CN114330760A - Equipment operation and maintenance management method and system - Google Patents
Equipment operation and maintenance management method and system Download PDFInfo
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- 238000012423 maintenance Methods 0.000 title claims abstract description 104
- 238000007726 management method Methods 0.000 title claims abstract description 80
- 238000012549 training Methods 0.000 claims abstract description 81
- 238000010801 machine learning Methods 0.000 claims abstract description 75
- 238000011156 evaluation Methods 0.000 claims abstract description 47
- 230000003993 interaction Effects 0.000 claims abstract description 47
- 238000000034 method Methods 0.000 claims description 25
- 230000008569 process Effects 0.000 claims description 17
- 238000012986 modification Methods 0.000 claims description 6
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- 230000002159 abnormal effect Effects 0.000 description 2
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- 238000013145 classification model Methods 0.000 description 1
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Abstract
The invention discloses an equipment operation and maintenance management method, which comprises the following steps: receiving data and equipment operation and maintenance management requirements through man-machine interaction; creating and training a machine learning model according to the requirements and the received data; evaluating the accuracy of the machine learning model to obtain an evaluation result; selecting a machine learning model according to the evaluation result; acquiring new equipment data through man-machine interaction by using the selected machine learning model to carry out equipment operation and maintenance management; an equipment operation and maintenance management system is also disclosed. The machine learning model can be automatically created according to the idea of the equipment manager and the data in hands; the model can be flexibly established according to the ideas and data of equipment personnel without compiling programs and complex settings; the system completes the tasks which can be completed by the people who need equipment personnel, data scientists and programmers to cooperate together or have the skills simultaneously, has the characteristics of automation, convenience and simplicity, and can greatly improve the efficiency of the equipment personnel.
Description
Technical Field
The invention relates to the technical field of equipment operation and maintenance management, in particular to an equipment operation and maintenance management method and system.
Background
At present, in the operation, maintenance, management and maintenance of equipment, the maintenance thought is that:
1. and (4) corrective maintenance: i.e. the equipment is repaired upon failure. In the absence of a fault, the maintenance team takes a rest. Currently, all large factories can do this regardless of size.
2. Preventive maintenance: in recent years, preventive maintenance has been increasingly emphasized. It is becoming increasingly appreciated that preventive maintenance works well and equipment is less prone to failure. The preventive maintenance mainly comprises: point inspection and periodic maintenance (such as monthly, quarterly and annual maintenance, primary maintenance, secondary maintenance and the like).
3. State-based maintenance: since various parameters of the device, such as current, voltage, temperature, pressure, etc., have a direct effect on whether the device is malfunctioning. Therefore, each large factory begins to implement production digitization, and corresponding parameters of the equipment are collected through DCS, BA, SCADA and other systems to obtain corresponding values so as to monitor the state of the equipment. When the parameters are abnormal, if the temperature exceeds the early warning value, maintenance personnel are arranged to check on site. The collected data can also be used for statistical analysis.
The above ideas are gradually from passive maintenance to active prevention from trend. But these are not enough. "corrective maintenance" is of a fire fighting nature and only if a fault occurs, processing begins. The next time a failure occurs is unpredictable. "preventative maintenance" works well to ensure proper operation of the equipment, but it is generally not possible to do strictly and effectively with all equipment and sub-equipment components. "maintenance based on state" usually takes measures after the equipment is abnormal. The parameters on each device are many, and small anomalies are likely to be ignored, resulting in missing opportunities to predict failures. Some plants that are moving ahead in terms of plant digitization may consider intelligent prediction of faults through machine learning. But without the associated theoretical knowledge and programming skills, even with good thinking, this cannot be achieved.
Disclosure of Invention
In view of the above-mentioned shortcomings, the present invention provides a method and system for managing operation and maintenance of equipment, which can automatically create a machine learning model according to the idea of the equipment manager and the data in hand, without the need for the equipment manager to understand the program and parameters of the model creation process.
In order to achieve the above purpose, the embodiment of the invention adopts the following technical scheme:
an equipment operation and maintenance management method comprises the following steps:
receiving data and equipment operation and maintenance management requirements through man-machine interaction;
creating and training a machine learning model according to the requirements and the received data;
evaluating the accuracy of the machine learning model to obtain an evaluation result;
selecting a machine learning model according to the evaluation result;
and acquiring new equipment data through man-machine interaction by using the selected machine learning model to carry out equipment operation and maintenance management.
According to one aspect of the invention, the equipment operation and maintenance management method comprises the following steps: and when the evaluation result is not ideal and has a new idea, returning the requirements of re-inputting data and equipment operation and maintenance management, and re-creating and training the machine learning model.
According to one aspect of the invention, the equipment operation and maintenance management method comprises the following steps: and when the evaluation result is not ideal, importing more training data for model training.
According to one aspect of the invention, the equipment operation and maintenance management method comprises the following steps: and carrying out visual display on the training process and the result of the machine learning model through human-computer interaction.
According to one aspect of the present invention, the evaluating the accuracy of the machine learning model to obtain an evaluation result comprises: and (4) visually displaying the evaluation result of the machine learning model through human-computer interaction, namely visually displaying the evaluation-related expected value, predicted value and difference value in a histogram and curve graph mode.
According to one aspect of the invention, the equipment operation and maintenance management method comprises the following steps: when the model is trained, the training data is uploaded through human-computer interaction, specifically, the training data is input on a human-computer interaction interface, sent to an equipment management service API, stored in a database and displayed through human-computer interaction.
According to one aspect of the invention, the equipment operation and maintenance management method comprises the following steps: the method comprises the steps of downloading training data from a database, displaying a data set through human-computer interaction, modifying the data set through human-computer interaction, adding a modification mark, sending the modified data set to a service API, then updating data in the database, and receiving the updated data and displaying the data set without the modification mark through a human-computer interaction interface.
According to one aspect of the invention, the acquiring new equipment data for equipment operation and maintenance management through man-machine interaction by using the selected machine learning model comprises the following steps: inputting data for prediction, predicting a result through a machine learning model, and taking corresponding measures by equipment maintenance personnel according to the predicted result.
An equipment operation and maintenance management system comprises a database and the following modules:
the human-computer interaction module is used for receiving data and the requirements of equipment operation and maintenance management and providing interface display of each process and result;
the model creating module is used for creating a corresponding machine learning model according to the received data and the requirement of equipment operation and maintenance management;
the model training module is used for carrying out model training on the machine learning model according to the received training data and outputting a training result;
the model evaluation module is used for evaluating the accuracy of the machine learning model to obtain an evaluation result;
and the operation and maintenance management module is used for selecting the machine learning model and carrying out equipment operation and maintenance management through man-machine interaction based on the machine learning model.
According to one aspect of the invention, the operation and maintenance management module comprises: the prediction module is used for receiving the prediction data, predicting the next state of the equipment by using a machine learning model and outputting a prediction result; and the treatment module is used for taking corresponding measures to treat according to the prediction result.
The implementation of the invention has the advantages that: the equipment operation and maintenance management method comprises the following steps: receiving data and equipment operation and maintenance management requirements through man-machine interaction; creating and training a machine learning model according to the requirements and the received data; evaluating the accuracy of the machine learning model to obtain an evaluation result; selecting a machine learning model according to the evaluation result; and acquiring new equipment data through man-machine interaction by using the selected machine learning model to carry out equipment operation and maintenance management. The machine learning model can be automatically created according to the idea of the equipment manager and the data in hands; the model can be flexibly established according to the ideas and data of equipment personnel without compiling programs and complex settings, and can be imported with data for multiple times and trained and adjusted repeatedly; the system completes the tasks which can be completed by the people who need equipment personnel, data scientists and programmers to cooperate together or have the skills simultaneously, has the characteristics of automation, convenience and simplicity, and can greatly improve the efficiency of the equipment personnel. Furthermore, an evaluation result curve of the model, a graphical representation result of the model training process and the like are automatically displayed through a human-computer interaction interface, visual display can visually see whether classification is accurate, whether the evaluation result is excellent and the like, and equipment management personnel who do not know computer programs are very visual and easy to understand, and can create and select the model without knowing professional computer skills.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic diagram of an apparatus operation and maintenance management method according to the present invention;
FIG. 2 is a schematic diagram of a model creation process according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a training data importing process according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of an update data set flow according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a model training process according to an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a model evaluation process according to an embodiment of the present invention;
fig. 7 is a schematic view of an actual application flow of the operation and maintenance management method according to the embodiment of the present invention;
FIG. 8 is a schematic view of a visualization display of an evaluation result according to an embodiment of the present invention;
fig. 9 is a schematic diagram of an apparatus operation and maintenance management system according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in fig. 1 to 8, an apparatus operation and maintenance management method includes the following steps:
step S1: receiving data and equipment operation and maintenance management requirements through man-machine interaction;
the step S1 of receiving data through human-computer interaction and the requirement of the device operation and maintenance management may specifically include importing training data. The training data refers to data provided to the machine learning model for training. Data is typically derived from (1) records of equipment maintenance personnel, and (2) automatic acquisition by systems such as DCS/SCADA. The equipment maintenance personnel summarize the data and make corresponding fault marks. It can be used for training. For example: the current, voltage, temperature, pressure, etc. of the device. The requirements of the operation and maintenance management of the equipment are requirements on the model, and specifically include model parameters (model name, type, input number, output number, reserved test line number), learning rate and the like.
Step S2: creating and training a machine learning model according to the requirements and the received data;
as shown in fig. 2, the step S2 of creating and training a machine learning model according to the requirement and the received data may specifically include:
step 201, the user sends the model parameters and the training data to the equipment management system API through the graphical interface.
At step 202, the device management system API writes the user's input to the database.
In step 203, the machine learning server obtains data from the database.
And step 204, the machine learning server creates a neural network model according to the model parameters of the user, and writes the model and the calculation result into a database.
In step 205, the device management server obtains the calculation result from the database.
In step 206, the device management server displays the calculation result on the user interface.
In practical application, the training process and the result of the machine learning model are visually displayed through man-machine interaction. The user can view visualization results of binary and multivariate classification, training history, evaluation training results and the like on the interface. The training result of the multivariate classification model is displayed, and whether the classification is accurate or not can be visually seen.
In practical application, as shown in fig. 3, when a model is trained, training data is uploaded through human-computer interaction, specifically, the training data is input on a human-computer interaction interface, sent to an equipment management service API, stored in a database, and the stored state is displayed through human-computer interaction.
In practical applications, as shown in fig. 4, the process of updating the data set is as follows: the method comprises the steps of downloading training data from a database, displaying a data set through human-computer interaction, modifying the data set through human-computer interaction, adding a modification mark, sending the modified data set to a service API, then updating data in the database, and receiving the updated data and displaying the data set without the modification mark through a human-computer interaction interface.
In practical applications, the training process may be as shown in fig. 5.
Step S3: evaluating the accuracy of the machine learning model to obtain an evaluation result;
the step S3 of evaluating the accuracy of the machine learning model to obtain an evaluation result may specifically include: and (4) visually displaying the evaluation result of the machine learning model through human-computer interaction, namely visually displaying the evaluation-related expected value, predicted value and difference value in a histogram and curve graph mode.
In practical application, when the evaluation result is not ideal and has a new idea, the requirements of re-inputting data and equipment operation and maintenance management are returned, and the machine learning model is re-created and trained.
In practical application, when the evaluation result is not ideal, more training data is imported for model training. The learning result of the model is not ideal, and the training data is possibly insufficient, so that more training data is provided, and the training accuracy is improved.
In practical applications, the evaluation process is shown in fig. 6.
In practical application, as shown in fig. 8, a schematic diagram is displayed for visualization of the evaluation result curve.
Step S4: selecting a machine learning model according to the evaluation result;
and when the evaluation result is not ideal and has a new idea, returning the requirements of re-inputting data and equipment operation and maintenance management, and re-creating and training the machine learning model.
Step S5: and acquiring new equipment data through man-machine interaction by using the selected machine learning model to carry out equipment operation and maintenance management.
The method specifically comprises the following steps: inputting data for prediction, predicting a result through a machine learning model, and taking corresponding measures by equipment maintenance personnel according to the predicted result. The data used for prediction refers to device parameter data that is consistent with the training data.
In practical application, as shown in fig. 7, the whole device operation and maintenance management process may be as follows:
1. model name
2. Model type (Standard/time series)
3. Input quantity
4. Number of outputs
5. Reserving test data
The system will automatically create the corresponding model. Conventionally, a Python or R script is used to create a model, and corresponding parameters are set therein. Knowledge of data science, as well as programming techniques, is required. This creates difficulty for equipment maintenance personnel. It is difficult to realize his idea by machine learning. By means of the machine learning module of the present embodiment, a model can be created simply and quickly.
Step 102, importing training data. The training data refers to data provided to the machine learning model for training. Data is typically derived from (1) records of equipment maintenance personnel, and (2) automatic acquisition by systems such as DCS/SCADA. The equipment maintenance personnel summarize the data and make corresponding fault marks. It can be used for training. For example: the current, voltage, temperature, pressure, etc. of the device.
And 103, training. The learning rate is set and training is started. The training time may take several seconds to several minutes depending on the amount of data and the model.
And 104, evaluating the learning result of the model. The system automatically generates an evaluation curve.
Step 105, if the result of the evaluation is good enough, step 106 is entered. Otherwise, step 108 is entered.
Step 106, data for prediction is input. Predictive data refers to device parameter data that is consistent with training data.
And step 107, predicting. The system predicts the result, and the equipment maintenance personnel can take corresponding measures according to the predicted result.
In step 108, if the evaluation result of the model is not ideal, if the equipment personnel has a new idea, the process can return to step 101 to recreate the model. Otherwise, step 109 may be entered.
Step 109, import more training data. The learning result of the model is not ideal, and the training data is possibly insufficient, so that more training data is provided, and the training accuracy is improved.
Example two
As shown in fig. 9, an equipment operation and maintenance management system includes a database 6 and the following modules:
the human-computer interaction module 1 is used for receiving data and equipment operation and maintenance management requirements and providing interface display of each process and result;
the model creating module 2 is used for creating a corresponding machine learning model according to the received data and the requirement of equipment operation and maintenance management;
the model training module 3 is used for carrying out model training on the machine learning model according to the received training data and outputting a training result;
the model evaluation module 4 is used for evaluating the accuracy of the machine learning model to obtain an evaluation result;
and the operation and maintenance management module 5 is used for selecting the machine learning model and carrying out equipment operation and maintenance management through man-machine interaction based on the machine learning model.
In practical application, the operation and maintenance management module comprises: a prediction module 51, configured to receive prediction data, predict a next state of the device using a machine learning model, and output a prediction result; and a disposal module 52, configured to take corresponding measures to dispose according to the prediction result.
The operation principle of the embodiment is as follows:
1. model name
2. Model type (Standard/time series)
3. Input quantity
4. Number of outputs
5. Reserving test data
The system will automatically create the corresponding model. Conventionally, a Python or R script is used to create a model, and corresponding parameters are set therein. Knowledge of data science, as well as programming techniques, is required. This creates difficulty for equipment maintenance personnel. It is difficult to realize his idea by machine learning. By means of the machine learning module of the present embodiment, a model can be created simply and quickly.
Step 102, importing training data. The training data refers to data provided to the machine learning model for training. Data is typically derived from (1) records of equipment maintenance personnel, and (2) automatic acquisition by systems such as DCS/SCADA. The equipment maintenance personnel summarize the data and make corresponding fault marks. It can be used for training. For example: the current, voltage, temperature, pressure, etc. of the device.
And 103, training. The learning rate is set and training is started. The training time may take several seconds to several minutes depending on the amount of data and the model.
And 104, evaluating the learning result of the model. The system automatically generates an evaluation curve.
Step 105, if the result of the evaluation is good enough, step 106 is entered. Otherwise, step 108 is entered.
Step 106, data for prediction is input. Predictive data refers to device parameter data that is consistent with training data.
And step 107, predicting. The system predicts the result, and the equipment maintenance personnel can take corresponding measures according to the predicted result.
In step 108, if the evaluation result of the model is not ideal, if the equipment personnel has a new idea, the process can return to step 101 to recreate the model. Otherwise, step 109 may be entered.
Step 109, import more training data. The learning result of the model is not ideal, and the training data is possibly insufficient, so that more training data is provided, and the training accuracy is improved.
The implementation of the invention has the advantages that: the equipment operation and maintenance management method comprises the following steps: receiving data and equipment operation and maintenance management requirements through man-machine interaction; creating and training a machine learning model according to the requirements and the received data; evaluating the accuracy of the machine learning model to obtain an evaluation result; selecting a machine learning model according to the evaluation result; and acquiring new equipment data through man-machine interaction by using the selected machine learning model to carry out equipment operation and maintenance management. The machine learning model can be automatically created according to the idea of the equipment manager and the data in hands; the model can be flexibly established according to the ideas and data of equipment personnel without compiling programs and complex settings, and can be imported with data for multiple times and trained and adjusted repeatedly; the system completes the tasks which can be completed by the people who need equipment personnel, data scientists and programmers to cooperate together or have the skills simultaneously, has the characteristics of automation, convenience and simplicity, and can greatly improve the efficiency of the equipment personnel. Furthermore, an evaluation result curve of the model, a graphical representation result of the model training process and the like are automatically displayed through a human-computer interaction interface, visual display can visually see whether classification is accurate, whether the evaluation result is excellent and the like, and equipment management personnel who do not know computer programs are very visual and easy to understand, and can create and select the model without knowing professional computer skills.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention disclosed herein are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An equipment operation and maintenance management method is characterized by comprising the following steps:
receiving data and equipment operation and maintenance management requirements through man-machine interaction;
creating and training a machine learning model according to the requirements and the received data;
evaluating the accuracy of the machine learning model to obtain an evaluation result;
selecting a machine learning model according to the evaluation result;
and acquiring new equipment data through man-machine interaction by using the selected machine learning model to carry out equipment operation and maintenance management.
2. The device operation and maintenance management method according to claim 1, wherein the device operation and maintenance management method comprises: and when the evaluation result is not ideal and has a new idea, returning the requirements of re-inputting data and equipment operation and maintenance management, and re-creating and training the machine learning model.
3. The device operation and maintenance management method according to claim 1, wherein the device operation and maintenance management method comprises: and when the evaluation result is not ideal, importing more training data for model training.
4. The device operation and maintenance management method according to claim 1, wherein the device operation and maintenance management method comprises: and carrying out visual display on the training process and the result of the machine learning model through human-computer interaction.
5. The method for managing operation and maintenance of equipment according to claim 1, wherein the evaluating the accuracy of the machine learning model to obtain an evaluation result comprises: and (4) visually displaying the evaluation result of the machine learning model through human-computer interaction, namely visually displaying the evaluation-related expected value, predicted value and difference value in a histogram and curve graph mode.
6. The device operation and maintenance management method according to claim 1, wherein the device operation and maintenance management method comprises: when the model is trained, the training data is uploaded through human-computer interaction, specifically, the training data is input on a human-computer interaction interface, sent to an equipment management service API, stored in a database and displayed through human-computer interaction.
7. The device operation and maintenance management method according to claim 6, wherein the device operation and maintenance management method comprises: the method comprises the steps of downloading training data from a database, displaying a data set through human-computer interaction, modifying the data set through human-computer interaction, adding a modification mark, sending the modified data set to a service API, then updating data in the database, and receiving the updated data and displaying the data set without the modification mark through a human-computer interaction interface.
8. The equipment operation and maintenance management method according to any one of claims 1 to 7, wherein the obtaining of new equipment data through human-computer interaction by using the selected machine learning model for equipment operation and maintenance management comprises: inputting data for prediction, predicting a result through a machine learning model, and taking corresponding measures by equipment maintenance personnel according to the predicted result.
9. The equipment operation and maintenance management system is characterized by comprising a database and the following modules:
the human-computer interaction module is used for receiving data and the requirements of equipment operation and maintenance management and providing interface display of each process and result;
the model creating module is used for creating a corresponding machine learning model according to the received data and the requirement of equipment operation and maintenance management;
the model training module is used for carrying out model training on the machine learning model according to the received training data and outputting a training result;
the model evaluation module is used for evaluating the accuracy of the machine learning model to obtain an evaluation result;
and the operation and maintenance management module is used for selecting the machine learning model and carrying out equipment operation and maintenance management through man-machine interaction based on the machine learning model.
10. The device operation and maintenance management method according to claim 9, wherein the operation and maintenance management module comprises: the prediction module is used for receiving the prediction data, predicting the next state of the equipment by using a machine learning model and outputting a prediction result; and the treatment module is used for taking corresponding measures to treat according to the prediction result.
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| KR101966557B1 (en) * | 2017-12-08 | 2019-04-05 | 세종대학교산학협력단 | Repairing-part-demand forecasting system and method using big data and machine learning |
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