CN119398742A - An equipment operation and maintenance management system based on tobacco processing production line - Google Patents
An equipment operation and maintenance management system based on tobacco processing production line Download PDFInfo
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Abstract
本发明公开了一种基于烟草加工生产线的设备运维管理系统，涉及工业自动化技术领域，通过安装传感器实时采集设备运行数据，对采集到的不同时间段内的设备运行数据进行预处理，对设备运行数据中的特征参数进行提取，基于所提取的特征参数，计算特征参数在不同时间段内的变化情况，通过加权平均计算和状态划分精准评估设备整体运行状态，并结合风险分析制定差异化维护策略，实现设备的实时监控和故障预警，减少设备故障频率，优化维护计划，降低维护成本，提升烟草加工生产线的生产效率和产品质量，确保生产线的高效、稳定运行。
The invention discloses an equipment operation and maintenance management system based on a tobacco processing production line, which relates to the technical field of industrial automation. The invention collects equipment operation data in real time by installing sensors, pre-processes the collected equipment operation data in different time periods, extracts characteristic parameters from the equipment operation data, calculates changes of the characteristic parameters in different time periods based on the extracted characteristic parameters, accurately evaluates the overall operation status of the equipment through weighted average calculation and state division, and formulates differentiated maintenance strategies in combination with risk analysis, thereby realizing real-time monitoring and fault warning of equipment, reducing the frequency of equipment failures, optimizing maintenance plans, reducing maintenance costs, improving the production efficiency and product quality of the tobacco processing production line, and ensuring efficient and stable operation of the production line.
Description
Technical Field
The invention relates to the technical field of industrial automation, in particular to an equipment operation and maintenance management system based on a tobacco processing production line.
Background
The equipment operation and maintenance management of the tobacco processing production line means that various equipment on the production line is maintained and managed through a systematic method and tools, so that the efficient and stable operation of the equipment is ensured. This process includes routine inspection of the equipment, fault detection and repair, operational status monitoring, and data analysis and optimization to improve the overall efficiency and product quality of the production line.
Specifically, the equipment operation and maintenance management system collects operation data of the equipment in real time through sensors installed on the equipment, and transmits the data to the central control system for analysis. By utilizing data analysis and a machine learning algorithm, the system can predict possible faults of equipment, give out early warning in advance and automatically generate a maintenance plan, thereby reducing equipment fault rate, reducing maintenance cost and ensuring continuous and stable operation of a production line.
The defects in the prior art are that:
Because the existing operation and maintenance management system of the tobacco processing production line equipment relies on manual inspection, the efficiency is low, the error is large, the equipment state cannot be monitored in real time, faults are difficult to find in time, and in addition, data records are dispersed, so that data analysis and decision support are inconvenient, equipment faults are frequent due to the defects, the maintenance cost is high, the production efficiency and the product quality are seriously influenced, and the production line is difficult to maintain to operate efficiently and stably.
Disclosure of Invention
The invention aims to provide a device operation and maintenance management system based on a tobacco processing production line, which aims to solve the defects of the background technology.
In order to achieve the aim, the invention provides the technical scheme that the equipment operation and maintenance management system based on the tobacco processing production line comprises a data acquisition module, a characteristic parameter extraction module, a calculation module, a state division module and a maintenance management module;
the management system acquires the quantity and type information of all the devices in the tobacco processing production line through a factory management platform, a plurality of sensors are arranged on the devices, and the operation data of the devices in different time periods are acquired in real time through the sensors;
The characteristic parameter extraction module is used for preprocessing the collected equipment operation data in different time periods, extracting characteristic parameters in the equipment operation data, calculating the change condition of the characteristic parameters in different time periods based on the extracted characteristic parameters and analyzing the change trend of the equipment operation state;
The calculation module is used for carrying out weight assignment on the characteristic parameters of each device according to the change trend of the device running state in different time periods, carrying out weighted average calculation, and evaluating the overall running state of the device according to the calculation result;
The state dividing module is used for analyzing the overall operation state of the equipment, dividing the overall operation state into different state levels, and dividing the equipment in the tobacco processing production line into a stable operation state, a possible stable operation state and an unstable operation state;
And the maintenance management module is used for further analyzing the equipment in the possible stable running state, determining the risk of the fault event according to the analysis result and formulating a corresponding maintenance strategy.
In a preferred embodiment, the characteristic parameters in the equipment operation data are extracted, including an oscillation frequency fluctuation index of equipment and an overall temperature deviation index of the equipment in the equipment operation data, the change condition of the characteristic parameters in different time periods is calculated based on the extracted characteristic parameters, and the change trend of the equipment operation state is analyzed.
In a preferred embodiment, the method for obtaining the vibration frequency fluctuation index is as follows:
using vibration sensors at sampling frequency Collecting vibration signals of equipment, sampling time length is T seconds, and obtaining time sequence data x (n), wherein,N=X T is the number of sampling points;
preprocessing the acquired vibration signal, performing fast Fourier transform on the preprocessed signal, and converting the time domain signal into a frequency domain to obtain a frequency spectrum X (k), wherein k is a frequency index X (k) = Wherein e and j are constant terms, and the amplitude of the frequency spectrum is calculated to obtain the amplitude spectrum of the frequency component,=Wherein, the method comprises the steps of,AndThe real and imaginary parts of the spectrum X (k), respectively; from amplitude spectraIn (1) identifying the maximum frequency componentAnd its amplitudeWhereinRepresent the firstA maximum frequency component, calculating an average frequency of the maximum frequency component,=Wherein M is the number of main frequency components, and the vibration frequency fluctuation index is calculated according to the maximum frequency component, and the specific calculation expression is as follows: In the formula (I), in the formula (II), Is the vibration frequency fluctuation index.
In a preferred embodiment, the overall temperature shift index is obtained by:
Setting an initial temperature state Initial covariance matrixPredicting the temperature value at the next moment according to the temperature state of the equipment at the moment t,=Wherein, the method comprises the steps of,For the estimated value of the temperature at time t, a is the state transition matrix, the prediction error covariance,=Wherein, the method comprises the steps of,For the covariance matrix at time T, Q is the process noise covariance, T is the matrix transpose, calculate the Kalman gain,=Wherein, the method comprises the steps of,The Kalman gain value of the covariance moment at the moment t, H is a measurement matrix, R is measurement noise covariance, the state and covariance matrix are updated according to the measurement values,=;For the updated temperature estimate at time t,Calculating the whole temperature deviation index according to the updated temperature estimated value, wherein the whole temperature deviation index is Temp=Wherein Temp is the global temperature shift index.
In a preferred embodiment, the vibration frequency fluctuation index and the integral temperature deviation index are converted into first feature vectors, the first feature vectors are used as inputs of a machine learning model, the machine learning model uses weight assignment labels of the equipment operation state feature parameters in different time periods of each group of first feature vectors as prediction targets, the sum of prediction errors of the weight assignment labels of the equipment operation state feature parameters in all time periods is minimized as a training target, the machine learning model is trained until the sum of the prediction errors reaches convergence, model training is stopped, weight assignment of the equipment operation state feature parameters in different time periods is determined according to model output results, the machine learning model is a polynomial regression model, and the integral operation state coefficient of the equipment is calculated after weighted average calculation of the weight assignment of the equipment operation state feature parameters in different time periods.
In a preferred embodiment, comparing the obtained overall operation state coefficient of the device with a gradient standard threshold, wherein the gradient standard threshold comprises a first standard threshold and a second standard threshold, the first standard threshold is smaller than the second standard threshold, and comparing the obtained overall operation state coefficient of the device with the first standard threshold and the second standard threshold respectively;
if the integral running state coefficient of the equipment is larger than a second standard threshold value, dividing the equipment in the tobacco processing production line into a stable running state, and needing no additional maintenance;
if the integral running state coefficient of the equipment is larger than or equal to a first standard threshold value and smaller than or equal to a second standard threshold value, dividing the equipment in the tobacco processing production line into possible stable running states, and further analyzing and maintaining prophylactically;
If the integral running state coefficient of the equipment is smaller than a first standard threshold value, dividing the equipment in the tobacco processing production line into unstable running states, and immediately taking maintenance measures.
In a preferred embodiment, when the equipment in the tobacco processing production line is divided into possible stable operation states, that is, the overall operation state coefficient of the equipment generated in a fixed time period is greater than or equal to a first standard threshold value and less than or equal to a second standard threshold value, the overall operation state coefficient of the equipment which is greater than or equal to the first standard threshold value and less than or equal to the second standard threshold value in a subsequent fixed time period is collected, a corresponding data set is established, the mean value and standard deviation of the data set are calculated, and the mean value and standard deviation of the data set are analyzed to determine the risk of occurrence of a fault event.
In a preferred embodiment, if the average value of the overall operation state coefficients of the devices in the data set is greater than or equal to the reference threshold value of the average value of the overall operation state coefficients of the devices, and the standard deviation of the overall operation state coefficients of the devices is smaller than the reference threshold value of the standard deviation of the overall operation state coefficients of the devices, no early warning signal is generated at the moment, the corresponding devices are marked as devices with extremely low risk levels, regular monitoring is continued, and regular maintenance is maintained;
If the average value of the overall operation state coefficients of the equipment is larger than or equal to a reference threshold value of the average value of the overall operation state coefficients of the equipment, and the standard deviation of the overall operation state coefficients of the equipment is larger than or equal to the reference threshold value of the standard deviation of the overall operation state coefficients of the equipment, generating three-level early warning signals at the moment, marking the corresponding equipment as low-risk-level equipment, performing preventive maintenance, and increasing the monitoring frequency;
If the average value of the overall operation state coefficients of the equipment is smaller than the reference threshold value of the average value of the overall operation state coefficients of the equipment, and the standard deviation of the overall operation state coefficients of the equipment is larger than or equal to the reference threshold value of the standard deviation of the overall operation state coefficients of the equipment, generating a secondary early warning signal, marking the corresponding equipment as equipment with a medium risk grade, and immediately arranging deep inspection and repair;
if the average value of the overall operation state coefficients of the equipment is smaller than the reference threshold value of the average value of the overall operation state coefficients of the equipment, and the standard deviation of the overall operation state coefficients of the equipment is smaller than the reference threshold value of the standard deviation of the overall operation state coefficients of the equipment, a first-level early warning signal is generated at the moment, the corresponding equipment is marked as high-risk-level equipment, regular monitoring is continued, maintenance frequency is reduced, and routine inspection is maintained.
In the technical scheme, the invention has the technical effects and advantages that:
1. According to the invention, the sensor is arranged on the equipment to collect the operation data in different time periods in real time, the characteristic parameter extraction module and the calculation module are utilized to analyze the change trend of the operation state of the equipment, and the weight assignment and the weighted average calculation are carried out on the characteristic parameters of the operation state of the equipment based on the polynomial regression model, so that the overall operation state of the equipment is accurately estimated. The method not only improves the accuracy of equipment state evaluation, but also can discover potential faults in time, reduces equipment downtime, optimizes maintenance resource allocation, reduces maintenance cost, and improves production efficiency and product quality.
2. According to the invention, the running state of the equipment is divided into three states of stable running, possible stable running and unstable running through the state dividing module, further risk analysis is carried out on the equipment in the possible stable running state, an early warning signal is generated, and a corresponding maintenance strategy is formulated. The extremely low risk equipment continues to monitor and maintain regularly, the low risk equipment performs preventive maintenance and increases the monitoring frequency, the medium risk equipment is arranged to be inspected and repaired deeply, and the high risk equipment reduces the maintenance frequency while continuing to monitor. The management strategies of different risk levels ensure that equipment can be timely maintained and repaired, prevent faults from being expanded, ensure high-efficiency and stable operation of the production line, thereby realizing long-term safe, stable and high-efficiency operation of the production line and improving the overall benefit of the production line.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for those skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a block diagram of a system according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1 and 2, the system for managing equipment operation and maintenance based on a tobacco processing production line according to the present embodiment includes a data acquisition module, a feature parameter extraction module, a calculation module, a status dividing module, and a maintenance management module;
the management system acquires the quantity and type information of all the devices in the tobacco processing production line through a factory management platform, a plurality of sensors are arranged on the devices, and the operation data of the devices in different time periods are acquired in real time through the sensors;
The characteristic parameter extraction module is used for preprocessing the collected equipment operation data in different time periods, extracting characteristic parameters in the equipment operation data, calculating the change condition of the characteristic parameters in different time periods based on the extracted characteristic parameters and analyzing the change trend of the equipment operation state;
The calculation module is used for carrying out weight assignment on the characteristic parameters of each device according to the change trend of the device running state in different time periods, carrying out weighted average calculation, and evaluating the overall running state of the device according to the calculation result;
The state dividing module is used for analyzing the overall operation state of the equipment, dividing the overall operation state into different state levels, and dividing the equipment in the tobacco processing production line into a stable operation state, a possible stable operation state and an unstable operation state;
And the maintenance management module is used for further analyzing the equipment in the possible stable running state, determining the risk of the fault event according to the analysis result and formulating a corresponding maintenance strategy.
In the data acquisition module, a management system acquires the quantity and type information of all equipment in a tobacco processing production line through a factory management platform, a plurality of sensors are arranged on the equipment, and operation data of the equipment in different time periods are acquired in real time through the sensors.
The management system obtains detailed information of all devices on the production line through the factory management platform, wherein the detailed information comprises the number and the type of each device.
The factory management platform stores detailed information of all devices of the production line. The management system may obtain this information through an interface or database query. The information includes the model number, function, location, number, etc. of the devices. For example, there may be shredders, dryers, packaging machines, etc. on a tobacco processing line.
Suitable sensors are installed on each device on the production line to monitor and collect data on the operating conditions of the device. According to the type of the equipment and the parameters to be monitored, a proper sensor is selected. Such as temperature sensors, pressure sensors, vibration sensors, etc. Sensors are installed at strategic locations on each device to ensure accurate collection of operational data for the device. The installation location should ensure the accuracy of the data and the safety of the sensor.
The running state of the equipment is monitored in real time through the sensor, various key parameter data are collected, and a basis is provided for subsequent data analysis.
And acquiring the operation data of the equipment in different time periods in real time through each sensor. All sensors are connected to the data acquisition system. The system is capable of collecting, storing and processing sensor data in real time. The data collected by the sensors are transmitted to a central database or management system through a wired or wireless network. The production process is divided into a plurality of time periods, and the operation data of the equipment are collected and recorded periodically (such as every second and every minute). For example, data such as temperature, pressure, vibration, etc. are recorded once per minute.
The characteristic parameter extraction module is used for preprocessing the collected equipment operation data in different time periods, extracting the characteristic parameters in the equipment operation data, calculating the change condition of the characteristic parameters in different time periods based on the extracted characteristic parameters and analyzing the change trend of the equipment operation state.
Preprocessing the collected equipment operation data in different time periods, wherein the preprocessing mainly comprises the following steps of:
Data cleaning, namely removing missing values, abnormal values and noise. For example, rejecting unreasonable temperature readings or vibration values.
And smoothing the data by using methods such as moving average, filtering and the like to reduce random fluctuation in the data.
Data alignment-ensuring that the data of different sensors are aligned in time so that subsequent analysis is consistent.
Extracting characteristic parameters in the equipment operation data, including vibration frequency fluctuation indexes of equipment and overall temperature deviation indexes of the equipment in the equipment operation data, calculating the change condition of the characteristic parameters in different time periods based on the extracted characteristic parameters, and analyzing the change trend of the equipment operation state.
The method for acquiring the vibration frequency fluctuation index comprises the following steps:
using vibration sensors at sampling frequency The vibration signal of the device is acquired, the sampling duration is T seconds, and time series data x (N) is obtained, wherein n=0, 1,2,..X T is the number of sampling points;
preprocessing the acquired vibration signal, including removing direct current components and noise;
Performing fast fourier transform on the preprocessed signal, and converting the time domain signal into a frequency domain to obtain a frequency spectrum X (k), wherein k is a frequency index X (k) = Wherein e and j are constant terms, and the amplitude of the frequency spectrum is calculated to obtain the amplitude spectrum of the frequency component,=Wherein, the method comprises the steps of,AndThe real and imaginary parts of the spectrum X (k), respectively; from amplitude spectraIn (1) identifying the maximum frequency componentAnd its amplitudeWhereinRepresent the firstA maximum frequency component, calculating an average frequency of the maximum frequency component,=Wherein M is the number of main frequency components, and the vibration frequency fluctuation index is calculated according to the maximum frequency component, and the specific calculation expression is as follows: In the formula (I), in the formula (II), Is the vibration frequency fluctuation index.
The larger the Vibration Frequency Fluctuation Index (VFFI) is, the more unstable the change trend of the running state of the equipment is. A high VFFI value generally means that the vibration frequency of the device fluctuates over a large range, which may be caused by loosening, wear or malfunction of the internal components of the device. Such fluctuations not only lead to reduced efficiency of the equipment, but may also accelerate degradation and damage of the equipment, increasing maintenance and repair costs.
In particular, when the vibration frequency fluctuation index of the apparatus is large, the operation state of the apparatus exhibits large uncertainty and fluctuation. Such unstable vibration frequency fluctuations may cause more equipment failures, affecting the normal operation of the production line. Operators need to monitor and maintain equipment more frequently to ensure continuity of the production line and stability of product quality.
Furthermore, an increase in the device vibration frequency fluctuation index may be indicative of a potential device failure risk. When the vibration frequency of the device fluctuates beyond the normal range, abnormal wear or malfunction may occur due to some parts inside the device. At this time, it is necessary to perform fault diagnosis and preventive maintenance in time to avoid sudden faults of the equipment during operation, resulting in production downtime and economic loss.
The method for obtaining the integral temperature deviation index comprises the following steps:
Setting an initial temperature state Initial covariance matrixPredicting the temperature value at the next moment according to the temperature state of the equipment at the moment t,=Wherein, the method comprises the steps of,For the estimated value of the temperature at time t, a is the state transition matrix, the prediction error covariance,=Wherein, the method comprises the steps of,For the covariance matrix at time T, Q is the process noise covariance, T is the matrix transpose, calculate the Kalman gain,=Wherein, the method comprises the steps of,The Kalman gain value of the covariance moment at the moment t, H is a measurement matrix, R is measurement noise covariance, the state and covariance matrix are updated according to the measurement values,=;For the updated temperature estimate at time t,Calculating the whole temperature deviation index according to the updated temperature estimated value, wherein the whole temperature deviation index is Temp=Wherein Temp is the global temperature shift index.
The larger the overall temperature shift index, the more unstable the trend of the equipment operation state. A higher temperature excursion index indicates a greater temperature fluctuation of the device, which may be due to wear, malfunction or changes in environmental conditions of the internal components of the device. Such temperature fluctuations not only affect the operating efficiency of the equipment, but may also accelerate the aging and damage of the equipment, increasing the cost of maintenance and repair.
Specifically, when the overall temperature shift index of the apparatus is large, the operation state of the apparatus exhibits large uncertainty and fluctuation. Such unstable temperature changes may lead to increased thermal stresses in the equipment, causing more malfunctions and downtime. Operators need to monitor and maintain equipment more frequently to ensure continuity of the production line and stability of product quality.
Furthermore, an increase in the overall temperature excursion index of the device may be indicative of a potential risk of device failure. When the temperature fluctuation of the equipment exceeds the normal range, the problems of abnormal wear, poor lubrication or poor heat dissipation of certain parts in the equipment can occur. At this time, it is necessary to perform fault diagnosis and preventive maintenance in time to avoid sudden faults of the equipment during operation, resulting in production downtime and economic loss.
And the calculation module is used for carrying out weight assignment on the characteristic parameters of each device according to the change trend of the device running state in different time periods, carrying out weighted average calculation, and evaluating the overall running state of the device according to the calculation result.
Converting the vibration frequency fluctuation index and the integral temperature deviation index into first feature vectors, taking the first feature vectors as input of a machine learning model, taking a weight assignment label of the equipment operation state feature parameters in different time periods of prediction of each group of first feature vectors as a prediction target, taking the sum of prediction errors of the weight assignment labels of the equipment operation state feature parameters in all time periods as a training target, training the machine learning model until the sum of the prediction errors reaches convergence, stopping model training, and determining weight assignment of the equipment operation state feature parameters in different time periods according to model output results, wherein the machine learning model is a polynomial regression model. And calculating the overall running state coefficient of the equipment after carrying out weighted average calculation on the weight assignment of the running state characteristic parameters of the equipment in different time periods.
The method for acquiring the weight assignment of the equipment running state characteristic parameters in different time periods comprises the steps of acquiring corresponding function expressions from first characteristic vector training data of a machine learning model after training: In the formula (I), in the formula (II), Is an output function of the model and,For the vibration frequency fluctuation index, temp is the integral temperature deviation index, and FK is the weight assignment of the equipment operation state characteristic parameters in different time periods.
And the state dividing module is used for analyzing the whole operation state of the equipment, dividing the whole operation state into different state levels, and dividing the equipment in the tobacco processing production line into a stable operation state, a possible stable operation state and an unstable operation state.
Comparing and analyzing the acquired overall operation state coefficient of the equipment with a gradient standard threshold, wherein the gradient standard threshold comprises a first standard threshold and a second standard threshold, the first standard threshold is smaller than the second standard threshold, and comparing the acquired overall operation state coefficient of the equipment with the first standard threshold and the second standard threshold respectively;
If the integral running state coefficient of the equipment is larger than a second standard threshold value, dividing the equipment in the tobacco processing production line into a stable running state, wherein the equipment is good in running and does not need additional maintenance;
If the overall running state coefficient of the equipment is greater than or equal to a first standard threshold value and less than or equal to a second standard threshold value, dividing the equipment in the tobacco processing production line into possible stable running states, wherein the possible stable running states indicate that the equipment has potential problems and needs further analysis and preventive maintenance;
If the integral running state coefficient of the equipment is smaller than a first standard threshold value, dividing the equipment in the tobacco processing production line into unstable running states, indicating that the equipment runs abnormally, and immediately taking maintenance measures.
In the embodiment, a management system acquires the quantity and type information of all equipment in a tobacco processing production line through a factory management platform, installs a plurality of sensors on the equipment, acquires operation data of the equipment in different time periods in real time through the sensors, pre-processes the acquired equipment operation data in the different time periods, extracts characteristic parameters in the equipment operation data, calculates the change condition of the characteristic parameters in different time periods based on the extracted characteristic parameters, analyzes the change trend of the equipment operation state, carries out weight assignment on the characteristic parameters of the equipment in the whole operation state according to the change trend of the equipment operation state in the different time periods, carries out weighted average calculation, evaluates the whole operation state of the equipment according to the calculation result, analyzes the whole operation state of the equipment, divides the equipment in the tobacco processing production line into different state levels, divides the equipment in the stable operation state, and possibly stable operation state, and can realize the improvement of the equipment operation efficiency, preventive maintenance and fault prediction, accurate state division and decision strategy, maintenance and the support of maintenance cost and data driving. These benefits together promote the operational efficiency, reliability and economic benefits of the tobacco processing line.
Example 2
And the maintenance management module is used for further analyzing the equipment in the possible stable running state, determining the risk of the fault event according to the analysis result and formulating a corresponding maintenance strategy.
When equipment in a tobacco processing production line is divided into possible stable operation states, namely the integral operation state coefficient of the equipment generated in a fixed time period is larger than or equal to a first standard threshold value and smaller than or equal to a second standard threshold value, collecting the integral operation state coefficient of the equipment which is larger than or equal to the first standard threshold value and smaller than or equal to the second standard threshold value in a subsequent fixed time period, establishing a corresponding data set, calculating the mean value and standard deviation of the data set, analyzing the mean value and standard deviation of the data set, and determining the risk of occurrence of a fault event.
If the average value of the overall operation state coefficients of the equipment in the data set is larger than or equal to the reference threshold value of the average value of the overall operation state coefficients of the equipment, and the standard deviation of the overall operation state coefficients of the equipment is smaller than the reference threshold value of the standard deviation of the overall operation state coefficients of the equipment, no early warning signal is generated at the moment, the corresponding equipment is marked as equipment with extremely low risk level, regular monitoring is continued, and routine maintenance is kept;
If the average value of the overall operation state coefficients of the equipment is larger than or equal to a reference threshold value of the average value of the overall operation state coefficients of the equipment, and the standard deviation of the overall operation state coefficients of the equipment is larger than or equal to the reference threshold value of the standard deviation of the overall operation state coefficients of the equipment, generating three-level early warning signals at the moment, marking the corresponding equipment as low-risk-level equipment, performing preventive maintenance, increasing the monitoring frequency, and analyzing the potential problems in detail;
If the average value of the overall operation state coefficients of the equipment is smaller than the reference threshold value of the average value of the overall operation state coefficients of the equipment, and the standard deviation of the overall operation state coefficients of the equipment is larger than or equal to the reference threshold value of the standard deviation of the overall operation state coefficients of the equipment, generating a secondary early warning signal, marking the corresponding equipment as equipment with a medium risk grade, immediately arranging deep inspection and repair, and considering replacement of possibly damaged parts;
If the average value of the overall operation state coefficients of the equipment is smaller than the reference threshold value of the average value of the overall operation state coefficients of the equipment, and the standard deviation of the overall operation state coefficients of the equipment is smaller than the reference threshold value of the standard deviation of the overall operation state coefficients of the equipment, a first-level early warning signal is generated at the moment, the corresponding equipment is marked as high-risk-level equipment, and the equipment is continuously monitored at regular intervals, but the maintenance frequency can be reduced, and the routine inspection is kept.
It is to be noted that, the management level of the first-level early warning signal is greater than that of the second-level early warning signal, and the management level of the second-level early warning signal is greater than that of the third-level early warning signal, so as to take corresponding treatment measures aiming at different risk levels, ensure the normal operation of the equipment, improve the overall efficiency and stability of the production line, and reduce the fault risk and maintenance cost.
In this embodiment, when equipment in the tobacco processing production line is classified into a possible stable operation state, failure risk is determined by collecting overall operation state coefficient data of the equipment and analyzing a mean value and a standard deviation thereof, risk classification is performed on the equipment according to an analysis result, and a corresponding early warning signal is generated. The method comprises the steps of continuously and regularly monitoring and maintaining the equipment with extremely low risk level, generating three-level early warning signals for low risk level equipment, performing preventive maintenance and increasing the monitoring frequency, generating two-level early warning signals for medium risk level equipment, immediately arranging deep inspection and repair, generating one-level early warning signals for high risk level equipment, and continuously monitoring and reducing the maintenance frequency. The management level of different early warning signals is reduced from one level to three levels in sequence, normal operation of equipment is ensured, the overall efficiency and stability of a production line are improved, and meanwhile, the fault risk and maintenance cost are reduced.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the invention, which is defined by the appended claims.
It is noted that relational terms such as first and second, and the like, if any, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of 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.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. The storage medium includes a U disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
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| CN119915542A (en) * | 2025-04-02 | 2025-05-02 | 知行(丽水)精密轴承有限公司 | Bearing processing equipment operation quality detection system and method based on data analysis |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN119915542A (en) * | 2025-04-02 | 2025-05-02 | 知行(丽水)精密轴承有限公司 | Bearing processing equipment operation quality detection system and method based on data analysis |
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