CN117538659A - Mine equipment fault detection method based on intelligent power distribution cabinet electrical parameters - Google Patents
Mine equipment fault detection method based on intelligent power distribution cabinet electrical parameters Download PDFInfo
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- CN117538659A CN117538659A CN202311534902.9A CN202311534902A CN117538659A CN 117538659 A CN117538659 A CN 117538659A CN 202311534902 A CN202311534902 A CN 202311534902A CN 117538659 A CN117538659 A CN 117538659A
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- 238000001514 detection method Methods 0.000 title claims abstract description 28
- 238000009826 distribution Methods 0.000 title claims abstract description 10
- 238000005065 mining Methods 0.000 claims abstract description 33
- 238000010586 diagram Methods 0.000 claims abstract description 32
- 238000000034 method Methods 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 5
- 238000013528 artificial neural network Methods 0.000 claims description 16
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- 239000000284 extract Substances 0.000 claims description 3
- 238000002372 labelling Methods 0.000 claims description 3
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- 239000013598 vector Substances 0.000 claims description 3
- 238000013480 data collection Methods 0.000 claims 1
- 238000012423 maintenance Methods 0.000 abstract description 6
- 238000004519 manufacturing process Methods 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- E—FIXED CONSTRUCTIONS
- E21—EARTH OR ROCK DRILLING; MINING
- E21F—SAFETY DEVICES, TRANSPORT, FILLING-UP, RESCUE, VENTILATION, OR DRAINING IN OR OF MINES OR TUNNELS
- E21F17/00—Methods or devices for use in mines or tunnels, not covered elsewhere
- E21F17/18—Special adaptations of signalling or alarm devices
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R19/00—Arrangements for measuring currents or voltages or for indicating presence or sign thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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Abstract
The invention relates to a mining equipment fault detection method based on intelligent power distribution cabinet electrical parameters, which comprises the following steps: collecting operation current voltage data, transmitting the operation current voltage data, generating an operation current voltage time domain diagram and judging fault occurrence. In the practical implementation process, the cloud server can monitor each mine equipment in real time, the fault detection system can rapidly and accurately alarm the fault type, compared with the mode of staring at the equipment, the detection alarm mode is used for monitoring the mine equipment, and the cloud server has the advantages that the fault can be predicted, the fault type can be rapidly analyzed, and the corresponding type of alarm can be sent out, so that maintenance personnel can rapidly respond, the maintenance efficiency can be effectively improved, and major accidents can be avoided.
Description
Technical Field
The invention relates to the technical field of mining equipment detection, in particular to a mining equipment fault detection method based on intelligent power distribution cabinet electrical parameters.
Background
Mines generally refer to independent production and operation units of excavated ores with a certain exploitation environment, one mine comprises one or more production workshops (or mining workshops, pitheads, mines and the like) and process production workshops, and most of the mines also comprise ore selection sites.
In the traditional production management mode, most mines adopt a mode of staring at people and equipment, the mode of staring at equipment on the one hand is too dependent on manual work, and mine equipment is numerous, but mine staff is less, so that the reliability of the operation of the equipment staring at people is reduced, the problem of failure of the mine equipment in the operation process cannot be directly solved, and in addition, the problem of delay exists in the fault message of the mine equipment under the condition, and the staff cannot perceive in the first time. In addition, the mode of the equipment is generally only perceived when the equipment suddenly stops due to a large fault of the mining equipment, which has a great influence on the production efficiency of the mine. In order to further improve the safety and stability of mining equipment in production, a real-time fault early warning detection method for the mining equipment is urgently needed. Research shows that when the mining equipment fails or is about to fail, abnormal fluctuation of voltage and current in the mining equipment occurs, which is the key of real-time fault early warning detection of the mining equipment.
Disclosure of Invention
First, the technical problem to be solved
In order to solve the problems in the prior art, the invention provides a mining equipment fault detection method based on the electrical parameters of an intelligent power distribution cabinet, which utilizes a neural network method to analyze current and voltage data of the mining equipment in an operation state so as to judge the operation state of the mining equipment and pre-warn whether the equipment is about to have faults.
(II) technical scheme
In order to achieve the above purpose, the main technical scheme adopted by the invention comprises the following steps:
a mining equipment fault detection method based on intelligent power distribution cabinet electrical parameters comprises the following steps of;
x1, collecting running current and voltage data: starting each mine equipment, monitoring that each mine equipment is in an operating state by a cloud server, and sending an acquisition instruction to a current acquisition sensor and a voltage acquisition sensor arranged on each mine equipment through a communication module;
x2, transmitting operation current voltage data: after receiving the acquisition instructions, the current acquisition sensor and the voltage acquisition sensor start to acquire the current running current and voltage data of each mining device, and upload the acquired current and voltage data to the communication module at intervals of preset time;
x3, generating an operation current voltage time domain diagram: the communication module uploads the data of the current and the voltage to the cloud server, extracts the voltage and the current change information of the mining equipment in the operation process through the data processing module to form an operation current voltage time domain diagram, and sends the operation current voltage time domain diagram into the mining equipment fault detection system;
x4, judging that the fault occurs: the mine fault detection system compares the running current voltage time domain diagram with a current voltage time domain diagram database model; when the mine equipment fails or is predicted to fail, the corresponding failure early warning alarm is sent out by combining the alarm module to prompt the staff.
Optionally, the establishing of the current-voltage time domain diagram database model comprises the following steps of;
s1, building an artificial neural network;
s2, collecting data: collecting current and voltage data of various mining equipment in the normal operation process, in the fault process and in the fault process, and labeling according to the equipment type;
s3, generating a current-voltage time domain diagram database model: feeding the labeled current and voltage data into an artificial neural network, converting the current and voltage data into a current and voltage time domain diagram by the artificial neural network according to a ruler with a length of 25ms and a frame of 8.5ms, and integrating the current and voltage time domain diagrams of all types to form a database model;
s4, optimizing a database model: by increasing the time domain diagram of the feeding current and voltage and increasing the training time, the artificial neural network can optimize the parameters of the database model, so as to predict the occurrence time and the fault type of the mining equipment before the fault occurs.
Optionally, the collecting of the current-voltage data in step S3 includes the following steps;
a1: selecting the same type of equipment;
a2: the current and voltage data of the equipment when the equipment fails and before the failure occurs are called;
a3: and comparing the current voltage data with the fault types, so that the fault types correspond to the current voltage data one by one, and extracting fault identification feature vectors.
(III) beneficial effects
The beneficial effects of the invention are as follows: in the practical implementation process, the cloud server can monitor each mine equipment in real time, the fault detection system can rapidly and accurately alarm the fault type, compared with the mode of staring at the equipment, the detection alarm mode is used for monitoring the mine equipment, and the cloud server has the advantages that the fault can be predicted, the fault type can be rapidly analyzed, and the corresponding type of alarm can be sent out, so that maintenance personnel can rapidly respond, the maintenance efficiency can be effectively improved, and major accidents can be avoided.
Drawings
FIG. 1 is a fault detection and early warning flow chart of an embodiment of the present invention;
FIG. 2 is a flow chart of a database creation of a current-voltage time domain diagram according to an embodiment of the present invention;
FIG. 3 is a block diagram of a real-time fault early warning architecture according to an embodiment of the present invention;
description of the reference numerals
Mining equipment 1, a cloud server 2, a communication module 3, a current acquisition sensor 4, a voltage acquisition sensor 5, a data processing module 6, a database model 7, a fault detection system 8 and an alarm module 9.
Detailed Description
The invention will be better explained by the following detailed description of the embodiments with reference to the drawings.
Referring to fig. 1-3, the mining equipment fault detection method based on the intelligent power distribution cabinet electrical parameters of the invention comprises the following steps of;
x1, collecting running current and voltage data: starting each mine equipment 1, monitoring that each mine equipment 1 is in an operating state by a cloud server 2, and sending an acquisition instruction to a current acquisition sensor 4 and a voltage acquisition sensor 5 arranged on each mine equipment 1 through a communication module 3;
x2, transmitting operation current voltage data: after receiving the acquisition instructions, the current acquisition sensor 4 and the voltage acquisition sensor 5 start to acquire the current running current and voltage data of each mining device 1, and upload the acquired current and voltage data to the communication module 3 at intervals of preset time;
x3, generating an operation current voltage time domain diagram: the communication module 3 uploads the data of the current and the voltage to the cloud server 2, extracts the voltage and the current change information of the mining equipment 1 in the operation process through the data processing module 6 to form an operation current voltage time domain diagram, and sends the operation current voltage time domain diagram to the fault detection system 8;
x4, judging that the fault occurs: the fault detection system 8 compares the running current voltage time domain diagram with the database model 7 of the current voltage time domain diagram; when the mining equipment 1 fails or is predicted to be in failure, the corresponding failure early warning alarm is sent out by combining the alarm module 9 to prompt the staff.
In the actual implementation process, the cloud server 2 can monitor each mine equipment 1 in real time, the fault detection system 8 can rapidly and accurately alarm the fault type, compared with the mode of staring at the equipment by people, the mine equipment 1 is monitored by the detection alarm mode, and the cloud server has the advantages that the fault can be predicted, the fault type can be rapidly analyzed, and the corresponding type of alarm can be sent out, so that maintenance personnel can rapidly respond, the maintenance efficiency can be effectively improved, and major accidents can be avoided.
Optionally, the establishing of the database model of the current-voltage time domain diagram comprises the following steps;
s1, building an artificial neural network;
s2, collecting data: collecting current and voltage data of various mining equipment 1 in the normal operation process, in the fault process and in the fault process, and labeling according to the equipment type;
s3, generating a database model 7 of a current-voltage time domain diagram: feeding the labeled current and voltage data into an artificial neural network, converting the current and voltage data into a current and voltage time domain diagram by the artificial neural network according to a ruler with a length of 25ms and a frame of 8.5ms, and integrating the current and voltage time domain diagrams of all types to form a database 7 model;
s4, optimizing a database 7 model: by increasing the time-domain map of the feed current and voltage and increasing the training time, the artificial neural network can optimize the model parameters of the database 7 to predict the time of occurrence and the type of failure immediately before the failure of the mining equipment 1 occurs.
In the actual implementation process, the current-voltage time domain graph database 7 model can be continuously optimized by continuously feeding data into the artificial neural network, so that the fault detection system 8 is more accurate and efficient when comparing the running current-voltage data.
Optionally, the collecting of the current-voltage data in step S3 includes the following steps;
a1: selecting the same type of equipment;
a2: the current and voltage data of the equipment when the equipment fails and before the failure occurs are called;
a3: and comparing the current voltage data with the fault types, so that the fault types correspond to the current voltage data one by one, and extracting fault identification feature vectors.
In the actual implementation process, the data fed into the artificial neural network can be more targeted by the data collecting mode, so that the artificial neural network can be conveniently analyzed.
The basic principle and main characteristics of the invention and the advantages of the invention are shown and described above, standard parts used by the invention can be purchased from market, special-shaped parts can be customized according to the description of the specification and the drawings, the specific connection modes of the parts adopt conventional means such as mature bolt rivets and welding in the prior art, the machinery, the parts and the equipment adopt conventional models in the prior art, and the circuit connection adopts conventional connection modes in the prior art, so that the description is omitted.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent changes made by the specification and drawings of the present invention, or direct or indirect application in the relevant art, are included in the scope of the present invention.
Claims (3)
1. The mining equipment fault detection method based on the intelligent power distribution cabinet electrical parameters is characterized by comprising the following steps of;
x1, collecting running current and voltage data: starting each mine equipment, monitoring that each mine equipment is in an operating state by a cloud server, and sending an acquisition instruction to a current acquisition sensor and a voltage acquisition sensor arranged on each mine equipment through a communication module;
x2, transmitting operation current voltage data: after receiving the acquisition instructions, the current acquisition sensor and the voltage acquisition sensor start to acquire the current running current and voltage data of each mining device, and upload the acquired current and voltage data to the communication module at intervals of preset time;
x3, generating an operation current voltage time domain diagram: the communication module uploads the data of the current and the voltage to the cloud server, extracts the voltage and the current change information of the mining equipment in the operation process through the data processing module to form an operation current voltage time domain diagram, and sends the operation current voltage time domain diagram to the fault detection system;
x4, judging that the fault occurs: the fault detection system compares the running current voltage time domain diagram with a current voltage time domain diagram database model; when the mine equipment fails or is predicted to fail, the corresponding failure early warning alarm is sent out by combining the alarm module to prompt the staff.
2. The mining equipment fault detection method based on the intelligent power distribution cabinet electrical parameters as claimed in claim 1, wherein the establishment of the current-voltage time domain diagram database model comprises the following steps of;
s1, building an artificial neural network;
s2, collecting data: collecting current and voltage data of various mining equipment in the normal operation process, in the fault process and in the fault process, and labeling according to the equipment type;
s3, generating a current-voltage time domain diagram database model: feeding the labeled current and voltage data into an artificial neural network, converting the current and voltage data into a current and voltage time domain diagram by the artificial neural network according to a ruler with a length of 25ms and a frame of 8.5ms, and integrating the current and voltage time domain diagrams of all types to form a database model;
s4, optimizing a database model: by increasing the time domain diagram of the feeding current and voltage and increasing the training time, the artificial neural network can optimize the parameters of the database model, so as to predict the occurrence time and the fault type of the mining equipment before the fault occurs.
3. The mining equipment fault detection method based on the intelligent power distribution cabinet electrical parameters as claimed in claim 2, wherein the current and voltage data collection in the step S3 comprises the following steps of;
a1: selecting the same type of equipment;
a2: the current and voltage data of the equipment when the equipment fails and before the failure occurs are called;
a3: and comparing the current voltage data with the fault types, so that the fault types correspond to the current voltage data one by one, and extracting fault identification feature vectors.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311534902.9A CN117538659A (en) | 2023-11-17 | 2023-11-17 | Mine equipment fault detection method based on intelligent power distribution cabinet electrical parameters |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202311534902.9A CN117538659A (en) | 2023-11-17 | 2023-11-17 | Mine equipment fault detection method based on intelligent power distribution cabinet electrical parameters |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118171538A (en) * | 2024-04-25 | 2024-06-11 | 江苏中天互联科技有限公司 | Fault time prediction method, electronic device and storage medium |
| CN118375484A (en) * | 2024-04-18 | 2024-07-23 | 山东黄金矿业(莱州)有限公司三山岛金矿 | Unmanned aerial vehicle-based mine inspection equipment and inspection method thereof |
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2023
- 2023-11-17 CN CN202311534902.9A patent/CN117538659A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118375484A (en) * | 2024-04-18 | 2024-07-23 | 山东黄金矿业(莱州)有限公司三山岛金矿 | Unmanned aerial vehicle-based mine inspection equipment and inspection method thereof |
| CN118171538A (en) * | 2024-04-25 | 2024-06-11 | 江苏中天互联科技有限公司 | Fault time prediction method, electronic device and storage medium |
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