CN112859769B - Energy consumption monitoring device in intelligent production equipment and operation method thereof - Google Patents
Energy consumption monitoring device in intelligent production equipment and operation method thereof Download PDFInfo
- Publication number
- CN112859769B CN112859769B CN202011640533.8A CN202011640533A CN112859769B CN 112859769 B CN112859769 B CN 112859769B CN 202011640533 A CN202011640533 A CN 202011640533A CN 112859769 B CN112859769 B CN 112859769B
- Authority
- CN
- China
- Prior art keywords
- equipment
- data
- energy consumption
- module
- current
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005265 energy consumption Methods 0.000 title claims abstract description 139
- 238000004519 manufacturing process Methods 0.000 title claims abstract description 77
- 238000000034 method Methods 0.000 title claims abstract description 24
- 238000012806 monitoring device Methods 0.000 title claims abstract description 16
- 238000007781 pre-processing Methods 0.000 claims abstract description 55
- 238000012544 monitoring process Methods 0.000 claims abstract description 36
- 230000002159 abnormal effect Effects 0.000 claims description 47
- 238000012549 training Methods 0.000 claims description 28
- 238000013528 artificial neural network Methods 0.000 claims description 20
- 238000010219 correlation analysis Methods 0.000 claims description 11
- 230000005856 abnormality Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 2
- 238000005516 engineering process Methods 0.000 abstract description 13
- 238000007418 data mining Methods 0.000 abstract description 8
- 230000007547 defect Effects 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 238000002372 labelling Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 3
- 238000010220 Pearson correlation analysis Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003490 calendering Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32339—Object oriented modeling, design, analysis, implementation, simulation language
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- General Factory Administration (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention relates to an energy consumption monitoring device in intelligent production equipment and an operation method thereof, which comprises the following steps: the method comprises the steps of collecting original data of each information collection point in real time, conducting data preprocessing, placing the data into a streaming message queue, collecting data from the streaming message queue by using a big data technology, preprocessing the data, constructing an operation state view of equipment, establishing an operation state standard under a complex production environment by using a data mining technology, monitoring the energy consumption state of the equipment, judging whether the equipment is in a normal operation state or not, displaying the result and conducting corresponding early warning operation. The invention uses big data mining technology, flexibly analyzes the equipment running state under complex conditions, establishes the running state correlation model among different equipment, optimizes the originally used machinery, and only based on a single-target early warning mode, so that the monitoring result is more convincing, and the cost of enterprises is saved.
Description
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to an energy consumption monitoring device in intelligent production equipment and an operation method thereof.
Background
In recent years, big data technology plays a very important role in the development of intelligent manufacturing; in production, the equipment running state of various production machines similar to PVC calenders is always an important index, and is an index for monitoring the key point of intelligent manufacturing. In the production process, the enterprise can be lost due to any abnormal operation of the equipment, the produced product quality can be influenced due to the abnormal indexes such as the energy consumption of the motor of the equipment, energy waste can be caused if the energy consumption is abnormally high, and then the enterprise is directly or indirectly lost. In the background of intelligent manufacturing, most indexes are obtained by manual table look-up and calculation, so that the labor cost is high, the operation complexity is high, and the requirement of real-time monitoring cannot be met, so that the production and manufacturing intellectualization is difficult to realize. So people are eventually focusing their attention on smart manufacturing today where big data technologies are gaining a lot of growth.
The chinese utility model with publication number CN202677152U is dedicated to 2013, month 1 and month 16, and discloses an energy consumption monitoring device, through presetting various operation parameters in the central control device, and connect at least one remote monitoring device and at least one real-time signal acquisition device, each remote monitoring device can inquire and monitor various operation and energy consumption parameters collected by each real-time signal acquisition device at any time, and compare and analyze with preset values and historical data in a database, so as to make feasible energy-saving and consumption-reducing measures, and intervene and execute in time, the efficiency of work is greatly improved, and energy consumption data informatization, real-time and controllability are realized, accurate monitoring and effective energy conservation are realized, but the device has a single early warning mode, and a certain uncertainty exists in the monitoring result.
Disclosure of Invention
The invention provides an energy consumption monitoring device in intelligent production equipment and an operation method thereof, aiming at overcoming the technical defects of single early warning mode and uncertain monitoring result of the existing energy consumption monitoring device.
In order to solve the technical problems, the technical scheme of the invention is as follows:
an energy consumption monitoring device in intelligent production equipment comprises a data acquisition module, a data preprocessing module, an equipment current grouping module, an equipment running state module and an online monitoring module; wherein:
the data acquisition module is arranged on the intelligent production equipment, acquires energy consumption data of the intelligent production equipment and transmits the energy consumption data to the data preprocessing module;
the data preprocessing module is used for preprocessing energy consumption data to obtain node grouping state Sn < Sn node 1 current, Sn node 2 current, Sn node 3 current. > and operation state Un < all node data > of equipment n, wherein n belongs to <1,2,3. >;
the device current grouping module is used for constructing a device current grouping model, judging the association abnormity of energy consumption among devices in a group according to the node grouping state of the devices and outputting the energy consumption abnormity;
the equipment running state module is used for constructing an equipment running state model and judging the energy consumption abnormity of a single piece of equipment according to the running state and the energy consumption abnormity of the equipment;
and the online monitoring module displays and gives an alarm to abnormal energy consumption equipment according to the output results of the equipment current grouping module and the equipment running state module.
The data acquisition module comprises a plurality of sensors and a data analyzer; wherein:
the sensor is internally or externally connected to the intelligent production equipment and is used for acquiring energy consumption data of the intelligent production equipment; transmitting the energy consumption data to the data analyzer;
the data analyzer is used for analyzing the energy consumption data, transmitting the analyzed data into a streaming message queue, and waiting for the data preprocessing module to call.
The energy consumption data comprise the switching value, the temperature, the current, the rotating speed and the pressure of each acquisition node of each intelligent production device.
Wherein, in the data preprocessing module, the preprocessing process includes:
the data preprocessing module is used for removing current data of current acquisition nodes of each device from the streaming data and grouping the current data according to the correlation analysis of the current nodes; constructing a node grouping state of the equipment according to the correlation analysis result;
the data preprocessing module collects the running state of the equipment from the streaming message queue.
In the scheme, current node correlation grouping in a data preprocessing module is performed, circuit node current data of all devices are obtained from streaming data, a data set S < node 1 current, node 2 current, node 3 current, node 4 current, node 5 current and node 6 current is formed, correlation calculation is performed on all currents by using a Pearson correlation analysis formula in mathematical statistics, correlation among all node currents is obtained, and the node currents with large correlation are put into a group of circuit node current grouping states S1< S1 node 1 current, S1 node 2 current, S1 node 3 current, S2< S2 node 4 current, S2 node 5 current, S2 node 6 current.
In the equipment current grouping module, historical data of node grouping states of equipment are used, a BP neural network algorithm is adopted, training is carried out, and an equipment current grouping model is obtained; and outputting the node grouping state constructed by the data preprocessing module as the equipment current grouping model, judging the association abnormity of energy consumption among the equipment in the grouping, and outputting the energy consumption abnormity.
In the above scheme, the device current grouping model construction process specifically includes:
(1) data collected by a quality node and a production node of a production line are used as indexes for judging whether the production state is healthy or not, the indexes are mapped to a healthy state value, the healthy state value is adopted to label equipment current grouping state data Sn, and the following results are obtained: labeling data Sn in a healthy state, wherein n belongs to <1,2,3. >, namely obtaining a data set S' n, wherein n belongs to <1,2,3. >: s '1 < S' 1 node 1 current, S '1 node 2 current, S' 1 node 3 current, health state >, S '2 < S' 2 node 4 current, S '2 node 5 current, S' 2 node 6 current, health state >, wherein n belongs to <1,2,3 >;
(2) aiming at the grouping S' n, wherein n belongs to the group of 1,2 and 3, a BP neural network algorithm is constructed, model training is carried out, and an equipment current grouping algorithm model is obtained;
in the equipment running state module, historical data of each equipment running state is used, a BP neural network algorithm is adopted, training is carried out, and an equipment running state model is obtained; and taking the equipment running state constructed by the data preprocessing module and the energy abnormity generated by the equipment current grouping module as input, and judging the energy consumption abnormity of the single equipment.
In the above scheme, the process of constructing the device operation state model specifically includes:
(1) the data collected by the quality nodes and the output nodes of the production line are used as indexes for judging whether the production state is healthy or not, and the equipment current grouping state data Sn are marked to obtain the following results: labeling data Un in a healthy state, wherein n belongs to <1,2,3. >, namely obtaining a data set U' n, wherein n belongs to <1,2,3. >: u '1 < all node data of the device 1, health state >, U' 2< all node data of the device 2, health state >, wherein n belongs to <1,2,3. >;
and aiming at the running state U' n of the equipment, wherein n belongs to <1,2 and 3 >, constructing a BP neural network algorithm, and performing the algorithm to obtain an equipment state algorithm model.
The online monitoring module comprises a microprocessor, a display module, an input module and an alarm module; wherein:
the input module is used for inputting an operation instruction, and the output end of the input module is electrically connected with the input end of the microprocessor;
the output end of the microprocessor is electrically connected with the signal input end of the display module;
the microprocessor is electrically connected with the control end of the alarm module;
the input end of the microprocessor is electrically connected with the output end of the equipment running state module.
An operation method of an energy consumption monitoring device in intelligent production equipment comprises the following steps:
s1: a data acquisition module is arranged on the intelligent production equipment to acquire energy consumption data of the intelligent production equipment;
s2: preprocessing the energy consumption data through a data preprocessing module to obtain a node grouping state and an operation state of the equipment;
s3: training an equipment current grouping model through an equipment current grouping module, taking a node grouping state of equipment as the input of the equipment current grouping model, judging the association abnormality of energy consumption among the equipment in a group, and outputting the energy consumption abnormality;
s4: training an equipment running state model through an equipment running state module, taking the running state and the energy consumption abnormity of the equipment as the input of the equipment running state model, and judging the energy consumption abnormity of the single equipment;
s5: and according to the output results of the equipment current grouping module and the equipment running state module, displaying and alarming the equipment with abnormal energy consumption on the online monitoring module.
In step S1, acquiring energy consumption data of the intelligent production equipment by internally or externally connecting a sensor to the intelligent production equipment; and analyzing the energy consumption data, transmitting the analyzed data into a streaming message queue, and waiting for the data preprocessing module to call.
In step S3, training a BP neural network by using historical data of a node grouping state of the device as a training set to obtain a device current grouping model; outputting a node grouping state constructed by the data preprocessing module as an equipment current grouping model, judging the association abnormity of energy consumption among equipment in a grouping, and outputting the energy consumption abnormity;
in step S4, training a BP neural network by using the historical data of the operating state of each device as a training set to obtain a device operating state model; and taking the equipment running state constructed by the data preprocessing module and the energy abnormity generated by the equipment current grouping module as input, and judging the energy consumption abnormity of the single equipment.
In the scheme, the running state data of the equipment, including the data of each acquisition node of each equipment, is acquired through a sensor built in or externally connected with the intelligent equipment, and the data is stored in a streaming message queue corresponding to the sensor;
preprocessing data: acquiring current data of current collection nodes of each device, and constructing a current collection node grouping state Sn < Sn node 1 current, Sn node 2 current and Sn node 3 current according to a grouping result of correlation analysis, wherein n is epsilon <1,2 and 3. >; acquiring all node data of a single device from a streaming message queue, and constructing a device running state Un < all node data of device n >, wherein n belongs to <1,2,3. >;
inputting an equipment current grouping algorithm model according to a view of grouping Sn, wherein n belongs to <1,2,3. >, monitoring the energy consumption state of equipment in real time, judging whether each group is in energy consumption abnormity in the whole production flow, and judging whether the relevance of the current state of each group of equipment is abnormal; if a certain group of Sk1 is in a normal state, wherein k1 is epsilon <1,2,3. >, the energy consumption of all the devices in the Sk1 group is in a normal state; if a certain group of Sk2, where k2 ∈ <1,2,3. >, is in an abnormal state, it indicates that the energy consumption of some devices in the Sk2 group is in an abnormal state, and it needs to further determine which device has abnormal energy consumption through a device state algorithm model;
from all exception packets Sk, k ∈ <1,2,3. >, a list of all devices contained is obtained. The equipment states of the equipment contained in the abnormal group are input into the equipment state model one by one for matching detection, which one or more equipment is/are in the energy consumption abnormal state is judged, whether the energy consumption of the single equipment is abnormal or not is judged, and all the equipment in the energy consumption abnormal state is output;
displaying, reminding and early warning the energy consumption abnormal equipment on a relevant controller, an alarm or a display screen, and correspondingly controlling the abnormal equipment in subsequent work;
in the scheme, the scheme relates to the monitoring and early warning of indexes such as the on-off state of each equipment switch, the rotating speeds of a plurality of rolling shafts of equipment, the energy consumption state of a multipoint motor of the equipment and the like, the original data of each information acquisition point is acquired in real time, data preprocessing is carried out and put into a streaming message queue, the data is collected from the streaming message queue by using a big data technology, the data is preprocessed, the running state view of the equipment is constructed, the running state standard under the complex production environment is established by using a data mining technology, the energy consumption state of the equipment is monitored, whether the equipment is in the normal running state or not is judged, the result is displayed, and corresponding early warning operation is carried out. The invention uses big data mining technology, flexibly analyzes the equipment running state under complex conditions, establishes the running state correlation model among different equipment, optimizes the originally used machinery, and only based on a single-target early warning mode, so that the monitoring result is more convincing, and the cost of enterprises is saved.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides an energy consumption monitoring device in intelligent production equipment and an operation method thereof, which use a big data mining technology to flexibly analyze the operation state of the equipment under complex conditions, establish an operation state association model among different equipment, optimize the early warning mode which is originally used and is only based on a single target, enable the monitoring result to be more convincing and save the cost for enterprises.
Drawings
FIG. 1 is a schematic diagram of the module connection of the apparatus of the present invention;
FIG. 2 is a schematic flow chart of the operation method of the present invention;
wherein: 1. a data acquisition module; 2. a data preprocessing module; 3. a device current grouping module; 4. an equipment running state module; 5. an online monitoring module; 51. a microprocessor; 52. a display module; 53. an input module; 54. and an alarm module.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
As shown in fig. 1, an energy consumption monitoring device in an intelligent production facility includes a data acquisition module 1, a data preprocessing module 2, a facility current grouping module 3, a facility running state module 4, and an online monitoring module 5; wherein:
the data acquisition module 1 is arranged on the intelligent production equipment, acquires energy consumption data of the intelligent production equipment and transmits the energy consumption data to the data preprocessing module 2;
the data preprocessing module 2 preprocesses the energy consumption data to obtain a node grouping state and an operation state of the equipment;
the device current grouping module 3 is used for constructing a device current grouping model, judging the association abnormity of energy consumption among devices in a group according to the node grouping state of the devices and outputting the energy consumption abnormity;
the equipment running state module 4 is used for constructing an equipment running state model and judging the energy consumption abnormity of a single piece of equipment according to the running state and the energy consumption abnormity of the equipment;
and the online monitoring module 5 displays and gives an alarm to abnormal energy consumption equipment according to the output results of the equipment current grouping module 3 and the equipment running state module 4.
More specifically, the data acquisition module 1 comprises a plurality of sensors and a data analyzer; wherein:
the sensor is internally or externally connected to the intelligent production equipment and is used for acquiring energy consumption data of the intelligent production equipment; transmitting the energy consumption data to the data analyzer;
the data analyzer is used for analyzing the energy consumption data, transmitting the analyzed data into a streaming message queue, and waiting for the data preprocessing module 2 to call.
More specifically, the energy consumption data includes the switching value, the temperature, the current, the rotating speed and the pressure of each acquisition node of each intelligent production device.
More specifically, in the data preprocessing module 2, the preprocessing process includes:
the data preprocessing module 2 is used for removing current data of current acquisition nodes of each device from the streaming data and grouping the current data according to the correlation analysis of the current nodes; constructing a node grouping state of the equipment according to the correlation analysis result;
the data preprocessing module 2 collects the running state of the device from the streaming message queue.
In a specific implementation process, a current node correlation grouping in the data preprocessing module 1 obtains circuit node current data of all devices from streaming data to form a data set S < node 1 current, node 2 current, node 3 current, node 4 current, node 5 current and node 6 current. >, performs correlation calculation on all currents by using a Pearson correlation analysis formula in mathematical statistics to obtain correlation among all node currents, and puts the node currents with large correlation into a group of circuit node current grouping states S1< S1 node 1 current, S1 node 2 current, S1 node 3 current. >, S2< S2 node 4 current, S2 node 5 current, S2 node 6 current. >, Sn < > wherein n ∈ <1,2,3. >.
More specifically, in the device current grouping module 3, historical data of a node grouping state of the device is used, a BP neural network algorithm is adopted, and training is performed to obtain a device current grouping model; and outputting the node grouping state constructed by the data preprocessing module 2 as the equipment current grouping model, judging the association abnormality of energy consumption among the equipment in the grouping, and outputting the energy consumption abnormality.
In the above scheme, the device current grouping model construction process specifically includes:
(1) data collected by a quality node and a production node of a production line are used as indexes for judging whether the production state is healthy or not, the indexes are mapped to a health state value, the health state value is adopted to label the equipment current grouping state data Sn, and the following results are obtained: labeling the data Sn in a healthy state, wherein n belongs to <1,2,3. >, namely obtaining a data set S' n, wherein n belongs to <1,2,3. >: s '1 < S' 1 node 1 current, S '1 node 2 current, S' 1 node 3 current, health state >, S '2 < S' 2 node 4 current, S '2 node 5 current, S' 2 node 6 current, health state >, wherein n belongs to <1,2,3 >;
(2) aiming at the grouping S' n, wherein n belongs to <1,2,3. >, a BP neural network algorithm is constructed, model training is carried out, and an equipment current grouping algorithm model is obtained;
more specifically, in the device running state module 4, historical data of each device running state is used, a BP neural network algorithm is adopted, and training is performed to obtain a device running state model; and taking the equipment running state constructed by the data preprocessing module 2 and the energy abnormity generated by the equipment current grouping module 3 as input, and judging the energy consumption abnormity of a single equipment.
In a specific implementation process, the device operation state model construction process specifically includes:
(2) and marking the equipment current grouping state data Sn by taking the data acquired by the quality node and the output node of the production line as indexes for judging whether the production state is healthy or not, and obtaining the following results: labeling data Un in a healthy state, wherein n belongs to <1,2,3. >, namely obtaining a data set U' n, wherein n belongs to <1,2,3. >: u '1 < all node data of the device 1, health state >, U' 2< all node data of the device 2, health state >, wherein n belongs to <1,2,3. >;
and aiming at the running state U' n of the equipment, wherein n belongs to <1,2 and 3 >, constructing a BP neural network algorithm, and performing the algorithm to obtain an equipment state algorithm model.
More specifically, the online monitoring module 5 includes a microprocessor 51, a display module 52, an input module 53 and an alarm module 54; wherein:
the input module 53 is used for inputting an operation instruction, and an output end of the input module is electrically connected with an input end of the microprocessor 51;
the output end of the microprocessor 51 is electrically connected with the signal input end of the display module 52;
the microprocessor 51 is electrically connected with the control end of the alarm module 54;
the input end of the microprocessor 51 is electrically connected with the output end of the equipment running state module 4.
In the specific implementation process, the device flexibly analyzes the running state of the equipment under the complex condition by using a big data mining technology, establishes a running state correlation model among different equipment, optimizes an early warning mode which is originally used and is only based on a single standard, enables the monitoring result to be more convincing, and saves the cost for enterprises.
Example 2
More specifically, on the basis of embodiment 1, as shown in fig. 2, an operation method of an energy consumption monitoring device in an intelligent production facility includes the following steps:
s1: a data acquisition module 1 is arranged on the intelligent production equipment to acquire energy consumption data of the intelligent production equipment;
s2: preprocessing the energy consumption data through a data preprocessing module 2 to obtain a node grouping state and an operation state of the equipment;
s3: training an equipment current grouping model through an equipment current grouping module 3, taking the node grouping state of the equipment as the input of the equipment current grouping model, judging the association abnormality of energy consumption among the equipment in the grouping, and outputting the energy consumption abnormality;
s4: training an equipment running state model through an equipment running state module 4, taking the running state and the energy consumption abnormity of the equipment as the input of the equipment running state model, and judging the energy consumption abnormity of the single equipment;
s5: and according to the output results of the equipment current grouping module 3 and the equipment running state module 4, displaying and alarming the equipment with abnormal energy consumption on the online monitoring module 5.
More specifically, in step S1, energy consumption data of the intelligent production equipment is acquired by a sensor built in or externally connected to the intelligent production equipment; and analyzing the energy consumption data, transmitting the analyzed data into a streaming message queue, and waiting for the calling of the data preprocessing module 2.
More specifically, in step S3, training the BP neural network by using the historical data of the node grouping state of the device as a training set, so as to obtain a device current grouping model; outputting the node grouping state constructed by the data preprocessing module 2 as an equipment current grouping model, judging the association abnormity of energy consumption among equipment in a group, and outputting the energy consumption abnormity;
in step S4, training a BP neural network by using the historical data of the operating state of each device as a training set to obtain a device operating state model; and the equipment running state constructed by the data preprocessing module 2 and the energy abnormity generated by the equipment current grouping module 3 are used as input, and the energy consumption abnormity of a single equipment is judged.
In the specific implementation process, the running state data of the equipment, including the data of each acquisition node of each equipment, is acquired through a sensor built in or externally connected with the intelligent equipment, and the data is stored in a streaming message queue corresponding to the sensor;
preprocessing data: acquiring current data of current collection nodes of each device, and constructing a current collection node grouping state Sn < Sn node 1 current, Sn node 2 current and Sn node 3 current according to a grouping result of correlation analysis, wherein n is epsilon <1,2 and 3. >; acquiring all node data of a single device from a streaming message queue, and constructing a device running state Un < all node data of a device n >, wherein n belongs to <1,2,3. >;
inputting an equipment current grouping algorithm model according to a view of grouping Sn, wherein n belongs to <1,2,3. >, monitoring the energy consumption state of equipment in real time, judging whether each group is in energy consumption abnormity in the whole production flow, and judging whether the relevance of the current state of each group of equipment is abnormal; if a certain group of Sk1 is in a normal state, wherein k1 is epsilon <1,2,3. >, the energy consumption of all the devices in the Sk1 group is in a normal state; if a certain group of Sk2, where k2 ∈ <1,2,3. >, is in an abnormal state, it indicates that the energy consumption of some devices in the Sk2 group is in an abnormal state, and it needs to further determine which device has abnormal energy consumption through a device state algorithm model;
from all exception packets Sk, k ∈ <1,2,3. >, a list of all devices contained is obtained. The equipment states of the equipment contained in the abnormal group are input into the equipment state model one by one for matching detection, which one or more equipment is/are in the energy consumption abnormal state is judged, whether the energy consumption of the single equipment is abnormal or not is judged, and all the equipment in the energy consumption abnormal state is output;
displaying, reminding and early warning the energy consumption abnormal equipment on a relevant controller, an alarm or a display screen, and correspondingly controlling the abnormal equipment in subsequent work;
in the specific implementation process, the scheme relates to the monitoring and early warning of indexes such as the on-off state of each equipment switch, the rotating speeds of a plurality of rolling shafts of equipment, the energy consumption state of a multipoint motor of the equipment and the like. The invention uses big data mining technology, flexibly analyzes the equipment running state under complex conditions, establishes the running state correlation model among different equipment, optimizes the originally used machinery, and only based on a single-target early warning mode, so that the monitoring result is more convincing, and the cost of enterprises is saved.
Example 3
On the basis of embodiments 1 and 2, aiming at the defects of the old technology, the scheme provides a monitoring and early warning method for indexes such as the running state, the energy consumption state and the like of each device in the intelligent manufacturing production process of big data analysis, and the monitoring of the energy consumption state in the complex environment is realized by means of data mining and deep learning technologies, the result is displayed and early warned, and powerful help is provided for enterprises to calculate loss and make decisions; the invention analyzes indexes such as equipment energy consumption and the like by analyzing the equipment running states under different conditions by means of an algorithm model, optimizes the originally used machinery, and only based on a single standard and a single early warning mode, so that the monitoring result is more convincing, and the cost is saved for enterprises.
The scheme comprises five parts: firstly, a data acquisition module 1; a data preprocessing module 2; device current grouping module 3; an equipment running state module 4; fifthly, monitoring the module 5 on line;
the data acquisition module 1: acquiring equipment running state data including switching value, temperature, current, rotating speed, pressure and the like of each acquisition node of each equipment through a sensor built in or externally connected to the intelligent equipment, performing normalization processing, and storing the data into topic corresponding to the sensor in a kafka message queue;
the data preprocessing module 2:
firstly, current data of current collection nodes of each device are obtained from kafka, and grouping is carried out according to current node correlation analysis. Then, according to a correlation analysis result, current collection node grouping states Sn < Sn node 1 current, Sn node 2 current and Sn node 3 current are constructed, wherein n is belonged to <1,2 and 3. >;
and acquiring equipment operation state Un < all node data of equipment n from kafka, wherein n belongs to <1,2,3. >, and all node data comprise equipment switches, temperature, current, rotating speed and pressure.
Device current grouping module 3: using historical state data of the current grouping state Sn of the equipment, wherein n is epsilon <1,2 and 3 >, adopting a BP neural network algorithm, training, and constructing an equipment current grouping model for judging the grouping energy consumption abnormal state;
the equipment running state module 4: singly using historical data of the running state Un of each device, wherein n belongs to <1,2 and 3 >, adopting a BP neural network algorithm, training, and constructing a device running state model for judging whether each device is in an energy consumption abnormal state;
the online monitoring module 5: and combining the output results of the equipment current grouping model 3 and the equipment running state model 4 to perform interface display and alarm prompt on the equipment with abnormal energy consumption.
More specifically, data collected by a quality node and a production node of a production line are used as indexes for judging whether a production state is healthy or not, equipment current grouping state data Sn are labeled, and a PVC calendering equipment is taken as an example, wherein the quality node comprises: the flaw detection result and the thickness gauge result are obtained, and data are collected to indicate whether the product has flaws and the thickness of the product; the output nodes comprise the collection nodes of the last curling wheel: weighing instrument result, length measuring instrument result, and data acquisition representing product weight and product length; according to the collected data, the specific conditions can be as follows:
if the product is flawless, and the thickness, length and weight of the finished product exceed the minimum threshold value and are within the standard range, the production state is normal, and the quality of the produced finished product is over-qualified;
if the product has defects, the thickness, the length and the weight of the finished product are all in a range exceeding the minimum threshold and are in a standard range, which indicates that the production state is abnormal and the quality of the produced finished product is not relevant;
if the product is not flawed, but the thickness, the length and the weight of the finished product are not in the standard range, the production state is abnormal, and the quality of the produced finished product is not over-critical;
if the product has defects, the thickness, the length and the weight of the finished product are not in the standard range, the production state is abnormal, and the quality of the produced finished product is not over-qualified;
the module 3 is constructed based on the device current grouping:
(1) the data collected by the quality nodes and the output nodes of the production line are used as indexes for judging whether the production state is healthy or not, and the equipment current grouping state data Sn are marked to obtain the following results:
data Sn in the state a can be considered to be in a healthy state, wherein n ∈ <1,2,3. >, and is labeled, so that a data set S' n is obtained, wherein n ∈ <1,2,3. >: s '1 < S' 1 node 1 current, S '1 node 2 current, S' 1 node 3 current, health >, S '2 < S' 2 node 4 current, S '2 node 5 current, S' 2 node 6 current, health >, wherein n ∈ <1,2,3. >;
and secondly, the data Sn in the states b, c and d can be considered to be in an unhealthy state, wherein n belongs to <1,2,3. >, and the data Sn is labeled to obtain a data set S' n, wherein n belongs to <1,2,3. >: s '1 < S' 1 node 1 current, S '1 node 2 current, S' 1 node 3 current, unhealthy >, S '2 < S' 2 node 4 current, S '2 node 5 current, S' 2 node 6 current, unhealthy >, wherein n belongs to <1,2,3. >;
(2) aiming at the grouping S' n, wherein n belongs to <1,2 and 3 >, a BP neural network algorithm is constructed, and an algorithm is carried out to obtain an equipment current grouping algorithm model;
based on the device operating state module 4:
(1) the data collected by the quality nodes and the output nodes of the production line are used as indexes for judging whether the production state is healthy or not, and the equipment running state data Un is labeled to obtain the following results:
marking the data Un in the state a as a healthy state, wherein n is belonged to <1,2,3. >, so as to obtain a data set U' n, wherein n is belonged to <1,2,3. >: u '1 < all node data of the equipment 1, health >, U' 2< all node data of the equipment 2, health >, wherein n belongs to <1,2,3. >;
and secondly, data Un in states b, c and d can be considered to be in an unhealthy state, wherein n belongs to <1,2,3. >, and is labeled, wherein n belongs to <1,2,3. >, namely a data set U' n is obtained, wherein n belongs to <1,2,3. >: u '1 < all node data of the device 1, unhealthy >, U' 2< all node data of the device 2, unhealthy >, wherein n belongs to <1,2,3. >;
(2) aiming at the running state U' n of the equipment, wherein n is the element of <1,2,3. >, a BP neural network algorithm is constructed, and the algorithm is carried out to obtain an equipment running state model;
the online monitoring module 5: displaying, reminding and early warning the energy consumption abnormal equipment on a related instrument or a display screen, and correspondingly controlling the abnormal equipment in subsequent work;
the real-time monitoring process specifically comprises the following steps:
(1) acquiring equipment running state data including data of each acquisition node of each equipment through a sensor built in or externally connected to the intelligent equipment, and storing the data into topic corresponding to the sensor in kafka;
(2) preprocessing data: firstly, acquiring current data of each equipment current collection node from kafka, grouping results according to current node correlation analysis, and constructing a current collection node grouping state Sn < Sn node 1 current, Sn node 2 current and Sn node 3 current, wherein n is belonged to <1,2 and 3. >; acquiring the running state Un of the equipment from kafka, wherein n belongs to the node data of 1,2 and 3. >;
(3) inputting an equipment current grouping algorithm model according to a view of grouping Sn, wherein n belongs to <1,2,3. >, monitoring the energy consumption state of equipment in real time, judging whether each group is in energy consumption abnormity in the whole production flow, and judging whether the relevance of the current state of each group of equipment is abnormal; if a certain group of Sk1 is in a normal state, wherein k1 belongs to <1,2,3. >, the energy consumption of all devices in the Sk1 group is in a normal state, and no treatment is needed to the devices; if a certain group of Sk2, where k2 ∈ <1,2,3. >, is in an abnormal state, it indicates that the energy consumption of some devices in the Sk2 group is in an abnormal state, and it needs to further determine which device has abnormal energy consumption through a device state algorithm model;
(4) from all exception packets Sk, k ∈ <1,2,3. >, a list of all devices contained is obtained. Acquiring equipment running state views Un of all equipment needing to be detected, inputting equipment state models one by one for matching detection, judging which one or more pieces of equipment are in an energy consumption abnormal state, judging whether the energy consumption of single equipment is abnormal or not, and outputting all the equipment in the energy consumption abnormal state;
(5) displaying, reminding and early warning the energy consumption abnormal equipment on a relevant controller, an alarm or a display screen, and correspondingly controlling the abnormal equipment in subsequent work;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (2)
1. An energy consumption monitoring device in intelligent production equipment is characterized by comprising a data acquisition module (1), a data preprocessing module (2), an equipment current grouping module (3), an equipment running state module (4) and an online monitoring module (5); wherein:
the data acquisition module (1) is arranged on the intelligent production equipment, acquires energy consumption data of the intelligent production equipment and transmits the energy consumption data to the data preprocessing module (2); the data acquisition module (1) comprises a plurality of sensors and a data analyzer; wherein: the sensor is internally or externally connected to the intelligent production equipment and used for acquiring energy consumption data of the intelligent production equipment and transmitting the energy consumption data to the data analyzer; the data analyzer is used for analyzing the energy consumption data, transmitting the analyzed data into a streaming message queue and waiting for the data preprocessing module (2) to call;
the energy consumption data comprises the switching value, the temperature, the current, the rotating speed and the pressure of each acquisition node of each intelligent production device;
the data preprocessing module (2) preprocesses the energy consumption data to obtain a node grouping state and an operation state of the equipment; the pretreatment process comprises the following steps: the data preprocessing module (2) acquires current data of current acquisition nodes of each device from streaming data, and groups the current data according to the correlation analysis of the current nodes; constructing a node grouping state of the equipment according to the correlation analysis result; the data preprocessing module (2) collects the running state of the equipment from the streaming message queue;
the device current grouping module (3) is used for constructing a device current grouping model, judging the association abnormity of energy consumption among devices in a group according to the node grouping state of the devices and outputting the energy consumption abnormity; in the equipment current grouping module (3), historical data of node grouping states of equipment are used, a BP neural network algorithm is adopted, training is carried out, and an equipment current grouping model is obtained; outputting the node grouping state constructed by the data preprocessing module (2) as the equipment current grouping model, judging that the association of energy consumption among the equipment in the grouping is abnormal, and outputting the energy consumption abnormality;
the equipment running state module (4) is used for constructing an equipment running state model and judging the energy consumption abnormity of a single piece of equipment according to the running state and the energy consumption abnormity of the equipment; in the equipment running state module (4), historical data of each equipment running state is used, a BP neural network algorithm is adopted, training is carried out, and an equipment running state model is obtained; the equipment running state constructed by the data preprocessing module (2) and the energy consumption abnormity generated by the equipment current grouping module (3) are used as input, and the energy consumption abnormity of a single piece of equipment is judged;
the online monitoring module (5) displays and gives an alarm to abnormal energy consumption equipment according to the output results of the equipment current grouping module (3) and the equipment running state module (4); the online monitoring module (5) comprises a microprocessor (51), a display module (52), an input module (53) and an alarm module (54); wherein: the input module (53) is used for inputting an operation instruction, and the output end of the input module is electrically connected with the input end of the microprocessor (51); the output end of the microprocessor (51) is electrically connected with the signal input end of the display module (52); the microprocessor (51) is electrically connected with the control end of the alarm module (54); the input end of the microprocessor (51) is electrically connected with the output end of the equipment running state module (4).
2. The method for operating the energy consumption monitoring device in the intelligent production equipment, as recited in claim 1, comprises the following steps:
s1: a data acquisition module (1) is arranged on the intelligent production equipment to acquire energy consumption data of the intelligent production equipment; the energy consumption data of the intelligent production equipment is acquired by internally arranging or externally connecting a sensor on the intelligent production equipment; analyzing the energy consumption data, transmitting the analyzed data into a streaming message queue, and waiting for the calling processing of the data preprocessing module (2);
s2: preprocessing the energy consumption data through a data preprocessing module (2) to obtain a node grouping state and an operation state of the equipment;
s3: training an equipment current grouping model through an equipment current grouping module (3), taking the node grouping state of the equipment as the input of the equipment current grouping model, judging the association abnormality of energy consumption among the equipment in the grouping, and outputting the energy consumption abnormality; taking historical data of a node grouping state of the equipment as a training set, and training a BP neural network to obtain an equipment current grouping model; outputting a node grouping state constructed by the data preprocessing module (2) as an equipment current grouping model, judging that the association of energy consumption among the equipment in a group is abnormal, and outputting the energy consumption abnormality;
s4: training an equipment running state model through an equipment running state module (4), taking the running state and the energy consumption abnormity of the equipment as the input of the equipment running state model, and judging the energy consumption abnormity of the single equipment; taking historical data of the running state of each device as a training set, and training a BP neural network to obtain a device running state model; the equipment running state constructed by the data preprocessing module (2) and the energy consumption abnormity generated by the equipment current grouping module (3) are used as input, and the energy consumption abnormity of a single piece of equipment is judged;
s5: and according to the output results of the equipment current grouping module (3) and the equipment running state module (4), displaying and alarming the equipment with abnormal energy consumption on the online monitoring module (5).
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011640533.8A CN112859769B (en) | 2020-12-31 | 2020-12-31 | Energy consumption monitoring device in intelligent production equipment and operation method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011640533.8A CN112859769B (en) | 2020-12-31 | 2020-12-31 | Energy consumption monitoring device in intelligent production equipment and operation method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112859769A CN112859769A (en) | 2021-05-28 |
| CN112859769B true CN112859769B (en) | 2022-09-06 |
Family
ID=76000653
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011640533.8A Active CN112859769B (en) | 2020-12-31 | 2020-12-31 | Energy consumption monitoring device in intelligent production equipment and operation method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112859769B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113485279B (en) * | 2021-08-19 | 2022-04-01 | 安徽三马信息科技有限公司 | Factory equipment start-stop energy-saving management system based on full-view artificial intelligence |
| CN113464414A (en) * | 2021-08-23 | 2021-10-01 | 广东鑫钻节能科技股份有限公司 | Energy-saving operation method of air compression station |
| CN114200984B (en) * | 2021-12-10 | 2022-09-27 | 黄山奥仪电器有限公司 | 15 way combination unification electric current type temperature controller |
| CN117684243B (en) * | 2024-02-04 | 2024-04-09 | 深圳市海里表面技术处理有限公司 | Intelligent electroplating control system and control method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107271764A (en) * | 2017-06-19 | 2017-10-20 | 宁波三星医疗电气股份有限公司 | A kind of electrical appliance power consumption method for detecting abnormality and device |
| CN109051104A (en) * | 2018-06-29 | 2018-12-21 | 江苏高远智能科技有限公司 | A kind of bottle placer operating status intelligent Detection and method |
| CN109613908A (en) * | 2018-11-23 | 2019-04-12 | 浙江永发机电有限公司 | A kind of intelligent electric machine system with cloud big data platform |
| CN110217656A (en) * | 2019-06-20 | 2019-09-10 | 江苏正一物联科技有限公司 | Elevator group controller monitoring system and method with energy consumption analysis |
| CN111191881A (en) * | 2019-12-13 | 2020-05-22 | 大唐东北电力试验研究院有限公司 | Thermal power generating unit industrial equipment state monitoring method based on big data |
| CN111611254A (en) * | 2020-04-30 | 2020-09-01 | 广东良实机电工程有限公司 | Equipment energy consumption abnormity monitoring method and device, terminal equipment and storage medium |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| BRPI0705569A2 (en) * | 2007-09-11 | 2009-05-05 | Univ Minas Gerais | method for measurement and monitoring |
| AU2013101573A4 (en) * | 2013-11-29 | 2014-01-09 | Macau University Of Science And Technology | Method for predicting and detecting network intrusion into a computer network |
| CN106126385B (en) * | 2016-06-14 | 2018-09-07 | 电子科技大学 | A kind of unit exception real-time detection method based on synchronous data flow compression |
| CN107666399A (en) * | 2016-07-28 | 2018-02-06 | 北京京东尚科信息技术有限公司 | A kind of method and apparatus of monitoring data |
| CN106484971B (en) * | 2016-09-23 | 2019-07-02 | 北京清控人居环境研究院有限公司 | A kind of automatic identifying method of drainage pipeline networks monitoring point |
| CN106959394B (en) * | 2017-02-15 | 2019-11-22 | 清华大学 | A kind of high-voltage large-capacity STATCOM state evaluating method and system |
| CN110469496B (en) * | 2019-08-27 | 2021-04-09 | 苏州热工研究院有限公司 | Intelligent early warning method and system for water pump |
| CN111080466A (en) * | 2019-12-03 | 2020-04-28 | 广东工业大学 | Calender calendering quality early warning system based on big data |
-
2020
- 2020-12-31 CN CN202011640533.8A patent/CN112859769B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107271764A (en) * | 2017-06-19 | 2017-10-20 | 宁波三星医疗电气股份有限公司 | A kind of electrical appliance power consumption method for detecting abnormality and device |
| CN109051104A (en) * | 2018-06-29 | 2018-12-21 | 江苏高远智能科技有限公司 | A kind of bottle placer operating status intelligent Detection and method |
| CN109613908A (en) * | 2018-11-23 | 2019-04-12 | 浙江永发机电有限公司 | A kind of intelligent electric machine system with cloud big data platform |
| CN110217656A (en) * | 2019-06-20 | 2019-09-10 | 江苏正一物联科技有限公司 | Elevator group controller monitoring system and method with energy consumption analysis |
| CN111191881A (en) * | 2019-12-13 | 2020-05-22 | 大唐东北电力试验研究院有限公司 | Thermal power generating unit industrial equipment state monitoring method based on big data |
| CN111611254A (en) * | 2020-04-30 | 2020-09-01 | 广东良实机电工程有限公司 | Equipment energy consumption abnormity monitoring method and device, terminal equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112859769A (en) | 2021-05-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112859769B (en) | Energy consumption monitoring device in intelligent production equipment and operation method thereof | |
| KR102427205B1 (en) | Apparatus and method for generating training data of artificial intelligence model | |
| CN116185757B (en) | Intelligent monitoring system for energy consumption of machine room | |
| CN113657221A (en) | Power plant equipment state monitoring method based on intelligent sensing technology | |
| CN113177646A (en) | Power distribution equipment online monitoring method and system based on self-adaptive edge proxy | |
| CN110728381A (en) | Intelligent power plant inspection method and system based on RFID and data processing | |
| CN109598309B (en) | Detection system and monitoring method of metal packaging punching machine | |
| CN108093210A (en) | A kind of transformer oil level warning system and its alarm method | |
| CN113420162B (en) | Equipment operation chain state monitoring method based on knowledge graph | |
| CN113611096A (en) | Electric energy consumption monitoring and early warning system for air compression station | |
| CN110514699B (en) | Cable defect on-line measuring system | |
| TW201017447A (en) | An early stage abnormality prediction device for fabrication process and its prediction method | |
| CN116277001A (en) | Continuous casting robot management method and system based on digital twin | |
| CN116399462A (en) | Digital visual cable temperature sensing early warning method and system | |
| CN115526095A (en) | Digital twin system of flexible brushless motor production line | |
| CN115378140A (en) | Unmanned aerial vehicle power equipment inspection system and method based on image recognition | |
| CN106059093A (en) | Smart substation health condition monitoring system | |
| TWM604689U (en) | Intelligent monitoring system applied to cutting process | |
| CN112612360A (en) | Three-dimensional visualization equipment monitoring system and method based on VR technology | |
| CN205139687U (en) | Online management system of quality is being pressed to pencil | |
| CN215642375U (en) | Equipment centralized monitoring system | |
| CN115049277A (en) | Production equipment management method and system | |
| CN113740588B (en) | Intelligent early warning method and system for industrial robot | |
| CN117629265A (en) | Underwater wireless energy transmission coil monitoring system and method | |
| CN113720458A (en) | Biological body temperature monitoring and inspection system and equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |