An industrial internet device running state online monitoring method and system

Abstract

This invention discloses an online monitoring method and system for the operational status of industrial internet equipment, belonging to the field of industrial monitoring technology. The system integrates sensor groups installed on industrial processing equipment and utilizes LoRa wireless transmission technology to achieve real-time acquisition and transmission of equipment operational status data. After preprocessing and feature extraction of key characteristics, the extracted operational status dataset is analyzed to calculate vibration state index Zd, current harmonic index Dl, and equipment acoustic wave index Sb, enabling timely detection of abnormalities in equipment operation. When the equipment fault score Pf exceeds a preset fault assessment threshold G, the system automatically generates an early warning message, promptly alerting relevant personnel to the operational status of the industrial processing equipment. Upon detecting an anomaly, the system combines historical data and equipment operation logs to confirm the specific fault type and propose maintenance suggestions.

Description

Technical Field

[0001] This invention relates to the field of industrial monitoring technology, specifically to a method and system for online monitoring of the operating status of industrial internet devices. Background Technology

[0002] Industrial Internet (IIoT) devices are those connected to industrial control systems and operating equipment via the internet. These devices are typically used to collect, transmit, and analyze data from industrial environments to improve production efficiency, reduce costs, and optimize operations. In manufacturing, IIoT devices are used to monitor the real-time status of various machines and processing equipment on production lines. By analyzing the collected data, equipment failures can be predicted, preventative maintenance can be scheduled, thereby reducing downtime and improving production efficiency. Furthermore, data analytics can optimize production processes and improve product quality.

[0003] Current traditional monitoring systems often focus only on single types of data analysis, such as vibration analysis, while neglecting other abnormal signals that may occur during equipment operation. A comprehensive scoring method combining current harmonics and acoustic characteristics can overcome this deficiency. Through multi-dimensional analysis of equipment operating status, the system can more accurately assess equipment malfunctions. However, existing monitoring systems still suffer from inefficiencies and insufficient data processing in data transmission, preprocessing, and feature extraction. This results in a lack of accuracy and real-time performance in equipment fault prediction, failing to reflect the true operating status of the equipment in a timely manner. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for online monitoring of the operating status of industrial internet devices, solving the problems mentioned in the background section.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solution: including a data acquisition module, a data transmission and preprocessing module, a vibration calculation module, an equipment operating status calculation module, and an evaluation and optimization module;

[0006] The data acquisition module collects real-time operating status data of the industrial processing equipment by installing and integrating a group of sensors on the equipment, and then summarizes the operating status data to generate an operating status dataset.

[0007] The data transmission and preprocessing module is used to transmit the collected operating status dataset to the data processing center and preprocess the operating status dataset.

[0008] The vibration calculation module extracts key feature parameters from the vibration spectrum based on the preprocessed operating status dataset, calculates and obtains the equipment vibration state index Zd, and performs a preliminary comparison and evaluation analysis of the abnormal conditions of the industrial processing equipment based on the preset vibration threshold Z according to the equipment vibration index.

[0009] When the equipment operation status calculation module identifies an abnormal situation in the industrial processing equipment, it extracts key feature parameters such as current harmonics, acoustic wave characteristics, and temperature change rate from the pre-processed operation status dataset, and calculates to obtain the current harmonic index Dl and the equipment acoustic wave index Sb.

[0010] The evaluation and optimization module is used to perform correlation calculations on the obtained vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb to obtain the equipment fault score Pf. Then, a second comparison evaluation is performed between the preset fault evaluation threshold G and the equipment fault score Pf to analyze the current equipment operating status.

[0011] Preferably, the data acquisition module is used to install an integrated sensor group on the industrial processing equipment to collect the operating data of the industrial processing equipment in real time, and to classify and summarize the collected operating data to generate an equipment operation dataset.

[0012] The integrated sensor group includes a vibration sensor, a current sensor, and an acoustic sensor;

[0013] The equipment operation dataset includes vibration spectrum, current harmonics, and acoustic characteristics.

[0014] Preferably, the data transmission and preprocessing module includes a data transmission unit and a data preprocessing unit;

[0015] The data transmission unit is used to install a LoRa module on industrial processing equipment and send the collected equipment operation dataset to the data processing center through the LoRa gateway;

[0016] The data preprocessing unit includes a processing unit and a feature extraction unit;

[0017] The processing unit is used to smooth the collected device operation dataset by calculating the average value of the data within a sliding window, and to eliminate interference data in the device operation dataset by using a filter.

[0018] The feature extraction unit is used to extract features from the data in the collected equipment operation dataset to obtain key parameters of the industrial processing equipment during operation. The specific extraction method and extraction results are as follows;

[0019] The vibration spectrum is processed by power density PSD and fast Fourier transform (FFT) to extract the vibration frequency component V and the vibration spectrum standard deviation BV.

[0020] The current harmonics are obtained by converting the current signal in the time domain into a frequency domain signal, extracting the current harmonic components I of each current harmonic of the industrial processing equipment, and calculating the standard deviation of the current harmonic components I to obtain the current harmonic standard deviation BI.

[0021] The acoustic wave characteristics are combined with time and frequency information to analyze the acoustic wave signal, extract the acoustic wave energy A of the acoustic wave signal, and calculate the standard deviation of the acoustic wave energy A to obtain the standard deviation BA of the acoustic wave energy.

[0022] Preferably, the vibration calculation module includes a vibration calculation unit and a device vibration state assessment unit;

[0023] The vibration calculation unit is used to construct a vibration state algorithm formula based on the extracted vibration frequency component V and vibration spectrum standard deviation BV. The vibration frequency component V and vibration spectrum standard deviation BV obtained in real time are input into the vibration state algorithm formula to calculate and obtain the vibration state index Zd, and analyze the internal mechanical faults of industrial processing equipment.

[0024] The vibration state index Zd is obtained using the following algorithm formula;

[0025]

[0026] In the formula, V i V represents the i-th vibration frequency component. pj Let a1 represent the average value of the vibration frequency components, log represent the logarithmic function, and n represent the total number of vibration frequency components in the vibration spectrum. Part of it is calculating the frequency component V for each vibration frequency. i The average value V of the vibration frequency component pj The deviation is then normalized by dividing by the standard deviation. The resulting square value represents the contribution of each frequency component to the overall vibration characteristics, eliminating the magnitude difference between frequency components, highlighting the influence of abnormal vibration, and improving the sensitivity of fault detection.

[0027] Preferably, the vibration state assessment unit is used to construct a vibration threshold Z based on the average of historical normal vibration data of industrial processing equipment, and then conduct a preliminary comparison assessment with the obtained vibration state index Zd to analyze the vibration state of the equipment under the current operating state. The specific assessment scheme is as follows:

[0028] When the vibration state index Zd > the vibration threshold Z, it indicates that the industrial processing equipment is vibrating abnormally during operation, and further analysis and evaluation should be carried out.

[0029] When the vibration state index Zd ≤ vibration threshold Z, it indicates that the vibration of the current industrial processing equipment is normal and the operating status of the industrial processing equipment is normal.

[0030] Preferably, the equipment operating status calculation module includes an equipment current harmonic calculation unit and an equipment acoustic wave characteristic calculation unit;

[0031] The current harmonic calculation unit is used to construct a current harmonic algorithm formula based on the current harmonic component I and the current harmonic standard deviation BI of each current harmonic extracted from the industrial processing equipment. The real-time acquired current harmonic component I and current harmonic standard deviation BI are input into the constructed current harmonic algorithm formula to calculate and obtain the current harmonic index Dl, and to detect the faults of the motor and electrical equipment of the industrial processing equipment.

[0032] The current harmonic index Dl is obtained through the following algorithm formula;

[0033]

[0034] In the formula, I j I represents the j-th current harmonic component. pj a1 represents the average value of the current harmonic components, a2 represents the second weight value, and m represents the number of current harmonic components I in the current signal.

[0035] Preferably, the equipment acoustic feature calculation unit is used to construct an acoustic signal algorithm formula based on the acoustic energy A of the extracted acoustic signal of the industrial processing equipment, and input the acoustic energy A of the real-time extracted acoustic signal into the calculation of the equipment acoustic index Sb to identify mechanical faults of the industrial processing equipment.

[0036] The acoustic wave index Sb of the device is obtained through the following algorithm formula;

[0037]

[0038] In the formula, A k Let A represent the k-th sound wave energy component. pj a3 represents the average value of the sound wave energy, a3 represents the third weight value, P represents the total sound wave energy of the sound wave signal, and e represents the exponential function.

[0039] Preferably, the evaluation and optimization module includes a comprehensive scoring unit and an evaluation and optimization unit;

[0040] The comprehensive scoring unit is used to perform dimensionless processing on the obtained vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb, and then correlate them to calculate the equipment fault score Pf, thereby scoring the industrial processing equipment for faults.

[0041] The equipment fault score Pf is obtained using the following algorithm formula;

[0042] Pf=[(w1*Zd)+(w2*Dl)+(w3*Sb)]+W;

[0043] In the formula, w1, w2 and w3 represent the preset weight values ​​of the vibration state index Zd, the current harmonic index Dl and the equipment acoustic index Sb, respectively, and w1+w2+w3=1. The specific parameters are set by the user, and W represents the correction constant.

[0044] Preferably, the evaluation and optimization unit is used to preset the fault evaluation threshold G and the equipment fault score Pf, and perform a secondary comparative evaluation to further analyze the current fault status of the industrial processing equipment. The specific evaluation scheme is as follows:

[0045] When the equipment fault score Pf > the fault assessment threshold G, it indicates that there is an abnormality in the current equipment. At this time, an early warning message is generated. By combining historical data and equipment operation logs, the specific fault type is confirmed and maintenance suggestions are made.

[0046] When the equipment fault score Pf ≤ fault assessment threshold G, it indicates that the current industrial processing equipment is in normal condition. At this time, a second early warning message is generated to perform maintenance on the industrial processing equipment.

[0047] A method for online monitoring of the operating status of industrial internet devices includes the following steps:

[0048] S1. First, install an integrated sensor group on the industrial processing equipment to collect the operating status data of the industrial processing equipment in real time, and summarize the operating status data of the processing equipment to generate an operating status dataset.

[0049] S2. Then, using LoRa module wireless transmission technology, the device's operating dataset is transmitted to the data processing center. Data transmission and reception are performed through the LoRa gateway. At the data processing center, the data is preprocessed, and key feature parameters are extracted after preprocessing.

[0050] S3. Extract key feature parameters of the vibration spectrum through power density PSD and fast Fourier transform FFT, and construct vibration state algorithm formula. Input the extracted frequency components and standard deviation into the formula to calculate the vibration state index Zd, and compare the vibration state index Zd with the preset vibration threshold Z.

[0051] S4. When an abnormal situation is detected in the industrial processing equipment, the key feature parameters of current harmonics, acoustic wave characteristics and temperature change rate are extracted from the pre-processed operating status dataset, and the current harmonic index Dl and the equipment acoustic wave index Sb are calculated and obtained.

[0052] S5. Finally, the obtained vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb are correlated and calculated to obtain the equipment fault score Pf. Then, the preset fault assessment threshold G and the equipment fault score Pf are compared and evaluated a second time to analyze the current equipment operating status.

[0053] This invention provides a method and system for online monitoring of the operating status of industrial internet devices. It has the following beneficial effects:

[0054] (1) By installing integrated sensor groups on industrial processing equipment and utilizing LoRa wireless transmission technology, the system can achieve real-time acquisition and transmission of equipment operating status data. After preprocessing and feature extraction of key characteristics, the extracted operating status dataset is used to calculate and analyze the vibration state index Zd, current harmonic index Dl, and equipment acoustic wave index Sb, thereby promptly detecting abnormalities in equipment operation. When the equipment fault score Pf exceeds the preset fault assessment threshold G, the system can automatically generate early warning information to promptly inform relevant personnel of the operating status of the industrial processing equipment. After detecting an anomaly, the system combines historical data and equipment operation logs to confirm the specific fault type and propose maintenance suggestions to avoid production stoppages and economic losses caused by equipment failures.

[0055] (2) The multi-module design of this system enables more comprehensive and in-depth monitoring of equipment operation status. Through the analysis of multi-dimensional data such as vibration spectrum, current harmonics, and acoustic characteristics, the system can not only identify mechanical faults but also detect electrical faults and acoustic anomalies. The comprehensive scoring unit correlates and calculates various indicators to generate an equipment fault score Pf, which is then evaluated a second time by the evaluation and optimization unit, making the equipment status assessment more accurate and reliable. The system's automated monitoring and evaluation functions enable continuous monitoring and real-time analysis of the operating status of industrial processing equipment. When the system detects that the equipment fault score Pf exceeds the preset fault assessment threshold G, it will automatically generate an early warning message and, in conjunction with historical data and equipment operation logs, propose specific maintenance suggestions. This early warning and maintenance suggestion function can help enterprises take preventive measures in advance, reduce equipment downtime, lower maintenance costs, and improve the overall efficiency of equipment maintenance and management. At the same time, for normally operating equipment, the system will also generate maintenance prompts to ensure that the equipment is always in optimal operating condition. Attached Figure Description

[0056] Figure 1This is a schematic diagram of the process of an online monitoring system for the operating status of industrial internet devices according to the present invention;

[0057] Figure 2 This is a schematic diagram illustrating the steps of an online monitoring method for the operating status of industrial internet devices according to the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1

[0060] Please see Figure 1 This invention provides an online monitoring system for the operating status of industrial Internet equipment. To achieve the above objectives, this invention is implemented through the following technical solutions: including a data acquisition module, a data transmission and preprocessing module, a vibration calculation module, an equipment operating status calculation module, and an evaluation and optimization module;

[0061] The data acquisition module collects real-time operating status data of industrial processing equipment by installing and integrating sensor groups on the equipment, and then summarizes the operating status data to generate an operating status dataset.

[0062] The data transmission and preprocessing module is used to transmit the collected operational status dataset to the data processing center and preprocess the operational status dataset.

[0063] The vibration calculation module extracts key feature parameters from the vibration spectrum based on the preprocessed operating status dataset, calculates and obtains the equipment vibration state index Zd, and sets a preset vibration threshold Z based on the equipment vibration index. It then performs a preliminary comparative evaluation and analysis of the abnormal conditions of the industrial processing equipment by comparing the threshold with the obtained vibration state index Zd.

[0064] When an abnormal situation is detected in the industrial processing equipment, the equipment operation status calculation module extracts key feature parameters such as current harmonics, acoustic wave characteristics and temperature change rate from the pre-processed operation status dataset, and calculates to obtain the current harmonic index Dl and the equipment acoustic wave index Sb.

[0065] The evaluation and optimization module is used to perform correlation calculations on the acquired vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb to obtain the equipment fault score Pf. Then, a second comparison evaluation is performed between the preset fault evaluation threshold G and the equipment fault score Pf to analyze the current equipment operating status.

[0066] In this embodiment, the system integrates vibration sensors, current sensors, and acoustic sensors. The data acquisition module can acquire real-time operating status data of industrial processing equipment and generate an operating status dataset. The data transmission and preprocessing module is responsible for transmitting this data to the data processing center and performing preprocessing to ensure the accuracy and reliability of the data. This comprehensive data acquisition and preprocessing method significantly improves the accuracy of fault detection compared to traditional single-data-source monitoring methods, and can comprehensively reflect the true operating status of the equipment. The vibration calculation module extracts key feature parameters from the vibration spectrum, calculates the equipment vibration state index Zd, and compares it with a preset vibration threshold Z to preliminarily assess the abnormality of the equipment. When an anomaly is detected, the equipment operating status calculation module further analyzes current harmonics, acoustic characteristics, and temperature change rate to calculate the current harmonic index Dl and the equipment acoustic index Sb. This multi-dimensional feature parameter analysis method enables the system to not only quickly identify mechanical faults but also detect electrical and acoustic anomalies, significantly improving the efficiency and accuracy of fault identification compared to traditional methods that rely on a single fault index. The evaluation and optimization module correlates and calculates the vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb to generate an equipment fault score Pf, which is then compared with a preset fault assessment threshold G. This comprehensive evaluation and optimization method enables the system to analyze the current operating status of the equipment comprehensively and accurately, and to promptly detect and warn of potential faults. Compared to traditional maintenance methods that rely on manual judgment, this system not only improves the automation level of equipment maintenance but also reduces misjudgments caused by human factors, significantly improving the efficiency and reliability of equipment management. By achieving these improvements, the system effectively extends the service life of the equipment and reduces maintenance costs.

[0067] Example 2

[0068] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 Specifically: The data acquisition module is used to install and integrate sensor groups on industrial processing equipment to collect real-time operating data of the industrial processing equipment during operation, and to classify and summarize the collected operating data to generate equipment operation datasets;

[0069] The integrated sensor group includes vibration sensors, current sensors, and acoustic sensors;

[0070] The equipment operation dataset includes vibration spectrum, current harmonics, and acoustic characteristics.

[0071] The data transmission and preprocessing module includes a data transmission unit and a data preprocessing unit;

[0072] The data transmission unit is used to install LoRa modules on industrial processing equipment and send the collected equipment operation datasets to the data processing center through the LoRa gateway;

[0073] The data preprocessing unit includes a processing unit and a feature extraction unit;

[0074] The processing unit is used to smooth the collected device operation dataset by calculating the average value of the data within a sliding window, and to eliminate interfering data in the device operation dataset by using a filter;

[0075] The feature extraction unit is used to extract features from the collected equipment operation dataset to obtain key parameters of industrial processing equipment during operation. The specific extraction methods and results are as follows.

[0076] The vibration spectrum is analyzed by power density PSD and fast Fourier transform (FFT) to extract the vibration frequency component V and the vibration spectrum standard deviation BV.

[0077] Current harmonics are obtained by converting the current signal in the time domain into a frequency domain signal, extracting the current harmonic components I of each current harmonic of the industrial processing equipment, and calculating the standard deviation of the current harmonic components I to obtain the current harmonic standard deviation BI.

[0078] The acoustic wave signal is analyzed by combining acoustic wave characteristics with time and frequency information, the acoustic wave energy A of the acoustic wave signal is extracted, and the standard deviation of the acoustic wave energy A is calculated to obtain the standard deviation BA of the acoustic wave energy.

[0079] In this embodiment, the system acquires detailed operational data of industrial processing equipment in real time by installing an integrated sensor group. Vibration spectrum, current harmonics, and acoustic wave characteristics—multi-dimensional data—compose a comprehensive equipment operation dataset. The data transmission and preprocessing module utilizes a LoRa module for data transmission and employs preprocessing techniques such as smoothing and filtering to eliminate noise and interference in the data. This multi-source data acquisition and reliable transmission method significantly improves the comprehensiveness and reliability of the data compared to traditional single-source monitoring methods. The feature extraction unit in the data preprocessing unit accurately extracts the vibration frequency component V and vibration spectrum standard deviation BV of the vibration spectrum using algorithms such as power density spectrum (PSD) and fast Fourier transform (FFT). Current harmonics are extracted through frequency domain signal analysis to obtain the current harmonic component I and its standard deviation BI. Acoustic wave characteristics are combined with time and frequency information to extract the acoustic wave energy A and its standard deviation BA. This multi-dimensional, high-precision feature parameter extraction method enables the system to accurately capture subtle changes in equipment operation, significantly improving the accuracy of data analysis and the sensitivity of fault detection compared to traditional simple signal analysis.

[0080] Example 3

[0081] This embodiment is an explanation based on Embodiment 2. Please refer to it. Figure 1 Specifically: the vibration calculation module includes a vibration calculation unit and an equipment vibration status assessment unit;

[0082] The vibration calculation unit is used to construct a vibration state algorithm formula based on the extracted vibration frequency component V and vibration spectrum standard deviation BV. The vibration frequency component V and vibration spectrum standard deviation BV obtained in real time are input into the vibration state algorithm formula to calculate and obtain the vibration state index Zd, and analyze the internal mechanical faults of industrial processing equipment.

[0083] The vibration state index Zd is obtained using the following algorithm formula;

[0084]

[0085] In the formula, V i V represents the i-th vibration frequency component. pj Let a1 represent the average value of the vibration frequency components, log represent the logarithmic function, and n represent the total number of vibration frequency components in the vibration spectrum. Part of it is calculating the frequency component V for each vibration frequency. i The average value V of the vibration frequency component pj The deviation is then normalized by dividing by the standard deviation. The resulting square value represents the contribution of each frequency component to the overall vibration characteristics, eliminating the magnitude difference between frequency components, highlighting the influence of abnormal vibration, and improving the sensitivity of fault detection.

[0086] The vibration state assessment unit is used to construct a vibration threshold Z based on the average of historical normal vibration data of industrial processing equipment, and then conduct a preliminary comparison and assessment with the obtained vibration state index Zd to analyze the vibration state of the equipment under the current operating state. The specific assessment scheme is as follows:

[0087] When the vibration state index Zd > the vibration threshold Z, it indicates that the industrial processing equipment is vibrating abnormally during operation, and further analysis and evaluation should be carried out.

[0088] When the vibration state index Zd ≤ vibration threshold Z, it indicates that the vibration of the current industrial processing equipment is normal and the operating status of the industrial processing equipment is normal.

[0089] In this embodiment, the system, through a vibration calculation module and a vibration calculation unit, constructs a vibration state algorithm formula using the extracted vibration frequency component V and the vibration spectrum standard deviation BV. This formula can calculate the vibration state index Zd in real time, and by normalizing the deviation of the vibration frequency component from the average value, the accuracy of vibration characteristic analysis is significantly improved. After eliminating the magnitude difference between frequency components, the system can more sensitively detect abnormal vibrations, ensuring timely detection of internal mechanical faults in the equipment. By calculating the vibration state index Zd and comparing it with the vibration threshold Z of historical data, the vibration state assessment unit can perform a preliminary assessment of the equipment's vibration state. When the vibration state index Zd exceeds the preset vibration threshold Z, the system identifies abnormal vibrations in the equipment operation and immediately performs further fault analysis. This process improves the sensitivity of fault detection, ensuring that the equipment receives timely warnings and handling in the early stages of faults, preventing more serious problems from occurring.

[0090] Example 4

[0091] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically: the equipment operating status calculation module includes an equipment current harmonic calculation unit and an equipment acoustic wave characteristic calculation unit;

[0092] The current harmonic calculation unit is used to construct a current harmonic algorithm formula based on the current harmonic component I and the current harmonic standard deviation BI of each current harmonic extracted from the industrial processing equipment. The real-time acquired current harmonic component I and current harmonic standard deviation BI are input into the constructed current harmonic algorithm formula to calculate and obtain the current harmonic index Dl, and to detect the faults of the motor and electrical equipment of the industrial processing equipment.

[0093] The current harmonic index Dl is obtained using the following algorithm formula;

[0094]

[0095] In the formula, I j I represents the j-th current harmonic component. pj a1 represents the average value of the current harmonic components, a2 represents the second weight value, and m represents the number of current harmonic components I in the current signal.

[0096] The equipment acoustic wave feature calculation unit is used to construct an acoustic wave signal algorithm formula based on the acoustic wave energy A of the extracted acoustic wave signal of the industrial processing equipment. The acoustic wave energy A of the real-time extracted acoustic wave signal is input to calculate the equipment acoustic wave index Sb to identify mechanical faults of the industrial processing equipment.

[0097] The acoustic performance index Sb of the equipment is obtained using the following algorithm formula;

[0098]

[0099] In the formula, A k Let A represent the k-th sound wave energy component. pj a3 represents the average value of the sound wave energy, a3 represents the third weight value, P represents the total sound wave energy of the sound wave signal, and e represents the exponential function.

[0100] In this embodiment, the system uses the current harmonic calculation unit in the equipment operation status calculation module to acquire the current harmonic component I and the current harmonic standard deviation BI in real time, and calculates the current harmonic index Dl using the current harmonic algorithm formula. This index accurately reflects the operating status of motors and electrical equipment, and can detect abnormalities and faults in the electrical equipment of industrial processing equipment in a timely manner. Compared with traditional detection methods, this method based on current harmonic analysis greatly improves the accuracy and timeliness of fault detection and reduces the potential risk of equipment failure. The equipment acoustic wave feature calculation unit extracts the acoustic wave energy A of the acoustic wave signal and calculates the equipment acoustic wave index Sb by constructing an acoustic wave signal algorithm formula based on the acoustic wave energy standard deviation BA. This method can accurately identify mechanical faults in industrial processing equipment. The sensitivity of the acoustic wave energy index enables the system to identify potential mechanical faults at an early stage, avoiding the delays caused by insufficient identification sensitivity in traditional methods, and ensuring the stability and safety of equipment operation.

[0101] Example 5

[0102] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 Specifically: the evaluation and optimization module includes a comprehensive scoring unit and an evaluation and optimization unit;

[0103] The comprehensive scoring unit is used to perform dimensionless processing on the obtained vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb, and then correlate them to calculate the equipment fault score Pf, thereby scoring the fault of industrial processing equipment.

[0104] The equipment failure score Pf is obtained using the following algorithm formula;

[0105] Pf=[(w1*Zd)+(w2*Dl)+(w3*Sb)]+W;

[0106] In the formula, w1, w2 and w3 represent the preset weight values ​​of the vibration state index Zd, the current harmonic index Dl and the equipment acoustic index Sb, respectively, and w1+w2+w3=1. The specific parameters are set by the user, and W represents the correction constant.

[0107] The evaluation and optimization unit is used to preset the fault evaluation threshold G and the equipment fault score Pf, and to conduct a secondary comparative evaluation to further analyze the current fault status of the industrial processing equipment. The specific evaluation scheme is as follows:

[0108] When the equipment fault score Pf > the fault assessment threshold G, it indicates that there is an abnormality in the current equipment. At this time, an early warning message is generated. By combining historical data and equipment operation logs, the specific fault type is confirmed and maintenance suggestions are made.

[0109] When the equipment fault score Pf ≤ fault assessment threshold G, it indicates that the current industrial processing equipment is in normal condition. At this time, a second early warning message is generated to perform maintenance on the industrial processing equipment.

[0110] In this embodiment, the system uses the vibration state index Zd, current harmonic index Dl, and equipment acoustic wave index Sb in the comprehensive scoring unit of the evaluation and optimization module to perform dimensionless processing and correlation calculation to obtain the equipment fault score Pf. This comprehensive scoring combines multiple key indicators and effectively assesses the failure probability of industrial processing equipment through user-defined weight values ​​and correction constants. Compared with traditional single-indicator evaluation methods, this comprehensive evaluation improves the accuracy and precision of fault prediction, enabling earlier detection of potential equipment failure signs and facilitating early maintenance and repair measures, thereby reducing production interruptions and losses caused by equipment failures. The evaluation and optimization unit of the evaluation and optimization module performs a secondary comparison evaluation based on the equipment fault score Pf and a preset fault evaluation threshold G. When the equipment fault score exceeds the set threshold, the system generates an immediate warning message, indicating an abnormal situation in the equipment. At this time, combined with historical data and equipment operation logs, the specific fault type can be quickly identified, and corresponding maintenance suggestions can be proposed, effectively reducing production downtime and losses caused by equipment failures. Conversely, when the equipment fault score does not exceed the threshold, the system generates a second-level warning message, indicating that the equipment status is normal, but still recommending routine maintenance to ensure long-term stable operation of the equipment.

[0111] Example 6

[0112] Please see Figure 1 and Figure 2 A method for online monitoring of the operating status of industrial internet devices includes the following steps:

[0113] S1. First, install an integrated sensor group on the industrial processing equipment to collect the operating status data of the industrial processing equipment in real time, and summarize the operating status data of the processing equipment to generate an operating status dataset.

[0114] S2. Then, using LoRa module wireless transmission technology, the device's operating dataset is transmitted to the data processing center. Data transmission and reception are performed through the LoRa gateway. At the data processing center, the data is preprocessed, and key feature parameters are extracted after preprocessing.

[0115] S3. Extract key feature parameters of the vibration spectrum through power density PSD and fast Fourier transform FFT, and construct vibration state algorithm formula. Input the extracted frequency components and standard deviation into the formula to calculate the vibration state index Zd, and compare the vibration state index Zd with the preset vibration threshold Z.

[0116] S4. When an abnormal situation is detected in the industrial processing equipment, the key feature parameters of current harmonics, acoustic wave characteristics and temperature change rate are extracted from the pre-processed operating status dataset, and the current harmonic index Dl and the equipment acoustic wave index Sb are calculated and obtained.

[0117] S5. Finally, the obtained vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb are correlated and calculated to obtain the equipment fault score Pf. Then, the preset fault assessment threshold G and the equipment fault score Pf are compared and evaluated a second time to analyze the current equipment operating status.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. An online monitoring system for the operating status of industrial internet equipment, characterized in that: It includes a data acquisition module, a data transmission and preprocessing module, a vibration calculation module, an equipment operating status calculation module, and an evaluation and optimization module; The data acquisition module collects real-time operating status data of the industrial processing equipment by installing and integrating a group of sensors on the equipment, and then summarizes the operating status data to generate an operating status dataset. The data transmission and preprocessing module is used to transmit the collected operating status dataset to the data processing center and preprocess the operating status dataset. The vibration calculation module extracts key feature parameters from the vibration spectrum based on the preprocessed operating status dataset, calculates and obtains the equipment vibration state index Zd, and performs a preliminary comparison and evaluation analysis of the abnormal conditions of the industrial processing equipment based on the preset vibration threshold Z according to the equipment vibration index. The vibration calculation module includes a vibration calculation unit and a device vibration status assessment unit; The vibration calculation unit is used to construct a vibration state algorithm formula based on the extracted vibration frequency component V and vibration spectrum standard deviation BV. The vibration frequency component V and vibration spectrum standard deviation BV obtained in real time are input into the vibration state algorithm formula to calculate and obtain the vibration state index Zd, and analyze the internal mechanical faults of industrial processing equipment. The vibration state index Zd is obtained using the following algorithm formula; In the formula, V i V represents the i-th vibration frequency component. pj denoted as the average value of the vibration frequency components, a1 represents the first weight value, log represents the logarithmic function, and n represents the total number of vibration frequency components in the vibration spectrum; The equipment operating status calculation module includes an equipment current harmonic calculation unit and an equipment acoustic wave characteristic calculation unit. The current harmonic calculation unit is used to construct a current harmonic algorithm formula based on the current harmonic component I and the current harmonic standard deviation BI of each current harmonic extracted from the industrial processing equipment. The real-time acquired current harmonic component I and current harmonic standard deviation BI are input into the constructed current harmonic algorithm formula to calculate and obtain the current harmonic index Dl, and to detect the faults of the motor and electrical equipment of the industrial processing equipment. The current harmonic index Dl is obtained through the following algorithm formula; In the formula, I j I represents the j-th current harmonic component. pj a1 represents the average value of the current harmonic components, a2 represents the second weighting value, and m represents the number of current harmonic components I in the current signal. The device acoustic feature calculation unit is used to construct an acoustic signal algorithm formula based on the acoustic energy A of the extracted acoustic signal from the industrial processing equipment, and input the acoustic energy A of the real-time extracted acoustic signal into the calculation of the device acoustic index Sb to identify mechanical faults of the industrial processing equipment. The acoustic wave index Sb of the device is obtained through the following algorithm formula; In the formula, A k Let A represent the k-th sound wave energy component. pj a3 represents the average value of the sound wave energy, P represents the total sound wave energy of the sound wave signal, and e represents the exponential function. When the equipment operation status calculation module identifies an abnormal situation in the industrial processing equipment, it extracts key feature parameters such as current harmonics, acoustic wave characteristics, and temperature change rate from the pre-processed operation status dataset, and calculates to obtain the current harmonic index Dl and the equipment acoustic wave index Sb. The evaluation and optimization module is used to perform correlation calculations on the obtained vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb to obtain the equipment fault score Pf. Then, a second comparison evaluation is performed between the preset fault evaluation threshold G and the equipment fault score Pf to analyze the current equipment operating status.

2. The industrial internet equipment operation status online monitoring system according to claim 1, characterized in that: The data acquisition module is used to install and integrate sensor groups on industrial processing equipment to collect real-time operating data of the industrial processing equipment during operation, and to classify and summarize the collected operating data to generate equipment operation dataset. The integrated sensor group includes a vibration sensor, a current sensor, and an acoustic sensor; The equipment operation dataset includes vibration spectrum, current harmonics, and acoustic characteristics.

3. The industrial internet equipment operation status online monitoring system according to claim 2, characterized in that: The data transmission and preprocessing module includes a data transmission unit and a data preprocessing unit; The data transmission unit is used to install a LoRa module on industrial processing equipment and send the collected equipment operation dataset to the data processing center through the LoRa gateway; The data preprocessing unit includes a processing unit and a feature extraction unit; The processing unit is used to smooth the collected device operation dataset by calculating the average value of the data within a sliding window, and to eliminate interference data in the device operation dataset by using a filter. The feature extraction unit is used to extract features from the data in the collected equipment operation dataset to obtain key parameters of the industrial processing equipment during operation. The specific extraction method and extraction results are as follows; The vibration spectrum is processed by power density PSD and fast Fourier transform (FFT) to extract the vibration frequency component V and the vibration spectrum standard deviation BV. The current harmonics are obtained by converting the current signal in the time domain into a frequency domain signal, extracting the current harmonic components I of each current harmonic of the industrial processing equipment, and calculating the standard deviation of the current harmonic components I to obtain the current harmonic standard deviation BI. The acoustic wave characteristics are combined with time and frequency information to analyze the acoustic wave signal, extract the acoustic wave energy A of the acoustic wave signal, and calculate the standard deviation of the acoustic wave energy A to obtain the standard deviation BA of the acoustic wave energy.

4. The industrial internet equipment operation status online monitoring system according to claim 1, characterized in that: The vibration state assessment unit is used to construct a vibration threshold Z based on the average of historical normal vibration data of industrial processing equipment, and then conduct a preliminary comparison and assessment with the obtained vibration state index Zd to analyze the vibration state of the equipment under the current operating state. The specific assessment scheme is as follows: When the vibration state index Zd > the vibration threshold Z, it indicates that the industrial processing equipment is vibrating abnormally during operation, and further analysis and evaluation should be carried out. When the vibration state index Zd ≤ vibration threshold Z, it indicates that the vibration of the current industrial processing equipment is normal and the operating status of the industrial processing equipment is normal.

5. The industrial internet equipment operation status online monitoring system according to claim 1, characterized in that: The evaluation and optimization module includes a comprehensive scoring unit and an evaluation and optimization unit; The comprehensive scoring unit is used to perform dimensionless processing on the obtained vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb, and then correlate them to calculate the equipment fault score Pf, thereby scoring the industrial processing equipment for faults. The equipment fault score Pf is obtained using the following algorithm formula; Pf=[(w1*Zd)+(w2*Dl)+(w3*Sb)]+W; In the formula, w1, w2 and w3 represent the preset weight values ​​of the vibration state index Zd, the current harmonic index Dl and the equipment acoustic index Sb, respectively, and w1+w2+w3=1. The specific parameters are set by the user, and W represents the correction constant.

6. The industrial internet equipment operation status online monitoring system according to claim 5, characterized in that: The evaluation and optimization unit is used to preset the fault evaluation threshold G and the equipment fault score Pf, and perform a secondary comparative evaluation to further analyze the current fault status of the industrial processing equipment. The specific evaluation scheme is as follows: When the equipment fault score Pf > the fault assessment threshold G, it indicates that there is an abnormality in the current equipment. At this time, an early warning message is generated. By combining historical data and equipment operation logs, the specific fault type is confirmed and maintenance suggestions are made. When the equipment fault score Pf ≤ fault assessment threshold G, it indicates that the current industrial processing equipment is in normal condition. At this time, a second early warning message is generated to perform maintenance on the industrial processing equipment.

7. A method for online monitoring of the operating status of industrial internet devices, comprising the online monitoring system for the operating status of industrial internet devices as described in any one of claims 1 to 6, characterized in that: Includes the following steps: S1. First, install an integrated sensor group on the industrial processing equipment to collect the operating status data of the industrial processing equipment in real time, and summarize the operating status data of the processing equipment to generate an operating status dataset. S2. Then, using LoRa module wireless transmission technology, the device's operating dataset is transmitted to the data processing center. Data transmission and reception are performed through the LoRa gateway. At the data processing center, the data is preprocessed, and key feature parameters are extracted after preprocessing. S3. Extract key feature parameters of the vibration spectrum through power density PSD and fast Fourier transform FFT, and construct vibration state algorithm formula. Input the extracted frequency components and standard deviation into the formula to calculate the vibration state index Zd, and compare the vibration state index Zd with the preset vibration threshold Z. S4. When an abnormal situation is detected in the industrial processing equipment, the key feature parameters of current harmonics, acoustic wave characteristics and temperature change rate are extracted from the pre-processed operating status dataset, and the current harmonic index Dl and the equipment acoustic wave index Sb are calculated and obtained. S5. Finally, the obtained vibration state index Zd, current harmonic index Dl, and equipment acoustic index Sb are correlated and calculated to obtain the equipment fault score Pf. Then, the preset fault assessment threshold G and the equipment fault score Pf are compared and evaluated a second time to analyze the current equipment operating status.

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