Equipment operation and maintenance management system based on tobacco processing production line

Abstract

The invention discloses an equipment operation and maintenance management system based on a tobacco processing production line, and relates to the technical field of industrial automation. Equipment operation data are collected in real time by installing a sensor, the collected equipment operation data in different time periods are preprocessed, characteristic parameters in the equipment operation data are extracted, and the characteristic parameters are extracted; based on the extracted characteristic parameters, the change conditions of the characteristic parameters in different time periods are calculated, the overall operation state of the equipment is accurately evaluated through weighted average calculation and state division, and a differentiated maintenance strategy is formulated in combination with risk analysis, so that real-time monitoring and fault early warning of the equipment are realized, the equipment fault frequency is reduced, the maintenance plan is optimized, and the maintenance efficiency is improved. The maintenance cost is reduced, the production efficiency and product quality of a tobacco processing production line are improved, and efficient and stable operation of the production line is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation, and in particular to an equipment operation and maintenance management system based on a tobacco processing production line. Background Art

[0002] Equipment operation and maintenance management of tobacco processing production lines refers to the maintenance and management of various equipment on the production line through systematic methods and tools to ensure its efficient and stable operation. This process includes daily inspection of equipment, fault detection and repair, operation status monitoring, and data analysis and optimization to improve the overall efficiency of the production line and product quality.

[0003] Specifically, the equipment operation and maintenance management system collects equipment operation data in real time through sensors installed on the equipment, and transmits the data to the central control system for analysis. Using data analysis and machine learning algorithms, the system can predict possible equipment failures, issue early warnings, and automatically generate maintenance plans, thereby reducing equipment failure rates, reducing maintenance costs, and ensuring the continuous and stable operation of the production line.

[0004] The shortcomings of the prior art:

[0005] Since the existing tobacco processing production line equipment operation and maintenance management system relies on manual inspections, it is inefficient and has large errors. It is unable to monitor the equipment status in real time and it is difficult to detect faults in a timely manner. In addition, data records are scattered, which is not convenient for data analysis and decision support. These shortcomings lead to frequent equipment failures and high maintenance costs, which seriously affect production efficiency and product quality, making it difficult for the production line to maintain efficient and stable operation. Summary of the invention

[0006] The purpose of the present invention is to provide an equipment operation and maintenance management system based on a tobacco processing production line to overcome the shortcomings of the background technology.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an equipment operation and maintenance management system based on a tobacco processing production line, comprising a data acquisition module, a characteristic parameter extraction module, a calculation module, a state division module and a maintenance management module;

[0008] Data acquisition module: The management system obtains the quantity and type information of all equipment in the tobacco processing production line through the factory management platform, and installs several sensors on the equipment to collect the operation data of the equipment in different time periods in real time through each sensor;

[0009] Characteristic parameter extraction module: pre-process the collected equipment operation data in different time periods, extract characteristic parameters from the equipment operation data, calculate the changes of characteristic parameters in different time periods based on the extracted characteristic parameters, and analyze the change trend of equipment operation status;

[0010] Calculation module: The overall operation status of the equipment is weighted according to the change trend of the equipment operation status in different time periods, and the weighted average calculation is performed. Based on the calculation results, the overall operation status of the equipment is evaluated;

[0011] State classification module: Analyze the overall operating status of the equipment and divide it into different status levels, and divide the equipment in the tobacco processing production line into stable operating status, possible stable operating status and unstable operating status;

[0012] Maintenance management module: further analyze the equipment that may be in a stable operating state, determine the risk of failure events based on the analysis results, and formulate corresponding maintenance strategies.

[0013] In a preferred embodiment, characteristic parameters in the equipment operation data are extracted, including the equipment's vibration frequency fluctuation index and the equipment's overall temperature deviation index in the equipment operation data. Based on the extracted characteristic parameters, the changes in the characteristic parameters in different time periods are calculated to analyze the changing trend of the equipment's operating status.

[0014] In a preferred embodiment, the method for obtaining the vibration frequency fluctuation index is:

[0015] Use vibration sensor to sample frequency The vibration signal of the equipment is collected, the sampling time is T seconds, and the time series data x(n) is obtained, where ,N= ×T is the number of sampling points;

[0016] The collected vibration signal is preprocessed, and the preprocessed signal is subjected to fast Fourier transform to convert the time domain signal into the frequency domain to obtain the spectrum X(k), where k is the frequency index: X(k)= ; In the formula, e and j are constant terms, and the amplitude of the spectrum is calculated to obtain the amplitude spectrum of the frequency component , = ;in, and are the real and imaginary parts of the spectrum X(k) respectively; from the amplitude spectrum Identify the maximum frequency component in and its amplitude ,in Indicates maximum frequency components, calculate the average frequency of the maximum frequency components, = ; Where M is the number of main frequency components, and the vibration frequency fluctuation index is calculated based on the maximum frequency component. The specific calculation expression is: ; In the formula, is the vibration frequency fluctuation index.

[0017] In a preferred embodiment, the method for obtaining the overall temperature deviation index is:

[0018] Set the initial temperature state and the initial covariance matrix , predict the temperature value at the next moment according to the device temperature status at moment t, = ;in, is the estimated value of the temperature at time t, A is the state transfer matrix, the prediction error covariance, = ;in, is the covariance matrix at time t, Q is the process noise covariance, and T is the matrix transpose; calculate the Kalman gain, = ;in, is the Kalman gain value of the covariance moment at time t, H is the measurement matrix, R is the measurement noise covariance, and the state and covariance matrix are updated according to the measurement value. = ; is the updated estimated temperature at time t, is the estimated temperature at time t-1; the overall temperature offset index is calculated based on the updated estimated temperature: Temp = ; Where Temp is the overall temperature offset index.

[0019] In a preferred embodiment, the vibration frequency fluctuation index and the overall temperature offset index are converted into a first eigenvector, and the first eigenvector is used as the input of a machine learning model. The machine learning model uses each group of first eigenvectors to predict the weighted assignment labels of the equipment operation status characteristic parameters in different time periods as the prediction target, and takes minimizing the sum of the prediction errors of the weighted assignment labels of the equipment operation status characteristic parameters in all time periods as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The weighted assignments of the equipment operation status characteristic parameters in different time periods are determined according to the model output results, wherein the machine learning model is a polynomial regression model; the weighted average calculation of the weighted assignments of the equipment operation status characteristic parameters in different time periods is performed, and then the overall operation status coefficient of the equipment is calculated.

[0020] In a preferred embodiment, the overall operating state coefficient of the acquired device is compared and analyzed with a gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the overall operating state coefficient of the acquired device is compared with the first standard threshold and the second standard threshold respectively;

[0021] If the overall operating status coefficient of the equipment is greater than the second standard threshold, the equipment in the tobacco processing production line is classified as a stable operating state and no additional maintenance is required;

[0022] If the overall operating status coefficient of the equipment is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the equipment in the tobacco processing production line is classified as a possible stable operating state for further analysis and preventive maintenance;

[0023] If the overall operating status coefficient of the equipment is less than the first standard threshold, the equipment in the tobacco processing production line is classified as an unstable operating state, and maintenance measures are taken immediately.

[0024] In a preferred embodiment, when the equipment in the tobacco processing production line is classified into a possible stable operating state, that is, the overall operating state coefficient of the equipment generated within a fixed time period is greater than or equal to a first standard threshold and less than or equal to a second standard threshold, the overall operating state coefficient of the equipment that is greater than or equal to the first standard threshold and less than or equal to the second standard threshold in a subsequent fixed time period is collected, and a corresponding data set is established, and the mean and standard deviation of the data set are calculated, and the mean and standard deviation of the data set are analyzed to determine the risk of a failure event.

[0025] In a preferred embodiment, if the mean value of the overall operating state coefficient of the equipment in the data set is greater than or equal to the reference threshold value of the mean value of the overall operating state coefficient of the equipment, and the standard deviation of the overall operating state coefficient of the equipment is less than the reference threshold value of the standard deviation of the overall operating state coefficient of the equipment, then no warning signal is generated, and the corresponding equipment is marked as an extremely low risk level equipment, and regular monitoring continues to be maintained;

[0026] If the mean value of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a third-level warning signal is generated at this time, and the corresponding equipment is marked as a low-risk level equipment, and preventive maintenance is carried out and the monitoring frequency is increased;

[0027] If the mean value of the overall operating status coefficient of the equipment is less than the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a secondary warning signal is generated, and the corresponding equipment is marked as a medium-risk level equipment, and in-depth inspection and repair are arranged immediately;

[0028] If the mean value of the overall operating status coefficient of the equipment is less than the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is less than the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a first-level warning signal is generated and the corresponding equipment is marked as a high-risk level equipment. Regular monitoring continues, maintenance frequency is reduced, and routine inspections are maintained.

[0029] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0030] 1. The present invention collects operating data in different time periods in real time by installing sensors on the equipment, analyzes the changing trend of the equipment operating status by using the characteristic parameter extraction module and the calculation module, and performs weight assignment and weighted average calculation on the characteristic parameters of the equipment operating status based on the polynomial regression model, so as to accurately evaluate the overall operating status of the equipment. It not only improves the accuracy of equipment status evaluation, but also can timely discover potential faults, reduce equipment downtime, optimize maintenance resource allocation, reduce maintenance costs, and improve production efficiency and product quality.

[0031] 2. The present invention divides the equipment operation status into three states: stable operation, possible stable operation and unstable operation through the state classification module, and conducts further risk analysis on the equipment in the possible stable operation state, generates early warning signals and formulates corresponding maintenance strategies. Extremely low-risk equipment continues to be regularly monitored and routinely maintained; low-risk equipment undergoes preventive maintenance and increases the monitoring frequency; medium-risk equipment is arranged for in-depth inspection and repair; high-risk equipment continues to be monitored but the maintenance frequency is reduced. Management strategies for different risk levels ensure that equipment can be maintained and repaired in a timely manner, prevent faults from expanding, and ensure the efficient and stable operation of the production line, thereby achieving long-term safe, stable and efficient operation of the production line and improving the overall efficiency of the production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0033] Figure 1 The present invention is a flow chart of the method.

[0034] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Example 1

[0037] See also Figure 1 , 2 As shown, the equipment operation and maintenance management system based on the tobacco processing production line described in this embodiment includes a data acquisition module, a characteristic parameter extraction module, a calculation module, a state division module and a maintenance management module;

[0038] Data acquisition module: The management system obtains the quantity and type information of all equipment in the tobacco processing production line through the factory management platform, and installs several sensors on the equipment to collect the operation data of the equipment in different time periods in real time through each sensor;

[0039] Characteristic parameter extraction module: pre-process the collected equipment operation data in different time periods, extract characteristic parameters from the equipment operation data, calculate the changes of characteristic parameters in different time periods based on the extracted characteristic parameters, and analyze the change trend of equipment operation status;

[0040] Calculation module: The overall operation status of the equipment is weighted according to the change trend of the equipment operation status in different time periods, and the weighted average calculation is performed. Based on the calculation results, the overall operation status of the equipment is evaluated;

[0041] State classification module: Analyze the overall operating status of the equipment and divide it into different status levels, and divide the equipment in the tobacco processing production line into stable operating status, possible stable operating status and unstable operating status;

[0042] Maintenance management module: further analyze the equipment that may be in a stable operating state, determine the risk of failure events based on the analysis results, and formulate corresponding maintenance strategies.

[0043] Among them, in the data acquisition module, the management system obtains the quantity and type information of all equipment in the tobacco processing production line through the factory management platform, and installs several sensors on the equipment to collect the operation data of the equipment in different time periods in real time through each sensor.

[0044] The management system obtains detailed information of all equipment on the production line through the factory management platform, including the quantity and type of each equipment.

[0045] The factory management platform stores detailed information about all equipment on the production line. The management system can obtain this information through interfaces or database queries. The information includes: equipment model, function, location, quantity, etc. For example, there may be a shredder, dryer, packaging machine, etc. on a tobacco processing production line.

[0046] Install appropriate sensors on each device on the production line to monitor and collect data on the device's operating status. Select appropriate sensors based on the type of device and the parameters that need to be monitored. For example, temperature sensors, pressure sensors, vibration sensors, etc. Install sensors at key locations on each device to ensure accurate collection of device operating data. The installation location should ensure data accuracy and sensor safety.

[0047] Sensors are used to monitor the operating status of equipment in real time and collect data on various key parameters, providing a basis for subsequent data analysis.

[0048] Collect the operation data of the equipment in different time periods in real time through various sensors. Connect all sensors to the data acquisition system. The system can collect, store and process sensor data in real time. Transmit the data collected by the sensors to the central database or management system through wired or wireless networks. Divide the production process into several time periods and collect and record the operation data of the equipment regularly (such as every second or every minute). For example, record temperature, pressure, vibration and other data once a minute.

[0049] Feature parameter extraction module: pre-process the collected equipment operation data in different time periods, extract the feature parameters in the equipment operation data, calculate the changes of the feature parameters in different time periods based on the extracted feature parameters, and analyze the change trend of the equipment operation status.

[0050] Preprocess the collected equipment operation data in different time periods, mainly including:

[0051] Data cleaning: Remove missing values, outliers, and noise. For example, remove unreasonable temperature readings or vibration values.

[0052] Data smoothing: Use moving average, filtering and other methods to smooth data and reduce random fluctuations in the data.

[0053] Data alignment: Ensure that data from different sensors are aligned in time so that subsequent analysis is consistent.

[0054] The characteristic parameters in the equipment operation data are extracted, including the equipment's vibration frequency fluctuation index and the equipment's overall temperature deviation index. Based on the extracted characteristic parameters, the changes in the characteristic parameters in different time periods are calculated to analyze the changing trend of the equipment's operating status.

[0055] Among them, the method for obtaining the vibration frequency fluctuation index is:

[0056] Use vibration sensor to sample frequency The vibration signal of the equipment is collected with a sampling time of T seconds to obtain time series data x(n), where n=0,1,2,…,N−1, N= ×T is the number of sampling points;

[0057] Preprocess the collected vibration signal, including removing DC components and noise;

[0058] Perform fast Fourier transform on the preprocessed signal to convert the time domain signal to the frequency domain and obtain the spectrum X(k), where k is the frequency index: X(k)= ; In the formula, e and j are constant terms, and the amplitude of the spectrum is calculated to obtain the amplitude spectrum of the frequency component , = ;in, and are the real and imaginary parts of the spectrum X(k) respectively; from the amplitude spectrum Identify the maximum frequency component in and its amplitude ,in Indicates maximum frequency components, calculate the average frequency of the maximum frequency components, = ; Where M is the number of main frequency components, and the vibration frequency fluctuation index is calculated based on the maximum frequency component. The specific calculation expression is: ; In the formula, is the vibration frequency fluctuation index.

[0059] The larger the vibration frequency fluctuation index (VFFI), the more unstable the trend of the equipment's operating status. A high VFFI value usually means that the equipment's vibration frequency fluctuates within a large range, which may be caused by looseness, wear or failure of internal components of the equipment. Such fluctuations will not only lead to reduced equipment efficiency, but may also accelerate equipment aging and damage, increasing maintenance and repair costs.

[0060] Specifically, when the vibration frequency fluctuation index of the equipment is large, the operating status of the equipment presents greater uncertainty and volatility. This unstable vibration frequency fluctuation may cause more equipment failures and affect the normal operation of the production line. Operators need to monitor and maintain the equipment more frequently to ensure the continuity of the production line and the stability of product quality.

[0061] In addition, an increase in the vibration frequency fluctuation index of the equipment may indicate a potential risk of equipment failure. When the vibration frequency fluctuation of the equipment exceeds the normal range, it may be due to abnormal wear or failure of certain components inside the equipment. At this time, timely fault diagnosis and preventive maintenance are necessary to avoid sudden failures of the equipment during operation, resulting in production downtime and economic losses.

[0062] The overall temperature deviation index is obtained as follows:

[0063] Set the initial temperature state and the initial covariance matrix , predict the temperature value at the next moment according to the device temperature status at moment t, = ;in, is the estimated value of the temperature at time t, A is the state transfer matrix, the prediction error covariance, = ;in, is the covariance matrix at time t, Q is the process noise covariance, and T is the matrix transpose; calculate the Kalman gain, = ;in, is the Kalman gain value of the covariance moment at time t, H is the measurement matrix, R is the measurement noise covariance, and the state and covariance matrix are updated according to the measurement value. = ; is the updated estimated temperature at time t, is the estimated temperature at time t-1; the overall temperature offset index is calculated based on the updated estimated temperature: Temp = ; Where Temp is the overall temperature offset index.

[0064] The larger the overall temperature excursion index, the more unstable the change trend of the equipment's operating status. A higher temperature excursion index indicates that the equipment's temperature fluctuates greatly, which may be caused by wear and tear of the equipment's internal components, failures, or changes in environmental conditions. Such temperature fluctuations not only affect the equipment's operating efficiency, but may also accelerate the aging and damage of the equipment, increasing the cost of maintenance and repairs.

[0065] Specifically, when the overall temperature excursion index of the equipment is large, the operating status of the equipment presents greater uncertainty and volatility. This unstable temperature change may lead to increased thermal stress on the equipment, causing more failures and downtime. Operators need to monitor and maintain the equipment more frequently to ensure the continuity of the production line and the stability of product quality.

[0066] In addition, an increase in the overall temperature excursion index of the equipment may indicate a potential risk of equipment failure. When the temperature fluctuation of the equipment exceeds the normal range, it may be due to abnormal wear, poor lubrication or poor heat dissipation of certain components inside the equipment. At this time, timely fault diagnosis and preventive maintenance are necessary to avoid sudden failures of the equipment during operation, resulting in production downtime and economic losses.

[0067] Calculation module: The overall operation status of the equipment is weighted according to the changing trend of the equipment operation status in different time periods, and a weighted average calculation is performed. Based on the calculation results, the overall operation status of the equipment is evaluated.

[0068] The vibration frequency fluctuation index and the overall temperature offset index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model predicts the weighted labels of the equipment operation status characteristic parameters in different time periods with each group of first eigenvectors as the prediction target, and minimizes the sum of the prediction errors of the weighted labels of the equipment operation status characteristic parameters in all time periods as the training target. The machine learning model is trained until the sum of the prediction errors reaches convergence, and the model training is stopped. The weighted values ​​of the equipment operation status characteristic parameters in different time periods are determined according to the model output results, wherein the machine learning model is a polynomial regression model. The overall operation status coefficient of the equipment is calculated after the weighted average calculation of the weighted values ​​of the equipment operation status characteristic parameters in different time periods.

[0069] The method for obtaining the weight assignment of the characteristic parameters of the equipment operation status in different time periods is as follows: from the first characteristic vector training data of the trained machine learning model, the corresponding function expression is obtained: ; In the formula, is the output function of the model, is the vibration frequency fluctuation index, Temp is the overall temperature deviation index, and FK is the weight assignment of the characteristic parameters of the equipment operation status in different time periods.

[0070] State division module: Analyze the overall operating status of the equipment, divide it into different status levels, and divide the equipment in the tobacco processing production line into stable operating status, possibly stable operating status and unstable operating status.

[0071] Compare and analyze the obtained overall operation status coefficient of the device with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and compare the obtained overall operation status coefficient of the device with the first standard threshold and the second standard threshold respectively;

[0072] If the overall operating status coefficient of the equipment is greater than the second standard threshold, the equipment in the tobacco processing production line is classified as a stable operating state, indicating that the equipment operates well and does not require additional maintenance;

[0073] If the overall operating status coefficient of the equipment is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the equipment in the tobacco processing production line is classified as a possible stable operating state, indicating that the equipment may have potential problems and requires further analysis and preventive maintenance;

[0074] If the overall operating status coefficient of the equipment is less than the first standard threshold, the equipment in the tobacco processing production line is classified as an unstable operating state, indicating that the equipment is operating abnormally and maintenance measures need to be taken immediately.

[0075] In this embodiment, the management system obtains the quantity and type information of all equipment in the tobacco processing production line through the factory management platform, and installs several sensors on the equipment, and collects the operation data of the equipment in different time periods in real time through each sensor; pre-processes the collected equipment operation data in different time periods, extracts the characteristic parameters in the equipment operation data, calculates the change of the characteristic parameters in different time periods based on the extracted characteristic parameters, and analyzes the change trend of the equipment operation status; the overall operation status of the equipment assigns weights to the characteristic parameters of each equipment according to the change trend of the equipment operation status in different time periods, and performs weighted average calculation, and evaluates the overall operation status of the equipment according to the calculation results; analyzes the overall operation status of the equipment, divides it into different state levels, and divides the equipment in the tobacco processing production line into stable operation state, possible stable operation state and unstable operation state; it can achieve the improvement of equipment operation efficiency, preventive maintenance and fault prediction, accurate state division and maintenance strategy, maintenance cost reduction and data-driven decision support. These benefits jointly improve the operation efficiency, reliability and economic benefits of the tobacco processing production line.

[0076] Example 2

[0077] Maintenance management module: further analyze the equipment that may be in a stable operating state, determine the risk of failure events based on the analysis results, and formulate corresponding maintenance strategies.

[0078] When the equipment in the tobacco processing production line is classified into a possible stable operating state, that is, the overall operating state coefficient of the equipment generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the overall operating state coefficient of the equipment that is greater than or equal to the first standard threshold and less than or equal to the second standard threshold in a subsequent fixed time period is collected, and a corresponding data set is established, and the mean and standard deviation of the data set are calculated. The mean and standard deviation of the data set are analyzed to determine the risk of a failure event.

[0079] If the mean value of the overall operating status coefficient of the equipment in the data set is greater than or equal to the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is less than the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, no warning signal will be generated at this time, and the corresponding equipment will be marked as an extremely low risk level equipment, and regular monitoring will continue to be maintained;

[0080] If the mean value of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a third-level warning signal is generated, and the corresponding equipment is marked as a low-risk level equipment, and preventive maintenance is carried out, the monitoring frequency is increased, and potential problems are analyzed in detail;

[0081] If the mean value of the overall operating status coefficient of the equipment is less than the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a secondary warning signal is generated, and the corresponding equipment is marked as a medium-risk level equipment, and an in-depth inspection and repair are immediately arranged, and replacement of potentially damaged parts is considered;

[0082] If the mean value of the overall operating status coefficient of the equipment is less than the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is less than the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a first-level warning signal is generated and the corresponding equipment is marked as a high-risk level equipment. Regular monitoring will continue, but the maintenance frequency can be reduced and regular inspections will be maintained.

[0083] It should be noted here that the management level of the first-level warning signal is greater than that of the second-level warning signal, and the management level of the second-level warning signal is greater than that of the third-level warning signal. According to different risk levels, corresponding processing measures should be taken to ensure the normal operation of equipment, improve the overall efficiency and stability of the production line, and reduce the risk of failure and maintenance costs.

[0084] In this embodiment, when the equipment in the tobacco processing production line is classified as a possible stable operating state, the failure risk is determined by collecting the equipment's overall operating state coefficient data and analyzing its mean and standard deviation. The equipment is divided into risk levels according to the analysis results, and corresponding warning signals are generated. For extremely low risk level equipment, regular monitoring and routine maintenance continue; for low risk level equipment, a third-level warning signal is generated, preventive maintenance is performed and the monitoring frequency is increased; for medium risk level equipment, a second-level warning signal is generated, and in-depth inspection and repair are immediately arranged; for high risk level equipment, a first-level warning signal is generated, and monitoring continues but the maintenance frequency is reduced. The management level of different warning signals decreases from level one to level three, ensuring the normal operation of the equipment, improving the overall efficiency and stability of the production line, and reducing the risk of failure and maintenance costs.

[0085] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0086] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0087] It should be noted that, in this article, if there are first and second, etc., relational terms are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or still includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0088] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0089] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0091] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0092] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0094] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0095] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An equipment operation and maintenance management system based on a tobacco processing production line, characterized by: It includes data acquisition module, characteristic parameter extraction module, calculation module, state division module and maintenance management module; Data acquisition module: The management system obtains the quantity and type information of all equipment in the tobacco processing production line through the factory management platform, and installs several sensors on the equipment to collect the operation data of the equipment in different time periods in real time through each sensor; Characteristic parameter extraction module: pre-process the collected equipment operation data in different time periods, extract characteristic parameters from the equipment operation data, calculate the changes of characteristic parameters in different time periods based on the extracted characteristic parameters, and analyze the change trend of equipment operation status; Calculation module: The overall operation status of the equipment is weighted according to the change trend of the equipment operation status in different time periods, and the weighted average calculation is performed. Based on the calculation results, the overall operation status of the equipment is evaluated; State classification module: Analyze the overall operating status of the equipment and divide it into different status levels, and divide the equipment in the tobacco processing production line into stable operating status, possible stable operating status and unstable operating status; Maintenance management module: further analyze the equipment that may be in a stable operating state, determine the risk of failure events based on the analysis results, and formulate corresponding maintenance strategies.

2. According to claim 1, the equipment operation and maintenance management system based on the tobacco processing production line is characterized by: The characteristic parameters in the equipment operation data are extracted, including the equipment's vibration frequency fluctuation index and the equipment's overall temperature deviation index. Based on the extracted characteristic parameters, the changes in the characteristic parameters in different time periods are calculated to analyze the changing trend of the equipment's operation status.

3. According to claim 2, an equipment operation and maintenance management system based on a tobacco processing production line is characterized in that: The method for obtaining the vibration frequency fluctuation index is: Use vibration sensor to sample frequency The vibration signal of the equipment is collected, the sampling time is T seconds, and the time series data x(n) is obtained, where ,N= ×T is the number of sampling points; The collected vibration signal is preprocessed, and the preprocessed signal is subjected to fast Fourier transform to convert the time domain signal into the frequency domain to obtain the spectrum X(k), where k is the frequency index: X(k)= ; In the formula, e and j are constant terms, and the amplitude of the spectrum is calculated to obtain the amplitude spectrum of the frequency component , = ;in, and are the real and imaginary parts of the spectrum X(k) respectively; from the amplitude spectrum Identify the maximum frequency component in and its amplitude ,in Indicates maximum frequency components, calculate the average frequency of the maximum frequency components, = ; Where M is the number of main frequency components, and the vibration frequency fluctuation index is calculated based on the maximum frequency component. The specific calculation expression is: ; In the formula, is the vibration frequency fluctuation index.

4. The equipment operation and maintenance management system based on a tobacco processing production line according to claim 3 is characterized in that: The overall temperature deviation index is obtained as follows: Set the initial temperature state and the initial covariance matrix , predict the temperature value at the next moment according to the device temperature status at moment t, = ;in, is the estimated value of the temperature at time t, A is the state transfer matrix, the prediction error covariance, = ;in, is the covariance matrix at time t, Q is the process noise covariance, and T is the matrix transpose; calculate the Kalman gain, = ;in, is the Kalman gain value of the covariance moment at time t, H is the measurement matrix, R is the measurement noise covariance, and the state and covariance matrix are updated according to the measurement value. = ; is the updated estimated temperature at time t, is the estimated temperature at time t-1; the overall temperature offset index is calculated based on the updated estimated temperature: Temp = ; Where Temp is the overall temperature offset index.

5. The equipment operation and maintenance management system based on a tobacco processing production line according to claim 4, characterized in that: The vibration frequency fluctuation index and the overall temperature offset index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model uses each group of first eigenvectors to predict the weighted assignment labels of the equipment operation status characteristic parameters in different time periods as the prediction target, and takes minimizing the sum of the prediction errors of the weighted assignment labels of the equipment operation status characteristic parameters in all time periods as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The weight assignment of the equipment operation status characteristic parameters in different time periods is determined according to the model output results, wherein the machine learning model is a polynomial regression model; the weighted average calculation of the weight assignment of the equipment operation status characteristic parameters in different time periods is performed to calculate the overall operation status coefficient of the equipment.

6. The equipment operation and maintenance management system based on a tobacco processing production line according to claim 5, characterized in that: Compare and analyze the obtained overall operation status coefficient of the device with the gradient standard threshold, the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and compare the obtained overall operation status coefficient of the device with the first standard threshold and the second standard threshold respectively; If the overall operating status coefficient of the equipment is greater than the second standard threshold, the equipment in the tobacco processing production line is classified as a stable operating state and no additional maintenance is required; If the overall operating status coefficient of the equipment is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the equipment in the tobacco processing production line is classified as a possible stable operating state for further analysis and preventive maintenance; If the overall operating status coefficient of the equipment is less than the first standard threshold, the equipment in the tobacco processing production line is classified as an unstable operating state, and maintenance measures are taken immediately.

7. The equipment operation and maintenance management system based on a tobacco processing production line according to claim 6, characterized in that: When the equipment in the tobacco processing production line is classified into a possible stable operating state, that is, the overall operating state coefficient of the equipment generated within a fixed time period is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the overall operating state coefficient of the equipment that is greater than or equal to the first standard threshold and less than or equal to the second standard threshold in a subsequent fixed time period is collected, and a corresponding data set is established, and the mean and standard deviation of the data set are calculated. The mean and standard deviation of the data set are analyzed to determine the risk of a failure event.

8. The equipment operation and maintenance management system based on a tobacco processing production line according to claim 7, characterized in that: If the mean value of the overall operating status coefficient of the equipment in the data set is greater than or equal to the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is less than the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, no warning signal will be generated at this time, and the corresponding equipment will be marked as an extremely low risk level equipment, and regular monitoring will continue to be maintained; If the mean value of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a third-level warning signal is generated at this time, and the corresponding equipment is marked as a low-risk level equipment, and preventive maintenance is carried out and the monitoring frequency is increased; If the mean value of the overall operating status coefficient of the equipment is less than the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is greater than or equal to the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a secondary warning signal is generated, and the corresponding equipment is marked as a medium-risk level equipment, and in-depth inspection and repair are arranged immediately; If the mean value of the overall operating status coefficient of the equipment is less than the reference threshold value of the mean value of the overall operating status coefficient of the equipment, and the standard deviation of the overall operating status coefficient of the equipment is less than the reference threshold value of the standard deviation of the overall operating status coefficient of the equipment, a first-level warning signal is generated and the corresponding equipment is marked as a high-risk level equipment. Regular monitoring continues, maintenance frequency is reduced, and routine inspections are maintained.

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