CN117455460A - Equipment predictive maintenance equipment and method based on embedded intelligent board card - Google Patents

Equipment predictive maintenance equipment and method based on embedded intelligent board card Download PDF

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CN117455460A
CN117455460A CN202311524446.XA CN202311524446A CN117455460A CN 117455460 A CN117455460 A CN 117455460A CN 202311524446 A CN202311524446 A CN 202311524446A CN 117455460 A CN117455460 A CN 117455460A
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equipment
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board card
early warning
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曹根军
单兰秋
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Suzhou Shannon Technology Co ltd
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Abstract

The invention discloses equipment predictive maintenance equipment based on an embedded intelligent board card, which comprises the following components: the work stations are interconnected with the preventive maintenance system work platform through a server, and the preventive maintenance system work platform is also interconnected with terminal equipment; the work station comprises at least one board card which is used for being interconnected with a plurality of devices, and the board card acquires the operation information of the devices and provides the operation information for the working platform of the preventive maintenance system; and the preventive maintenance system working platform feeds back the operation information processing to the terminal equipment. The invention discloses equipment predictive maintenance equipment based on an embedded intelligent board card and a verification method thereof, which can fully integrate and comprehensively analyze hardware monitoring and a production manufacturing execution system, and improve the accuracy while meeting the actual production requirements.

Description

Equipment predictive maintenance equipment and method based on embedded intelligent board card
Technical Field
The invention relates to the technical field of equipment predictive maintenance, in particular to equipment predictive maintenance equipment based on an embedded intelligent board card and a method thereof.
Background
Traditional equipment state monitoring mainly relies on manual periodic acquisition of equipment parameters to achieve limited state monitoring and maintenance.
With the development of automation technology, equipment state monitoring and diagnosis technology requires the addition of sensors to the equipment and the accurate diagnostic prediction and analysis of the monitored data. However, in the specific pushing process, a great number of cable laying problems exist due to the fact that the existing predictive maintenance system monitors and analyzes data by using the sensors, so that a plurality of data lines and power lines are arranged on a working site, and construction and maintenance are difficult; the existing predictive maintenance system does not fully integrate and comprehensively analyze hardware monitoring and Manufacturing Execution Systems (MES), so that the system has certain limitation, and an embedded intelligent board card-based equipment predictive maintenance device and a verification method thereof are required to be developed in order to meet the actual production needs and improve the accuracy.
Disclosure of Invention
The invention overcomes the defects of the prior art, provides the equipment predictive maintenance equipment based on the embedded intelligent board card and the verification method thereof, can fully integrate and comprehensively analyze hardware monitoring and a Manufacturing Execution System (MES), and improves the accuracy while meeting the actual production needs.
In order to achieve the above purpose, the invention adopts the following technical scheme: an embedded smart card based device predictive maintenance device comprising: the work stations are interconnected with the preventive maintenance system work platform through a server, and the preventive maintenance system work platform is also interconnected with terminal equipment;
the work station comprises at least one board card which is used for being interconnected with a plurality of devices, and the board card acquires the operation information of the devices and provides the operation information for the working platform of the preventive maintenance system;
and the preventive maintenance system working platform feeds back the operation information processing to the terminal equipment.
In a preferred aspect of the invention, the apparatus comprises an on-site production facility; or/and, the workstation is interconnected with a sensor arranged on the processing equipment to acquire data acquired by the sensor.
Specifically, a user in the preventive maintenance system working platform can log in the preventive maintenance system working platform to perform parameter setting and data analysis, such as setting user personnel, authority, working range and the like; and analyzing the running state change trend of the equipment according to the historical data in the server.
In a preferred scheme of the invention, the maintenance system in the preventive maintenance system working platform comprises a collection module, an inference module, an MES data extraction module, a state judgment module, a message notification module and a data storage module which are mutually connected.
In a preferred embodiment of the present invention, the acquisition module: the data acquisition module is responsible for data collection;
the reasoning module is used for: inputting the collected data into a training model for reasoning, and outputting a reasoning result;
the MES data extraction module is used for: extracting quality inspection data of a process corresponding to the current product from an MES system, and performing SPC analysis to obtain an SPC analysis result;
the state judging module is used for: the inference result of the inference module is extracted, then the SPC analysis result is extracted, firstly, the inference result and the SPC analysis result are respectively judged to be the early warning or normal according to the set threshold value, the conclusion is normal if the inference result and the SPC analysis result are both normal, and otherwise, the conclusion is abnormal;
the message notification module: when the state judgment module is abnormal, pushing the message to a preventive maintenance system working platform;
the data storage module: and storing the acquired data and the judging result to a system server, and locally storing the data in a set time period.
In a preferred scheme of the invention, a training model and a TensorFlow computing frame are arranged in the board card; the training model adopts a convolutional neural network training model, and the training model adopts a classifier method to classify data.
Specifically, the board card adopts a microsatellite embedded main board and a linux system; the training model is a model trained according to the previous data, and is updated periodically in the use process; the machine tool communication protocol adopted by the workstation is OPCUA, MTConnect, umati protocol; the communication protocol between the workstation and the peripheral equipment is OPC and modbus.
In a preferred scheme of the invention, the maintenance method of the equipment predictive maintenance equipment based on the embedded intelligent board card comprises the following steps:
and S1, installing corresponding sensors, such as a torque measuring sensor, on equipment to be monitored for data acquisition.
And S2, inputting data acquired by one device or a group of devices with similar distances into the same intelligent board card, wherein the board card and the devices form a workstation. The workstation is responsible for data acquisition and data processing work and pushes the results to the preventive maintenance system work platform.
Step S3, the whole system can be provided with one or more workstations, and the workstations are determined according to the actual layout of the site, and the whole system is subjected to whole dispersion and local concentration as criteria;
step S4, the workstation is connected with the server through a network cable;
and S5, the server runs a preventive maintenance system working platform, and the platform pushes the received abnormal information to the corresponding terminal equipment.
Further, in step S5, the terminal device further includes a corresponding responsible person APP, and performs a level 1 early warning, a level 2 early warning, or a level N early warning according to the abnormal level; and after receiving the early warning, carrying out equipment maintenance.
In a preferred embodiment of the invention, the SPC analysis employs SPC algorithms, including the following:
the SPC analysis employs SPC algorithms, including the following:
x is the sample value involved in the calculation; USL is the upper specification limit; LSL is the lower specification limit;is an estimated sigma; n is the total number of samples; />Is the average value of all the samples,
calculating Mean:a total average of all means in the subgroup number;
max maximum is calculated: max=x max The maximum average value in the subgroup number;
calculating Min minimum: min=x min The smallest average value in the subgroup number;
calculating StdDev standard deviation:
when the subgroup capacity is greater than 25, the formula can be used: />
3 is the standard deviation multiple of control, C 4 Is a constant related to sample volume;
calculating an offset coefficient:
the offset coefficient is a value of k,wherein->Average data value of upper and lower specification limit, < ->A total average of all sample values; mu represents a distribution center and belongs to a process average value; m represents a specification center, m= (USL-LSL)/2, USL is an upper specification limit, and LSL is a lower specification limit;
the workstation judges the abnormal level according to the threshold value set by the offset coefficient, judges that the state is normal when the offset coefficient k is smaller than the secondary early warning value, judges that the state is second-level abnormal when the offset coefficient k is larger than the secondary early warning value and smaller than the primary early warning value, and judges that the state is first-level abnormal when the offset coefficient k is larger than the primary early warning value.
The invention solves the defects existing in the background technology:
the equipment predictive maintenance equipment based on the embedded intelligent board card and the verification method thereof can fully integrate and comprehensively analyze hardware monitoring and a Manufacturing Execution System (MES), thereby meeting the actual production requirement and improving the accuracy.
Drawings
The invention will be further described with reference to the drawings and examples.
FIG. 1 is a schematic view of the construction of a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram of a maintenance system in a preventive maintenance system work platform according to a preferred embodiment of the present invention;
fig. 3 is a workflow diagram of a workstation in a preferred embodiment of the present invention.
Detailed Description
The invention will now be described in further detail with reference to the drawings and examples, which are simplified schematic illustrations of the basic structure of the invention, which are presented only by way of illustration, and thus show only the structures that are relevant to the invention.
Example 1
As shown in fig. 1-3, an apparatus predictive maintenance apparatus based on an embedded smart card includes: the work stations are interconnected with the preventive maintenance system work platform through a server, and the preventive maintenance system work platform is also interconnected with terminal equipment; the work station comprises at least one board card which is used for being interconnected with a plurality of devices, and the board card acquires the operation information of the devices and provides the operation information for the working platform of the preventive maintenance system; and the preventive maintenance system working platform feeds back the operation information processing to the terminal equipment.
Further, the apparatus includes an on-site production apparatus; the workstation is interconnected with a sensor mounted on the processing equipment to acquire data acquired by the sensor. Specifically, a user in the preventive maintenance system working platform can log in the preventive maintenance system working platform to perform parameter setting and data analysis, such as setting user personnel, authority, working range and the like; and analyzing the running state change trend of the equipment according to the historical data in the server.
Specifically, the maintenance system in the preventive maintenance system working platform comprises a collection module, an inference module, an MES data extraction module, a state judgment module, a message notification module and a data storage module which are mutually interconnected. The acquisition module is used for: the data acquisition module is responsible for data collection; the reasoning module is used for: inputting the collected data into a training model for reasoning, and outputting a reasoning result; the MES data extraction module is used for: extracting quality inspection data of a process corresponding to the current product from an MES system, and performing SPC analysis to obtain an SPC analysis result; the state judging module is used for: the inference result of the inference module is extracted, then the SPC analysis result is extracted, firstly, the inference result and the SPC analysis result are respectively judged to be the early warning or normal according to the set threshold value, the conclusion is normal if the inference result and the SPC analysis result are both normal, and otherwise, the conclusion is abnormal; the message notification module: when the state judgment module is abnormal, pushing the message to a preventive maintenance system working platform; the data storage module: and storing the acquired data and the judging result to a system server, and locally storing the data in a set time period.
Specifically, a training model and a TensorFlow computing frame are installed in the board card; the training model adopts a convolutional neural network training model, and the training model adopts a classifier method to classify data.
Specifically, the board card adopts a microsatellite embedded main board and a linux system; the training model is a model trained according to the previous data, and is updated periodically in the use process; the machine tool communication protocol adopted by the workstation is OPCUA, MTConnect, umati protocol; the communication protocol between the workstation and the peripheral equipment is OPC and modbus.
Example two
As shown in fig. 1-3, a maintenance method for predictive maintenance of equipment based on an embedded smart card includes the following steps:
and S1, installing corresponding sensors, such as a torque measuring sensor, on equipment to be monitored for data acquisition.
And S2, inputting data acquired by one device or a group of devices with similar distances into the same intelligent board card, wherein the board card and the devices form a workstation. The workstation is responsible for data acquisition and data processing work and pushes the results to the preventive maintenance system work platform.
Step S3, the whole system can be provided with one or more workstations, and the workstations are determined according to the actual layout of the site, and the whole system is subjected to whole dispersion and local concentration as criteria;
step S4, the workstation is connected with the server through a network cable;
and S5, the server runs a preventive maintenance system working platform, and the platform pushes the received abnormal information to the corresponding terminal equipment.
Further, in step S5, the terminal device further includes a corresponding responsible person APP, and performs a level 1 early warning, a level 2 early warning, or a level N early warning according to the abnormal level; and after receiving the early warning, carrying out equipment maintenance.
In a preferred embodiment of the invention, the SPC analysis employs SPC algorithms, including the following:
the SPC analysis employs SPC algorithms, including the following:
x is the sample value involved in the calculation; USL is the upper specification limit; LSL is the lower specification limit;is an estimated sigma; n is the total number of samples; />Is the average value of all the samples,
calculating Mean:the total average of all the averages (field names) in the subgroup number;
max maximum is calculated: max=x max The maximum average value in the subgroup number;
calculating Min minimum: min=x min The smallest average value in the subgroup number;
calculating StdDev standard deviation:
when the subgroup capacity is greater than 25, the formula can be used: />
3 is the standard deviation multiple of control, C 4 Is a constant related to sample size (number of data points per set);
calculating an offset coefficient:
the offset coefficient is a value of k,wherein->Average data value of upper and lower specification limit, < ->A total average of all sample values; mu represents a distribution center and belongs to a process average value; m represents a specification center, m= (USL-LSL)/2, USL is an upper specification limit, and LSL is a lower specification limit;
the workstation judges the abnormal level according to the threshold value set by the offset coefficient, judges that the state is normal when the offset coefficient k is smaller than the secondary early warning value, judges that the state is second-level abnormal when the offset coefficient k is larger than the secondary early warning value and smaller than the primary early warning value, and judges that the state is first-level abnormal when the offset coefficient k is larger than the primary early warning value.
Working principle:
as shown in figures 1-3, the coverage area is wide, one set of system can cover the whole factory, and the network coverage area, namely the working range, is not limited by wiring such as signal wires, power wires and the like. The method has low dependence on the server, and model reasoning, SPC and other calculations are put on each workstation, so that the data processing work is dispersed, and the pressure of the server is reduced. The invention reduces hardware cost and saves a large number of signal wires and power wires. The system is convenient to maintain, and the system uses a structure with overall dispersion and local concentration, so that the association among all working stations is reduced, and faults are easy to be checked. The system can be independently formed according to actual conditions of factories or used together with an MES system to improve accuracy. The system can be configured accordingly as needed to accommodate plants of different sizes.
In view of the foregoing, it will be apparent to those skilled in the art from this disclosure that various changes and modifications can be made herein without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.

Claims (9)

1. An embedded smart card-based device predictive maintenance device, comprising: the work stations are interconnected with the preventive maintenance system work platform through a server, and the preventive maintenance system work platform is also interconnected with terminal equipment;
the work station comprises at least one board card which is used for being interconnected with a plurality of devices, and the board card acquires the operation information of the devices and provides the operation information for the working platform of the preventive maintenance system;
and the preventive maintenance system working platform feeds back the operation information processing to the terminal equipment.
2. The embedded smart card-based device predictive maintenance device of claim 1, wherein: the apparatus includes an on-site production apparatus; or/and, the workstation is interconnected with a sensor arranged on the processing equipment to acquire data acquired by the sensor.
3. The embedded smart card-based device predictive maintenance device of claim 2, wherein: the maintenance system in the preventive maintenance system working platform comprises a collection module, an inference module, an MES data extraction module, a state judgment module, a message notification module and a data storage module which are mutually connected.
4. A device predictive maintenance device based on an embedded smart card as claimed in claim 3, wherein: the acquisition module is used for: the data acquisition module is responsible for data collection;
the reasoning module is used for: inputting the collected data into a training model for reasoning, and outputting a reasoning result;
the MES data extraction module is used for: extracting quality inspection data of a process corresponding to the current product from an MES system, and performing SPC analysis to obtain an SPC analysis result;
the state judging module is used for: the inference result of the inference module is extracted, then the SPC analysis result is extracted, firstly, the inference result and the SPC analysis result are respectively judged to be the early warning or normal according to the set threshold value, the conclusion is normal if the inference result and the SPC analysis result are both normal, and otherwise, the conclusion is abnormal;
the message notification module: when the state judgment module is abnormal, pushing the message to a preventive maintenance system working platform;
the data storage module: and storing the acquired data and the judging result to a system server, and locally storing the data in a set time period.
5. The embedded smart card based device predictive maintenance device of claim 4, wherein: a training model and a TensorFlow computing frame are installed in the board card; the training model adopts a convolutional neural network training model, and the training model adopts a classifier method to classify data.
6. The embedded smart card based device predictive maintenance device of claim 5, wherein: the board card adopts a microsatellite embedded main board and a linux system; the training model is a model trained according to the previous data, and is updated periodically in the use process; the machine tool communication protocol adopted by the workstation is OPCUA, MTConnect, umati protocol; the communication protocol between the workstation and the peripheral equipment is OPC and modbus.
7. A maintenance method for equipment predictive maintenance equipment based on an embedded intelligent board card, which is characterized by comprising the following steps of:
step S1, installing corresponding sensors on equipment to be monitored to acquire data, such as a torque measurement sensor and the like;
s2, inputting data acquired by a device or a group of devices with similar distances into the same intelligent board card, wherein the board card and the devices form a workstation; the workstation is responsible for data acquisition and data processing work and pushes the result to a preventive maintenance system working platform;
step S3, the whole system can be provided with one or more workstations, and the workstations are determined according to the actual layout of the site, and the whole system is subjected to whole dispersion and local concentration as criteria;
step S4, the workstation is connected with the server through a network cable;
and S5, the server runs a preventive maintenance system working platform, and the platform pushes the received abnormal information to the corresponding terminal equipment.
8. The maintenance method for the device predictive maintenance device based on the embedded smart card according to claim 7, wherein:
in step S5, the terminal equipment comprises a corresponding responsible person APP, and performs 1-level early warning or 2-level early warning or N-level early warning according to the abnormal level; and after receiving the early warning, carrying out equipment maintenance.
9. A method for maintaining a predictive maintenance device for a device based on an embedded smart card as claimed in claim 7 or 8, wherein:
the SPC analysis employs SPC algorithms, including the following:
x is the sample value involved in the calculation; USL is the upper specification limit; LSL is the lower specification limit;is an estimated sigma; n is the total number of samples;is the average value of all the samples,
calculating Mean:a total average of all means in the subgroup number;
max maximum is calculated: max=x max The maximum average value in the subgroup number;
calculating Min minimum: min=x min The smallest average value in the subgroup number;
calculating StdDev standard deviation:
when the subgroup capacity is greater than 25, the formula can be used: />
3 is the standard deviation multiple of control, C 4 Is a constant related to sample volume;
calculating an offset coefficient:
the offset coefficient is a value of k,wherein->Average data value of upper and lower specification limit, < ->A total average of all sample values; mu represents a distribution center and belongs to a process average value; m represents a specification center, m= (USL-LSL)/2, USL is an upper specification limit, and LSL is a lower specification limit;
the workstation judges the abnormal level according to the threshold value set by the offset coefficient, judges that the state is normal when the offset coefficient k is smaller than the secondary early warning value, judges that the state is second-level abnormal when the offset coefficient k is larger than the secondary early warning value and smaller than the primary early warning value, and judges that the state is first-level abnormal when the offset coefficient k is larger than the primary early warning value.
CN202311524446.XA 2023-11-16 2023-11-16 Equipment predictive maintenance equipment and method based on embedded intelligent board card Pending CN117455460A (en)

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CN202311524446.XA CN117455460A (en) 2023-11-16 2023-11-16 Equipment predictive maintenance equipment and method based on embedded intelligent board card

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