Intelligent state monitoring and life prediction system and method for industrial equipment

Abstract

The invention discloses a state intelligent monitoring and life prediction system and method for industrial equipment. The system comprises a multi-physical field sensor module, a data processing module, a model construction module, an analysis and prediction platform and an output and alarm module. The infrared sensing sub-module, the ultrasonic sensing sub-module and the electromagnetic sensing sub-module are respectively used for detecting surface temperature change, internal damage and material performance change of parts. The data processing module receives sensor data, and data standardization is carried out through the ultrasonic and infrared processing sub-module. The model construction module constructs and updates a wear model in real time based on monitoring data, and adjusts model parameters in combination with a wear state. And the analysis and prediction platform fuses historical and real-time data, calculates the current wear rate, predicts the remaining service life and generates a wear report. According to the invention, through multi-physical field data fusion and intelligent analysis, the accuracy and stability of wear monitoring are improved, and the method is suitable for industrial equipment in extreme temperature and high dust environment.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical monitoring, and particularly relates to a system and method for intelligent monitoring of the state and life prediction of industrial equipment. Background Art

[0002] The intelligent monitoring and life prediction technology of industrial equipment has been gradually developed to improve equipment reliability and optimize maintenance costs. Traditional condition monitoring methods mainly rely on single-sensor data such as vibration, temperature, current, etc., and combine statistical analysis or physical modeling for equipment health assessment. However, in complex working conditions, a single data source is difficult to comprehensively reflect the operating state of the equipment.

[0003] Although the existing industrial equipment condition monitoring and life prediction technologies have made certain progress, there are still the following key problems in extreme environments (such as harsh working conditions like high temperature, high dust, strong vibration, etc.). First, the stability of data acquisition is limited. Traditional sensors are easily affected by environmental factors such as high temperature and dust, resulting in data distortion and inability to obtain accurate monitoring data, thus reducing the reliability of the monitoring system. For example, a vibration sensor may generate errors due to the thermal drift effect in a high-temperature environment, and it is difficult to distinguish the temperature difference caused by actual wear of components from the infrared images detected by an infrared sensor affected by extreme environmental temperatures such as high or low temperatures, and dust accumulation will affect the accuracy of optical sensors. Second, the accuracy of data analysis is limited. Most current life prediction technologies rely on single-sensor data or historical failure data, while ignoring the influence of environmental variables on the equipment aging process, resulting in insufficient generalization ability of the prediction model. In addition, the existing intelligent monitoring systems lack adaptability. Due to the complex and changeable operating environment of the equipment, fixed-parameter models are difficult to adjust in the face of dynamic working conditions, resulting in large deviations in prediction results, which in turn affect the accuracy of maintenance decisions. More importantly, the existing maintenance strategies often rely on preset thresholds rather than the actual health state of the equipment, which is prone to problems of "over-maintenance" or "under-maintenance", increasing both the operation and maintenance costs and possibly leading to sudden failures.

[0004] Based on this, the present invention proposes a system and method for intelligent monitoring of the state and life prediction of industrial equipment, which utilizes environment-adaptive data acquisition, multi-source data fusion, deep learning life prediction, and intelligent maintenance decision optimization technologies, aiming to construct an efficient, accurate, and stable equipment health management system to solve the problems of poor data stability, low monitoring accuracy, and inaccurate maintenance strategies in the existing technology under extreme environments. Summary of the Invention

[0005] The object of the present invention is to provide a system for intelligent monitoring of the state and life prediction of industrial equipment, which has the advantage of effectively improving the accuracy of wear detection.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions:

[0007] An intelligent state monitoring and life prediction system for industrial equipment, comprising:

[0008] A multi-physical field sensor module, the multi-physical field sensor module includes an infrared sensing sub-module, an ultrasonic sensing sub-module, and an electromagnetic sensing sub-module. The infrared sensing sub-module is used to detect the temperature change on the surface of the component; the ultrasonic sensing sub-module is used to detect internal damage of the component; the electromagnetic sensing sub-module is used to detect performance changes of the component.

[0009] A data processing module, the data processing module is used to receive and process real-time data from the multi-physical field sensor module to obtain monitoring data;

[0010] A model construction module, used to construct a wear model of the component and update the wear model in real time according to the monitoring data;

[0011] An analysis and prediction platform, used to predict the remaining service life of the component according to the wear model and generate a wear report;

[0012] An output and alarm module, used to send the wear report to the user and issue an alarm when the remaining service life reaches a predetermined threshold.

[0013] Further setting: The ultrasonic sensing sub-module specifically includes an ultrasonic sensor and a correction unit. The ultrasonic sensor is arranged at a position facing the target component, and the correction unit is arranged on the propagation path of the ultrasonic sensor. The ultrasonic sensor is used to transmit ultrasonic waves and receive ultrasonic signals reflected back by the component to obtain ultrasonic detection data; the correction unit is used to generate an electrical signal according to the received ultrasonic wave and generate ultrasonic correction data according to the electrical signal.

[0014] Further setting: The correction unit is made of piezoelectric material.

[0015] Further setting: The data processing module includes an ultrasonic processing sub-module. The ultrasonic processing sub-module analyzes and calculates the ultrasonic detection data and the ultrasonic correction data to obtain ultrasonic standard data. The specific steps are as follows:

[0016] Extract the original flight time and the original echo amplitude from the ultrasonic detection data, and extract the time correction parameter and the amplitude correction parameter from the ultrasonic correction data;

[0017] Based on the time correction parameter, calculate and compensate the delay deviation of the propagation speed of ultrasonic waves in the current environment, so as to correct the original flight time to obtain the standardized time;

[0018] Based on the amplitude correction parameter, calculate and compensate for the energy attenuation or scattering loss of ultrasonic waves in the current environment, so as to correct the original echo amplitude to obtain a standardized amplitude;

[0019] Obtain ultrasonic standard data according to the standardized amplitude and the standardized amplitude.

[0020] Further set: The infrared sensing sub-module detects the temperature change on the surface of the component to obtain infrared image data. The data processing module includes an infrared processing sub-module. The infrared processing sub-module analyzes and calculates the infrared detection data to obtain infrared standard data. The specific steps are as follows:

[0021] Preprocess the infrared image data to obtain an infrared analysis image for enhancing the quality of the infrared image;

[0022] Divide the infrared analysis image into multiple grid regions and calculate the average temperature and temperature fluctuation coefficient of each grid region;

[0023] According to a preset temperature fluctuation threshold, screen out the grid regions with a temperature fluctuation coefficient less than the temperature fluctuation threshold as stable regions;

[0024] Exclude the noise regions in the stable regions according to the morphological analysis method. The noise regions represent grid regions with too small an area or irregular shapes;

[0025] Calculate the average temperature of the stable region as the reference temperature;

[0026] Calculate the measured temperature of the edge wear region according to the reference temperature and correct the measured temperature based on the heat conduction equation to obtain infrared standard data. The edge wear region is characterized as the region where the working surface of the component is located.

[0027] Further set: The model construction module is used to construct a wear model of the component and update the wear model in real time according to the monitoring data. The specific steps include the following:

[0028] Establish a wear model according to a preset wear calculation framework and the material parameters of the component;

[0029] Judge the wear state of the component according to the monitoring data. The wear states include normal wear state, accelerated wear state and critical failure state;

[0030] Update and correct the wear model according to the monitoring data and the wear state to obtain a new wear model.

[0031] Further set: The analysis and prediction platform is used to predict the remaining service life of the component according to the wear model and generate a wear report. Specifically, it includes:

[0032] Obtain the cumulative wear amount, current wear rate, and wear state according to the wear model;

[0033] Calculate the remaining service life based on the cumulative wear amount, current wear rate, and wear state;

[0034] Generate a maintenance plan based on the remaining service life and form a wear report.

[0035] Another object of the present invention is to provide a method for intelligent state monitoring and life prediction of industrial equipment, which is applied to a system for intelligent state monitoring and life prediction of industrial equipment described above.

[0036] The above technical object of the present invention is achieved through the following technical solutions:

[0037] A method for intelligent state monitoring and life prediction of industrial equipment specifically includes the following steps:

[0038] Obtain the surface temperature change, internal damage condition, and material property change of components through a multi-physical field sensor module respectively to form multi-physical field monitoring data;

[0039] Receive the monitoring data and perform ultrasonic signal correction and infrared temperature data calibration respectively. Among them, for ultrasonic detection data, extract the original flight time and echo amplitude, and correct the time delay and amplitude attenuation based on the ultrasonic correction data of the correction unit to obtain ultrasonic standard data; for infrared detection data, calculate the temperature fluctuation coefficient of multiple grid regions, screen out the stable regions, and correct the temperature data of the edge wear regions based on the average temperature of the stable regions and the heat conduction equation to obtain infrared standard data;

[0040] Construct a wear model of components and update the wear model in real time according to the monitoring data;

[0041] Predict the remaining service life of components according to the wear model and generate a wear report;

[0042] Send the wear report to the user and issue an alarm when the remaining service life reaches a predetermined threshold.

[0043] In summary, the present invention has the following beneficial effects:

[0044] 1. Adopt a multi-physical field sensor module combined with infrared, ultrasonic, and electromagnetic sensors. Compared with a single sensing method, it can effectively reduce the influence of factors such as environmental temperature fluctuations and dust interference on the measurement results, improve the reliability of monitoring data, and enable the system to still operate stably in high-temperature, low-temperature, and dust-intensive environments.

[0045] 2. The infrared processing sub-module and the ultrasonic processing sub-module of the data processing module respectively correct and standardize the temperature measurement and ultrasonic signals. The infrared processing sub-module uses temperature grid analysis, morphological analysis, and heat conduction model correction to ensure that the temperature data excludes the influence of the ambient temperature and improves the temperature measurement accuracy of the worn areas of the components. The ultrasonic processing sub-module eliminates the influence of dust on the ultrasonic propagation characteristics based on time correction and amplitude compensation, making the internal damage detection more accurate.

[0046] 3. The model construction module is adopted to update the wear model in real time based on the monitoring data, which can dynamically adjust the wear parameters and improve the accuracy of depicting the component state. The system can identify three states: normal wear, accelerated wear, and critical failure, and adjust the wear calculation method according to the state to ensure the adaptability of the model to complex wear conditions.

[0047] 4. The calculation logic of the remaining service life can be adjusted according to different wear stages to ensure that the prediction results are more in line with the actual working conditions, thereby providing a scientific basis for equipment maintenance and avoiding losses caused by premature replacement or delayed maintenance.

[0048] 5. Through the output and alarm module, when the remaining service life reaches the preset threshold, the system can automatically send an alarm to remind the user to take maintenance measures in advance, reducing the risk of equipment downtime caused by sudden failures. Combined with the wear report, the user can optimize the maintenance plan based on the system's suggestions, improve the equipment management efficiency, and extend the service life of the components. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is the overall structural block diagram of the embodiment. DETAILED DESCRIPTION OF THE EMBODIMENT

[0050] The present invention will be further described in detail below with reference to the accompanying drawings.

[0051] Embodiment:

[0052] As Figure 1 shown, a state intelligent monitoring and life prediction system for industrial equipment includes:

[0053] A multi-physical field sensor module, the multi-physical field sensor module includes an infrared sensing sub-module, an ultrasonic sensing sub-module, and an electromagnetic sensing sub-module. The infrared sensing sub-module is used to detect the temperature change on the surface of the component; the ultrasonic sensing sub-module is used to detect the internal damage of the component; the electromagnetic sensing sub-module is used to detect the performance change of the component;

[0054] A data processing module, the data processing module is used to receive and process the real-time data from the multi-physical field sensor module to obtain the monitoring data;

[0055] A model construction module, configured to construct a wear model of components and update the wear model in real time according to the monitoring data;

[0056] An analysis and prediction platform, configured to predict the remaining service life of components according to the wear model and generate a wear report;

[0057] An output and alarm module, configured to send the wear report to the user and issue an alarm when the remaining service life reaches a predetermined threshold.

[0058] Among them, the ultrasonic sensing sub-module specifically includes an ultrasonic sensor and a correction unit. The ultrasonic sensor is arranged at a position facing the target component, and the correction unit is arranged on the propagation path of the ultrasonic sensor. The ultrasonic sensor is used to emit ultrasonic waves and receive the ultrasonic signal reflected by the component to obtain ultrasonic detection data; the correction unit is used to generate an electrical signal according to the received ultrasonic wave and generate ultrasonic correction data according to the electrical signal.

[0059] The correction unit is made of piezoelectric material. The piezoelectric material has the piezoelectric effect, that is, it generates electric charges when subjected to pressure or tension. Conversely, when an electric field is applied, the piezoelectric material will deform and generate mechanical vibration. When ultrasonic waves propagate to the piezoelectric material in a dusty environment, the energy carried by them will cause the piezoelectric material to generate an electrical signal. By detecting the changes in these electrical signals, the ultrasonic propagation characteristics can be obtained, similar to the way magneto-optical materials indirectly obtain ultrasonic information through the magneto-optical effect. Therefore, the piezoelectric material is used to capture the changes in ultrasonic signals and compensate for the influence of factors such as dust in the environment on ultrasonic propagation, thereby improving the detection accuracy.

[0060] The data processing module includes an ultrasonic processing sub-module, an infrared processing sub-module, and an electromagnetic processing sub-module. The ultrasonic processing sub-module analyzes and calculates according to the ultrasonic detection data and the ultrasonic correction data to obtain ultrasonic standard data. The specific steps are as follows:

[0061] Extract the original flight time and the original echo amplitude according to the ultrasonic detection data, and extract the time correction parameter and the amplitude correction parameter according to the ultrasonic correction data;

[0062] Based on the time correction parameter, calculate and compensate for the delay deviation of the ultrasonic wave propagation speed in the current environment, so as to correct the original flight time to obtain the standardized time; in an environment with high dust or low / high temperature, there is a deviation between the actual propagation speed of the ultrasonic wave and the reference speed, resulting in the echo flight time not conforming to the ideal value. Since the preset correction unit knows its set position and the distance from the ultrasonic sensor, the obtained time correction parameter can reflect this deviation. This delay deviation in the propagation speed can be calculated based on the pre-known distance, the time when the ultrasonic sensor emits the ultrasonic wave, and the time when the correction unit generates an electrical signal to obtain the actual detected ultrasonic wave propagation speed and the theoretical ultrasonic wave propagation speed. According to this wave speed ratio, the time correction parameter can be obtained. Correcting the original flight time according to the time correction parameter can obtain the true time when the ultrasonic sensor emits the ultrasonic wave and receives the echo without being affected by the dust environment, that is, the standardized time.

[0063] Based on the amplitude correction parameter, calculate and compensate for the energy attenuation or scattering loss of the ultrasonic wave in the current environment, so as to correct the original echo amplitude to obtain the standardized amplitude; dust or temperature changes not only affect the propagation speed of the ultrasonic wave, but also cause an increase in energy attenuation or waveform distortion. Therefore, peak amplitude detection, RMS (root mean square) calculation, or integral energy analysis is performed on the electrical signal generated by the correction unit when receiving the ultrasonic wave to measure the actual intensity of the ultrasonic wave at the position of the correction unit. Map this intensity index (such as peak voltage, RMS value, etc.) into an amplitude correction parameter for attenuation correction. When correcting the original echo amplitude, multiplicative correction is used for the original echo amplitude.

[0064] Obtain the ultrasonic standard data according to the standardized amplitude and the standardized amplitude.

[0065] The infrared sensing sub-module detects the temperature change on the surface of the component to obtain infrared image data. The data processing module includes an infrared processing sub-module. The infrared processing sub-module analyzes and calculates the infrared detection data to obtain the infrared standard data. The specific steps are as follows:

[0066] 1. Preprocess the infrared image data to obtain an infrared analysis image for enhancing the quality of the infrared image;

[0067] The preprocessing specifically includes:

[0068] Denoising processing: Use an adaptive filtering algorithm (such as bilateral filtering or wavelet transform denoising) to remove the noise in the image to ensure that the temperature data is not interfered by the environment;

[0069] Contrast enhancement: Use Histogram Equalization (HE) or Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the image contrast, making the temperature distribution more clearly distinguishable.

[0070] Edge detection: Apply the Canny operator or Sobel operator to enhance the edges of the image, making the structural features of the components more obvious

[0071] Divide the infrared analysis image into multiple grid regions and calculate the average temperature and temperature standard deviation of each grid region. Calculate the temperature fluctuation coefficient of each grid region according to the temperature standard deviation of each grid region. The calculation formula is:

[0072]

[0073] Among them, σ i is the temperature standard deviation, T i is the average temperature, and W i is the temperature fluctuation coefficient.

[0074] 2. According to the preset temperature fluctuation threshold, screen out the grid regions with temperature fluctuation coefficients less than the temperature fluctuation threshold as stable regions, which are used to represent the influence of the ambient temperature;

[0075] 4. Exclude the noise regions in the stable regions according to the morphological analysis method. The noise regions represent grid regions with too small area or irregular shape, ensuring that the detected stable regions conform to the structural characteristics of the components.

[0076] 5. Calculate the average temperature of the stable region as the reference temperature, calculate the measured temperature of the edge wear region according to the reference temperature, and correct the measured temperature based on the heat conduction equation to obtain the infrared standard data. The edge wear region is characterized as the region where the working surface of the component is located.

[0077] The calculation formula for the reference temperature is:

[0078]

[0079] Among them, T s is the reference temperature, M is the number of pixel points in the stable region, and T j is the average temperature of each stable region.

[0080] The calculation formula for calculating the measured temperature is:

[0081] T b = T measured - T s

[0082] Among them, T bis the deviation temperature, T measured is the temperature detected by this pixel point in the edge wear area. After subtracting the reference temperature from the detected temperature, the offset of the overall ambient temperature can be removed, but the heat conduction effect is still included. Therefore, it is necessary to correct the temperature in combination with the heat conduction equation.

[0083] Correct the temperature in combination with the heat conduction equation:

[0084]

[0085] Among them, q is the heat flux density, that is, the heat transferred from the environment to the component, k is the thermal conductivity of the component material, d is the thickness of the heat conduction path, and the distance from the working surface at the edge of the component to the temperature stable area is taken. is the final infrared standard data, which can reflect the temperature rise of the component due to wear.

[0086] The model construction module is used to construct the wear model of the component and update the wear model in real time according to the monitoring data. The specific steps are as follows:

[0087] The model construction module is used to construct the wear model of the component and update the wear model in real time according to the monitoring data. The specific steps are as follows:

[0088] Establish a wear model according to the preset wear calculation framework and the material parameters of the component;

[0089] Judge the wear state of the component according to the monitoring data. The wear state includes normal wear state, accelerated wear state and critical failure state;

[0090] Update and correct the wear model according to the monitoring data and the wear state to obtain a new wear model.

[0091] In this embodiment, the Archard wear model is adopted for the wear calculation framework, and the initial parameters are set according to the specific type of the component, including material hardness H, wear coefficient K, load P, and relative movement speed v.

[0092] The identification of the wear state is obtained by analyzing the monitoring data through the LSTM (Long Short-Term Memory Network) or the Bayesian update model. Among them, the infrared standard data obtained by the infrared sensing sub-module is the temperature T(t), which is used to correct the wear caused by thermal stress. The ultrasonic standard data obtained by the ultrasonic sensing sub-module is the damage depth d(t), which reflects the material loss caused by crack propagation. The electromagnetic standard data detected by the electromagnetic sensing sub-module is the change in material properties Δσ(t), which reflects the influence of metal fatigue on wear.

[0093] In the normal wear state: T(t) changes slowly, d(t) is approximately 0, and Δσ(t) is stable. At this time, when adjusting the parameters of the wear model, a low wear coefficient K1 is adopted, and the friction term of the Archard model is mainly used:

[0094]

[0095] In the accelerated wear state: T(t) increases, d(t) increases, and Δσ(t) decreases. The wear model is adjusted to:

[0096]

[0097] where K2 is greater than K1, which means the wear rate increases.

[0098] In the critical failure state: T(t) changes violently, d(t) increases sharply, and Δσ(t) shows a mutation. The weight of the wear model is further increased:

[0099]

[0100] This means that the early warning mechanism will be activated and an alarm will be issued to recommend maintenance.

[0101] The Kalman filter (KF) is used to dynamically adjust the parameters of the wear model. The state equation:

[0102] X t+1 = AX t + BU t + w t

[0103] where the state vector:

[0104]

[0105] The wear state is used to adjust the state transition matrix A to make the model adapt to different wear stages.

[0106] The analysis and prediction platform is used to predict the remaining service life of parts according to the wear model and generate a wear report, which specifically includes:

[0107] Obtain the cumulative wear amount W(t), the current wear rate and the wear state according to the wear model;

[0108] Calculate the remaining service life according to the cumulative wear amount, the current wear rate, and the wear state;

[0109] The current wear rate is obtained from the change in the wear amount at consecutive time points of the monitoring data:

[0110]

[0111] In the normal wear state, the current wear rate changes slowly, and the remaining service life can be calculated using the linear wear rate:

[0112]

[0113] Where W failure is the critical wear threshold, which is determined according to the design standards of the component and represents the maximum wear amount that the component can withstand before failure.

[0114] In the accelerated wear state, the current wear rate increases, and the remaining service life needs to incorporate wear trend prediction:

[0115]

[0116] Where is the rate of change of the wear rate (acceleration), and λ1 is an adjustment coefficient to make the RUL prediction more stable.

[0117] In the critical failure state, the current wear rate reaches the highest, and the calculation method is adjusted:

[0118]

[0119] Where f warning is a non-linear correction term based on the fault threshold to make the prediction more accurate, and λ2 and λ3 are adaptive adjustment coefficients used to adjust the sensitivity of the RUL calculation.

[0120] Generate a maintenance plan based on the remaining service life and form a wear report. The analysis and prediction platform needs to automatically generate a wear report, provide data visualization and maintenance suggestions, and use tools such as Power BI, Matplotlib, or D3.js to real-time plot: wear trend curve, temperature-damage correlation curve, predicted RUL curve.

[0121] Based on the wear prediction results, the analysis and prediction platform generates a maintenance plan: in the normal state (RUL > 1000 hours), no maintenance is required; in the warning state (100 ≤ RUL ≤ 1000 hours), inspection is recommended; in the emergency state (RUL < 100 hours), component replacement is recommended.

[0122] A method for intelligent condition monitoring and life prediction of industrial equipment specifically includes the following steps:

[0123] Obtain the surface temperature change, internal damage condition, and material property change of the component through the multi-physical field sensor module respectively to form multi-physical field monitoring data;

[0124] Receive the monitoring data and perform ultrasonic signal correction and infrared temperature data calibration respectively. For the ultrasonic detection data, extract the original flight time and echo amplitude, and correct the time delay and amplitude attenuation based on the ultrasonic correction data of the correction unit to obtain the ultrasonic standard data. For the infrared detection data, calculate the temperature fluctuation coefficients of multiple grid regions, screen out the stable regions, and correct the temperature data of the edge wear regions based on the average temperature of the stable regions and the heat conduction equation to obtain the infrared standard data;

[0125] Construct a wear model of the component and update the wear model in real time according to the monitoring data;

[0126] Predict the remaining service life of the component according to the wear model and generate a wear report;

[0127] Send the wear report to the user and issue an alarm when the remaining service life reaches a predetermined threshold.

[0128] In summary, the present embodiment has the following beneficial effects:

[0129] Adopt a multi-physical field sensor module combined with infrared, ultrasonic and electromagnetic sensors. Compared with a single sensing method, it can effectively reduce the influence of factors such as environmental temperature fluctuations and dust interference on the measurement results, improve the reliability of the monitoring data, and enable the system to still operate stably in high-temperature, low-temperature and dust-intensive environments.

[0130] Through the infrared processing sub-module and ultrasonic processing sub-module of the data processing module, correct and standardize the temperature measurement and ultrasonic signals respectively. The infrared processing sub-module adopts temperature grid analysis, morphological analysis and heat conduction model correction to ensure that the temperature data excludes the influence of the environmental temperature and improves the temperature measurement accuracy of the component wear regions. The ultrasonic processing sub-module eliminates the influence of dust on the ultrasonic propagation characteristics based on time correction and amplitude compensation, making the internal damage detection more accurate.

[0131] Adopt a model construction module to update the wear model in real time based on the monitoring data, which can dynamically adjust the wear parameters and improve the accuracy of depicting the component state. The system can identify three states: normal wear, accelerated wear and critical failure, and adjust the wear calculation method according to the state to ensure the adaptability of the model to complex wear conditions.

[0132] The calculation logic of the remaining service life can be adjusted according to different wear stages to ensure that the prediction results are more in line with the actual working conditions, thereby providing a scientific basis for equipment maintenance and avoiding losses caused by premature replacement or delayed maintenance.

[0133] Through the output and alarm module, when the remaining service life reaches the preset threshold, the system can automatically send an alarm to remind the user to take maintenance measures in advance, reducing the risk of equipment downtime caused by sudden failures. Combining with the wear report, the user can optimize the maintenance plan based on the system's suggestions, improve the efficiency of equipment management, and extend the service life of components.

[0134] The above-described embodiments do not constitute a limitation on the protection scope of the technical solution. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the above embodiments shall be included in the protection scope of the technical solution.

Claims

1. A system for intelligent status monitoring and life prediction of industrial equipment, characterized in that: include: A multi-physics sensor module, the multi-physics sensor module includes an infrared sensor submodule, an ultrasonic sensor submodule and an electromagnetic sensor submodule, the infrared sensor submodule is used to detect temperature changes on the surface of a component; the ultrasonic sensor submodule is used to detect damage inside a component; the electromagnetic sensor submodule is used to detect performance changes of a component; A data processing module, the data processing module is used to receive and process real-time data from the multi-physics field sensor module to obtain monitoring data; Model building module, used to build the wear model of parts and components and update the wear model in real time according to monitoring data; An analysis and prediction platform, which is used to predict the remaining service life of components based on wear models and generate wear reports; Output and alarm modules to send wear reports to the user and generate alarms when the remaining service life reaches a predefined threshold.

2. The intelligent state monitoring and life prediction system for industrial equipment according to claim 1 is characterized in that: The ultrasonic sensor submodule specifically includes an ultrasonic sensor and a correction unit. The ultrasonic sensor is arranged at a position facing the target component, and the correction unit is arranged on the propagation path of the ultrasonic sensor. The ultrasonic sensor is used to transmit ultrasonic waves and receive ultrasonic signals reflected from the components to obtain ultrasonic detection data; the correction unit is used to generate an electrical signal based on the received ultrasonic wave, and generate ultrasonic correction data based on the electrical signal.

3. The intelligent state monitoring and life prediction system for industrial equipment according to claim 2 is characterized in that: The correction unit is made of piezoelectric material.

4. The intelligent state monitoring and life prediction system for industrial equipment according to claim 3 is characterized in that: The data processing module includes an ultrasonic processing submodule, and the ultrasonic processing submodule performs analysis and calculation based on the ultrasonic detection data and the ultrasonic correction data to obtain ultrasonic standard data. The specific steps are as follows: Extract the original flight time and the original echo amplitude according to the ultrasonic detection data, and extract the time correction parameter and the amplitude correction parameter according to the ultrasonic correction data; Based on the time correction parameter, calculate and compensate for the delay deviation of the propagation speed of the ultrasonic wave in the current environment, so as to correct the original flight time to obtain the standardized time; Based on the amplitude correction parameter, calculate and compensate for the energy attenuation or scattering loss of the ultrasonic wave in the current environment, thereby correcting the original echo amplitude to obtain a standardized amplitude; The ultrasonic standard data is obtained according to the standardized amplitude and the standardized amplitude.

5. The intelligent state monitoring and life prediction system for industrial equipment according to claim 1 is characterized in that: The infrared sensor submodule detects the temperature change on the surface of the component to obtain infrared image data. The data processing module includes an infrared processing submodule. The infrared processing submodule analyzes and calculates the infrared detection data to obtain infrared standard data. The specific steps are as follows: Preprocessing the infrared image data to obtain an infrared analysis image for enhancing the quality of the infrared image; Divide the infrared analysis image into a plurality of grid areas and calculate the average temperature and temperature fluctuation coefficient of each grid area; According to the preset temperature fluctuation threshold, the grid area whose temperature fluctuation coefficient is less than the temperature fluctuation threshold is selected as the stable area; Eliminate noise regions in stable regions according to a morphological analysis method, wherein the noise regions represent grid regions that are too small or irregular in shape; Calculate the average temperature of the stable area as the reference temperature; The measured temperature of the edge wear area is calculated according to the reference temperature, and the measured temperature is corrected based on the heat conduction equation to obtain infrared standard data. The edge wear area is characterized as the area where the working surface of the component is located.

6. The system for intelligent state monitoring and life prediction of industrial equipment according to claim 1, characterized in that: The model building module is used to build a wear model of parts and components, and update the wear model in real time according to monitoring data. Specifically, it includes the following steps: Establish a wear model based on the preset wear calculation framework and material parameters of components; Determine the wear state of the parts according to the monitoring data, wherein the wear state includes normal wear state, accelerated wear state and critical failure state; The wear model is updated and corrected according to the monitoring data and wear status to obtain a new wear model.

7. The system for intelligent state monitoring and life prediction of industrial equipment according to claim 6, characterized in that: The analysis and prediction platform is used to predict the remaining service life of parts according to the wear model and generate a wear report, which specifically includes: According to the wear model, the accumulated wear amount, current wear rate and wear state are obtained; The remaining service life is calculated based on the accumulated wear, current wear rate and wear status; Generate maintenance plans and wear reports based on remaining service life.

8. A method for intelligent state monitoring and life prediction of industrial equipment, applied to the system for intelligent state monitoring and life prediction of industrial equipment as claimed in any one of claims 1 to 7, characterized in that: The specific steps include: The multi-physics field sensor module is used to obtain the surface temperature changes, internal damage conditions and material performance changes of components to form multi-physics field monitoring data; The monitoring data is received, and ultrasonic signal correction and infrared temperature data calibration are performed respectively, wherein, for ultrasonic detection data, the original flight time and echo amplitude are extracted, and the time delay and amplitude attenuation are corrected based on the ultrasonic correction data of the correction unit to obtain ultrasonic standard data; for infrared detection data, the temperature fluctuation coefficients of multiple grid areas are calculated, stable areas are screened out, and the temperature data of the edge wear area is corrected based on the average temperature of the stable area and the heat conduction equation to obtain infrared standard data; Build a wear model for components and update the wear model in real time based on monitoring data; Predict the remaining service life of components based on wear models and generate wear reports; Wear reports are sent to users and alerts are issued when the remaining service life reaches a pre-defined threshold.

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