Factory industrial equipment electromechanical installation state monitoring and early warning system

Abstract

The invention discloses a factory industrial equipment electromechanical installation state monitoring and early warning system, and relates to the technical field of electromechanical equipment monitoring, and the system comprises a sensing module which collects the equipment number and the three-dimensional installation coordinate of each piece of industrial equipment, matches the installation coordinate with a structure drawing or a CAD model, extracts the corresponding structure node attribute, and transmits the structure node attribute to an early warning module; judging whether the position is a structure energy convergence sensitive area or not; and the data acquisition module is used for acquiring vibration signals and stress response data in the operation process of the equipment, binding the vibration signals and the stress response data with the equipment number and the installation coordinates, performing frequency domain analysis on the vibration signals to extract a dominant frequency component, and calculating the total amount of vibration energy. According to the invention, a complete system architecture composed of a sensing module, a data acquisition module, a data analysis module, a structure state monitoring module and an early warning module is established, so that full-process monitoring from equipment space positioning to structure risk early warning is realized.

Description

Technical Field

[0001] This invention relates to the field of electromechanical equipment monitoring technology, specifically to a monitoring and early warning system for the electromechanical installation status of factory industrial equipment. Background Technology

[0002] With the increasing number and operating density of electromechanical equipment in modern factories, the structural coupling and energy transfer phenomena between equipment are becoming increasingly complex. The vibration, stress, and temperature changes generated by equipment during long-term operation not only reflect its own operating status but also affect each other through the foundation structure. Existing electromechanical equipment condition monitoring is mostly based on the vibration or stress signals of single equipment. Although it can identify local faults, it is difficult to accurately reflect the structural resonance effect and energy transfer path between multiple devices. Especially in large factory buildings or complex steel structure environments, the resonance behavior between devices often has spatial correlation and dynamic evolution characteristics. In industrial production scenarios, the installation location, support structure, and connection method of equipment directly affect its operational stability. Different equipment installed on different structural units such as beam nodes, wall boundaries, or hoisting platforms exhibit significant differences in stiffness characteristics and energy coupling. If multiple devices generate superimposed vibrations within a similar frequency range, local energy convergence areas may form, leading to potential hazards such as structural fatigue, crack propagation, or foundation resonance. Therefore, establishing a monitoring system based on equipment installation space information and structural characteristics that can identify resonance paths, track structural responses, and provide dynamic risk warnings has become an important research direction for the safe operation of industrial equipment. In recent years, the application of sensing technology and industrial communication networks has provided conditions for real-time monitoring of equipment operating status. By collaboratively collecting vibration, stress, and temperature data from multiple sources of sensors and combining them with digital factory models, a comprehensive analysis of the relationship between equipment operation and structural response can be achieved. However, existing systems still have room for improvement in spatial information association, temporal alignment, and multi-point data fusion, making it difficult to form a continuous logical chain from "installation coordinate identification - vibration characteristic analysis - structural risk early warning". Therefore, this invention proposes a monitoring and early warning system for the electromechanical installation status of factory industrial equipment. Summary of the Invention

[0003] The purpose of this invention is to provide a monitoring and early warning system for the electromechanical installation status of factory industrial equipment, so as to solve the problems mentioned in the background art.

[0004] The present invention can be achieved through the following technical solution: a monitoring and early warning system for the electromechanical installation status of factory industrial equipment, comprising a sensing module, a data acquisition module, a data analysis module, a structural status monitoring module, and an early warning module; The sensing module is used to collect the three-dimensional spatial position information of all industrial equipment in the factory, including the equipment number and installation coordinates of each piece of equipment in the factory coordinate system; The perception module compares the installation coordinates of each device with the type of installation foundation structure (such as beam nodes, wall boundaries, and hoisting surfaces) indicated in the factory structure drawings or CAD models, maps the installation coordinates to the corresponding structural unit area, identifies the type of structural element at that location, and extracts the structural node attributes corresponding to that installation location. Structural node attributes include, but are not limited to: node type (such as beam intersection, slab edge, wall corner), stiffness level, connection characteristics, and structural coupling level; Based on whether it is located at a structural interface, a region of abrupt change in stiffness, or a point of coupling of multiple structural sources, it can be determined whether the location of the device is in a sensitive area for structural energy convergence.

[0005] The data acquisition module is used to collect vibration signals and stress response data of each industrial device during operation and bind them to their corresponding device number and installation coordinates; The data acquisition module deploys vibration sensors and strain detection units at key structural locations in each industrial device. Vibration sensors are used to acquire vibration signals during the operation of industrial equipment in real time, and strain detection units are used to acquire stress response data of corresponding structural parts of the equipment; all acquisition operations are bound to the equipment number and installation coordinates of the equipment to ensure that the acquisition results have clear spatial positioning information; Subsequently, the data acquisition module synchronously samples the vibration signals and stress response data with a unified timestamp, and filters and compresses the collected vibration signals and stress response data through the local computing unit to form structured sampling data units. Finally, the data acquisition module performs a fast Fourier transform on the collected vibration signal to extract the main frequency component and its corresponding amplitude in the frequency range of 1Hz to 5kHz, and calculates the total vibration energy. Finally, the data acquisition module outputs a data packet containing the device number, acquisition timestamp, installation coordinates, vibration frequency distribution, amplitude, real-time acceleration, total vibration energy, and stress response data, which is then sent to the data analysis module.

[0006] The data analysis module identifies potential resonance coupling paths and structural risk areas based on the installation coordinates of each industrial device, structural node attributes, and total vibration energy. The data analysis module calculates the spatial distance between the installation coordinates of each device, and combines the structural drawings or CAD models with the structural node attributes to determine whether there is a structural coupling relationship between the devices. If there is a coincidence of the main frequencies within the range of ±5 Hz in the vibration frequency distributions of multiple devices, and the total vibration energy of each of them exceeds the set energy threshold, the data analysis module determines that this structural path is a potential resonance coupling path and performs frequency energy superposition according to the structural positions. If a certain structural position corresponds to multiple device numbers and has structural node attributes such as beam intersections or high structural coupling levels, and is accompanied by a continuous upward trend in the total vibration energy, then this position is marked as a sensitive area for structural energy convergence and is assigned a unique resonance node identifier. The data analysis module outputs the resonance node identifier, the set of corresponding device numbers, the set of installation coordinates, the frequency coincidence degree, the trend of the total vibration energy, and the marking information of this high-risk resonance node.

[0007] The structural state monitoring module is used to conduct key health monitoring on the structural positions (i.e., the sensitive areas for structural energy convergence confirmed by data analysis) that have been assigned resonance node identifiers. This structural state monitoring module activates the strain gauges, laser displacement sensors, and thermal expansion measurement devices at the corresponding positions to obtain the structural state indicators in real time, including the strain rate, displacement change, and thermal expansion length. If the strain rate continuously increases, or the displacement change and the total vibration energy show a synchronous growth trend, or the thermal expansion length exceeds the heat capacity limit of the node material, and any of the above states persists for more than the set period, it is determined that the structure is in an abnormal state, and a structural risk event package is generated, including the resonance node identifier, the set of device numbers, the set of installation coordinates, the strain rate, the displacement change, the thermal expansion length, and the duration of the abnormal state.

[0008] The warning module is used to perform a risk score on the structural health state and generate a structural risk warning message based on the structural risk event package output by the structural state monitoring module, combined with the device numbers, installation coordinates, and structural node attributes provided by the sensing module, the total vibration energy provided by the data acquisition module, the resonance node identifier and frequency coincidence degree provided by the data analysis module, and the strain rate, displacement change, thermal expansion length, and duration of the abnormal state provided by the structural state monitoring module. The warning module normalizes the following input indicators and assigns weights through a weighted scoring function, including: the total vibration energy, the frequency coincidence degree, the strain rate, the displacement change, the thermal expansion length, and the duration of the abnormal state, and forms the final risk score value according to the weighted calculation result. According to this risk score value, it is divided into low-risk, medium-risk, high-risk, and extremely high-risk levels, and risk warning data including the resonance node identifier, the set of device numbers, the set of installation coordinates, the risk score value, the risk level, and the detailed list of triggering factors is output for subsequent linkage response or maintenance decision-making.

[0009] A further technical improvement of the present invention is that the data analysis module and the structural condition monitoring module realize data interaction through an industrial Ethernet communication interface, and use a time synchronization protocol to align the data collected by different modules with a unified time reference to ensure the timing consistency of the generation of resonance node identifiers and subsequent structural condition monitoring.

[0010] A further technical improvement of the present invention is that: when generating a resonance node identifier, the data analysis module stores the identifier along with the corresponding set of device numbers and the set of installation coordinates in a node information table. The node information table is used by the structural condition monitoring module to achieve continuous monitoring of specific resonance locations.

[0011] A further technical improvement of the present invention is that: the data analysis module performs differential calculations on the installation coordinates of each industrial device within a continuous sampling period to obtain the vibration displacement vector of each device, and analyzes the structural coupling relationship between the devices by combining the structural connection relationship in the structural drawings or CAD model, and identifies the propagation direction of vibration in the structure based on the trend of the total vibration energy change and the direction relationship of the vibration displacement vector, so as to distinguish the main vibration-generating device and the disturbed device that causes vibration in the resonance node. By identifying the main vibration-generating device and the disturbed device in the resonance node, the weight allocation of the risk score value is optimized during the calculation. This solves the technical problem that existing systems cannot identify the direction of vibration propagation, resulting in the inability to accurately determine the location of the vibration source and the path of its influence.

[0012] A further technical improvement of the present invention is that the data analysis module compares the amplitude of the dominant frequency component in the vibration frequency distribution of each industrial device in the current sampling period with the average amplitude of the same frequency band in multiple historical sampling periods. If the change in the amplitude of the main frequency component compared to the historical average exceeds the preset amplitude change judgment threshold; Furthermore, the amplitude decreased and fell back to the range of the historical average ± fluctuation range of this frequency band in subsequent sampling periods. Furthermore, within the frequency range adjacent to this main frequency component, no other devices were detected to exhibit a coordinated upward trend in amplitude. If any two of the three judgment conditions are met, the current period is determined to be a non-periodic disturbance event, and the subsequent processing of the event corresponding to the device in the resonance node identification process and structural risk event is suspended.

[0013] A further technical improvement of the present invention is that: the data analysis module marks the preset high-temperature equipment location, determines the installation coordinate range of the high-temperature equipment in the factory coordinate system, and establishes a set of heat source interference areas corresponding to the high-temperature equipment; During the process of identifying potential resonance paths or generating resonance node identifiers, the system obtains the installation coordinates of all industrial equipment involved in the resonance path and determines whether they have a spatial overlap with the installation coordinate range of any high-temperature equipment in the above set of heat source interference areas. If there is spatial overlap, further analyze the vibration frequency distribution of the equipment within the path to determine whether it contains the dominant frequency component within the set heat source disturbance frequency range. If both spatial overlap and frequency matching conditions are met simultaneously, the resonance path is marked as a heat source interference path, and the preset processing procedure is executed.

[0014] A further technical improvement of the present invention is that: the data acquisition module calculates the amplitude change rate corresponding to the main frequency component of each industrial device based on the vibration frequency distribution within a continuous sampling period; If the rate of change of the amplitude of the main frequency component exceeds the set amplitude change detection threshold, or if the increase in the total vibration energy exceeds the preset energy jump judgment threshold in two or more consecutive sampling periods, the system determines that the device is in a change trend state. In response to the sudden change trend, the data acquisition module increases the current sampling frequency from the basic sampling frequency to a preset high-frequency sampling frequency, and then uses the preset high sampling frequency to collect vibration signals in the next preset number of sampling periods. If, during a continuous sampling period of acquiring vibration signals at a preset high sampling frequency, the rate of change of the main frequency component is continuously within the set stable threshold range, or the total vibration energy within this range does not exceed the preset energy fluctuation tolerance threshold, the system will automatically restore the sampling frequency to the basic sampling frequency.

[0015] A further technical improvement of the present invention is that the structural condition monitoring module calculates the structural response sensitivity weights based on the thermal capacity coefficient, stiffness level, and connection degrees of freedom of the structural nodes. Based on the temperature change rate and structural response sensitivity weights within a continuous sampling period, a correction factor is generated to dynamically adjust the threshold values ​​for judging strain rate, displacement change, and thermal expansion length. The differences in structural state changes over multiple sampling periods are summed to obtain a cumulative trend value. If any cumulative trend value exceeds the corresponding dynamic threshold, the structural state is determined to be abnormal, and a structural trend abnormality event data packet is output.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention establishes a complete system architecture consisting of a sensing module, a data acquisition module, a data analysis module, a structural status monitoring module, and an early warning module, realizing full-process monitoring from equipment spatial positioning to structural risk early warning. By collecting equipment number and three-dimensional installation coordinates through the sensing module and matching them with structural drawings or CAD models, information such as node type, stiffness level, and structural coupling level is extracted, realizing the spatial binding of equipment installation location and structural characteristics, and providing accurate basic data for subsequent structural energy convergence determination. Furthermore, the data acquisition module and the data analysis module work together to identify frequency overlap relationships and structural coupling paths between multiple devices through time-synchronous acquisition and frequency domain analysis of multi-point vibration and stress data, thereby generating resonance node identifiers and locating potential energy convergence areas. The structural condition monitoring module takes the resonance node as the monitoring target and continuously acquires the corresponding strain rate, displacement change, and thermal expansion length to ensure real-time health monitoring of key structural areas. It can dynamically reflect changes in structural stress and vibration state, and achieve accurate identification and long-term tracking of resonance risks. The early warning module integrates multiple indicators such as total vibration energy, frequency overlap, strain rate, displacement change, thermal expansion length, and duration of abnormal state, and uses a weighted scoring mechanism to form a quantitative risk level. This achieves an intuitive mapping from multi-source physical quantities to structural safety status. Combined with the industrial Ethernet communication interface, time synchronization protocol, and node information table mechanism in the subordinate technical solution, it has high reliability and scalability in terms of data transmission, time consistency, and monitoring object management. In summary, through this invention, factories can achieve real-time monitoring, quantitative assessment, and early warning response of the operating status and structural risks of electromechanical equipment, which helps to improve the safety of industrial equipment operation and the scientific nature of maintenance decisions. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 This is a schematic diagram of the system logic of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0020] Example 1 Please see Figure 1As shown, the present invention provides a monitoring and early warning system for the electromechanical installation status of factory industrial equipment, including a sensing module, a data acquisition module, a data analysis module, a structural status monitoring module, and an early warning module; The sensing module is used to collect the three-dimensional spatial location information of all industrial equipment in the factory, including the equipment number and installation coordinates of each piece of equipment in the factory coordinate system; Equipment numbers and installation coordinates are obtained through laser ranging, UWB positioning, or QR code recognition, and then matched with structural drawings or CAD models; The perception module compares the installation coordinates of each device with the type of installation foundation structure (such as beam nodes, wall boundaries, and hoisting surfaces) indicated in the factory structure drawings or CAD models, maps the installation coordinates to the corresponding structural unit area, identifies the type of structural element at that location, and extracts the structural node attributes corresponding to that installation location. Structural node attributes include, but are not limited to: node type (such as beam intersection, slab edge, wall corner), stiffness level, connection characteristics, and structural coupling level; Based on whether it is located at a structural interface, a region of abrupt change in stiffness, or a point of coupling of multiple structural sources, it can be determined whether the location of the device is in a sensitive area for structural energy convergence.

[0021] The data acquisition module is used to collect vibration signals and stress response data of each industrial device during operation and bind them to their corresponding device number and installation coordinates; The data acquisition module deploys vibration sensors and strain detection units at key structural locations in each industrial device. Vibration sensors are used to acquire vibration signals during the operation of industrial equipment in real time, and strain detection units are used to acquire stress response data of corresponding structural parts of the equipment; all acquisition operations are bound to the equipment number and installation coordinates of the equipment to ensure that the acquisition results have clear spatial positioning information; Subsequently, the data acquisition module synchronously samples the vibration signals and stress response data with a unified timestamp, and filters and compresses the collected vibration signals and stress response data through the local computing unit to form structured sampling data units. Finally, the data acquisition module performs a fast Fourier transform on the collected vibration signal to extract the main frequency component and its corresponding amplitude in the frequency range of 1Hz to 5kHz, and calculates the total vibration energy. Finally, the data acquisition module outputs a data packet containing the device number, acquisition timestamp, installation coordinates, vibration frequency distribution, amplitude, real-time acceleration, total vibration energy, and stress response data, which is then sent to the data analysis module.

[0022] The data acquisition module calculates the amplitude change rate corresponding to the main frequency component of each industrial device based on the vibration frequency distribution within a continuous sampling period. If the rate of change of the amplitude of the main frequency component exceeds the set amplitude change detection threshold, or if the increase in the total vibration energy exceeds the preset energy jump judgment threshold in two or more consecutive sampling periods, the system determines that the device is in a change trend state. In response to the sudden change trend, the data acquisition module increases the current sampling frequency from the basic sampling frequency to a preset high-frequency sampling frequency, and then uses the preset high sampling frequency to collect vibration signals in the next preset number of sampling periods. If, during a continuous sampling period of acquiring vibration signals at a preset high sampling frequency, the rate of change of the main frequency component is continuously within the set stable threshold range, or the total vibration energy within this range does not exceed the preset energy fluctuation tolerance threshold, the system will automatically restore the sampling frequency to the basic sampling frequency.

[0023] Specifically, the data acquisition module first performs fast Fourier transform processing on the vibration signals collected by each industrial device within multiple consecutive sampling periods, extracts the dominant frequency component in each period, that is, the frequency point with the largest amplitude in the vibration frequency distribution, and records the amplitude corresponding to the dominant frequency component. Subsequently, the system calculates the rate of change of the amplitude of the main frequency component based on the amplitude difference between the current and previous sampling periods. The rate of change of the amplitude of the main frequency component = the amplitude of the main frequency component in the current cycle minus the amplitude of the main frequency component in the previous cycle, and then divided by the sampling period length. If the rate of change of amplitude exceeds the system's preset threshold for detecting abrupt amplitude changes, the device is considered to have obvious frequency energy fluctuation characteristics. Meanwhile, the system further compares and analyzes the total vibration energy of the device in the current and previous sampling cycles. If the energy increase in two or more consecutive sampling cycles exceeds the preset energy jump judgment threshold, the device can also be judged to be in a sudden change trend state. If any of the above mutation conditions are met, the system will determine the device as being in a "mutation trend state" and trigger the sampling strategy adjustment process. In response to the mutation trend, the data acquisition module will switch the current sampling frequency from the default basic sampling frequency to the preset high-frequency sampling frequency, and will continue to record and analyze the vibration signal of the device in the high sampling frequency mode for the next preset number of sampling periods. During the high sampling frequency sampling process, the system continues to monitor the rate of change of the main frequency component and the trend of change of the total vibration energy in each cycle; if the rate of change of the main frequency component is continuously within the preset stability threshold range, or the periodic variation amplitude of the total vibration energy is lower than the energy fluctuation tolerance threshold defined by the system, it indicates that the equipment state tends to be stable. Once any of the above stability conditions is met for a set number of periods, the system will automatically restore the sampling frequency from the high-frequency mode to the basic sampling frequency, resume the normal sampling strategy, and ensure that the overall resource utilization efficiency and data processing load of the system are within a reasonable range. Through the aforementioned adaptive adjustment mechanism of the sampling period, the system can provide high-resolution vibration data acquisition capabilities when the equipment operating status changes significantly, while maintaining low-frequency and efficient sampling when the equipment status is stable. This effectively improves the sensitivity and accuracy of the response to changes in the main frequency, providing a more timely and targeted data foundation for subsequent data analysis and structural health assessment.

[0024] The data analysis module identifies potential resonance coupling paths and structural risk areas based on the installation coordinates of each industrial device, structural node attributes, and total vibration energy. The data analysis module calculates the spatial distance between the installation coordinates of each device, and combines the structural drawings or CAD models with the structural node attributes to determine whether there is a structural coupling relationship between the devices. If the main frequencies of multiple devices overlap within a range of ±5Hz in their vibration frequency distributions, and their total vibration energy exceeds the set energy threshold, the data analysis module will determine the structural path as a potential resonant coupling path and perform frequency energy superposition according to the structural location. If a structural location corresponds to multiple equipment numbers and has structural node attributes such as beam intersection or high structural coupling level, and is accompanied by a continuous upward trend in the total vibration energy, then the location is marked as a sensitive area for structural energy convergence and is assigned a unique resonance node identifier. The data analysis module outputs the resonance node identifier, the corresponding set of equipment numbers, the set of installation coordinates, the frequency overlap, the trend of total vibration energy, and the marking information of the high-risk resonance node.

[0025] The data analysis module compares the amplitude of the dominant frequency component in the vibration frequency distribution of each industrial device in the current sampling period with the average amplitude of the same frequency band in multiple historical sampling periods. If the change in the amplitude of the main frequency component compared to the historical average exceeds the preset amplitude change judgment threshold; Furthermore, the amplitude decreased and fell back to the range of the historical average ± fluctuation range of this frequency band in subsequent sampling periods. Furthermore, within the frequency range adjacent to this main frequency component, no other devices were detected to exhibit a coordinated upward trend in amplitude. If any two of the three judgment conditions are met, the current period is determined to be a non-periodic disturbance event, and the subsequent processing of the event corresponding to the device in the resonance node identification process and structural risk event is suspended.

[0026] Specifically, including: The system extracts the vibration frequency distribution of the target industrial equipment in multiple consecutive sampling periods and identifies the dominant frequency component in each period, i.e., the frequency point with the largest amplitude. Specifically, the dominant frequency component is the result extracted by the data acquisition module by performing a Fast Fourier Transform (FFT) on the vibration signal collected during the operation of the industrial equipment. This dominant frequency component corresponds to each frequency component and its corresponding amplitude in the vibration frequency distribution within a set frequency range. The system extracts the frequency value of the main frequency component in each continuous sampling period to form a frequency sequence of length N; If any period difference between adjacent main frequencies exceeds the set frequency deviation threshold, or if the standard deviation of the frequency sequence exceeds the frequency fluctuation range threshold, then the main frequency component is considered to lack stability.

[0027] The system performs trend calculations on the total vibration energy of the target device over multiple consecutive sampling periods, including: the system judges the trend of the total vibration energy of the device over M consecutive periods; If, in more than half of these M cycles, the total vibration energy is lower than the set energy effectiveness threshold, or if the slope of the fitted energy trend curve is negative or close to zero, then the device is considered to lack energy response continuity.

[0028] If the total vibration energy is consistently below the preset energy effectiveness threshold over multiple cycles, or does not show an increasing trend, then the vibration behavior is deemed to lack the energy continuity of the structural response. The system compares the amplitude of the current cycle's dominant frequency component with the average amplitude of that frequency band in historical cycles and makes the following judgments: b1. If the change ratio exceeds the set threshold for amplitude change judgment.

[0029] b2, and the amplitude then quickly falls back to the preset fluctuation range of the historical average; the fluctuation range is the range of fluctuation constructed based on the average amplitude of this frequency band over X historical periods; Specifically, it is the mean of the frequency band ± K × standard deviation, where K is the tolerance coefficient set by the system; If the current main frequency amplitude continues to decline in the next two or more cycles and stabilizes within the range, it is considered to have the "amplitude decline" characteristic.

[0030] b3. If no other devices show a coordinated upward trend in the adjacent frequency range, it is judged as an isolated non-periodic disturbance behavior; specifically, the system monitors the frequency components of other devices within the range of ±Δf of the current main frequency component; where Δf represents a set frequency tolerance range value. If, within the frequency window, the amplitude of other devices also increases within the same period, and the increase exceeds the set coordination judgment threshold, then a coordinated upward trend is considered to exist.

[0031] If any two of the three judgment steps b1, b2, and b3 meet the non-periodic disturbance condition, the system will mark the current period of the device as a "non-periodic disturbance event". Such events will not proceed to the subsequent resonance node identification, structural status monitoring, and risk scoring processes, but will be recorded in the disturbance event log for later tracking or screening analysis.

[0032] Furthermore, the data analysis module marks the preset locations of high-temperature equipment, determines the installation coordinate range of the high-temperature equipment in the factory coordinate system, and establishes a set of heat source interference areas corresponding to the high-temperature equipment. Specifically, the data analysis module identifies the installation coordinates of high-temperature equipment such as electric furnaces, heat treatment devices, and heating pipes based on the equipment installation information in the structural drawings or computer-aided design drawings provided by the perception module. It then extracts the three-dimensional installation coordinate range of these equipment within the plant coordinate system and establishes this coordinate range as a set of heat source interference regions. Each heat source interference region can be spatially defined using a set coordinate boundary box (such as minimum and maximum XYZ values).

[0033] During the process of identifying potential resonance paths or generating resonance node identifiers, the system obtains the installation coordinates of all industrial equipment involved in the resonance path and determines whether they have a spatial overlap with the installation coordinate range of any high-temperature equipment in the above set of heat source interference areas. If there is spatial overlap, further analyze the vibration frequency distribution of the equipment within the path to determine whether it contains the dominant frequency component within the set heat source disturbance frequency range. Specifically, for paths that are determined to have spatial overlap, the system further calls the vibration frequency distribution data provided by the data acquisition module to extract the dominant frequency component of each device and determine whether the dominant frequency component is within the heat source disturbance frequency range. The heat source disturbance frequency range is a predefined set of frequency bands (e.g., common disturbance frequency intervals of industrial heat sources) used to identify non-structural resonance behavior that may be caused by thermal environment fluctuations.

[0034] If both spatial overlap and frequency matching conditions are met simultaneously, the resonance path is marked as a heat source interference path, and the preset processing procedure is executed.

[0035] In this embodiment, if the two conditions of spatial coincidence and frequency matching are met, the system will mark this resonance path as a "heat source interference path"; for the heat source interference path, the preset processing flow includes: c1. Exclude this path from the resonance path recognition process to avoid misjudging it as a structural resonance risk path; c2. Delay the generation time of the resonance node identification for this path and conduct a reconfirmation in subsequent cycles; c3. Record this path in the "interference path log" for manual review or historical backtracking analysis.

[0036] The structural state monitoring module is used to conduct key health monitoring on the structural positions (i.e., the sensitive areas of structural energy convergence confirmed through data analysis) that have been given resonance node identifications; This structural state monitoring module activates the strain gauges, laser displacement sensors, and thermal expansion measurement devices at the corresponding positions to obtain the structural state indicators in real time, including the strain rate, displacement change, and thermal expansion length; If the strain rate continuously increases, or the displacement change and the total vibration energy show a synchronous growth trend, or the thermal expansion length exceeds the heat capacity limit of the node material, and any of the above states persists for more than the set period, it is determined that the structure is in an abnormal state, and a structural risk event package including the resonance node identification, equipment number set, installation coordinate set, strain rate, displacement change, thermal expansion length, and abnormal state duration is generated.

[0037] The warning module is used to, based on the structural risk event package output by the structural state monitoring module, combine the equipment number, installation coordinates, and structural node attributes provided by the sensing module, the total vibration energy provided by the data acquisition module, the resonance node identification and frequency coincidence degree provided by the data analysis module, and the strain rate, displacement change, thermal expansion length, and abnormal state duration provided by the structural state monitoring module, conduct a risk scoring on the structural health state, and generate a structural risk warning message; The warning module conducts normalization processing and assigns weights to the following input indicators through a weighted scoring function, including: the total vibration energy, frequency coincidence degree, strain rate, displacement change, thermal expansion length, and abnormal state duration, and forms the final risk scoring value according to the weighted calculation result. According to this risk scoring value, it is divided into low risk, medium risk, high risk, and extremely high risk levels, and risk warning data including the resonance node identification, equipment number set, installation coordinate set, risk scoring value, risk level, and trigger factor details are output for subsequent linkage response or maintenance decision-making; Specifically, the early warning module normalizes each indicator, including total vibration energy, frequency overlap, strain rate, displacement change, thermal expansion length, and duration of abnormal state, by converting each indicator value into a standardized value between 0 and 1 to eliminate the influence between different physical dimensions. For each normalized indicator, the system assigns a corresponding weight based on its sensitivity to structural risk or its empirical importance, for example: The total vibration energy and strain rate are given high weight because they are directly related to structural fatigue and damage. Frequency overlap and thermal expansion length are weighted secondarily; The displacement change and the duration of the abnormal state are assigned moderate weights based on their correlation with historical faults. In this embodiment, the weights can be set by expert experience or optimized by machine learning after deployment.

[0038] The normalized value of each indicator is multiplied by its corresponding weight, and the results are added together to obtain a comprehensive structural risk score. The structural risk score is between 0 and 1, with a higher value indicating a higher risk level.

[0039] Based on the numerical range of the final risk score, the system is divided into four levels: 0.0–0.25: Low risk; 0.26–0.50: Medium risk; 0.51–0.75: High risk; 0.76–1.00: Extremely high risk; The system ultimately outputs a structural risk warning data record, which includes: Resonance node identifier; The associated set of device IDs; Installation coordinate set; Risk score; Risk level; A list of the main factors that trigger this score and their detailed indicator values; The output will be used for subsequent maintenance prioritization or automated response.

[0040] The data analysis module and the structural condition monitoring module communicate with each other via an industrial Ethernet interface. A time synchronization protocol is used to align the data collected by different modules with a unified time reference to ensure the consistency of the generation of resonance node identifiers with the timing of subsequent structural condition monitoring.

[0041] In this embodiment, the industrial Ethernet communication interface adopts a wired communication method compliant with the IEEE 802.3 standard, supporting full-duplex real-time transmission and multi-node interconnection. This interface is used to transmit the resonance node identifier, the corresponding set of device numbers, and the set of installation coordinates to the structural condition monitoring module in the form of structured data packets after the data analysis module generates the resonance node identifier.

[0042] By adopting an industrial Ethernet communication interface, stable data transmission can be achieved in factory environments with high electromagnetic interference, long-distance cabling, or multiple devices operating in parallel, avoiding the problem of asynchronous structural monitoring information caused by delays, packet loss, or signal attenuation.

[0043] In addition, the industrial Ethernet communication interface is scalable, allowing multiple structural condition monitoring modules to be connected in parallel, realizing centralized aggregation and distributed management of multi-point monitoring data, thereby improving the overall scalability of the system and the reliability of data interaction.

[0044] The system uses a time synchronization protocol to align the data collected by different modules with a unified time reference, so as to ensure the timing consistency between the generation of resonance node identifiers and subsequent structural status monitoring.

[0045] The time synchronization protocol is based on a network clock synchronization mechanism. The master clock node broadcasts a standard timestamp signal to the data acquisition module, data analysis module, and structural status monitoring module according to a preset synchronization period. After receiving the timestamp signal, each module resets its local sampling clock to a standard time reference consistent with the master clock.

[0046] When the data acquisition module, data analysis module, and structural state monitoring module perform acquisition tasks at different sampling periods, the time synchronization protocol ensures that the time base of the data acquired by each module remains consistent, so that the generation time of the resonance node identifier and the acquisition time of the structural state monitoring data strictly correspond within the millisecond range.

[0047] This unified time reference enables the establishment of a continuous and consistent time series within the system, ensuring a one-to-one correspondence between the duration of abnormal states, strain rate changes, and total vibration energy trends in the structural risk event package across the time dimension. This improves the timing accuracy of structural risk assessment and the reliability of early warning output.

[0048] When generating resonance node identifiers, the data analysis module stores the identifier along with the corresponding set of device numbers and installation coordinates in the node information table. The node information table is used by the structural condition monitoring module to achieve continuous monitoring of specific resonance locations.

[0049] Specifically, the node information table adopts a hierarchical storage structure. The first layer records the resonant node identifier and its basic spatial information, the second layer records the number and coordinate mapping relationship of the associated devices, and the third layer records the structural node attributes and monitoring status indicators.

[0050] When the structural condition monitoring module starts the monitoring task, the system automatically calls the corresponding resonance node identifier in the node information table, extracts the associated installation coordinates and equipment number information, and activates the corresponding strain measurement, displacement measurement and thermal expansion measurement devices accordingly.

[0051] By adopting the above-mentioned node information table storage and retrieval mechanism, a clear resonance node data mapping relationship can be established between the data analysis module and the structural status monitoring module, realizing structured management of data flow within the system.

[0052] By using the resonance node identifier as the index key, the set of device numbers is bound and stored with the set of installation coordinates, so that each potential resonance path has a unique and traceable spatial identifier and device association, avoiding positioning deviations caused by inconsistent data sources or duplicate coordinates during multi-module collaborative monitoring.

[0053] This node information table serves as the core intermediate layer data structure of the system, enabling the structural status monitoring module to quickly locate the target monitoring area based on the resonance node identifier without having to re-retrieve or compare the original installation coordinate data, thereby significantly shortening the monitoring response time.

[0054] In addition, by continuously updating the structural node attribute fields and monitoring status flags in the node information table, the system can dynamically reflect the changes in the health status of sensitive areas where structural energy converges, providing a stable data index foundation for subsequent risk trend analysis and maintenance decisions.

[0055] Example 2 A monitoring and early warning system for the electromechanical installation status of factory industrial equipment, characterized in that it includes: The sensing module collects the equipment number and three-dimensional installation coordinates of each industrial device, matches the installation coordinates with structural drawings or CAD models, extracts the corresponding structural node attributes, and determines whether the location is a sensitive area for structural energy convergence. The data acquisition module collects vibration signals and stress response data during equipment operation and binds them to the equipment number and installation coordinates. The module performs frequency domain analysis on the vibration signals to extract the dominant frequency component and calculates the total vibration energy, forming a data package containing the equipment number, installation coordinates, vibration frequency distribution, and total vibration energy. The data analysis module identifies equipment pairs with overlapping frequencies and structural coupling based on installation coordinates, structural node attributes, and total vibration energy. If the preset judgment conditions are met, the pair is identified as a potential resonance path, and a resonance node identifier is generated at a specific location. The structural condition monitoring module is used to focus on monitoring the structural locations that have been assigned resonance node identifiers, collect the strain rate, displacement change and thermal expansion length at the location, and generate a structural risk event package containing the resonance node identifier and the duration of the abnormal state if any structural condition index is abnormal and continues to exceed a set period. The early warning module performs weighted calculations on the total vibration energy, frequency overlap, strain rate, displacement change, thermal expansion length, and duration of abnormal state to generate a risk score value and output structural risk early warning information including resonance node identifier, risk score value, and risk level.

[0056] Compared to Example 1, the data analysis module in Example 2 performs differential calculations on the installation coordinates of each industrial device within a continuous sampling period to obtain the vibration displacement vector of each device. It also analyzes the structural coupling relationship between devices by combining the structural connection relationship in the structural drawings or CAD model. Based on the trend of the total vibration energy change and the direction relationship of the vibration displacement vector, it identifies the direction of vibration propagation in the structure to distinguish between the main vibration-generating device and the disturbed device in the resonance node. By identifying the main vibration-generating device and the disturbed device in the resonance node, the weight allocation of the risk score value is optimized during the calculation. This solves the technical problem that existing systems cannot identify the direction of vibration propagation, resulting in the inability to accurately determine the location of the vibration source and the path of its influence.

[0057] Specifically, the data analysis module performs differential calculations on the installation coordinates of each industrial device over multiple consecutive sampling periods to obtain the vibration displacement vector of each device in the time series. This vector reflects the trend of minute spatial disturbances caused by the structural response of the device. Subsequently, the data analysis module combines the structural connection relationships between devices in the structural drawings or CAD models obtained by the perception module to filter out device pairs with physical connections or structural coupling relationships and establish a structural coupling network diagram between devices. Next, for each pair of devices with structural coupling, the data analysis module compares the trend of their total vibration energy and the directionality of their vibration displacement vector. If the total vibration energy of a certain device is significantly higher than that of its structural connection pair, and its vibration displacement vector is directed toward the other device, then the device is identified as the main vibration-generating device, and its connected device is identified as the disturbed device. When the subsequent early warning module calculates the structural risk score, the system will determine whether a certain device is the main vibration-generating device or the disturbed device based on the results output by the data analysis module. After identifying the device role, the early warning module performs differentiated weighting on the various input indicators used for risk scoring; For objects identified as the main vibration-generating devices, the system increases the importance of the corresponding "total vibration energy" and "frequency overlap" indicators during the risk scoring process. In this embodiment, the weight values ​​of these two indicators in the scoring function are increased to 1.3 to 1.5 times the original value on the default basis, so that the scoring result of the device occupies a larger proportion in the overall risk score, thereby reflecting its higher risk impact on structural health as a vibration source. For other input indicators, such as "strain rate", "displacement change", "thermal expansion length", and "duration of abnormal state", the risk scoring function adopts a standardized weighted model during implementation, and the sum of the weights of all indicators remains at 1.0. After identifying the main vibration-generating device, the weights of its corresponding total vibration energy and frequency overlap will be increased from the default value to 1.3 to 1.5 times the original value, while the weights of other indicators will be reduced proportionally to keep the total weight unchanged.

[0058] For objects identified as disturbed devices, the system appropriately reduces the weight of their "total vibration energy" and "frequency overlap" indicators. In this embodiment, the weight is reduced to 0.6 to 0.8 times the original weight, in order to reflect their relatively minor influence as vibration receivers rather than primary vibration sources.

[0059] By adjusting the corresponding weights, the risk scoring results can more accurately focus on the equipment causing the structural risk, improve the accuracy of the response to the vibration propagation path, and provide a basis for subsequent graded response, early warning priority judgment, and responsibility positioning.

[0060] Example 3 Compared to Examples 1 and 2, the structural state monitoring module in Example 3 calculates the structural response sensitivity weights based on the thermal capacity coefficient, stiffness level, and connection degrees of freedom of the structural nodes. Based on the temperature change rate and structural response sensitivity weights within a continuous sampling period, a correction factor is generated to dynamically adjust the threshold values ​​for judging strain rate, displacement change, and thermal expansion length. The differences in structural state changes over multiple sampling periods are summed to obtain a cumulative trend value. If any cumulative trend value exceeds the corresponding dynamic threshold, the structural state is determined to be abnormal, and a structural trend abnormality event data packet is output.

[0061] Specifically, based on the structural information of each node location extracted from the structural drawings or CAD drawings by the perception module, the system extracts the following parameters: Material heat capacity coefficient; Structural stiffness rating; The number of degrees of freedom connecting nodes; Based on the above parameters, the structural response sensitivity weights are calculated using the following calculation logic: Multiplying the heat capacity coefficient by the reciprocal of the stiffness grade, and then by the number of nodal degrees of freedom, yields the structural response sensitivity weight, i.e.: Structural response sensitivity weight = thermal capacity coefficient × (1 ÷ stiffness grade) × number of nodal degrees of freedom; Subsequently, for the target structural node location, the temperature change rate is calculated between the current cycle and the previous cycle, i.e.: temperature change rate = current cycle temperature - previous cycle temperature. And generate a corresponding trend threshold correction factor. In this embodiment, the correction factor = temperature change rate × structural response sensitivity weight. The correction factor is used to dynamically adjust the trend judgment threshold of the structural state index. That is, the original set threshold is multiplied by the correction factor to generate the dynamic trend judgment thresholds of strain rate, displacement change and thermal expansion length respectively. When the temperature changes drastically or the structure has a weak thermal response capability, the correction factor increases and the corresponding trend judgment threshold decreases, thereby enhancing the recognition sensitivity.

[0062] To capture structural performance degradation trends, the structural condition monitoring module performs interpolation processing on continuous periodic structural condition changes, including: For the target structure node, within each sampling period, calculate the three state changes between the current period and the previous period: Strain rate difference = current strain rate - strain rate of the previous cycle; Displacement change difference = Current displacement change - Previous period displacement change; Thermal expansion length difference = current thermal expansion length - previous cycle thermal expansion length; The differences in strain rate, displacement change, and thermal expansion length are accumulated over multiple sampling periods to form a sequence of corresponding differences. For each difference sequence, perform an accumulation process to obtain the cumulative value of strain trend, cumulative value of displacement trend, and cumulative value of thermal expansion trend.

[0063] The cumulative values ​​of strain trend, displacement trend, and thermal expansion trend are compared with their corresponding dynamic thresholds: If any cumulative value of strain trend, displacement trend, or thermal expansion trend exceeds its corresponding dynamic threshold, the structural state is determined to have a trend anomaly at that node location. The system records the current resonance node identifier, the cumulative values ​​of the three trends and their judgment results, and generates a data packet of structural trend anomaly events.

[0064] In this embodiment, structural trend abnormal event data packets are recorded to the abnormal trend recording unit in the structural status monitoring module, which is used to establish a historical archive of the trend response of a specific structural node under different operating stages. The system archives historical data of trend anomalies by labeling them, including resonance node identifiers, trend item types, cumulative trend values ​​and timestamp information, and forms a trend evolution trajectory based on the time dimension; The trend evolution trajectory can be used to assist in the formulation of subsequent operation and maintenance management strategies, such as: determining whether structural nodes need to be re-inspected regularly, whether the sampling frequency needs to be increased, and whether a specific observation window for health assessment needs to be added.

[0065] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A monitoring and early warning system for the electromechanical installation status of factory industrial equipment, characterized in that, include: The sensing module collects the equipment number and three-dimensional installation coordinates of each industrial device, matches the installation coordinates with structural drawings or CAD models, extracts the corresponding structural node attributes, and determines whether the location is a sensitive area for structural energy convergence. The data acquisition module collects vibration signals and stress response data during equipment operation and binds them to the equipment number and installation coordinates. The data acquisition module performs frequency domain analysis on the vibration signals to extract the dominant frequency component and calculates the total vibration energy, forming a data package containing the equipment number, installation coordinates, vibration frequency distribution, and total vibration energy. The data analysis module identifies equipment pairs with overlapping frequencies and structural coupling based on installation coordinates, structural node attributes, and total vibration energy. If the preset judgment conditions are met, the pair is identified as a potential resonance path, and a resonance node identifier is generated at a specific location. The structural condition monitoring module is used to focus on monitoring the structural locations that have been assigned resonance node identifiers, collect the strain rate, displacement change and thermal expansion length at the location, and generate a structural risk event package containing the resonance node identifier and the duration of the abnormal state if any structural condition index is abnormal and continues to exceed a set period. The early warning module performs weighted calculations on the total vibration energy, frequency overlap, strain rate, displacement change, thermal expansion length, and duration of abnormal state to generate a risk score value and output structural risk early warning information including resonance node identifier, risk score value, and risk level.

2. The factory industrial equipment electromechanical installation status monitoring and early warning system according to claim 1, characterized in that, In the data analysis module, the preset judgment conditions are: If the main frequencies of multiple devices overlap within a range of ±5Hz in their vibration frequency distributions, and their total vibration energy exceeds the set energy threshold, the data analysis module will determine the structural path as a potential resonant coupling path and perform frequency energy superposition according to the structural location. If a structural location corresponds to multiple equipment numbers and has structural node attributes such as beam intersection or high structural coupling level, and is accompanied by a continuous upward trend in the total vibration energy, then the location is marked as a sensitive area for structural energy convergence and assigned a unique resonance node identifier.

3. The electromechanical installation status monitoring and early warning system for factory industrial equipment according to claim 1, characterized in that, The data analysis module marks the preset locations of high-temperature equipment, determines the installation coordinate range of the high-temperature equipment in the factory coordinate system, and establishes a set of heat source interference areas corresponding to the high-temperature equipment. During the process of identifying potential resonance paths or generating resonance node identifiers, the system obtains the installation coordinates of all industrial equipment involved in the resonance path and determines whether they have a spatial overlap with the installation coordinate range of any high-temperature equipment in the above set of heat source interference areas. If there is spatial overlap, further analyze the vibration frequency distribution of the equipment within the path to determine whether it contains the dominant frequency component within the set heat source disturbance frequency range. If both spatial overlap and frequency matching conditions are met simultaneously, the resonance path is marked as a heat source interference path, and the preset processing procedure is executed.

4. The factory industrial equipment electromechanical installation status monitoring and early warning system according to claim 1, characterized in that, The data analysis module and the structural condition monitoring module interact via an industrial Ethernet communication interface, and a time synchronization protocol is used to align the data collected by different modules with a unified time reference to ensure the consistency of the generation of resonance node identifiers with the timing of subsequent structural condition monitoring.

5. A factory industrial equipment electromechanical installation status monitoring and early warning system according to claim 1, characterized in that, When generating a resonance node identifier, the data analysis module stores the identifier along with the corresponding set of device numbers and the set of installation coordinates in a node information table. This node information table is used by the structural condition monitoring module to achieve continuous monitoring of specific resonance locations.

6. The electromechanical installation status monitoring and early warning system for factory industrial equipment according to claim 1, characterized in that, The data analysis module obtains the vibration displacement vector of each piece of industrial equipment by performing differential calculations on the installation coordinates of each piece of equipment within a continuous sampling period. It also analyzes the structural coupling relationship between the equipment by combining the structural connection relationship in the structural drawings or CAD models. Based on the trend of the total vibration energy change and the direction of the vibration displacement vector, it identifies the direction of vibration propagation in the structure, so as to distinguish the main vibration-generating equipment and the disturbed equipment that cause vibration in the resonance node. By identifying the primary vibration-generating device and the disturbed device in the resonant node, the weighting of the risk score value during calculation is optimized.

7. A factory industrial equipment electromechanical installation status monitoring and early warning system according to claim 1, characterized in that, The data analysis module compares the amplitude of the dominant frequency component in the vibration frequency distribution of each industrial device in the current sampling period with the average amplitude of the same frequency band in multiple historical sampling periods. If the change in the amplitude of the main frequency component compared to the historical average exceeds the preset amplitude change judgment threshold; Furthermore, the amplitude decreased and fell back to the range of the historical average ± fluctuation range of this frequency band in subsequent sampling periods. Furthermore, within the frequency range adjacent to this main frequency component, no other devices were detected to exhibit a coordinated upward trend in amplitude. If any two of the three judgment conditions are met, the current period is determined to be a non-periodic disturbance event, and the process of entering the resonance node identification process and structural risk event handling process for the corresponding event of the device is stopped.

8. The electromechanical installation status monitoring and early warning system for factory industrial equipment according to claim 1, characterized in that, The data acquisition module calculates the amplitude change rate corresponding to the main frequency component of each industrial device based on the vibration frequency distribution within a continuous sampling period. If the rate of change of the amplitude of the main frequency component exceeds the set amplitude change detection threshold, or if the increase in the total vibration energy exceeds the preset energy jump judgment threshold in two or more consecutive sampling periods, the system determines that the device is in a change trend state. In response to the sudden change trend, the data acquisition module increases the current sampling frequency from the basic sampling frequency to a preset high-frequency sampling frequency, and then uses the preset high sampling frequency to collect vibration signals in the next preset number of sampling periods. If, within a continuous sampling period of acquiring vibration signals using a preset sampling frequency, the rate of change of the main frequency component is continuously within the set stable threshold range, or the total vibration energy within this range does not exceed the preset energy fluctuation tolerance threshold, the system will automatically restore the sampling frequency to the basic sampling frequency.

9. A factory industrial equipment electromechanical installation status monitoring and early warning system according to claim 1, characterized in that, The structural condition monitoring module calculates the structural response sensitivity weights based on the thermal capacity coefficient, stiffness level, and connection degrees of freedom of the structural nodes. Based on the temperature change rate and structural response sensitivity weights within a continuous sampling period, a correction factor is generated to dynamically adjust the threshold values ​​for judging strain rate, displacement change, and thermal expansion length. The differences in structural state changes over multiple sampling periods are summed to obtain a cumulative trend value. If any cumulative trend value exceeds the corresponding dynamic threshold, the structural state is determined to be abnormal, and a structural trend abnormality event data packet is output.

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