CN114442477B - Equipment health management system based on Internet of things - Google Patents
Equipment health management system based on Internet of things Download PDFInfo
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Abstract
The invention relates to an equipment health management system based on the Internet of things, which comprises: a device management means for device management; the equipment management device is provided with a recognition device, a control module and an auxiliary control module established by implanting a control program into equipment based on the control module; the control module has: the data receiving part is used for receiving the node data of the backup node transmitted by the data storage unit in real time; the analysis unit is used for analyzing the stability of the node data of the backup node in continuous time based on the parameter information of the data interface corresponding to the backup node; the invention forms an auxiliary control module in control elements such as a PLC, a controller, an industrial chip and the like, the auxiliary control module has the function of acquiring data of a data interface, transmits the acquired data to an equipment management device, and analyzes the stability of the node data of a backup node in continuous time through an analysis unit based on the parameter information of the data interface corresponding to the backup node.
Description
Technical Field
The invention relates to the technical field of equipment health management, in particular to an equipment health management system based on the Internet of things.
Background
The conventional industrial production line is provided with a large amount of devices, the normal operation of each device can ensure the normal operation of the whole industrial production line, and in the industrial production line, the normal operation is generally a production type enterprise which is uninterrupted for 24 hours, once the device fails, the whole production line is greatly lost, for example, in a chip manufacturing enterprise, once the device fails, the whole production line fails, and the input wafers become reported waste products. Therefore, the method has great significance for the healthy operation management of the equipment.
In the prior art, the following methods are generally adopted as the equipment health management.
Firstly, the method comprises the following steps: the health state of each device is analyzed by exporting data in each service in the maintenance platform through the existing maintenance platform, and the method is speculated and analyzed through historical maintenance records, has large error and is not suitable for the existing large-scale production line.
Secondly, the method comprises the following steps: the method is characterized in that an SCADA system is established, the SCADA system is a single operation equipment control platform, relevant equipment data are read in an existing centralized control interface, a protocol analysis processing assembly is adopted, all equipment needs to be integrated according to a certain mode, the cost is high, and the equipment cannot be unified.
Thirdly, the method comprises the following steps: various sensors are connected to the equipment to monitor the operation condition of the equipment, the mode is the same as the second mode, effective unification can not be carried out, and the existing production line equipment is provided with various sensors, so that the real-time operation condition of the equipment can be monitored.
Fourthly: with an industrial gateway box, reading the information of the devices through an access gateway requires that all devices be integrated under the same protocol. Therefore, different devices need to be modified.
Disclosure of Invention
In view of the above, the present invention is to provide an equipment health management system based on the internet of things, so as to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
equipment health management system based on thing networking includes:
a device management means for device management;
the equipment management device is provided with a recognition device, a control module and an auxiliary control module which is built by implanting a control program in the equipment based on the control module;
the auxiliary control module has:
the configuration unit is used for configuring a data storage unit of the auxiliary control module in the data backup process and configuring a backup node of each data interface;
the detection unit is used for detecting all data interfaces of the control unit, establishing a backup node at each detected data interface, and configuring a basic threshold value of the corresponding backup node based on each data interface;
the fault pre-detection unit is used for comparing the node data acquired based on the backup node with a basic threshold value so as to judge whether the equipment node corresponding to the data interface has a fault or not, and if the equipment node has the fault, sending a fault signal to the control unit so as to warn the fault;
the backup nodes are connected to a data storage unit, and the data storage unit is used for transmitting node data of each backup node acquired in real time to the control module;
the control module has:
the data receiving part is used for receiving the node data of the backup node transmitted by the data storage unit in real time;
the analysis unit is used for analyzing the stability of the node data of the backup node in continuous time based on the parameter information of the data interface corresponding to the backup node;
and the machine learning system is configured to adjust a basic threshold corresponding to the backup node, wherein iterative training of the machine learning system is configured through corresponding node data, life cycle working conditions of the equipment node represented by the data interface corresponding to the node data and deviation values of the node data and reference data in continuous time so as to enhance the cognition of the machine learning system on the deviation values, and thus the basic threshold corresponding to the backup node is adjusted.
Further, the auxiliary control module is established by the following method:
A1) after the equipment is connected with the equipment management device, the identification device sends an identification instruction to a control unit of the equipment to read basic parameters of the control unit; inputting the basic parameters into an identification module for identification processing so as to obtain the type of a control program corresponding to the control unit;
A2) according to the type of the control program corresponding to the control unit, the control module calls a corresponding control program data packet in the program library and sends the control program data packet to the control unit of the equipment;
A3) the control unit decompresses after receiving the control program data packet, triggers an initialization command of the driving program to execute initialization installation after decompression is completed, and forms an auxiliary control module in the control unit after initialization installation is completed.
Further, the data storage unit is configured by the following method:
B1) detecting the storage capacity of a data storage unit in a control unit and acquiring the available capacity of the data storage unit;
B2) if the storage capacity is larger than the set basic capacity and the available capacity is larger than the configuration capacity, the auxiliary control module divides a rated capacity in the data storage unit by using the dividing unit for basic configuration; during basic configuration, writing the rated capacity into a storage tag, and enabling only node data with corresponding attributes to be stored in a data storage unit;
if the storage capacity is smaller than the set basic capacity or the storage capacity is larger than the set basic capacity, the available capacity is smaller than the configuration capacity; the auxiliary control module drives the control unit to use a spare buffer arranged in the control unit for basic configuration; at the time of basic configuration, a storage tag is written in the reserve buffer, and only node data of a corresponding attribute can be stored in the data storage unit.
Further, the backup node is configured by the following method:
C1) after an auxiliary control module is formed in a control unit, a detection unit in the auxiliary control module performs pre-detection on all data interfaces of the control unit to acquire port information of each data port;
C2) according to the port information of each data port, the configuration unit correspondingly establishes a data node at each data port, the data node is used for backing up monitoring data of the equipment node received by the data port in real time, and a node label is written in the attribute of each backup node, so that the backup node is identified by the data storage unit.
Further, the method for configuring the base threshold of the corresponding backup node based on each data interface is as follows:
D1) after an auxiliary control module is formed in a control unit, a detection unit in the auxiliary control module performs pre-detection on all data interfaces of the control unit to acquire port information of each data port;
D2) correspondingly acquiring equipment parameters of equipment nodes according to the port information of each data port, and setting a reference threshold value based on the equipment parameters; firstly, carrying out fault detection initialization by using a reference threshold value;
D3) after the fault detection initialization is completed, the data storage unit transmits the node data of each backup node acquired in real time to the control module, the analysis unit analyzes a stable interval of the node data of the backup node in the set continuous time based on the parameter information of the data interface corresponding to the backup node, and the reference threshold is revised based on the stable interval to form a basic threshold.
Further, in step B2), at the time of the basic configuration, writing the rated capacity to the storage tag by the writing unit;
and, during the basic configuration, writing a memory tag in the reserve buffer through the write unit;
the write unit is provided in the auxiliary control module.
Further, in step C2), writing a node tag in the attribute of the backup node by the writing unit;
the write unit is provided in the auxiliary control module.
Further, the machine learning system has a plurality of neural network units;
each neural network unit corresponds to a backup node;
and each neural network unit is configured to adjust the basic threshold value of the corresponding backup node, wherein iterative training of the neural network units is configured through the corresponding node data, the life cycle working condition of the equipment node represented by the data interface corresponding to the node data and the deviation value of the node data from the reference data in continuous time, so as to enhance the cognition of the neural network units on the deviation value, and thus, the basic threshold value corresponding to the backup node is adjusted.
The invention is based on the scheme of basically adopting intelligent control in the existing large-scale industrial production line, for example, the monitoring of various data of each device can be realized, and the basic control is carried out by means of PLC and the like. According to the method, the control program is implanted into the control elements such as the PLC, the controller and the industrial chip, the control program can detect data interfaces in the control elements such as the PLC, the controller and the industrial chip after being implanted, a backup node is configured at each data interface, and the control program can be distinguished by writing in a corresponding attribute tag in each backup node.
After the control program is implanted, an auxiliary control module is formed in control elements such as a PLC, a controller and an industrial chip, the auxiliary control module has the functions of acquiring data of a data interface, detecting the fault of single equipment and storing and transmitting data, the acquired data is sent to an equipment management device, and the stability of the node data of the backup node in continuous time is analyzed through an analysis unit based on the parameter information of the data interface corresponding to the backup node.
In addition, because the basic threshold value of the equipment is continuously adjusted along with the use and the working condition of the equipment as the parts age, the line age and the like in the using process of the equipment, the machine learning system is configured to adjust the basic threshold value corresponding to the backup node, wherein the iterative training of the machine learning system is configured through the corresponding node data, the life cycle working condition of the equipment node represented by the data interface corresponding to the node data and the deviation value of the node data and the reference data in continuous time, so as to enhance the cognition of the machine learning system on the deviation value, and thus, the basic threshold value corresponding to the backup node is adjusted.
Drawings
FIG. 1 is a schematic diagram of the framework of the present invention;
FIG. 2 is a flow chart of a method for establishing an auxiliary control module according to the present invention;
FIG. 3 is a flow chart of a method for establishing a data storage unit according to the present invention;
fig. 4 is a flowchart of a method for establishing a backup node according to the present invention.
FIG. 5 is a flowchart of a method for configuring a base threshold of a corresponding backup node based on each data interface according to the present invention.
Detailed Description
The present invention is described in detail below with reference to the accompanying drawings, which refer to fig. 1 to 5.
The invention is based on the scheme of basically adopting intelligent control in the existing large-scale industrial production line, for example, the monitoring of various data of each device can be realized, and the basic control is carried out by means of PLC and the like. According to the method, the control program is implanted into the control elements such as the PLC, the controller and the industrial chip, the control program can detect data interfaces in the control elements such as the PLC, the controller and the industrial chip after being implanted, a backup node is configured at each data interface, and the control program can be distinguished by writing in a corresponding attribute tag in each backup node.
After the control program is implanted, an auxiliary control module is formed in control elements such as a PLC, a controller and an industrial chip, the auxiliary control module has the functions of acquiring data of a data interface, detecting the fault of single equipment and storing and transmitting data, the acquired data is sent to an equipment management device, and the stability of the node data of the backup node in continuous time is analyzed through an analysis unit based on the parameter information of the data interface corresponding to the backup node.
Referring to fig. 1, the present invention provides an equipment health management system based on the internet of things, including:
a device management means for device management;
the equipment management device is provided with a recognition device, a control module and an auxiliary control module which is built by implanting a control program in the equipment based on the control module;
referring to fig. 2, the supplementary control module is established by the following method:
A1) after the equipment is connected with the equipment management device, the identification device sends an identification instruction to a control unit of the equipment to read basic parameters of the control unit; inputting the basic parameters into an identification module for identification processing so as to obtain the type of a control program corresponding to the control unit;
A2) according to the type of the control program corresponding to the control unit, the control module calls a corresponding control program data packet in the program library and sends the control program data packet to the control unit of the equipment;
A3) the control unit decompresses after receiving the control program data packet, triggers an initialization command of the driving program to execute initialization installation after decompression is completed, and forms an auxiliary control module in the control unit after initialization installation is completed.
The auxiliary control module has:
the configuration unit is used for configuring a data storage unit of the auxiliary control module in the data backup process and configuring a backup node of each data interface;
referring to fig. 3, in the above, the data storage unit is configured by the following method:
B1) detecting the storage capacity of a data storage unit in a control unit and acquiring the available capacity of the data storage unit;
B2) if the storage capacity is larger than the set basic capacity and the available capacity is larger than the configuration capacity, the auxiliary control module divides a rated capacity in the data storage unit by using the dividing unit for basic configuration; during basic configuration, writing the rated capacity into a storage tag, and enabling only node data with corresponding attributes to be stored in a data storage unit;
if the storage capacity is smaller than the set basic capacity or the storage capacity is larger than the set basic capacity, the available capacity is smaller than the configuration capacity; the auxiliary control module drives the control unit to use a spare buffer arranged in the control unit for basic configuration; at the time of basic configuration, a storage tag is written in the reserve buffer, and only node data of a corresponding attribute can be stored in the data storage unit.
In step B2), at the time of basic configuration, writing the rated capacity to the storage tag by the writing unit;
and, during the basic configuration, writing a memory tag in the reserve buffer through the write unit;
the write unit is provided in the auxiliary control module.
Referring to fig. 4, in the above, the backup node is configured by the following method:
C1) after an auxiliary control module is formed in a control unit, a detection unit in the auxiliary control module performs pre-detection on all data interfaces of the control unit to acquire port information of each data port;
C2) according to the port information of each data port, the configuration unit correspondingly establishes a data node at each data port, the data node is used for backing up monitoring data of the equipment node received by the data port in real time, and a node label is written in the attribute of each backup node, so that the backup node is identified by the data storage unit.
The detection unit is used for detecting all data interfaces of the control unit, establishing a backup node at each detected data interface, and configuring a basic threshold value of the corresponding backup node based on each data interface;
in step C2), writing a node tag in the attribute of the backup node by the writing unit;
the write unit is provided in the auxiliary control module.
Referring to fig. 5, in the above, the method for configuring the base threshold of the corresponding backup node based on each data interface is as follows: D1) after an auxiliary control module is formed in a control unit, a detection unit in the auxiliary control module performs pre-detection on all data interfaces of the control unit to acquire port information of each data port;
D2) correspondingly acquiring equipment parameters of the equipment nodes according to the port information of each data port, and setting a reference threshold value based on the equipment parameters; firstly, carrying out fault detection initialization by using a reference threshold value;
D3) after the fault detection initialization is completed, the data storage unit transmits the node data of each backup node acquired in real time to the control module, the analysis unit analyzes a stable interval of the node data of the backup node in the set continuous time based on the parameter information of the data interface corresponding to the backup node, and the reference threshold is revised based on the stable interval to form a basic threshold.
The fault pre-detection unit is used for comparing the node data acquired based on the backup node with a basic threshold value so as to judge whether the equipment node corresponding to the data interface has a fault or not, and if the equipment node has the fault, sending a fault signal to the control unit so as to warn the fault;
the backup nodes are connected to a data storage unit, and the data storage unit is used for transmitting node data of each backup node acquired in real time to the control module;
the control module has:
the data receiving part is used for receiving the node data of the backup node transmitted by the data storage unit in real time;
the analysis unit is used for analyzing the stability of the node data of the backup node in continuous time based on the parameter information of the data interface corresponding to the backup node;
and the machine learning system is configured to adjust a basic threshold corresponding to the backup node, wherein iterative training of the machine learning system is configured through corresponding node data, life cycle working conditions of the equipment node represented by the data interface corresponding to the node data and deviation values of the node data and the reference data in continuous time, so as to enhance the cognition of the machine learning system on the deviation values, and accordingly, the basic threshold corresponding to the backup node is adjusted.
In the above, the machine learning system has a plurality of neural network units;
each neural network unit corresponds to a backup node;
each neural network unit is configured to adjust a basic threshold value of a corresponding backup node, wherein iterative training of the neural network unit is configured through corresponding node data, life cycle conditions of equipment nodes represented by data interfaces corresponding to the node data and deviation values of the node data and reference data in continuous time, and the basic threshold value of the neural network unit is continuously adjusted along with the use of the equipment and the working conditions of the equipment due to the problems of part aging, line aging and the like of the equipment in the using process of the equipment, and the machine learning system is configured to adjust the basic threshold value corresponding to the backup node, wherein iterative training of the machine learning system is configured through corresponding node data, life cycle conditions of the equipment nodes represented by data interfaces corresponding to the node data and deviation values of the node data and the reference data in continuous time, and enhancing the cognition of the machine learning system on the deviation value so as to adjust the basic threshold value corresponding to the backup node.
In some embodiments, the device parameters of the device node include a rated voltage, a rated current;
environmental factors of use: including temperature and humidity.
The invention also provides an equipment health management method based on the Internet of things, which comprises the following steps:
detecting all data interfaces of the control unit through the detection unit, establishing a backup node at each detected data interface, and configuring a basic threshold value of a corresponding backup node based on each data interface;
comparing node data acquired based on the backup node with a basic threshold value to judge whether equipment nodes corresponding to the data interfaces have faults or not, and if the equipment nodes have the faults, sending fault signals to a control unit to warn the faults;
meanwhile, the backup nodes are connected to a data storage unit, and the data storage unit is used for transmitting node data of each backup node acquired in real time to the control module;
the receiving data storage unit is used for receiving node data of the backup node transmitted by the data storage unit in real time;
the analysis unit analyzes the stability of the node data of the backup node in continuous time based on the parameter information of the data interface corresponding to the backup node.
In some embodiments, for example, the device is not provided with a corresponding sensor for monitoring the device, and the monitoring may be implemented by installing a sensor on the device and accessing the control system, which is also incorporated in the present application.
The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts of the present invention. The foregoing is only a preferred embodiment of the present invention, and it should be noted that there are objectively infinite specific structures due to the limited character expressions, and it will be apparent to those skilled in the art that a plurality of modifications, decorations or changes may be made without departing from the principle of the present invention, and the technical features described above may be combined in a suitable manner; such modifications, variations, combinations, or adaptations of the invention using its spirit and scope, as defined by the claims, may be directed to other uses and embodiments.
Claims (8)
1. Equipment health management system based on thing networking, its characterized in that includes:
a device management means for device management;
the equipment management device is provided with a recognition device, a control module and an auxiliary control module established by implanting a control program into equipment based on the control module;
the auxiliary control module has:
the configuration unit is used for configuring a data storage unit of the auxiliary control module in the data backup process and configuring a backup node of each data interface;
the detection unit is used for detecting all data interfaces of the control unit, establishing a backup node at each detected data interface, and configuring a basic threshold value of the corresponding backup node based on each data interface;
the fault pre-detection unit is used for comparing the node data acquired based on the backup node with a basic threshold value so as to judge whether the equipment node corresponding to the data interface has a fault or not, and if the equipment node has the fault, sending a fault signal to the control unit so as to warn the fault;
the backup nodes are connected to a data storage unit, and the data storage unit is used for transmitting node data of each backup node acquired in real time to the control module;
the control module has:
the data receiving part is used for receiving the node data of the backup node transmitted by the data storage unit in real time;
the analysis unit is used for analyzing the stability of the node data of the backup node in continuous time based on the parameter information of the data interface corresponding to the backup node;
and the machine learning system is configured to adjust a basic threshold corresponding to the backup node, wherein iterative training of the machine learning system is configured through corresponding node data, life cycle working conditions of the equipment node represented by the data interface corresponding to the node data and deviation values of the node data and the reference data in continuous time, so as to enhance the cognition of the machine learning system on the deviation values, and accordingly, the basic threshold corresponding to the backup node is adjusted.
2. The internet of things-based equipment health management system of claim 1, wherein the auxiliary control module is established by a method comprising:
A1) after the equipment is connected with the equipment management device, the identification device sends an identification instruction to a control unit of the equipment to read basic parameters of the control unit; inputting the basic parameters into an identification module for identification processing so as to obtain the type of a control program corresponding to the control unit;
A2) according to the type of the control program corresponding to the control unit, the control module calls a corresponding control program data packet in the program library and sends the control program data packet to the control unit of the equipment;
A3) the control unit decompresses after receiving the control program data packet, triggers an initialization command of the driving program to execute initialization installation after decompression is completed, and forms an auxiliary control module in the control unit after initialization installation is completed.
3. The internet of things-based device health management system of claim 1, wherein the data storage unit is configured by:
B1) detecting the storage capacity of a data storage unit in a control unit and acquiring the available capacity of the data storage unit;
B2) if the storage capacity is larger than the set basic capacity and the available capacity is larger than the configuration capacity, the auxiliary control module divides a rated capacity in the data storage unit by using the dividing unit for basic configuration; during basic configuration, writing the rated capacity into a storage tag, and enabling only node data with corresponding attributes to be stored in a data storage unit;
if the storage capacity is smaller than the set basic capacity or the storage capacity is larger than the set basic capacity, the available capacity is smaller than the configuration capacity; the auxiliary control module drives the control unit to use a spare buffer arranged in the control unit for basic configuration; at the time of basic configuration, a storage tag is written in the reserve buffer, and only node data of a corresponding attribute can be stored in the data storage unit.
4. The internet of things-based equipment health management system of claim 1, wherein the backup node is configured by:
C1) after an auxiliary control module is formed in a control unit, a detection unit in the auxiliary control module performs pre-detection on all data interfaces of the control unit to acquire port information of each data port;
C2) according to the port information of each data port, the configuration unit correspondingly establishes a data node at each data port, the data node is used for backing up monitoring data of the equipment node received by the data port in real time, and a node label is written in the attribute of each backup node, so that the backup node is identified by the data storage unit.
5. The internet of things-based equipment health management system of claim 1, wherein the method for configuring the base threshold of the corresponding backup node based on each data interface comprises the following steps:
D1) after an auxiliary control module is formed in a control unit, a detection unit in the auxiliary control module performs pre-detection on all data interfaces of the control unit to acquire port information of each data port;
D2) correspondingly acquiring equipment parameters of equipment nodes according to the port information of each data port, and setting a reference threshold value based on the equipment parameters; firstly, carrying out fault detection initialization by using a reference threshold value;
D3) after the fault detection initialization is completed, the data storage unit transmits the node data of each backup node acquired in real time to the control module, the analysis unit analyzes a stable interval of the node data of the backup node in the set continuous time based on the parameter information of the data interface corresponding to the backup node, and the reference threshold is revised based on the stable interval to form a basic threshold.
6. The IOT-based equipment health management system of claim 3, wherein in step B2), at the time of basic configuration, the rated capacity is written to the storage tag by the writing unit;
and, during the basic configuration, writing a memory tag in the reserve buffer through the write unit;
the write unit is provided in the auxiliary control module.
7. The internet-of-things-based equipment health management system according to claim 4, wherein in the step C2), a node tag is written in the attribute of the backup node through a writing unit;
the write unit is provided in the auxiliary control module.
8. The internet of things based device health management system of claim 4, wherein the machine learning system has a plurality of neural network elements;
each neural network unit corresponds to a backup node;
and each neural network unit is configured to adjust the basic threshold value of the corresponding backup node, wherein iterative training of the neural network units is configured through the corresponding node data, the life cycle working condition of the equipment node represented by the data interface corresponding to the node data and the deviation value of the node data from the reference data in continuous time, so as to enhance the cognition of the neural network units on the deviation value, and thus, the basic threshold value corresponding to the backup node is adjusted.
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