CN114861540A - Mine ventilator working state monitoring system based on WT-CNN and harmonic vibration feature fusion - Google Patents
Mine ventilator working state monitoring system based on WT-CNN and harmonic vibration feature fusion Download PDFInfo
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Abstract
The invention relates to a monitoring system for the working state of a mine ventilator based on WT-CNN and harmonic vibration feature fusion. The acquisition module comprises distributed audio sensing nodes and vibration sensing nodes, audio and vibration signals of the working state of the ventilator are fused, data are transmitted to a microprocessor of the analysis node through wired and wireless heterogeneous communication networks, the collected data are rapidly processed and analyzed by adopting a WT-CNN-based mine ventilator fault monitoring model, an abnormal diagnosis result is obtained, the abnormal diagnosis result is uploaded to an upper computer monitoring center through an industrial Ethernet, and the upper computer interface displays the working state characteristic information of the mine ventilator in real time. The mine ventilator monitoring system can monitor the working state of the mine ventilator in real time, provide a real-time alarm function and reduce the failure rate.
Description
Technical Field
The invention relates to the field of mine ventilator fault monitoring, in particular to a mine ventilator working state monitoring system based on WT-CNN and harmonic vibration feature fusion.
Background
The mine ventilator is one of important underground working equipment, plays a role of an air conveyor underground, is a life conveyor of underground workers, provides circulating air for the miners, discharges coal dust, reduces the concentration of harmful gas, ensures safe and orderly operation of underground production, needs long-time continuous operation and is heavy-load energy consumption equipment. In the actual working operation of the mine ventilator, abnormal states of a shaft, an impeller, a bearing, a motor and the like of the ventilator can cause abnormal work of the ventilator, and once the mine ventilator breaks down or stops working, the stability of a coal mine system and the life safety of underground workers are directly influenced. Therefore, the mine ventilator is directly related to safe production, and the working state of the mine ventilator needs to be detected in real time to avoid faults.
At present, the mine ventilation machine abnormity detection is realized by mainly collecting relevant parameters of the mine ventilation machine during working through a single vibration sensor or a temperature sensor, processing data and uploading the data to an upper computer for display.
Therefore, a monitoring system for the working state of the mine ventilator based on fusion of WT-CNN and harmonic vibration characteristics is needed, so that the mine ventilator can be timely processed once a fault occurs, and the mine ventilator can be prevented from getting in the bud.
Disclosure of Invention
The invention aims to provide a monitoring system for the working state of a mine ventilator based on WT-CNN and harmonic vibration feature fusion, which utilizes a sound sensor and a vibration sensor to collect audio and vibration signals of the mine ventilator in different running states, realizes on-line monitoring of the working state of the mine ventilator, can effectively prevent faults, and ensures safe and stable running of the mine ventilator.
The invention adopts the following technical scheme for realizing the purpose:
a mine ventilator working condition monitoring system based on WT-CNN and harmonic vibration feature fusion is characterized in that: the monitoring system comprises a collection node, an analysis node, an upper computer monitoring center and a handheld terminal, wherein the collection node comprises a sound sensor (1), a vibration sensor (2), a first microprocessor (301), a first communication module (401) and a first power module (501); the analysis module comprises a second microprocessor (302), a second communication module (402), a second power module (502), a memory (6) and a sound-light alarm (7); the data of the collection node and the analysis node are sent through serial ports of a first microprocessor (301) and a second microprocessor (302); and the second microprocessor (302) of the analysis node sends the processed data to the upper computer monitoring center through a second communication module (402).
Preferably, the working state monitoring system of the mine ventilator based on WT-CNN and harmonic vibration feature fusion is characterized in that: the collection node comprises a sound sensor (1), a vibration sensor (2), a first microprocessor (301), a first communication module (401) and a first power module (501); the sound sensor (1), the vibration sensor (2) are connected with an I/O port of the first microprocessor (301) through wires, and the first power module (501) supplies power to the sound sensor (1), the vibration sensor (2), the first microprocessor (301) and the first communication module (401) through wires.
Preferably, the working state monitoring system of the mine ventilator based on WT-CNN and harmonic vibration feature fusion is characterized in that: the analysis node comprises a second microprocessor (302), a second communication module (402), a second power module (502), a memory (6) and an acousto-optic alarm (7), wherein the second communication module (402), the memory (6) and the acousto-optic alarm (7) are connected with an I/O (input/output) port of the second microprocessor (302) through leads, and the second power module (402) supplies power to the second communication module (402), the memory (6) and the acousto-optic alarm (7) through leads.
Preferably, the working state monitoring system of the mine ventilator based on WT-CNN and harmonic vibration feature fusion is characterized in that: after the first microprocessor (301) is electrified and initialized, the sound sensor (1) and the vibration sensor (2) are controlled to collect audio signals and vibration signals during operation of the mine ventilator and transmit the audio signals and the vibration signals to the second microprocessor (302) through the first communication module (401), and the second microprocessor (302) processes and analyzes the collected signals, extracts characteristic information, diagnoses abnormity of the characteristic information and stores the characteristic information into the memory (6).
Preferably, the working state monitoring system of the mine ventilator based on WT-CNN and harmonic vibration feature fusion is characterized in that: the characteristic information abnormity diagnosis is to construct a WT-CNN-based fault classification network, and firstly, audio signals and vibration signals of different working states of the mine ventilator, which are acquired by nodes, are acquired; then, performing wavelet transformation on the original signals in batch to obtain a time-frequency diagram beneficial to CNN model training; then preprocessing the time-frequency diagram, constructing a CNN model for training, and determining optimal parameters according to a training result; and finally, applying the trained optimal model to monitoring the working state of the mine ventilator.
Preferably, the working state monitoring system of the mine ventilator based on WT-CNN and harmonic vibration feature fusion is characterized in that: the second microprocessor (302) performs wavelet transformation on original signals in batches, inputs a time-frequency graph after wavelet transformation into a CNN model, identifies the working state of the mine ventilator, stores mine working state information into a memory (6), sends the mine working state information through a second communication module (402), an upper computer receives the mine working state information through an industrial Ethernet, displays the working state information on an interface of the upper computer, sends the working state information to a handheld terminal through a Wi-Fi wireless network, if abnormity or fault occurs, an upper computer monitoring center or a mobile terminal immediately sends an alarm signal, the alarm signal is sent out through the industrial Ethernet, and after the second communication module (402) receives the alarm signal, the second microprocessor (302) immediately controls an audible and visual alarm (7) to alarm.
Compared with the prior art, the invention has the beneficial effects that:
1. the method has the advantages that the sound and vibration sensors are used for collecting multi-source information of the running state of the mine ventilator, a multi-information fusion data sensing layer containing distributed audio sensing nodes and vibration sensing nodes is constructed, the running state of the mine ventilator can be comprehensively represented, and the sound sensors are collected in a non-contact mode and are convenient to install.
2. The data transmission is carried out by utilizing wired and wireless heterogeneous communication networks, and the anti-interference capability is strong, the transmission efficiency is high, and the reliability is high.
3. The collected data are processed in real time, the running state of the mine ventilator can be detected in real time, the running state can be displayed and monitored on the upper computer, an alarm function is provided, and the accuracy of the identification of the working state of the mine ventilator is improved.
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FIG. 1 is a schematic view of the overall structure of the present invention;
FIG. 2 is a diagram of a collection node structure of the present invention;
FIG. 3 is a diagram of an analysis node structure of the present invention;
FIG. 4 is a diagnostic model structure of the operating condition of the WT-CNN mine ventilator.
Wherein, in fig. 2: 1-a sound sensor, 2-a vibration sensor, 301-a first microprocessor, 401-a first communication module, 501-a first power module; in fig. 3: 302-second microprocessor, 402-second communication module, 502-second power module, 6-memory, 7-audible and visual alarm.
Detailed Description
The invention is further illustrated by the following specific examples.
As shown in fig. 1, the overall structure of the system is schematically shown, and the system specifically comprises a collection node, an analysis node, an upper computer monitoring center and a handheld terminal. The whole system is implemented as follows:
the collection node comprises a sound sensor 1, a vibration sensor 2, a first microprocessor 301, a first communication module 401 and a first power module 501, wherein the first power module 501 supplies power to the sound sensor 1, the vibration sensor 2 and the first microprocessor 301 through conducting wires, after the first microprocessor 301 is powered on and initialized, the sound sensor 1 and the vibration sensor 2 are controlled to collect data, the distributed audio sensing nodes are composed of a plurality of directional and aluminum strip type sound pick-up devices, the audio sensing nodes are distributed annularly around the ventilator, the sound pick-up devices respectively point to the impeller of the ventilator, the non-driving side bearing and the driving side motor to collect audio signals of the ventilator in real time in the running process, and the high-fidelity audio line transmits the signals to the first microprocessor 301, the vibration sensor 2 is arranged near the motor shell and the rotating shaft to collect vibration signals, and the vibration signals are transmitted to the first microprocessor 301 through the RS485 communication module.
In the analysis node, a second power supply module 502 supplies power to a second microprocessor 302, a second communication module 402, a memory 6 and an audible and visual alarm 7, the second microprocessor receives audio signals and vibration signals after being electrified and initialized, analyzes and processes the audio signals and the vibration signals, diagnoses the working state of the ventilator through a WT-CNN fault diagnosis model, stores the diagnosis result into the memory 6, packs and compresses the diagnosis result by the memory 6, sends the diagnosis result to an upper computer monitoring center through an industrial Ethernet, and displays the diagnosis result on an upper computer interface in real time.
In the analysis node, a CNN network in a WT-CNN fault diagnosis model comprises an input layer, a convolution layer, a pooling layer and a full connection layer, wherein the input layer inputs audio and vibration signals and divides compressed images after wavelet transformation, the convolution layer extracts the characteristics of the images, the pooling layer reduces the dimensionality of a characteristic diagram in a maximum pooling mode, and finally obtains deep characteristic information through the full connection layer, and classifies output results by using classification functions to obtain the working state category of the mine ventilator, wherein the expression of continuous wavelet transformation is as follows:
in the formula (1), f (t) represents the original signal, a represents the scale factor, τ represents the panning factor, ψ a,τ And (t) is a wavelet basis function, and a cmor wavelet basis is selected in the text because the function waveform of the continuous wavelet transform is similar to the characteristics of an analysis object when the wavelet basis is selected.
The upper computer monitoring center receives the diagnosis result sent by the memory 6 through the industrial Ethernet, displays the diagnosis result on an interface of the upper computer in real time, sends the diagnosis result to the handheld terminal through the Wi-Fi wireless network, compares and analyzes the diagnosis result at the upper computer monitoring center or the mobile terminal, judges the fault state of the equipment, immediately sends an alarm signal if the fault is judged, the alarm signal is sent through the industrial Ethernet, and the second microprocessor 302 immediately controls the audible and visual alarm 7 to give an alarm after the second communication module 402 receives the alarm signal.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (6)
1. A mine ventilator working condition monitoring system based on WT-CNN and harmonic vibration feature fusion is characterized in that: the monitoring system comprises a collection node, an analysis node, an upper computer monitoring center and a handheld terminal, wherein the collection node comprises a sound sensor (1), a vibration sensor (2), a first microprocessor (301), a first communication module (401) and a first power module (501); the analysis module comprises a second microprocessor (302), a second communication module (402), a second power supply module (502), a memory (6) and an acousto-optic alarm (7); the data of the collection node and the analysis node are sent through serial ports of a first microprocessor (301) and a second microprocessor (302); and the second microprocessor (302) of the analysis node sends the processed data to the upper computer monitoring center through a second communication module (402).
2. The system for monitoring the working condition of the mine ventilator based on the WT-CNN and the acoustic vibration feature fusion of claim 1, wherein: the collection node comprises a sound sensor (1), a vibration sensor (2), a first microprocessor (301), a first communication module (401) and a first power module (501); the sound sensor (1), the vibration sensor (2) are connected with an I/O port of the first microprocessor (301) through wires, and the first power module (501) supplies power to the sound sensor (1), the vibration sensor (2), the first microprocessor (301) and the first communication module (401) through wires.
3. The system for monitoring the working condition of the mine ventilator based on the WT-CNN and the acoustic vibration feature fusion of claim 1, wherein: the analysis node comprises a second microprocessor (302), a second communication module (402), a second power module (502), a memory (6) and an acousto-optic alarm (7), wherein the second communication module (402), the memory (6) and the acousto-optic alarm (7) are connected with an I/O (input/output) port of the second microprocessor (302) through leads, and the second power module (402) supplies power to the second communication module (402), the memory (6) and the acousto-optic alarm (7) through leads.
4. The system for monitoring the working condition of the mine ventilator based on the WT-CNN and the acoustic vibration feature fusion of claim 1, wherein: after the first microprocessor (301) is electrified and initialized, the sound sensor (1) and the vibration sensor (2) are controlled to collect audio signals and vibration signals during operation of the mine ventilator and transmit the audio signals and the vibration signals to the second microprocessor (302) through the first communication module (401), and the second microprocessor (302) processes and analyzes the collected signals, extracts characteristic information, diagnoses abnormity of the characteristic information and stores the characteristic information into the memory (6).
5. The system for monitoring the working condition of the mine ventilator based on WT-CNN and harmonic vibration feature fusion is characterized in that: the characteristic information abnormity diagnosis is to construct a WT-CNN-based fault classification network, and firstly, audio signals and vibration signals of different working states of the mine ventilator, which are acquired by nodes, are acquired; then, performing wavelet transformation on the original signals in batch to obtain a time-frequency diagram beneficial to CNN model training; then preprocessing the time-frequency diagram, constructing a CNN model for training, and determining optimal parameters according to a training result; and finally, applying the trained optimal model to monitoring the working state of the mine ventilator.
6. The system for monitoring the working condition of the mine ventilator based on the WT-CNN and the acoustic vibration feature fusion of claim 1, wherein: the second microprocessor (302) performs wavelet transformation on original signals in batches, inputs a time-frequency graph after wavelet transformation into a CNN model, identifies the working state of the mine ventilator, stores mine working state information into a memory (6), sends the mine working state information through a second communication module (402), an upper computer receives the mine working state information through an industrial Ethernet, displays the working state information on an interface of the upper computer, sends the working state information to a handheld terminal through a Wi-Fi wireless network, if abnormity or fault occurs, an upper computer monitoring center or a mobile terminal immediately sends an alarm signal, the alarm signal is sent out through the industrial Ethernet, and after the second communication module (402) receives the alarm signal, the second microprocessor (302) immediately controls an audible and visual alarm (7) to alarm.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116049725A (en) * | 2023-03-29 | 2023-05-02 | 淄博热力有限公司 | Rotary machine fault diagnosis method based on improved deep learning classification model |
| CN116517862A (en) * | 2023-04-27 | 2023-08-01 | 淮南矿业(集团)有限责任公司煤业分公司 | Mine ventilator abnormality diagnosis system based on STFT-CNN |
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| CN204113701U (en) * | 2014-07-18 | 2015-01-21 | 河北联合大学 | A kind of mine fan on-line monitoring and fault diagnosis system |
| CN114035047A (en) * | 2021-11-22 | 2022-02-11 | 安徽理工大学 | Mine ventilation machine motor fault early warning device based on audio frequency identification |
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- 2022-05-12 CN CN202210518586.5A patent/CN114861540A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN204113701U (en) * | 2014-07-18 | 2015-01-21 | 河北联合大学 | A kind of mine fan on-line monitoring and fault diagnosis system |
| CN114035047A (en) * | 2021-11-22 | 2022-02-11 | 安徽理工大学 | Mine ventilation machine motor fault early warning device based on audio frequency identification |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116049725A (en) * | 2023-03-29 | 2023-05-02 | 淄博热力有限公司 | Rotary machine fault diagnosis method based on improved deep learning classification model |
| CN116049725B (en) * | 2023-03-29 | 2023-12-29 | 淄博热力有限公司 | Rotary machine fault diagnosis method based on improved deep learning classification model |
| CN116517862A (en) * | 2023-04-27 | 2023-08-01 | 淮南矿业(集团)有限责任公司煤业分公司 | Mine ventilator abnormality diagnosis system based on STFT-CNN |
| CN116517862B (en) * | 2023-04-27 | 2024-02-27 | 淮南矿业(集团)有限责任公司煤业分公司 | Abnormality diagnosis system for mine ventilator |
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