a method for monitoring and early warning of the status of the lifting plate in a calcining furnace based on infrared sensors.

[0044] Explanation of reference numerals in the attached drawings: 1: cylinder; 2: measuring plate; 3: sensor. Detailed Implementation

[0045] The present invention will be further illustrated below through specific embodiments.

[0046] Reference Figure 2 The infrared sensor-based method for monitoring and early warning of the status of the lifting plate in a calcining furnace (especially a rotary calcining furnace) includes the following steps:

[0047] (1) Temperature sensor installation

[0048] Temperature sensors are installed in one or more of the four key areas on the outside of the rotary kiln: the front, middle, middle-rear, and rear. For example, they can be installed on the outer side of each of these areas, spaced around the outer perimeter of the cylinder, preferably evenly spaced. Alternatively, to save costs, the temperature sensors can be distributed longitudinally along the cylinder, fixed to the outer side (e.g., above or below), at a certain distance from the outer wall, and not rotate with the cylinder. The number of temperature sensors should preferably cover the entire lifting plate working area of ​​each of the front, middle, middle-rear, and rear sections. The front section typically occupies about 1 / 6 to 1 / 4 of the rotary kiln's length (approximately 16% to 25% of the total length). This area is mainly used for preheating and initial heating of the material, and the temperature is relatively low. The middle section occupies approximately 1 / 3 to 1 / 2 of the rotary kiln's length (approximately 33% to 50% of the total length). This area is the main heating zone of the rotary kiln, where the temperature gradually increases, and the material undergoes calcination and heating reactions. The middle and rear sections typically comprise about 1 / 4 to 1 / 3 of the rotary kiln's length (approximately 25% to 33% of the total length). Temperatures are higher here, the material is nearly fully calcined, and the heat load on the lifting plates is also greater. The rear section typically comprises about 1 / 6 to 1 / 4 of the rotary kiln's total length (approximately 16% to 25% of the total length). This area is primarily used for the final heating of the material, resulting in relatively high temperatures and a greater heat load on the equipment.

[0049] The front end is the feed end of the rotary kiln, which is the part of the kiln body closest to where the raw material enters. Characteristics: The temperature in this area is relatively low because the kiln body has just begun heating, and the material is not yet fully heated. This area primarily handles the preheating and initial calcination of the material.

[0050] Mid-section: Located in the middle of the rotary kiln, between the front and rear ends. Characteristics: The temperature in this area begins to rise, and it is the main heating and reaction zone of the rotary kiln. Further calcination of materials typically occurs here, with a relatively uniform and high temperature.

[0051] Mid-to-rear end position: The area between the middle of the rotary kiln and the furnace outlet, located between the middle and rear ends. Characteristics: The temperature in this area is usually higher, as the material has already reached a higher temperature, the calcination process of the furnace body is complete, and it is close to the material discharge point.

[0052] Rear end location: The outlet end of the rotary kiln, where the material exits after calcination. Characteristics: The temperature in the rear end area is typically higher, but slightly lower than the middle and rear ends. This area is mainly used for material discharge; after final heating and calcination, the material enters the next process step. This division of areas helps to more accurately monitor the working status of the lifting plates and determine whether abnormalities have occurred in different areas based on temperature changes, enabling more precise location of detached lifting plates for timely handling.

[0053] like Figure 1 As shown, each sensor 3 measures the temperature change of the scraper plate within its monitoring area in real time according to its installation position, ensuring coverage of the entire scraper plate working area and comprehensive acquisition of temperature information. The infrared sensor works by detecting the temperature of the scraper plate surface in a non-contact manner. Although the infrared sensor is installed externally, it can indirectly reflect the status of the scraper plate by monitoring the temperature fluctuations of the rotary kiln surface in the corresponding area. When the scraper plate 2 malfunctions (such as falling off, being damaged, or deforming), it usually causes temperature fluctuations or changes. When the scraper plate inside the rotary kiln cylinder 1 operates at high temperatures, the internal heat is transferred outward through the rotary kiln wall. Even if the external sensor does not directly contact the scraper plate, the temperature of the scraper plate will affect the temperature of the outer surface of the cylinder through heat conduction. The external sensor indirectly infers the temperature of the scraper plate by measuring the temperature change of the furnace surface. The scraper plate surface also radiates heat, especially under high temperature conditions, the scraper plate surface will produce significant infrared radiation. This infrared radiation will be captured by the external infrared sensor and converted into temperature data. Therefore, the status of the scraper plate (e.g., whether it has fallen off, been damaged, or changed in heat distribution) will be reflected in the temperature changes detected by the external sensor. Temperature changes in the flip-flop are directly related to its operating status. For example, if the flip-flop detaches, deforms, or is damaged, the heat exchange efficiency between the flip-flop surface and the material will change, which will manifest as abnormal fluctuations or drastic changes in temperature. By combining real-time data analysis (RMS value calculation, temperature threshold judgment), the accuracy of early warning can be further improved. When temperature fluctuations exceed the normal range, the system can accurately determine whether there is a risk of flip-flop failure by calculating the RMS value and comparing it with the set threshold. The system uses machine learning (LSTM model) to predict future temperature trends, thereby detecting potential temperature anomalies earlier. This helps to identify potential problems with the flip-flop in advance; for example, a gradual increase in temperature fluctuations may indicate damage or detachment of the flip-flop.

[0054] (2) Temperature signal acquisition, amplification and analog-to-digital conversion

[0055] Each infrared sensor detects the radiant temperature of an object's surface using non-contact infrared light and converts it into an analog voltage signal. Since the analog signal output by the sensor is typically weak, it needs to be amplified by an amplifier circuit (such as an operational amplifier) ​​to ensure that the signal is strong enough for subsequent processing.

[0056] Signal amplification formula:

[0057] V amp =V out ·G

[0058] Among them, V amp V is the amplified voltage. outG is the sensor's original output voltage (a signal in the range of 0-1V, which we set to 1V here), and G is the amplification gain (which can be set to a large value so that the output voltage reaches or approaches 5V, which we set to 5 here).

[0059] Modular-to-digital conversion formula:

[0060]

[0061] Among them, V ref The reference voltage is 5V, N is the effective number of bits of the ADC (16), and D is the digitized signal.

[0062] In step (2), signal amplification and analog-to-digital conversion can be performed arbitrarily.

[0063] (3) Data processing and analysis

[0064] The data collected by the sensor is cleaned and filtered to remove abnormal data points.

[0065] Data cleaning: Using the Z-score method to detect and remove outliers:

[0066]

[0067] Where X is the observed value (the raw temperature value collected by the sensor at a certain time), μ is the data mean (the average of all observed values), and σ is the standard deviation. When the Z value exceeds a set threshold (e.g., Z>2), the data point is considered an anomaly and is removed.

[0068] Filtering and denoising: Low-pass filters are used to denoise the signal while maintaining the integrity of the effective signal.

[0069] (4) Feature extraction and anomaly detection

[0070] The root mean square (RMS) value of the signal is calculated to describe the intensity of temperature fluctuations. A larger RMS value may indicate a malfunction in the PCB, especially when problems such as PCB detachment or deformation occur, in which case temperature fluctuations will typically increase.

[0071] RMS value calculation formula:

[0072]

[0073] Among them, T i Let T be the temperature value at the i-th time point. i =a·V out +b, where a and b are sensor calibration constants (a = 10, b = 150); V out (The original output voltage of the sensor), or Where D is the digitized signal obtained from the above calculation;

[0074] N is the number of sampling points (the number of sampling points for the front end, middle end, middle and back end, or each of the back end). It is the average of the temperature data.

[0075] The system compares characteristic values ​​such as RMS value and maximum value with a set threshold to determine whether the board is in normal working condition.

[0076] In the front-end area, the lifting plates are mainly responsible for preheating and initial heating of the material. The temperature here is typically low with relatively small temperature fluctuations, and a low threshold is usually set. The middle area is the main heating zone of the rotary kiln, with relatively high temperatures and larger temperature fluctuations. The threshold for the middle area should be appropriately increased because temperature changes will be more pronounced. The rear-end area is near the furnace outlet, with high temperatures and fully heated material. Temperature fluctuations are more drastic here, and a higher threshold can be set due to these larger fluctuations. The rear-end area is where the material exits the furnace; the temperature is typically high and also fluctuates significantly, so a higher threshold should be set there as well.

[0077] Specifically, it can be set as follows:

[0078] area RMS threshold Maximum threshold front end RMS<5 200℃ Mid-range RMS<6 250℃ Mid-backend RMS<7 300℃ rear end RMS<7 350℃

[0079] Judgment criteria:

[0080] Normal state: If the RMS value is less than the set threshold (e.g., front-end RMS < 5; the RMS threshold setting should not be too strict or too lenient. If the threshold is too high (e.g., set to 10 or higher), some small abnormal fluctuations may be missed. If the threshold is too low (e.g., set to 2 or lower), it may cause false alarms, and the system will frequently trigger alarms), and the temperature value is within the set normal range (e.g., 10℃ < temperature < 200℃), then the reading board is considered to be in normal working state.

[0081] Abnormal conditions: If the RMS value is greater than the set threshold (e.g., front-end RMS > 5), or the temperature value exceeds the upper limit (e.g., temperature > 5), the abnormal conditions are considered abnormal.

[0082] If the temperature is below 200℃ or below the lower limit (e.g., temperature <10℃), it is determined that the reading board is malfunctioning.

[0083] (5) Alarm mechanism and response measures

[0084] Alarm triggering conditions:

[0085] If the front-end RMS value is greater than the set threshold of 5, or the temperature exceeds the set range (e.g., temperature > 200℃ or temperature < 10℃), the mid-end RMS value is greater than 6, or the mid-end or rear-end RMS value is greater than 7, then an alarm signal will be triggered.

[0086] The system immediately stopped the furnace operation to prevent more serious equipment damage or safety accidents caused by plate-flipping problems.

[0087] (6) LSTM model prediction

[0088] During the operation of the rotary calcining furnace, the lifting plates are subjected to enormous mechanical loads and high temperatures, making them prone to detachment or damage. If the lifting plate status is not monitored in a timely manner, it may lead to equipment damage and production interruptions. The current system uses temperature sensors to monitor the lifting plate's operating status in real time; however, to improve the accuracy and proactiveness of fault warnings, an LSTM-enhanced predictive model is employed to predict future temperature trends and detect lifting plate problems early.

[0089] Data preparation:

[0090] The input to an LSTM model is time-series data, which is the temperature data of a device over a period of time. For example, if we collect temperature data from the past 60 minutes, collecting one data point per minute, this forms a time-series dataset.

[0091] Data format:

[0092] Training set: Uses temperature data from the past 60 minutes (one data point per minute) to predict the temperature for the next 10 minutes.

[0093] Each sample data point has the shape of (number of samples, time step, number of features), for example (1000, 60, 1), which means that we use the data from the past 60 minutes (the number of features is 1, i.e., the temperature value per minute) to predict the future temperature.

[0094] LSTM model structure

[0095] The working principle of LSTM is as follows:

[0096] 1. Input Layer: The input is temperature data from the past 60 minutes (one data point per minute). Let's assume our input data is: X input =[55,56,58,57,53,···] Each data point is the temperature value over the past minute.

[0097] 2. LSTM Layer: The LSTM layer receives this time-series data and predicts future temperature trends based on historical data. The core of LSTM lies in its ability to capture temporal dependencies, such as the impact of the temperature value from the past minute on the current temperature. The LSTM calculation formula includes forget gates, input gates, and output gates, and learns the dependencies of historical data by updating memory cells.

[0098] 3. Output Layer: The output of the LSTM model is the temperature prediction for the next 10 minutes. These predictions will be used as inputs into the system for further analysis of future temperature trends.

[0099] LSTM model formula:

[0100] 1. The Gate of Oblivion:

[0101] f t =σ(W f ·[h t-1 ,x t ]+b f )

[0102] Among them, f t It is the output of the Forgotten Gate, W f It is the weight of the forget gate, x t It is the input data at the current moment, h t-1 It is the hidden state of the previous time step, σ is the activation function, and b f It is the bias term of the forget gate;

[0103] 2. Input Gate

[0104] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0105] Among them, i t It is the output of the Forgotten Gate, W i It is the weight of the forget gate, x t It is the input data at the current moment, h t-1 It is the hidden state of the previous time step, σ is the activation function, and b i It is the bias term of the forget gate;

[0106] 3. Update memory units

[0107] C t =f t ·C t-1 +i t ·tanh(W C ·[h t-1 ,x t ]+b C )

[0108] Among them, C t It represents the current state of the memory cell, W. c It is the weight matrix for updating memory units, C t-1 It is a memory unit from the previous moment;

[0109] 4. Output gate

[0110]

[0111] Among them, o t It is the output of the output gate, W o It is the weight matrix of the output gate, b o It is the bias term of the forget gate, h t It is the hidden state at the current moment, which serves as the output of the model.

[0112] LSTM prediction example:

[0113] Predict the temperature value for the next 10 minutes using an LSTM model. The model makes predictions based on data from the past 60 minutes. Here is an example:

[0114] time Actual temperature value (°C) Predicted temperature (°C) 10：00 150 151.2 10：01 152 153.1 10：02 153 154.2 10：03 154 155.3 10：04 155 156.5

[0115] Example

[0116] During the operation of a calcining furnace, the system monitored the temperature of the scraper plates in real time using infrared sensors installed in four areas of the furnace body. The system utilized data acquisition and analysis to assess whether the scraper plates were in normal working order and to provide early warnings and responses to potential anomalies.

[0117] Step 1: Real-time data acquisition

[0118] Four sensors are installed at the front, middle, middle-rear, and rear ends of the rotary calcining furnace to collect temperature data in real time for these areas. The following are examples of data collected by the sensors (unit: Pa).

[0119] area Collection time Original temperature value (°C) front end 10：00 150 Mid-range 10：00 180 Mid-backend 10：00 170 rear end 10：00 160

[0120] Step 2: Signal Processing and Analog-to-Digital Conversion

[0121] Each sensor converts the analog temperature signal it detects into a voltage signal, which is then amplified and converted from analog to digital into a digital signal.

[0122] The sensor's original output voltage will increase after passing through the signal amplification circuit. According to the amplification formula:

[0123] V amp =V out ·G = 1V·5 = 5V.

[0124] Taking the front-end area as an example, the original temperature value collected by the front-end sensor is 150℃. After signal amplification and analog-to-digital conversion, a digital signal D is obtained:

[0125]

[0126] Step 3: Data Cleaning and Noise Reduction

[0127] Data cleaning and noise reduction are crucial steps in real-time data processing. In this example, the Z-score method is used to remove outliers. Twenty temperature samples from different time points have already been collected from the front end.

[0128] [150, 151, 152, 149, 153, 148, 154, 151, 155, 152, 153, 151, 157, 158, 159, 150, 151, 155, 152, 149].

[0129] Detecting and removing outliers using the Z-score method:

[0130]

[0131] Where X is the observed value (temperature value of 20 temperature sample data), μ is the data mean (i.e. the average value of 20 temperature sample data), and σ is the standard deviation; when the Z value exceeds the set threshold (e.g. Z>2), the data point is considered abnormal and removed.

[0132]

[0133] Therefore, μ is 152 and σ is 3.22.

[0134] The current temperature value is 150℃, so the calculated Z value is:

[0135]

[0136] Since Z < 2 (the set threshold), this data point is not an outlier and therefore does not need to be deleted.

[0137] Step 4: Calculate the root mean square (RMS) value

[0138] Next, the system will calculate the root mean square (RMS) value of the temperature data for each region to assess the intensity of temperature fluctuations. The RMS value reflects the temperature fluctuations during the reverse engineering process; a large RMS value may indicate a malfunction in the reverse engineering process.

[0139] The following temperature data (in degrees Celsius) for the front area over the past five minutes are available:

[0140] [160,161,158,162,164],

[0141] Calculate the RMS value:

[0142]

[0143] An RMS value of 2 indicates that the temperature fluctuation in this area is small, suggesting that the reverse engineering process is stable.

[0144] Step 5: Compare with the threshold to determine whether it is normal or abnormal.

[0145] Now, the status of the reverse engineering board is determined based on the RMS value and temperature threshold.

[0146] Normal state: The current RMS value is less than the front-end set threshold of 5, and the temperature value is within the normal range (10℃ < temperature < 200℃).

[0147] (10℃ is the minimum temperature considered during normal heating startup, indicating that the temperature has started to rise, but may not yet be fully heated; 200℃ is considered because excessively high temperatures may damage the copying board, or indicate a malfunction in the equipment (such as uneven heat distribution causing overheating in some areas)). Therefore, it is considered that the copying...

[0148] The board is in normal working condition;

[0149] Abnormal conditions: If the front-end RMS value is greater than the front-end set threshold of 5, or the temperature value exceeds the set range (temperature > 200℃ or temperature > 50℃), the abnormal condition is indicated.

[0150] If the temperature range is less than 10℃ and 10℃ < temperature < 200℃, this temperature range is set based on the reasonable temperature range under normal operating conditions of the equipment. It is derived from experiments and historical data (the purpose is to ensure the safe operation of the plate-scraping and rotary kiln). If the temperature range is less than 10℃ and 10℃ < temperature < 200℃, then the plate-scraping is considered to be abnormal.

[0151] The RMS value reflects the magnitude of temperature fluctuations. A sudden increase in the RMS value usually indicates some kind of abnormal fluctuation, which may indicate a change in the operating status of the reverse engineering process. Here are a few reasons why setting the RMS threshold to 5 is reasonable.

[0152] a) Small fluctuations under normal operating conditions

[0153] Under normal circumstances, the temperature fluctuation during PCB reverse engineering is relatively stable. The operating temperature of the reverse engineering board fluctuates within a relatively stable range, and the amplitude of the temperature fluctuation is not too large.

[0154] If the RMS value is small (e.g., less than 5), it indicates that the temperature fluctuation is small, meaning that the reverse engineering board is in normal working condition.

[0155] b) Increased volatility due to abnormal stock price movement

[0156] Lifting plate detachment or damage: If the lifting plate detaches or is damaged, the contact between the lifting plate and the material deteriorates, potentially leading to uneven heating and significant temperature fluctuations. For example, the temperature in some areas may suddenly rise while other areas may cool down. This uneven temperature distribution results in increased temperature fluctuations.

[0157] Drastic temperature changes: Board detachment, deformation, or other malfunctions often lead to drastic temperature changes because heat conduction and radiation become uneven. This change will be reflected in the temperature data, thus affecting the RMS value.

[0158] In this example, the RMS value in the right-side region was 2, less than the set threshold of 5, and the temperature was also within the normal range (10℃ < temperature < 200℃). Therefore, the plate lifter was functioning normally. Conversely, the system would immediately issue a warning and automatically stop the rotary kiln to prevent further damage to the equipment caused by the plate lifter detaching or becoming damaged. The staff received an alarm message on the display screen, indicating a possible anomaly in the right-side plate lifter area. The staff immediately went to the area to inspect and found that the right-side plate lifter had partially detached. The staff re-secured and repaired the plate lifter to ensure smooth production. After the repair was completed, the system confirmed that the temperature data and RMS value in the right-side region had returned to normal levels, and the system automatically resumed kiln operation, with production continuing.

[0159] The data collected from the middle, middle-end, and back ends are processed in the same way as described above, according to steps 1 to 5, except that the thresholds are set to 6 for the middle end and 7 for the middle-end and back ends, respectively, to determine the board copying status of these parts and to perform corresponding processing.

[0160] The preferred embodiments of the present invention have been described above; however, this description is not intended to be limiting. Those skilled in the art can make many changes or modifications to the present invention without departing from its spirit and scope. Such changes or modifications should be included within the scope of the appended claims.

Claims

1. A method for monitoring and early warning of the status of the lifting plate in a calcining furnace based on infrared sensors, comprising the following steps: (1) Real-time data acquisition Temperature data of one or more locations are collected in real time by temperature sensors installed on one or more locations on the outside of the rotary calciner. Optional (2) Signal Processing and Analog-to-Digital Conversion Each sensor converts the analog temperature signal it detects into a voltage signal, which is then amplified and converted from analog to digital into a digital signal. (3) Data processing and analysis to eliminate abnormal data; (4) Calculate the root mean square (RMS) value Where N is the number of sampling points, T i Let be the temperature value at the i-th time point. It is the average of the temperature data; (5) Compare the root mean square (RMS) value with the threshold to determine whether it is normal or abnormal. Normal state: When the RMS value is less than the set threshold and the temperature value is within the normal range, the reverse engineering is considered to be in normal working state; Abnormal state: If the RMS value is greater than the set threshold and the temperature value exceeds the set range, the reverse engineering is considered to have malfunctioned.

2. The method for monitoring and early warning of the status of the lifting plate in a calcining furnace according to claim 1, characterized in that, The one or more parts are one or more external parts of the front end, middle end, middle and rear end of the rotary kiln.

3. The method for monitoring and early warning of the status of the lifting plate in a calcining furnace according to claim 1 or 2, characterized in that, In step (5), the front-end RMS threshold is set to <5, the middle-end RMS threshold to <6, the middle-end and back-end RMS thresholds to <7, and the back-end RMS threshold to <7.

4. The method for monitoring and early warning of the status of the lifting plate in a calcining furnace according to any one of claims 1-3, characterized in that, In step (2), signal processing and analog-to-digital conversion are performed as follows: After the sensor's original output voltage passes through the signal amplification circuit, the output voltage will increase, according to the amplification formula: V amp =V out ·G; Among them, V amp V is the amplified voltage. out The original output voltage of the sensor is the signal in the range of 0-1V, and G is the amplification gain. Modular-to-digital conversion formula: Among them, V ref The reference voltage is set to 5V, N is the effective number of bits of the ADC (16), and D is the digitized signal.

5. The method for monitoring and early warning of the status of the lifting plate in a calcining furnace according to any one of claims 1-4, characterized in that, In step (3), outliers are detected and removed using the Z-score method: Where X is the observed value, μ is the data mean, and σ is the standard deviation. When the Z value exceeds the set threshold, the data point is considered abnormal and is removed.

6. The method for monitoring and early warning of the status of the lifting plate in a calcining furnace according to any one of claims 1-5, characterized in that, In step (4), T i =a·V out +b, where a and b are sensor calibration constants: a = 10, b = 150, or Where D represents the digitized signal.

7. The method for monitoring and early warning of the status of the lifting plate in a calcining furnace according to any one of claims 1-6, characterized in that, When the RMS value exceeds the set threshold and the temperature exceeds the predetermined range, an alarm mechanism is triggered, and the furnace operation is stopped for maintenance.

8. The method for monitoring and early warning of the status of the lifting plate in a calcining furnace according to any one of claims 1-7, characterized in that, Data analysis and processing includes removing outlier data through data cleaning and noise reduction.

9. The method for monitoring and early warning of the calcining furnace lifting plate status according to any one of claims 1-8, further comprising LSTM model prediction, including the following steps: (1) Input temperature data over a period of time, with one data point every time interval; (2) The LSTM layer receives and processes this time series data; (3) The output of the LSTM model is the temperature prediction value for a period of time in the future. These prediction values ​​will be used as the basis for judging the future temperature trend.

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