A real-time signal processing early warning method

Abstract

The application discloses a kind of real-time signal processing early warning methods, and the application relates to adaptive signal processing technical field, and the early warning method specifically includes the following steps, vibration sensor is installed at the bearing seat of motor and shell, and ensure that the x direction of sensor is aligned with the direction of equipment operation, subsequently calibration is carried out to transmission, to ensure the accuracy of subsequent data acquisition;The collection interval time in data acquisition is set to 5-10S, and the data collected by vibration sensor is normalized and pretreated, and the application has the advantages that: by time domain and frequency domain analysis to vibration signal, using fast fourier transform (FFT) converts time domain signal into frequency domain signal, and the amplitude of each frequency point is calculated, this fusion processing mode can provide more in-depth understanding of motor vibration characteristics, effectively reveal the energy distribution of signal on different frequency components, help to identify potential faults or abnormal conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of adaptive signal processing, in particular to a real-time signal processing early warning method. BACKGROUND

[0002] With the continuous development of artificial intelligence, big data and Internet of Things technologies, signal processing technology is also continuously improving. Traditional signal processing methods have been difficult to meet the needs of modern applications for real-time, accuracy and efficiency. Therefore, real-time signal processing early warning methods have emerged. They combine the latest algorithms and hardware technologies to achieve fast and accurate processing of signals and timely early warnings. Real-time signal processing early warning methods are widely used in various fields such as industrial monitoring, security monitoring, traffic management and climate change monitoring. These fields have very high requirements for the real-time and accuracy of signals, thus driving the development of real-time signal processing early warning methods.

[0003] However, existing early warning processing methods mostly use high-performance processors and optimized algorithms to quickly process and analyze real-time collected signals by extracting key features from the signals and comparing them with preset thresholds or models to achieve early warning of potential risks. However, this early warning method does not have adaptive capabilities when faced with real-time changes in parameters such as frequency, amplitude and phase, and cannot adjust processing parameters in real time to adapt to signal changes. Therefore, we propose a real-time signal processing early warning method. SUMMARY

[0004] The purpose of the present application is to provide a real-time signal processing early warning method.

[0005] To solve the problems raised in the background technology, the present application provides the following technical solution: a real-time signal processing early warning method, which specifically includes the following steps:

[0006] Step one, install vibration sensors on the bearing seat and the casing of the motor, and ensure that the x direction of the sensor is aligned with the direction of the device operation. Then calibrate the transmission to ensure the accuracy of subsequent data collection.

[0007] Step two, set the collection interval time in data collection to 5-10S, and normalize the data collected by the vibration sensor for preprocessing, so that signals of different magnitudes are comparable, improving the accuracy and stability of subsequent processing.

[0008] Step three, under normal operating conditions of the motor, collect vibration signals for a period of time, then convert the collected vibration signals, convert time domain signals to frequency domain signals, and calculate the amplitude of each frequency point to display it in a broken line graph, where the calculation formula is kn, while x(K) is the frequency domain signal, x(n) is the time domain signal, N is the signal length, and k represents the frequency index;

[0009] Step four, calculate the mean deviation and variance of the amplitude of the vibration signal, wherein the mean value is the average value of the signal, which can reflect the overall level of the signal, and the variance measures the deviation between the sample value and the mean value, that is, the greater the variance, the greater the degree of dispersion, and the greater the difference between the signal value and the mean value;

[0010] Step five, extract the amplitude of each frequency point calculated in step three and name the feature as , and calculate the Poisson kernel Pb( ) by the formula , and further calculate the normalized Poisson kernel Pb( ) by the formula Pb( ) = *Pb( );

[0011] Step six, further analyze the characteristics of the normalized Poisson kernel, such as its change over time and its value at different frequencies, which can reflect the characteristics of the motor vibration signal, and when the Poisson kernel has a large change at a certain frequency, it means that the motor has abnormal vibration at that frequency;

[0012] Step seven, calculate the value range of the normalized Poisson kernel in the normal state and take the mean value plus or minus a certain multiple of the standard deviation as the threshold range, and set an abnormal threshold K= , compare the real-time calculated normalized Poisson kernel value with the set abnormal threshold, and when the Poisson kernel value exceeds the abnormal threshold range, the early warning mechanism will be triggered;

[0013] Step eight, the early warning mechanism can communicate with the outside through the wireless communication module, and when the early warning mechanism is triggered, the buzzer will be started by the controller to issue a buzzing alarm and the warning light will issue an alternating flashing light source to remind the on-site staff, and the abnormal information will be transmitted to the email of the relevant personnel through network communication.

[0014] As a further scheme of the present application: the normalization processing mentioned in step two includes but is not limited to deep cleaning of data to ensure the accuracy and consistency of data, and the interval time of collection is finely adjusted to 5-10S, aiming to improve the accuracy of data measurement and reserve sufficient calculation time for subsequent calculation process.

[0015] ​As a further scheme of the present application: the conversion process mentioned in step three is the conversion process of the vibration signal collected initially and its conversion from time domain to frequency domain, the vibration signal collected initially by the vibration sensor exists in the form of time domain, that is, the signal describes the change rule of vibration with time, and in order to analyze the vibration characteristics of the motor in depth, the time domain signal is converted into a frequency domain signal to reveal the energy distribution of the signal at different frequency components, in the conversion process, the fast Fourier transform (FFT) algorithm is adopted, and through the FFT conversion, the amplitude information of each frequency point is obtained, which directly reflects the vibration intensity of the motor at the frequency, and provides an important data basis for subsequent detailed analysis and processing.

[0016] As a further scheme of the present application: the mean and variance calculation formula mentioned in step four are respectively U= and a²= , wherein the mean U identifies the average level of the motor vibration signal in a period of time, which is obtained by summing the vibration signal values x (n) of the sampling points and then dividing by the total number of sampling points N, the value can reflect the overall trend of the motor vibration, and the variance a² is obtained by calculating the square of the difference between each sampling point vibration signal value x (n) and U, then summing the square differences and finally dividing by the total number of sampling points N.

[0017] As a further scheme of the present application: in step five, the amplitude characteristics of each frequency point calculated in step three need to be extracted and calculated through the Poisson kernel calculation formula.

[0018] As a further scheme of the present application: in step six, according to the curve graph of the Poisson kernel changing with time, we can intuitively observe the stability or volatility of the vibration signal in different time periods, and the value at different frequencies also reflects the intensity distribution of the motor vibration signal at different frequencies.

[0019] As a further scheme of the present application: the abnormal threshold K designed in step seven is between the mean U minus the standard deviation and the mean U plus the standard deviation , and k is 1.5, that is, the value range of the normalized Poisson kernel in the normal state is calculated, and the mean value plus or minus 1.5 times the standard deviation is taken as the upper and lower limits of the threshold range.

[0020] As a further scheme of the present application: the pre-warning mechanism mentioned in step eight also communicates with the outside through the wireless communication module. It can transmit abnormal information to the email of the relevant personnel through network communication, so that they can obtain abnormal information and process it in the first time. This remote communication mode not only improves the response speed, but also expands the range of information transmission, so that more people can participate in abnormal processing.

[0021] Compared with the prior art, the present application has the following advantages:

[0022] 1. The present application converts the time domain signal into a frequency domain signal by using fast Fourier transform (FFT) and calculates the amplitude of each frequency point through time domain and frequency domain analysis of the vibration signal. This fusion processing method can provide a more in-depth understanding of the vibration characteristics of the motor, effectively reveal the energy distribution of the signal at different frequency components, and help identify potential faults or abnormal conditions. In addition, the Poisson kernel is used for motor vibration signal analysis. By calculating the Poisson kernel and analyzing its normalized features such as changes over time and values at different frequencies, abnormal vibration of the motor at a specific frequency can be more sensitively captured, providing a new method and approach for motor fault monitoring, thereby increasing the adaptive ability.

[0023] 2. The present application calculates the value range of the normalized Poisson kernel under normal conditions, and takes the mean value plus or minus 1.5 times the standard deviation as the threshold range to fully consider the distribution of data, which can more reliably determine whether the motor has abnormal vibration. In addition, the mean value plus or minus the standard deviation method can effectively filter out some normal fluctuations and only issue a warning for situations that exceed the reasonable range, thereby greatly increasing the reliability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The figure is a schematic diagram of the method steps in the embodiment of the present application.

[0025] Figure 2 The figure is a schematic diagram of the conversion process in the embodiment of the present application. DETAILED DESCRIPTION

[0026] The specific embodiments of the present application will be further described below in conjunction with the drawings. It should be noted that the description of these embodiments is used to help understand the present application and does not constitute a limitation on the present application. In addition, the technical features involved in each embodiment of the present application described below can be combined with each other as long as they do not conflict with each other.

[0027] Embodiment one, please refer to the attached Figure 1 - attached Figure 2 The present application provides a technical solution: the pre-warning method specifically comprises the following steps:

[0028] Step 1: Install the vibration sensor on the motor's bearing seat and housing, ensuring that the sensor's x-direction is aligned with the direction of the device's operation. Then calibrate the actuator to ensure the accuracy of subsequent data collection.

[0029] Step 2: Set the data collection interval to 5-10 seconds and perform normalization preprocessing on the data collected by the vibration sensor to make signals of different magnitudes comparable, thereby improving the accuracy and stability of subsequent processing;

[0030] Step 3: When the motor is in normal operation, collect the vibration signal for a period of time, then convert the collected vibration signal, convert the time domain signal into the frequency domain signal, and calculate the amplitude of each frequency point and display it in a broken line graph. The calculation formula is: kn, and x(K) is the frequency domain signal, x(n) is the time domain signal, N is the signal length, and k represents the frequency index;

[0031] Step 4: Calculate the mean and variance of the vibration signal amplitude. The mean, as the average value of the signal, can reflect the overall level of the signal, while the variance measures the degree of deviation between the sample value and the mean. That is, the larger the variance, the higher the degree of dispersion and the greater the difference between the signal value and its mean.

[0032] Step 5: Extract the amplitude of each frequency point calculated in step 3 and name the feature and through Pb ( ) = Formula to calculate the Poisson kernel Pb ( ), and further through the formula Pb ( ) = *Pb( ) Calculate the normalized Poisson kernel Pb ( );

[0033] Step 6: Further analyze the characteristics of the normalized Poisson kernel, such as its changes over time and its values ​​at different frequencies. These frequencies can reflect the characteristics of the motor vibration signal. If the Poisson kernel shows a large change at a certain frequency, it means that the motor has abnormal vibration at that frequency.

[0034] Step 7: Calculate the value range of the normalized Poisson kernel and take the mean plus or minus a certain multiple of the standard deviation as the threshold range, and set an abnormal threshold K= , the normalized Poisson kernel value calculated in real time is compared with the set abnormal threshold. When the Poisson kernel value exceeds the abnormal threshold range, the early warning mechanism will be triggered;

[0035] Step eight, the early warning mechanism can communicate with the outside through the wireless communication module, when the early warning mechanism is triggered, the controller will start the buzzer to issue a buzzing alarm and the warning light to issue an alternating flashing light source, reminding the on-site staff, at the same time, the abnormal information will be transmitted to the email of the relevant personnel through network communication.

[0036] Please refer to the attached Figure 1 - attached Figure 2 , the normalization process mentioned in step two includes but is not limited to deep cleaning of data to ensure the accuracy and consistency of data, and we will fine-tune the interval time of collection to 5-10S, aiming to improve the accuracy of data measurement and reserve sufficient calculation time for subsequent calculation process, the conversion process mentioned in step three is the conversion process of the initial collected vibration signal from time domain to frequency domain, the vibration signal collected by the vibration sensor initially exists in the time domain, that is, these signals describe the change rule of vibration with time, in order to analyze the vibration characteristics of motor more deeply, these time domain signals are converted into frequency domain signals to reveal the energy distribution of signals at different frequency components, in this conversion process, we use fast Fourier transform (FFT) algorithm, and through FFT conversion, we obtain the amplitude information of each frequency point, which directly reflects the vibration intensity of motor at this frequency, and provides important data basis for subsequent detailed analysis and processing, the mean and variance calculation formula mentioned in step four are respectively U= and a²= , where the mean U represents the average level of motor vibration signal in a period of time, which is obtained by summing the vibration signal values x(n) of sampling points and then dividing by the total number of sampling points N, this value can reflect the overall trend of motor vibration, and the variance a² is obtained by calculating the square of the difference between each sampling point vibration signal value x(n) and U, then summing the square difference and finally dividing by the total number of sampling points N;

[0037] In this embodiment, the calculation of the mean U can be used to compare the vibration levels of different time periods or different motors to understand the changes in the motor operating state, and the variance a² reflects the stability of the motor vibration. The larger the variance, the higher the instability of the motor vibration, which may mean that there is a potential fault or abnormality in the motor. That is, the larger the variance, the greater the fluctuation of the motor vibration signal, that is, the difference between the value of each sampling point and the mean is large; if the variance is small, it means that the motor vibration signal is relatively stable, and the value of each sampling point is relatively close to the mean. In step three, the time domain vibration signal collected under the normal operating state of the motor is converted and the frequency domain signal is obtained using the FFT algorithm. Specifically, In other words, these amplitude data are substituted into the Poisson kernel calculation formula as input parameters, and the Poisson kernel is calculated using these amplitude data. The Poisson kernel value obtained through calculation is further used for subsequent normalization processing, providing more representative and comparable data for analyzing the characteristics of the motor vibration signal and judging whether there is abnormal vibration. In general, the frequency point amplitude data calculated in step three is an important basis and foundation for calculating the Poisson kernel in step five. They play a key role in connecting the previous and the next in the entire early warning method process. Through precise mathematical calculations and data transmission, the process of motor vibration signal from simple amplitude measurement to more in-depth feature analysis and abnormality judgment is realized.

[0038] When in use, the vibration signal is analyzed in the time domain and frequency domain, the time domain signal is converted into a frequency domain signal using the fast Fourier transform (FFT), and the amplitude of each frequency point is calculated. This fusion processing method can provide a deeper understanding of the motor vibration characteristics, effectively revealing the energy distribution of the signal at different frequency components, which helps to identify potential faults or abnormal conditions. In addition, the Poisson kernel is used to analyze the motor vibration signal. By calculating the Poisson kernel and analyzing its normalized characteristics, such as changes over time and values ​​at different frequencies, it can more keenly capture abnormal vibrations of the motor at specific frequencies, providing a new method and idea for motor fault monitoring, thereby increasing a certain degree of adaptive capability.

[0039] Example 2, please refer to the attached Figure 1 -Attached Figure 2 The present invention provides a technical solution: the early warning method specifically includes the following steps:

[0040] Step 1: Install the vibration sensor on the motor's bearing seat and housing, ensuring that the sensor's x-direction is aligned with the direction of the device's operation. Then calibrate the actuator to ensure the accuracy of subsequent data collection.

[0041] Step 2: Set the data collection interval to 5-10 seconds and perform normalization preprocessing on the data collected by the vibration sensor to make signals of different magnitudes comparable, thereby improving the accuracy and stability of subsequent processing;

[0042] Step three, in the normal operation state of the motor, a period of time of vibration signal is collected, then the collected vibration signal is converted, the time domain signal is converted into the frequency domain signal, and the amplitude of each frequency point is calculated and displayed in the broken line graph, wherein the calculation formula is kn, and x(K) is wherein is the frequency domain signal, x(n) is the time domain signal, N is the signal length, and k represents the frequency index;

[0043] Step four, the mean deviation and variance of the amplitude of the vibration signal are calculated, wherein the mean value is the average value of the signal, which can reflect the overall level of the signal, and the variance measures the deviation degree between the sample value and the mean value, that is, the greater the variance, the higher the degree of dispersion, and the greater the difference between the signal value and the mean value;

[0044] Step five, the amplitude of each frequency point calculated in step three is extracted and the feature is named , and the Poisson kernel Pb( ) is calculated through the formula , and the normalized Poisson kernel Pb( ) is further calculated through the formula = *Pb( );

[0045] Step six, the characteristics of the normalized Poisson kernel are further analyzed, such as the change over time and the value at different frequencies, and these frequencies can reflect the characteristics of the motor vibration signal. When the Poisson kernel has a larger change at a certain frequency, it means that the motor has abnormal vibration at the frequency;

[0046] Step seven, the value range of the normalized Poisson kernel in the normal state is calculated, and the mean value plus or minus a certain multiple of the standard deviation is taken as the threshold range, and an abnormal threshold K= is set. The real-time calculated normalized Poisson kernel value is compared with the set abnormal threshold value. When the Poisson kernel value exceeds the abnormal threshold range, the early warning mechanism is triggered;

[0047] Step eight, the early warning mechanism can communicate with the outside through the wireless communication module. When the early warning mechanism is triggered, the buzzer is started through the controller to issue a buzzing alarm and the warning light issues an alternating flashing light source to remind the on-site staff, and the abnormal information is transmitted to the email of the relevant personnel through network communication.

[0048] Please refer to the attached Figure 1 -attached Figure 2 ​, step five, the amplitude of each frequency point calculated in step three is extracted, and the Poisson kernel calculation formula is calculated, step six, according to the curve of Poisson kernel with time, we can intuitively observe the stability or volatility of the vibration signal in different time periods, and its value at different frequencies also reflects the intensity distribution of the motor vibration signal at different frequencies, step seven, the abnormal threshold K is calculated by the mean value U- standard deviation - mean value U + standard deviation ², and k is 1.5, that is, the value range of the normalized Poisson kernel under normal state is calculated, and the mean value plus or minus 1.5 times the standard deviation is taken as the upper and lower limits of the threshold range, step eight, the pre-warning mechanism mentioned in step eight also communicates with the outside through a wireless communication module. It can transmit abnormal information to the email of relevant personnel through network communication, so that they can obtain abnormal information and process it in the first time. This remote communication method not only improves the response speed, but also expands the range of information transmission, so that more people can participate in abnormal processing;

[0049] In this embodiment, the abnormal threshold is K, and the comparison method between the Poisson kernel Pb( ) and K is to compare the normalized Poisson kernel value calculated in real time with the abnormal threshold range set in advance. If the normalized Poisson kernel value is greater than the upper limit of the threshold or less than the lower limit of the threshold, that is, it is out of the abnormal threshold range, which means that the motor may have abnormal vibration under the current vibration state. For example, when the value of the Poisson kernel at a certain frequency exceeds the normal range, according to the analysis of step six, it means that the motor may have abnormal vibration at that frequency. If the normalized Poisson kernel value is within the abnormal threshold range, it means that the current vibration state of the motor is within the normal range, and real-time monitoring and data processing continue. When the Poisson kernel value exceeds the abnormal threshold range, the pre-warning mechanism is triggered, according to step eight, the wireless communication module communicates with the outside, the controller starts the buzzer to issue a buzzing alarm, and the warning light issues an alternating flashing light source to remind the on-site staff. At the same time, abnormal information is transmitted to the email of relevant personnel through network communication, so that relevant personnel can quickly take measures to troubleshoot and handle. Even after triggering the warning, the vibration of the motor still needs to be monitored and recorded for further analysis and diagnosis of the fault. At the same time, the pre-warning mechanism and the setting of the abnormal threshold can be optimized and adjusted according to the actual situation to improve the accuracy and reliability of the warning.

[0050] In use, by calculating the value range of the normalized Poisson kernel in the normal state, and taking the mean value plus or minus 1.5 times the standard deviation as the threshold range, the distribution of the data is fully considered, which can more reliably judge whether the motor has abnormal vibration, and the mean value plus or minus the standard deviation method can effectively filter out some normal fluctuations, and only the situation beyond the reasonable range is warned, thereby greatly increasing the reliability of the system.

[0051] Working principle:

[0052] Firstly, the vibration sensor is installed on the bearing seat and the shell of the motor, and it is necessary to ensure that the x direction of the sensor is strictly aligned with the direction of the equipment operation. Only the correct installation of the sensor can accurately capture the vibration condition of the motor during operation. After installation, the sensor is calibrated to eliminate possible errors and ensure the accuracy of subsequent data collection. The interval time for data collection is set to 5-10S, which can ensure that enough data points are collected to reflect the vibration characteristics of the motor, and at the same time, it will not produce too much data to cause processing difficulties. At the same time, the data collected by the vibration sensor is normalized and preprocessed, including but not limited to deep cleaning of data, to ensure the accuracy and consistency of the data. Then, under the normal operating state of the motor, a period of vibration signal is collected. Next, the fast Fourier transform (FFT) algorithm is used to convert the collected time domain vibration signal into a frequency domain signal, which reveals the energy distribution of the signal at different frequency components. The amplitude of each frequency point is calculated and displayed in the form of a broken line graph. These amplitudes directly reflect the vibration intensity of the motor at different frequencies, providing an important data basis for subsequent analysis. According to the mean and variance calculation formula, the mean U and variance a² of the vibration signal amplitude are calculated respectively. The mean reflects the average level of the motor vibration signal in a period of time, which is obtained by summing the vibration signal values x(n) of the sampling points and dividing by the total number of sampling points N. The variance measures the deviation between the sample value and the mean, which is obtained by calculating the square of the difference between each sampling point vibration signal value x(n) and the mean U, then summing these squared differences and finally dividing by the total number of sampling points N. Extract the amplitude characteristics of each frequency point calculated in step three, calculate the Poisson kernel Pb( ) through the Poisson kernel calculation formula, and further calculate the normalized Poisson kernel Pb( ), a curve graph of the Poisson kernel changing over time is drawn, stability or volatility of the vibration signal in different time periods is observed, and values of the Poisson kernel at different frequencies are analyzed to understand intensity distribution of the motor vibration signal at different frequencies, when the Poisson kernel has a larger change at a certain frequency, it means that the motor may have abnormal vibration at the frequency, a value range of the normalized Poisson kernel in a normal state is calculated, and a mean value plus or minus 1.5 times of a standard deviation is taken as an upper and lower limit of a threshold range, an abnormal threshold K is set, the normalized Poisson kernel value calculated in real time is compared with the set abnormal threshold, and once the abnormal threshold range is exceeded, the early warning mechanism is triggered, when the early warning mechanism is triggered, the wireless communication module is communicated with the outside, the controller starts the buzzer to issue a buzzer alarm, the warning light issues an alternating flashing light source, reminding the on-site staff, and abnormal information is transmitted to the email of the relevant personnel through network communication, so that they can obtain the abnormal information in the first time and process it.

[0053] Those skilled in the art should understand that the modules or steps of the present application described above can be realized by a general computer device, alternatively, they can be realized by program codes executable by a computing device, so that they can be stored in a storage device and executed by a computing device, or they can be respectively manufactured into integrated circuit modules, or a plurality of modules or steps thereof can be manufactured into a single integrated circuit module. The present application is not limited to any specific combination of hardware and software.

[0054] The above is only a preferred embodiment of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes, and any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0055] The above describes the specific embodiments of the present application in combination with the drawings, but is not used to limit the protection scope of the present application, and those skilled in the art should understand that various modifications or changes made on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A real-time signal processing early warning method, characterized by: The early warning method specifically comprises the following steps: Step 1: Install the vibration sensor on the motor's bearing seat and housing, ensuring that the sensor's x-direction is aligned with the direction of the device's operation. Then calibrate the actuator to ensure the accuracy of subsequent data collection. Step 2: Set the data collection interval to 5-10 seconds and perform normalization preprocessing on the data collected by the vibration sensor to make signals of different magnitudes comparable, thereby improving the accuracy and stability of subsequent processing; Step 3: When the motor is in normal operation, collect the vibration signal for a period of time, then convert the collected vibration signal, convert the time domain signal into the frequency domain signal, and calculate the amplitude of each frequency point and display it in a broken line graph. The calculation formula is: kn, and x(K) is the frequency domain signal, x(n) is the time domain signal, N is the signal length, and k represents the frequency index; Step 4: Calculate the mean and variance of the vibration signal amplitude. The mean, as the average value of the signal, can reflect the overall level of the signal, while the variance measures the degree of deviation between the sample value and the mean. That is, the larger the variance, the higher the degree of dispersion and the greater the difference between the signal value and its mean. Step 5: Extract the amplitude of each frequency point calculated in step 3 and name the feature and through Pb ( ) = Formula to calculate the Poisson kernel Pb ( ), and further through the formula Pb ( ) = *Pb( ) Calculate the normalized Poisson kernel Pb ( ); Step 6: Further analyze the characteristics of the normalized Poisson kernel, such as its changes over time and its values ​​at different frequencies. These frequencies can reflect the characteristics of the motor vibration signal. If the Poisson kernel shows a large change at a certain frequency, it means that the motor has abnormal vibration at that frequency. Step 7: Calculate the value range of the normalized Poisson kernel and take the mean plus or minus a certain multiple of the standard deviation as the threshold range, and set an abnormal threshold K= , the normalized Poisson kernel value calculated in real time is compared with the set abnormal threshold. When the Poisson kernel value exceeds the abnormal threshold range, the early warning mechanism will be triggered; Step eight, the early warning mechanism can communicate with the outside through the wireless communication module. When the early warning mechanism is triggered, the controller will start the buzzer alarm to sound a buzzer alarm and the warning light will emit alternating flashing light to remind the on-site staff. At the same time, the abnormal information will be transmitted to the email address of the relevant personnel through network communication.

2. A real-time signal processing early warning method according to claim 1, characterized in that: The normalization process mentioned in step 2 includes but is not limited to deep cleaning of the data to ensure data accuracy and consistency. We also fine-tune the acquisition interval to between 5 and 10 seconds to improve the accuracy of data measurement and reserve sufficient computing time for subsequent calculations.

3. The real-time signal processing early warning method according to claim 1, characterized in that: The conversion process mentioned in step three is the process of converting the initially collected vibration signal and its conversion from the time domain to the frequency domain. The vibration signal initially collected by the vibration sensor exists in the time domain, that is, these signals describe the change law of vibration over time. In order to more deeply analyze the vibration characteristics of the motor, these time domain signals are converted into frequency domain signals to reveal the energy distribution of the signal at different frequency components. In this conversion process, we use the fast Fourier transform (FFT) algorithm, and through FFT conversion, we obtain the amplitude information of each frequency point. These amplitudes directly reflect the vibration intensity of the motor at this frequency and provide an important data basis for subsequent detailed analysis and processing.

4. The real-time signal processing early warning method according to claim 1, characterized in that: The mean and variance calculation formulas mentioned in step 4 are U= and a²= , where the mean U represents the average level of the motor vibration signal over a period of time. It is obtained by summing the vibration signal values ​​x(n) at the sampling points and dividing it by the total number of sampling points N. This value can reflect the overall trend of the motor vibration. The variance a² is obtained by calculating the square of the difference between the vibration signal value x(n) at each sampling point and U, then summing these square differences and finally dividing them by the total number of sampling points N.

5. The real-time signal processing early warning method according to claim 1, characterized in that: In the step 5, the amplitude characteristics of each frequency point calculated in the step 3 need to be extracted and calculated using the Poisson kernel calculation formula.

6. The real-time signal processing early warning method according to claim 1, characterized in that: In step six, we need to draw a curve chart showing the change of the Poisson kernel over time. We can intuitively observe the stability or volatility of the vibration signal in different time periods, and its values ​​at different frequencies also reflect the intensity distribution of the motor vibration signal at different frequencies.

7. The real-time signal processing early warning method according to claim 1, characterized in that: The abnormal threshold K designed in step 7 is calculated by the mean U-standard deviation - mean U + standard deviation ², and k is 1.5, that is, the value range of the normalized Poisson kernel under normal conditions is calculated, and the standard deviation of its mean plus or minus 1.5 times is taken as the upper and lower limits of the threshold range.

8. The real-time signal processing early warning method according to claim 1, characterized in that: The early warning mechanism mentioned in step eight also communicates with the outside through a wireless communication module. It can transmit abnormal information to the email boxes of relevant personnel through network communication so that they can obtain and process the abnormal information as soon as possible. This remote communication method not only improves the response speed, but also expands the scope of information transmission, allowing more people to participate in abnormal processing.

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