CN116976862B - A factory equipment information management system and method - Google Patents
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Abstract
本发明涉及设备信息化领域，尤其涉及一种工厂设备信息化管理系统及方法，包括通过传感器和和监测设施对工厂设备数据信息进行采集，同时获取设备相关使用、维修与养护记录的数据信息；对收集到的工厂设备的数据信息预处理，将数据信息分类，计算彼此间相关联权重；通过对设备及其相关数据的分析，构建设备故障预测神经网络模型，实时监控工厂设备的运行状态，并生成相应的报警信号；根据分析得到的工厂设备故障情况，并结合维修时间和故障率进行综合评判，得到综合参量值再进行统筹管理。本发明可以实时监控和管理设备，优化生产计划和资源分配，提高生产效率和产能利用率，从而提高工厂的竞争力和经济效益。
The invention relates to the field of equipment informatization, and in particular to a factory equipment information management system and method, which includes collecting factory equipment data information through sensors and monitoring facilities, and at the same time obtaining data information related to equipment use, repair and maintenance records; Preprocess the collected data information of factory equipment, classify the data information, and calculate the associated weights; through the analysis of the equipment and its related data, build an equipment failure prediction neural network model to monitor the operating status of the factory equipment in real time. And generate corresponding alarm signals; conduct a comprehensive evaluation based on the analyzed factory equipment failure conditions, combined with maintenance time and failure rate, and obtain comprehensive parameter values for overall management. The invention can monitor and manage equipment in real time, optimize production plans and resource allocation, improve production efficiency and capacity utilization, thereby improving the competitiveness and economic benefits of the factory.
Description
Technical Field
The invention relates to the field of equipment informatization, in particular to a factory equipment informatization management system and method.
Background
In conventional factory production, the management and maintenance of equipment is typically based on manual operations and paper logging. The method has the problems of opaque information, low efficiency, difficulty in real-time monitoring and the like, and limits the efficiency and quality of factory production.
With the rapid development of information technology, the application of the industrial Internet and the Internet of things is mature gradually, and a new opportunity is provided for the management of factory equipment. Through the connected equipment, the remote monitoring and management of the equipment can be realized, and factory management personnel can monitor the state and the running condition of the equipment at any time and any place through the Internet and discover and solve the problems in time.
However, there are some problems in the factory equipment informatization management systems and methods on the market at present:
the informatization management of the factory equipment involves a plurality of data sources and modules, but the integration and the data consistency between the data sources and the modules are poor; the data processing process is complex and unclear, so that the data analysis is inaccurate; the system lacks powerful intelligent application functions, and cannot utilize data to perform fault prediction, performance optimization, maintenance strategies and other works.
Disclosure of Invention
The invention provides a factory equipment informatization management system and method, which aim to improve data processing and analysis capacity, identify potential optimization points and improve production efficiency and productivity utilization rate; forming intelligent integrated application functions, and predicting equipment faults, generating maintenance strategies and the like through processed data; different modules in the informationized management system can be integrated with high efficiency, so that data transmission and cooperative work among the modules are smoother, and data consistency and accuracy are ensured. The invention can monitor and manage equipment in real time, optimize production plan and resource allocation, and improve production efficiency and productivity utilization rate, thereby improving the competitiveness and economic benefit of factories.
The technical scheme of the invention is as follows:
an informatization management system and method for factory equipment specifically comprises the following steps:
step 1, acquiring data information of factory equipment through a sensor and a monitoring facility, and acquiring data information of relevant use, maintenance and maintenance records of the equipment;
step 2, preprocessing the collected data information of the factory equipment, classifying the data information, and calculating the associated weight;
step 3, constructing an equipment failure prediction neural network model through analyzing equipment and related data thereof, monitoring the running state of the factory equipment in real time, and generating a corresponding alarm signal;
and step 4, comprehensively judging according to the analyzed fault condition of the factory equipment and combining the maintenance time and the fault rate to obtain a comprehensive parameter value, and then carrying out overall management.
Further, the step 2 specifically includes:
classifying the plant equipment information obtained after preprocessing, calculating the correlation weights among the plant equipment information, and obtaining a correlation weight set of equipment information sets under each classification, including the correlation weight of the equipment information sets under the energy consumption classificationAssociated weights for security partition device information setsAssociated weights of automation degree dividing device information setsAssociated weights for capacity partitioning equipment information setsThe method comprises the steps of carrying out a first treatment on the surface of the And fine-tuning the obtained associated weight set, and then performing fusion calculation.
Further, the step 3 specifically includes:
based on the obtained association weight among the devices, performing fault prediction according to the preprocessed device data information, and establishing a device fault prediction neural network model, wherein the device fault prediction neural network model comprises an input layer, a convolution layer, a pooling layer, a prediction layer, an evaluation layer, an optimization layer, a full connection layer and an output layer.
Further, the full connection layer outputs the predicted result according to the device data information, and then transmits the predicted result to the output layer, and the output layer outputs the final predicted device fault condition, wherein the specific process is as follows:
；
wherein,representing an output of the output layer;representing the weight value;representing the bias of the output layer;and representing the output result of the optimization layer.
Further, the step 4 specifically includes:
is provided with a type of preset maintenance timeSecond class preset maintenance timeAnd (2) andthe method comprises the steps of carrying out a first treatment on the surface of the Maintenance time for the acquired equipmentRespectively withAnd (3) withComparing; the fault condition of the equipment can be divided into key faults, serious faults and general faults; the specific process is as follows:
in the comparison result of the maintenance time, the first processing unit judges the maintenance timeBelongs to key faults; meanwhile, the comparison result of the maintenance time meets the following condition；
In the second type of maintenance time comparison result, the first processing unit judges the maintenance timeBelongs to serious faults; meanwhile, the comparison result of the second class maintenance time meets the requirement；
In the comparison result of the three types of maintenance time, the first processing unit judges the maintenance timeBelongs to general faults; meanwhile, the comparison results of three types of maintenance time meet；
Calculating the failure rate of the equipment according to the acquired failure times of the equipment in the appointed time，，Wherein, the method comprises the steps of, wherein,representing the number of times a device fails in a certain failure situation within a specified time frame;indicating the total run time of the device within the same time frame.
Further, according to the failure rate of the equipmentTo determine the maintenance frequency of the equipment, the maintenance frequency including a higher maintenance frequency, a medium maintenance frequency, a lower maintenance frequency.
Further, according to the failure rateTo loss of productivityAnd adjusting, and then calculating the comprehensive parameter value by combining the maintenance frequency.
An informatization management system of factory equipment comprises the following contents:
the system comprises an information collection module, an information preprocessing module, an equipment association module, a fault analysis module, a maintenance planning module and an equipment overall planning module;
the information collection module is used for collecting data information recorded by the sensors and daily operation of the equipment and transmitting the data information to the equipment overall module and the information preprocessing module;
the information preprocessing module is used for preprocessing the equipment information acquired by the information collecting module to obtain preprocessed equipment data information, and transmitting the preprocessed equipment data information to the equipment association module and the equipment overall module in a data transmission mode;
the equipment association module is used for processing the preprocessed equipment information to obtain association weights among the equipment, and transmitting the association weights to the fault analysis module, the maintenance planning module and the equipment overall planning module through data;
the fault analysis module is used for analyzing the fault condition of the factory equipment to obtain the fault result of the equipment, generating an alarm signal and transmitting the result to the maintenance planning module and the equipment overall planning module;
the maintenance planning module is used for carrying out maintenance protection planning arrangement according to the analysis result of equipment faults and combining maintenance frequency and production capacity loss, and transmitting the maintenance protection planning arrangement to the equipment overall planning module through data; the maintenance planning module comprises a fault unit and a capacity unit;
the fault unit is used for obtaining maintenance frequency according to the maintenance time and the fault rate and providing a basis for obtaining the comprehensive parameter value;
the productivity unit calculates a comprehensive parameter value according to the maintenance frequency and the production capacity loss;
the equipment overall management module is used for overall management of all the modules in the system, monitoring the state and health condition of the equipment, carrying out maintenance task allocation and processing data transmission and message exchange among the equipment.
The beneficial effects are that:
1. according to the invention, the association weight among the devices is obtained through calculation, so that the dependency relationship and the influence degree among the devices can be determined, when one device fails, other devices related to the device can be checked and maintained in time according to the association weight, so that the fault diffusion or the chain reaction is prevented, the root cause of the fault is determined, and the repair and the recovery are performed more quickly; the influence degree of other equipment can be evaluated through the association weight, so that the risk management strategy and disaster recovery plans can be formulated, and potential production and business risks can be reduced.
2. The potential fault risk can be captured in time through the equipment fault prediction neural network model, an alarm is sent out in advance, preventive maintenance measures are taken, and loss of equipment faults to the working process is avoided; the severe damage of the equipment can be avoided, and the maintenance cost is reduced, so that the maintenance plan is reasonably arranged, the maintenance efficiency is improved, and the downtime is reduced; through predictive analysis modeling of equipment faults, a deep foundation is laid for the management process of equipment, decisions are made based on accurate data and information, and the accuracy and efficiency of equipment management are improved.
3. The invention can help to determine the maintenance strategy of the equipment by calculating the comprehensive parameter value. Through comprehensive evaluation of maintenance frequency and production capacity loss, the maintenance period and maintenance content of the equipment can be determined, so that preventive maintenance is realized, sudden faults and production interruption are avoided, and the service life of the equipment is prolonged to the greatest extent; meanwhile, the comprehensive parameter value provides an objective evaluation index for equipment management, and the effects of different equipment management schemes can be evaluated through analysis of the comprehensive parameter value, and the optimal management strategy is selected so as to achieve the aims of reducing cost and improving production efficiency and quality.
4. The invention is characterized in that the data is collected and represented in a matrix form, which is applicable to different types of data, is helpful for simplifying the data processing and analysis processes, and can accelerate the processes of executing data processing and modeling algorithm by utilizing the matrix; potential features in the data can be better revealed, and accuracy and efficiency are improved for subsequent information classification.
Drawings
FIG. 1 is a flow chart of a method for informationized management of plant equipment according to the present invention;
FIG. 2 is a block diagram of a factory equipment information management system of the present invention;
FIG. 3 is a block diagram of a maintenance planning module of the present invention;
fig. 4 is a flow chart of a device failure prediction neural network model of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will refer to the accompanying drawings and specific embodiments. It should also be understood that the particular embodiments described herein are illustrative only and are not intended to limit the invention.
Referring to fig. 1, the present embodiment provides a factory equipment informatization management method, which includes the following steps:
s1, installing various sensors on factory equipment, including a temperature sensor, a pressure sensor, a vibration sensor and the like, and monitoring various indexes and environmental conditions of the factory equipment in real time. The sensor needs to be selected and arranged according to the type of equipment and the monitoring environment, so that the data information such as parameters of working state, energy consumption and the like can be normally collected. And meanwhile, data information of relevant use, maintenance and maintenance records of the equipment is collected.
Acquiring factory equipment information according to the acquisition of the information collection module to obtain a factory equipment information set，WhereinRepresenting the total number of devices,any one of the collection elements is availableThe representation is made of a combination of a first and a second color,represent the firstInformation subsets of the individual devices;wherein, the method comprises the steps of, wherein,represent the firstDevice-related information quantity, aggregation of individual devicesAny one of the elements may be composed ofThe representation is made of a combination of a first and a second color,represent the firstFirst of the devicesA piece of equipment information; specifically, the obtained device information is represented as a matrix:
；
the invention is characterized in that the data is collected and represented in a matrix form, which is applicable to different types of data, is helpful for simplifying the data processing and analysis processes, and can accelerate the processes of executing data processing and modeling algorithm by utilizing the matrix; potential features in the data can be better revealed, and accuracy and efficiency are improved for subsequent information classification.
S2, preprocessing the collected data information of the factory equipment, classifying the data information, and calculating the associated weight.
By analyzing the working parameters of the equipment, the operation strategy of the equipment can be optimized, the operation efficiency and stability of the equipment are improved, the energy consumption is reduced, and the production benefit is improved.
Preprocessing the acquired data to obtain a preprocessed data setClassifying the factory equipment information, and classifying according to energy consumption levels, wherein the energy consumption levels comprise a high energy efficiency level, a medium energy efficiency level and a low energy efficiency level; the method comprises the following steps:
；
wherein,a subset representing a high energy efficiency level;a subset representing medium energy efficiency levels;a subset representing a low energy efficiency level; and is also provided with；
According to the safety grade of the equipment, the safety grade comprises a low safety grade, a medium safety grade, a high safety grade and a critical safety grade; the method comprises the following steps:
；
wherein,representing a subset of low security levels;a security level in the representation;representing a high security level;representing a critical security level; and is also provided with；
In one embodiment, the classification may also be as follows:
dividing according to the degree of automation, wherein the degree of automation comprises manual operation, semi-automatic, full-automatic and roboticized; is specifically shown as；
According to the division of the equipment capacity, the equipment capacity comprises small-sized equipment, medium-sized equipment, large-sized equipment and high-capacity equipment; is specifically shown as。
The method and the device can be used for classifying the preprocessed factory device information, calculating the mutual correlation weight, and helping to determine the interaction and the dependency relationship between the devices, and rapidly detecting and identifying the states of the devices. In the classification of the energy consumption of the equipment, the specific process is as follows:
the weight of the association between any two devices,
；
wherein,represent the firstEnergy consumption weight parameters of the individual devices;represent the firstEnergy consumption weight parameters of the individual devices;expressed in energy consumption classification setIn (a)Is the ratio of (2);representing energy consumption based classification setsApparatus and deviceAt the time of the firstProbability of occurrence of individual devices. From this, the associated weights of the device information sets under the energy consumption classification can be derived，Wherein, the method comprises the steps of, wherein,respectively represent the weight sets of the energy consumption dividing devices, the associated weight sets of any two energy consumption dividing devices,and (5) the associated weight set of each energy consumption dividing device.
A set of associated weights for the set of security partition device information may then be derived, defined asThe method comprises the steps of carrying out a first treatment on the surface of the The associated weight set for obtaining the automation degree dividing equipment information set is defined asThe method comprises the steps of carrying out a first treatment on the surface of the Obtaining an associated weight set of the capacity dividing equipment information set, which is defined as。
The obtained associated weight set is finely adjusted, and then fusion calculation is carried out, wherein the specific process is as follows:
；
wherein,representing the association weights among the fused devices;representing a correction factor;representing the fusion parameters;、、、respectively represent、、、Is used for correcting parameters;representing the gain value in the fusion.
According to the invention, the association weight among the devices is obtained through calculation, so that the dependency relationship and the influence degree among the devices can be determined, when one device fails, other devices related to the device can be checked and maintained in time according to the association weight, so that the fault diffusion or the chain reaction is prevented, the root cause of the fault is determined, and the repair and the recovery are performed more quickly; the influence degree of other equipment can be evaluated through the association weight, so that the risk management strategy and disaster recovery plans can be formulated, and potential production and business risks can be reduced.
S3, data analysis and processing are carried out in the data preprocessing module, the running state of the factory equipment is monitored in real time through analysis of the data information related to equipment work, whether the equipment has fault risks is judged, and corresponding alarm signals are generated.
In a specific application embodiment, an equipment failure prediction neural network model is established, and failure prediction is performed according to preprocessed equipment data information based on obtained association weights among the equipment. Training a device fault prediction neural network model consisting of deep learning, using a large number of data samples including sensor data, operation records and fault histories of the device and other information related to the state of the device, inputting the data into the neural network after multiple training, and enabling the device fault prediction neural network model to finally output accurate prediction results through multiple layers of calculation and parameter adjustment.
The trained equipment failure prediction neural network comprises an input layer, a convolution layer, a pooling layer, a prediction layer, an evaluation layer, an optimization layer, a full connection layer and an output layer.
Referring to fig. 4, the inputs defining the device failure prediction neural network areCurrent device data information sharingThe method is characterized in that an input layer is fully connected with a convolution layer, convolution operation is carried out on equipment information in the convolution layer, and the local characteristics of the equipment information are obtained, wherein the specific process is as follows:
；
；
wherein,an output representing a convolutional layer;representing a convolution operation;representing a convolution kernel;an index representing the output feature sequence in the convolutional layer;representing the bias in the convolution operation;an index representing a convolution kernel;representing the weight value;representing the bias in the convolutional layer.
Performing pooling operation in the pooling layer, and then characterizing the pooled device informationDelivering to a prediction layer where predictions are made based on data information, where the prediction layer is commonThe specific process of each neuron is as follows:
；
；
；
wherein,represent the firstA time step state of each neuron;represent the firstThe current time state of the individual neurons;represent the firstAn update gate for the individual neurons;represent the firstReset gates of individual neurons;represent the firstMemory gates of individual neurons;、、representing an activation function;、、representing the bias;、、representing the weight in the current time state;、、representing the iterated neuron weights;representing the first in the pooled device information featuresA neuron; the output of the prediction layer is thus:
；
wherein,representing the output result of the prediction layer;representing the learning factor. And then the prediction layer transmits the predicted result to the evaluation layer, and the evaluation fault is judged according to the set rule: if the predicted result meets the predefined range, namely:，according to experimental data or defined in advance by expert, has predictabilityThe evaluation fault transmits the result to an optimization layer for optimization; otherwise, the evaluation layer sends a command, and the input device data information enters the convolution layer again to perform secondary convolution action.
And optimizing the predicted result after being evaluated in the evaluation layer in the optimization layer, wherein the specific process is as follows:
；
wherein,representing an output result of the optimization layer;representing empirical parameters in the optimization layer;representing the mapping factor;representing the bias;representing an activation function in an optimization layer;representing the mapped parameters;representing a control factor;represent the firstThe prediction results corresponding to the input device information,。
after the secondary convolution operation is carried out in the convolution layer, the result is transmitted to the evaluation layer, and the iteration is sequentially circulated until the evaluation layer judges that the result passes.
And finally, outputting a predicted result according to the equipment data information by the full-connection layer, transmitting the predicted result to the output layer, and outputting a final predicted equipment fault condition by the output layer, wherein the specific process is as follows:
；
wherein,representing an output of the output layer;representing the weight value;representing the bias of the output layer.
And then optimizing parameters of the model by using a proper loss function so as to minimize the difference between the predicted result and the actual fault, and finally generating an alarm signal according to the fault condition of the factory equipment.
The potential fault risk can be captured in time through the equipment fault prediction neural network model, an alarm is sent out in advance, preventive maintenance measures are taken, and loss of equipment faults to the working process is avoided; the severe damage of the equipment can be avoided, and the maintenance cost is reduced, so that the maintenance plan is reasonably arranged, the maintenance efficiency is improved, and the downtime is reduced; through predictive analysis modeling of equipment faults, a deep foundation is laid for the management process of equipment, decisions are made based on accurate data and information, and the accuracy and efficiency of equipment management are improved.
S4, comprehensively judging according to the analyzed fault condition of the factory equipment and combining the maintenance time and the fault rate to obtain a comprehensive parameter value, and then carrying out overall management.
Predicting possible faults in the future by combining alarm signals with historical fault data, running conditions, maintenance records, capacity conditions of equipment and the like of the equipment; based on these predictions, an optimized maintenance plan is generated to minimize the impact of equipment failure on production. The maintenance cost is reduced, the work plan is improved, and the equipment utilization rate and the maintenance efficiency are improved.
First, according to the fault condition of the equipment, the maintenance frequency is adjustedAnd loss of productivityPresetting; the fault condition of the equipment can be divided into key faults, serious faults and general faults;
in the fault unit, a type of preset maintenance time is setSecond class preset maintenance timeAnd (2) andthe method comprises the steps of carrying out a first treatment on the surface of the Maintenance time for the acquired equipmentRespectively withAnd (3) withThe specific procedure for comparison is as follows:
in the comparison result of the maintenance time, the first processing unit judges the maintenance timeThe system is in a critical fault (the critical faults mentioned in the current period and later refer to the faults at the highest level, and the system can be completely stopped or can not normally operate); meanwhile, the comparison result of the maintenance time meets the following condition；
In the second type of maintenance time comparison result, the first processing unit judges the maintenance timeBelongs to serious faults; meanwhile, the comparison result of the second class maintenance time meets the requirement；
In the comparison result of the three types of maintenance time, the first processing unit judges the maintenance timeBelongs to general faults; meanwhile, the comparison results of three types of maintenance time meet。
Calculating the failure rate of the equipment in the failure unit according to the acquired failure times of the equipment in a certain time，，Wherein, the method comprises the steps of, wherein,representing the number of times a device fails in a certain failure situation within a certain time frame;indicating the total run time of the device within the same time frame. Then according to the failure rate of the equipmentTo determine the maintenance frequency of the equipment, the specific process is as follows:
if it isOr (b)Judging that the maintenance frequency is higher, namely, the higher maintenance frequency; if it isOr (b)Then the medium maintenance frequency is determined; if it isDetermining a low maintenance frequency; for the followingAndthe other conditions are low maintenance frequency; if it isThen the medium maintenance frequency is determined;
representing a failure rate of the device in the critical failure situation;representing the failure rate of the device in a severe failure situation;representing the failure rate of the device in a general failure situation;。
in the capacity unit, according to the failure rateTo loss of productivityThe adjustment is carried out so that the adjustment is carried out,representing the acquired device yield, defining a first preset yield versus parameterAnd a second preset output value contrast parameter，；
When (when)When the production capacity is lost, the like；
When (when)When the production capacity is lost, the like；
When (when)When the production capacity is lost, the production capacity is lost；
Wherein,adjusting parameters for a first preset yield value;parameters are adjusted for a second predetermined output value.
Calculating integrated parameter values based on maintenance frequency and production capacity lossDefinition of，
Wherein,parameters indicating maintenance efficiency;a parameter indicative of production loss capacity;a weight value representing a maintenance frequency;a weight value representing production loss capacity. Further, parameters of maintenance efficiency are determined according to maintenance efficiency and production loss capacityAnd production loss capacity parametersIs used for the value of (a) and (b),a parameter indicative of a predetermined maintenance frequency,the parameters representing the preset production loss capacity are as follows:
if the maintenance efficiency is higher maintenance frequency, the corresponding maintenance frequency parameter setting coefficient isSetting the parameters of maintenance frequency to beThe method comprises the steps of carrying out a first treatment on the surface of the If the maintenance efficiency is the medium maintenance frequency, the corresponding maintenance frequency parameter setting coefficient isSetting the parameters of maintenance frequency to beThe method comprises the steps of carrying out a first treatment on the surface of the If the maintenance efficiency is the medium maintenance frequency, the corresponding maintenance frequency parameter setting coefficient isSetting the parameters of maintenance frequency to be；
If the productivity loss is equal to the productivity loss, the parameter setting coefficient of the corresponding productivity loss isSetting the production capacity parameter asThe method comprises the steps of carrying out a first treatment on the surface of the If the production capacity loss is equal to the production capacity loss, the parameter setting coefficient of the corresponding production capacity loss isSetting the production capacity parameter asThe method comprises the steps of carrying out a first treatment on the surface of the If the production capacity loss is three, the parameter setting coefficient of the corresponding production capacity loss isSetting the production capacity parameter as。
And calculating a comprehensive parameter value according to the maintenance frequency and the production capacity loss, wherein the comprehensive parameter value is a characteristic parameter of the equipment operation and maintenance standard, and when the comprehensive parameter value becomes large, the maintenance frequency and the production capacity loss are described to be large, the equipment instability is enhanced, and a good maintenance plan is required to be obtained.
The invention can help to determine the maintenance strategy of the equipment by calculating the comprehensive parameter value. Through comprehensive evaluation of maintenance frequency and production capacity loss, the maintenance period and maintenance content of the equipment can be determined, so that preventive maintenance is realized, sudden faults and production interruption are avoided, and the service life of the equipment is prolonged to the greatest extent; meanwhile, the comprehensive parameter value provides an objective evaluation index for equipment management, and the effects of different equipment management schemes can be evaluated through analysis of the comprehensive parameter value, and the optimal management strategy is selected so as to achieve the aims of reducing cost and improving production efficiency and quality.
Referring to fig. 2, the present embodiment provides an informatization management system for factory equipment, which includes the following contents:
the system comprises an information collection module, an information preprocessing module, an equipment association module, a fault analysis module, a maintenance planning module and an equipment overall planning module;
the information collection module is used for collecting data information recorded by the sensors and daily operation of the equipment and transmitting the data information to the equipment overall module and the information preprocessing module;
the information preprocessing module is used for preprocessing the equipment information acquired by the information collecting module to obtain preprocessed equipment data information, and transmitting the preprocessed equipment data information to the equipment association module and the equipment overall module in a data transmission mode;
the equipment association module is used for processing the preprocessed equipment information to obtain association weights among the equipment, and transmitting the association weights to the fault analysis module, the maintenance planning module and the equipment overall planning module through data;
the fault analysis module is used for analyzing the fault condition of the factory equipment to obtain the fault result of the equipment, generating an alarm signal and transmitting the result to the maintenance planning module and the equipment overall planning module;
the maintenance planning module is used for carrying out maintenance protection planning arrangement according to the analysis result of equipment faults and combining maintenance frequency and production capacity loss, and transmitting the maintenance protection planning arrangement to the equipment overall planning module through data; the maintenance planning module comprises a fault unit and a capacity unit;
the fault unit is used for obtaining maintenance frequency according to the maintenance time and the fault rate and providing a basis for obtaining the comprehensive parameter value;
the productivity unit calculates a comprehensive parameter value according to the maintenance frequency and the production capacity loss;
the equipment overall management module is used for overall management of all the modules in the system, monitoring the state and health condition of the equipment, carrying out maintenance task allocation and processing data transmission and message exchange among the equipment.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
The above is only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited by this, and any modification made on the basis of the technical scheme according to the technical idea of the present invention falls within the protection scope of the claims of the present invention.
Claims (4)
1. An informatization management method for factory equipment is characterized by comprising the following steps:
step 1, acquiring data information of factory equipment through a sensor and a monitoring facility, and acquiring data information of relevant use, maintenance and maintenance records of the equipment;
step 2, preprocessing the collected data information of the factory equipment, classifying the data information, and calculating the associated weight;
classifying the plant equipment information obtained after preprocessing, calculating the correlation weights among the plant equipment information, and obtaining a correlation weight set of equipment information sets under each classification, including the correlation weight of the equipment information sets under the energy consumption classificationAssociated weight of security partition device information set +.>The degree of automation divides the associated weight of the device information set +.>The associated weight of the capacity division device information set +.>The method comprises the steps of carrying out a first treatment on the surface of the Will beThe obtained associated weight set is subjected to fine adjustment, and then fusion calculation is performed, wherein the specific process is as follows:
；
wherein,representing the association weights among the fused devices;Representing a correction factor;Representing the fusion parameters;、、、Respectively indicate->、、、Is used for correcting parameters;Representing the gain value in the fusion;
step 3, constructing an equipment failure prediction neural network model through analyzing equipment and related data thereof, monitoring the running state of the factory equipment in real time, and generating a corresponding alarm signal;
based on the obtained association weight among the devices, performing fault prediction according to the preprocessed device data information, and establishing a device fault prediction neural network model, wherein the device fault prediction neural network model comprises an input layer, a convolution layer, a pooling layer, a prediction layer, an evaluation layer, an optimization layer, a full connection layer and an output layer;
defining inputs of a device failure prediction neural network asCurrent device data information is common +.>The input layer is fully connected with the convolution layer, and convolution operation is carried out on the equipment information in the convolution layer to obtain local characteristics of the equipment information;
performing pooling operation in the pooling layer, and then characterizing the pooled device informationIs transferred to a prediction layer, where predictions are made based on data information, sharing +.>The specific process of each neuron is as follows:
；
；
；
wherein,indicate->A time step state of each neuron;Indicate->The current time state of the individual neurons;indicate->An update gate for the individual neurons;Indicate->Reset gates of individual neurons;Indicate->Memory gates of individual neurons;、、Representing an activation function;、、Representing the bias;、、Representing the weight in the current time state;、、Representing the iterated neuron weights;Representing +.>A neuron; the output of the prediction layer is thus:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Representing the output result of the prediction layer;Representing a learning factor;
then the prediction layer transmits the predicted result to the evaluation layer, and the evaluation layer judges according to the set rule: if the predicted result meets the predefined range, namely:，the method is finished according to experimental data or defined in advance by an expert, predictability is achieved, and an evaluation layer transmits results to an optimization layer for optimization; otherwise, the evaluation layer sends a command, and the input device data information enters the convolution layer again to perform secondary convolution action;
after performing secondary convolution operation in the convolution layer, transmitting the result to the evaluation layer, and sequentially performing loop iteration until the evaluation layer judges that the result passes;
step 4, comprehensively judging according to the analyzed fault condition of the factory equipment and combining the maintenance time and the fault rate to obtain a comprehensive parameter value, and then carrying out overall management;
is provided with a type of preset maintenance timeSecond class preset maintenance time->And->The method comprises the steps of carrying out a first treatment on the surface of the Then repair time for the acquired device +.>Respectively and->And->Comparing; the fault condition of the equipment can be divided into key faults, serious faults and general faults; the specific process is as follows:
in the comparison result of the maintenance time, the first processing unit judges the maintenance timeBelongs to key faults; meanwhile, the comparison result of the maintenance time satisfies +.>；
In the second type of maintenance time comparison result, the first processing unit judges the maintenance timeBelongs to serious faults; meanwhile, the comparison result of the second class maintenance time meets the requirement->；
In the comparison result of the three types of maintenance time, the first processing unit judges the maintenance timeBelongs to general faults; meanwhile, the comparison result of three types of maintenance time meets +.>；
Calculating the failure rate of the equipment according to the acquired failure times of the equipment in the appointed time，，Wherein->Representing the number of times a device fails in a certain failure situation within a specified time frame;Representing the total running time of the device in the same time range; according to failure rate of equipment>To determine a maintenance frequency of the device;
according to the failure rateLoss of productivity->Make adjustments and/or receive>Representing the obtained device yield, defining a first preset yield comparison parameter +.>And a second preset birth value comparison parameter +.>，；
When (when)In this case, the productivity is lost by one of the first and second>；
When (when)When the production capacity is lost, the like->；
When (when)When the production capacity is lost, the production capacity is lost three times>；
Wherein,adjusting parameters for a first preset yield value;Adjusting parameters for a second preset yield value;
calculating integrated parameter values based on maintenance frequency and production capacity lossDefinition of，
Wherein,parameters indicating maintenance efficiency;A parameter indicative of production loss capacity;A weight value representing a maintenance frequency;A weight value representing production loss capacity.
2. The method for informationized management of plant equipment according to claim 1, wherein the fully connected layer outputs the predicted result according to the equipment data information, and then transmits the predicted result to the output layer, and the output layer outputs the finally predicted equipment failure condition, wherein the specific process is as follows:
；
wherein,representing an output of the output layer;Representing the weight value;Representing the bias of the output layer;And representing the output result of the optimization layer.
3. The method for informationized management of plant equipment according to claim 1, wherein the failure rate of the plant equipment is determined byTo determine the maintenance frequency of the equipment, the maintenance frequency including a higher maintenance frequency, a medium maintenance frequency, a lower maintenance frequency.
4. A factory equipment informatization management system, which is applied to the factory equipment informatization management method of any one of claims 1 to 3, and is characterized by comprising the following contents:
the system comprises an information collection module, an information preprocessing module, an equipment association module, a fault analysis module, a maintenance planning module and an equipment overall planning module;
the information collection module is used for collecting data information recorded by the sensors and daily operation of the equipment and transmitting the data information to the equipment overall module and the information preprocessing module;
the information preprocessing module is used for preprocessing the equipment information acquired by the information collecting module to obtain preprocessed equipment data information, and transmitting the preprocessed equipment data information to the equipment association module and the equipment overall module in a data transmission mode;
the equipment association module is used for processing the preprocessed equipment information to obtain association weights among the equipment, and transmitting the association weights to the fault analysis module, the maintenance planning module and the equipment overall planning module through data;
the fault analysis module is used for analyzing the fault condition of the factory equipment to obtain the fault result of the equipment, generating an alarm signal and transmitting the result to the maintenance planning module and the equipment overall planning module;
the maintenance planning module is used for carrying out maintenance protection planning arrangement according to the analysis result of equipment faults and combining maintenance frequency and production capacity loss, and transmitting the maintenance protection planning arrangement to the equipment overall planning module through data; the maintenance planning module comprises a fault unit and a capacity unit;
the fault unit is used for obtaining maintenance frequency according to maintenance time and fault rate and providing a basis for obtaining comprehensive parameter values;
the productivity unit calculates a comprehensive parameter value according to the maintenance frequency and the production capacity loss;
the equipment overall management module is used for overall management of all modules in the system, monitoring the state and health condition of the equipment, carrying out maintenance task allocation and processing data transmission and message exchange among the equipment.
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Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130109506A (en) * | 2012-03-27 | 2013-10-08 | 한국남동발전 주식회사 | Risk based maintenance system for power generation facilities |
| JP2014016691A (en) * | 2012-07-06 | 2014-01-30 | Hitachi Ltd | Equipment maintenance and management support system, and method for the same |
| WO2015158198A1 (en) * | 2014-04-17 | 2015-10-22 | 北京泰乐德信息技术有限公司 | Fault recognition method and system based on neural network self-learning |
| CN108241343A (en) * | 2016-12-24 | 2018-07-03 | 青岛海尔模具有限公司 | A kind of intelligent plant management platform system |
| CN108995675A (en) * | 2018-06-28 | 2018-12-14 | 上海工程技术大学 | A kind of rail transportation operation risk intelligent recognition early warning system and method |
| CN110555477A (en) * | 2019-08-30 | 2019-12-10 | 青岛海信网络科技股份有限公司 | municipal facility fault prediction method and device |
| CN110874506A (en) * | 2018-08-31 | 2020-03-10 | 青岛大数华创科技有限公司 | Low-temperature equipment fault prediction method |
| CN111160685A (en) * | 2019-09-23 | 2020-05-15 | 上海安恪企业管理咨询有限公司 | A maintenance decision-making method based on comprehensive health status analysis and management of equipment |
| CN111815001A (en) * | 2020-07-16 | 2020-10-23 | 中国人民解放军空军工程大学 | A maintenance decision-making system, method and application for complex equipment |
| CN115407712A (en) * | 2022-08-04 | 2022-11-29 | 营口理工学院 | Intelligent maintenance system for hydraulic station of steel mill and working process |
| CN115526375A (en) * | 2022-07-26 | 2022-12-27 | 中国人民解放军63926部队 | Space flight equipment predictive maintenance system |
| CN115793590A (en) * | 2023-01-30 | 2023-03-14 | 江苏达科数智技术有限公司 | Data processing method and platform suitable for system safety operation and maintenance |
| CN116228186A (en) * | 2022-12-12 | 2023-06-06 | 武汉理工大学 | Intelligent operation and maintenance system of ship engine room based on human factors engineering |
| CN116307236A (en) * | 2023-03-31 | 2023-06-23 | 联想(北京)有限公司 | Fault prediction method, device, equipment and storage medium |
| WO2023134447A1 (en) * | 2022-01-12 | 2023-07-20 | 华为技术有限公司 | Data processing method and related device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11501132B2 (en) * | 2020-02-07 | 2022-11-15 | Hitachi, Ltd. | Predictive maintenance system for spatially correlated industrial equipment |
-
2023
- 2023-09-20 CN CN202311211160.6A patent/CN116976862B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130109506A (en) * | 2012-03-27 | 2013-10-08 | 한국남동발전 주식회사 | Risk based maintenance system for power generation facilities |
| JP2014016691A (en) * | 2012-07-06 | 2014-01-30 | Hitachi Ltd | Equipment maintenance and management support system, and method for the same |
| WO2015158198A1 (en) * | 2014-04-17 | 2015-10-22 | 北京泰乐德信息技术有限公司 | Fault recognition method and system based on neural network self-learning |
| CN108241343A (en) * | 2016-12-24 | 2018-07-03 | 青岛海尔模具有限公司 | A kind of intelligent plant management platform system |
| CN108995675A (en) * | 2018-06-28 | 2018-12-14 | 上海工程技术大学 | A kind of rail transportation operation risk intelligent recognition early warning system and method |
| CN110874506A (en) * | 2018-08-31 | 2020-03-10 | 青岛大数华创科技有限公司 | Low-temperature equipment fault prediction method |
| CN110555477A (en) * | 2019-08-30 | 2019-12-10 | 青岛海信网络科技股份有限公司 | municipal facility fault prediction method and device |
| CN111160685A (en) * | 2019-09-23 | 2020-05-15 | 上海安恪企业管理咨询有限公司 | A maintenance decision-making method based on comprehensive health status analysis and management of equipment |
| CN111815001A (en) * | 2020-07-16 | 2020-10-23 | 中国人民解放军空军工程大学 | A maintenance decision-making system, method and application for complex equipment |
| WO2023134447A1 (en) * | 2022-01-12 | 2023-07-20 | 华为技术有限公司 | Data processing method and related device |
| CN115526375A (en) * | 2022-07-26 | 2022-12-27 | 中国人民解放军63926部队 | Space flight equipment predictive maintenance system |
| CN115407712A (en) * | 2022-08-04 | 2022-11-29 | 营口理工学院 | Intelligent maintenance system for hydraulic station of steel mill and working process |
| CN116228186A (en) * | 2022-12-12 | 2023-06-06 | 武汉理工大学 | Intelligent operation and maintenance system of ship engine room based on human factors engineering |
| CN115793590A (en) * | 2023-01-30 | 2023-03-14 | 江苏达科数智技术有限公司 | Data processing method and platform suitable for system safety operation and maintenance |
| CN116307236A (en) * | 2023-03-31 | 2023-06-23 | 联想(北京)有限公司 | Fault prediction method, device, equipment and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 基于VB 6.0及Oracle 9i的工厂设备管理系统的设计;郭平;陈双一;;上海电力学院学报(第02期);全文 * |
| 基于物联网和运行大数据的设备状态监测诊断;高帆;王玉军;杨露霞;;自动化仪表(第06期);全文 * |
| 过程工业设备维修智能决策系统的开发与应用;王庆锋;杨剑锋;刘文彬;袁庆斌;马宏伟;;机械工程学报(第24期);全文 * |
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