CN111337234A - TBM scraper service life prediction system and method based on real-time monitoring - Google Patents
TBM scraper service life prediction system and method based on real-time monitoring Download PDFInfo
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Abstract
The invention belongs to the technical field of safe operation of TBM scrapers, and discloses a TBM scraper service life prediction system and method based on real-time monitoring, wherein the prediction system comprises: the lower computer acquisition system is used for acquiring state signals of the TBM scraper and preprocessing the state signals, and the state signals comprise eddy current signals and vibration signals; the preprocessing comprises weight processing, and the eddy current signal is weighted higher than the vibration signal; the neural network processing module is used for receiving the preprocessed state signal and predicting the residual life of the TBM scraper based on the preprocessed state signal so as to obtain a prediction result; in the invention, the real-time monitoring of the TBM scraper is realized by acquiring the eddy current signal and the vibration signal which can reflect the state of the TBM scraper; in the monitoring process, the eddy current signal is taken as a main part, and the vibration signal is taken as an auxiliary part, so that the inaccuracy of prediction caused by environmental noise interference and single data can be effectively reduced.
Description
Technical Field
The invention belongs to the technical field of safe operation of TBM scrapers, and particularly relates to a TBM scraper service life prediction system and method based on real-time monitoring.
Background
The TBM scraper is an important component for achieving TBM tunneling. Particularly, when the TBM is tunneled, the scraper is used for cutting rock and soil mass and needs to bear larger radial force and tangential force, so that the TBM scraper is easy to wear and fail due to overlarge stress; and the scraper is also severely rubbed by rock-soil body particles to generate abrasion, thereby further influencing the normal state of the TBM scraper.
Therefore, if the abrasion state and the residual service life of the TBM scraper can be known in time, the replacement time of the TBM scraper can be accurately judged, and the accurate replacement of the TBM scraper influences the TBM tunneling efficiency to a great extent.
Disclosure of Invention
In view of the above, the present invention provides a system and a method for predicting a lifetime of a TBM scraper based on real-time monitoring, and in particular, the present invention monitors an eddy current signal and a vibration signal of the TBM scraper in real time, and combines the monitoring signal with a neural network to achieve highly accurate lifetime prediction of the TBM scraper, thereby facilitating replacement of the TBM scraper to reduce production cost.
In order to achieve the purpose, the invention provides the following technical scheme:
TBM scraper life prediction system based on real-time monitoring
The prediction system comprises a lower computer acquisition system and a neural network processing module;
the lower computer acquisition system is used for acquiring state signals of the TBM scraper and preprocessing the state signals, and the state signals comprise eddy current signals and vibration signals; the lower computer acquisition system is also in wireless communication with the neural network processing module so as to transmit the state signal to the neural network processing module;
the neural network processing module is used for receiving the preprocessed state signal and predicting the residual life of the TBM scraper based on the preprocessed state signal so as to obtain a prediction result; the neural network processing module is also in wireless communication with an upper computer display system so as to transmit the prediction result to the upper computer display system;
wherein:
the preprocessing comprises weight processing, and the eddy current signal is weighted higher than the vibration signal;
the neural network processing module comprises a CNN-LSMT neural network framework, the CNN-LSMT neural network framework comprises a convolution layer, a pooling layer, a drop-out layer, a full-connection layer and an LSMT layer, and the CNN-LSMT neural network framework completes training based on a public data set to realize service life prediction of the TBM scraper.
Preferably, the lower computer acquisition system mainly comprises: the sensor acquisition module is used for acquiring eddy current signals and vibration signals from the TBM scraper; the data conversion module is used for converting the acquired eddy current signals and vibration signals into transmittable digital signals; the MCU data preprocessing module is used for preprocessing the digital signal with weight processing; and the wireless data transmission module transmits the preprocessed signals to the neural network processing module based on a wireless network.
Preferably, the sensor acquisition module comprises a vibration sensor and an eddy current sensor, and both are embedded in the TBM scraper.
Preferably, the data conversion module mainly comprises an acquisition card and an AD conversion chip, wherein: the acquisition card comprises an integrated amplification circuit and a filter circuit so as to convert the acquired eddy current signals and vibration signals into analog signals; the AD conversion chip is used for converting the analog signal into a digital signal.
Preferably, the wireless data transmission module adopts one of a bluetooth module or a WIFI module.
Preferably, the prediction system further comprises an upper computer display system: and the upper computer display system is used for acquiring a prediction result from the neural network processing module, and displaying and storing the prediction result.
Preferably, the upper computer display system mainly includes: the acquisition module is used for acquiring a prediction result from the neural network processing module; a database for storing the prediction results a plurality of times; the real-time data display module is used for displaying the prediction result; and the historical data query module is used for querying a historical prediction result from the database.
A TBM scraper life prediction method based on real-time monitoring comprises the following steps:
s1, constructing a lower computer acquisition system, and building and training a neural network processing module; the neural network processing module comprises a CNN-LSMT neural network framework, the CNN-LSMT neural network framework comprises a convolution layer, a pooling layer, a drop-out layer, a full-connection layer and an LSMT layer, and the CNN-LSMT neural network framework completes training based on a public data set;
s2, acquiring an eddy current signal and a vibration signal based on a lower computer acquisition system, and preprocessing, wherein the preprocessing comprises weight processing, and the weight of the eddy current signal is higher than that of the vibration signal;
s3, acquiring the preprocessed signal based on the neural network processing module, and predicting the service life of the TBM scraper based on the preprocessed signal to obtain a prediction result;
and S4, displaying and storing the prediction result in an upper computer display system.
Compared with the prior art, the invention has the following beneficial effects:
in the invention, a sensor is embedded into the TBM scraper so as to obtain an eddy current signal and a vibration signal which can reflect the state of the TBM scraper, thereby realizing the real-time monitoring of the TBM scraper; in the monitoring process, the eddy current signal is taken as a main part, and the vibration signal is taken as an auxiliary part, so that the inaccuracy of prediction caused by environmental noise interference and single data can be effectively reduced.
Based on the signals, processing by adopting a neural network to realize the real-time prediction of the residual life of the TBM scraper, so that whether the TBM scraper needs to be replaced or not can be judged in time;
aiming at the neural network, a CNN-LSMT neural network framework comprising a drop-out layer is adopted, and the framework is optimally trained by using an open data set, so that the prediction accuracy and robustness of the neural network are effectively improved.
In addition, the invention is also provided with an upper computer display system, so that the service life prediction result of the TBM scraper can be checked and stored in real time.
Drawings
FIG. 1 is a block diagram of a prediction system provided in the present invention;
FIG. 2 is a flow chart of a prediction method provided by the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
Referring to fig. 1, a structural block diagram of a TBM scraper life prediction system based on real-time monitoring provided by the present invention is shown, and it can be known from the figure that the overall prediction system includes three parts, namely, a lower computer acquisition system 1, a neural network processing module 2 and an upper computer display system 3:
as an implementable manner, the lower computer acquisition system 1 includes:
the sensor acquisition module mainly comprises a vibration sensor and an eddy current sensor which are embedded in the TBM scraper and is used for acquiring eddy current signals and vibration signals from the TBM scraper.
The data conversion module mainly comprises a collection card and an AD conversion chip and is used for converting the collected eddy current signals and vibration signals into transmittable digital signals;
specifically, the acquisition card comprises an integrated amplification circuit and a filter circuit, so as to convert the acquired eddy current signal and vibration signal into analog signals; the AD conversion chip is used for converting the analog signal into a digital signal.
The MCU data preprocessing module is used for preprocessing the digital signals with weight processing;
specifically, the weight of the eddy current signal is higher than that of the vibration signal, so that the overall signal acquisition is in a form that the eddy current signal is dominant and the vibration signal is subordinate.
The wireless data transmission module transmits the preprocessed signals to the neural network processing module based on a wireless network; specifically, the module adopts one of a Bluetooth module or a WIFI module.
In summary, in the present embodiment, the signal form with the eddy current signal as the main signal and the vibration signal as the auxiliary signal is collected, so that the problem of inaccurate prediction caused by environmental noise interference and single data can be effectively reduced.
As an implementable manner, the neural network processing module 2 mainly includes:
the CNN-LSMT neural network framework is built by a convolution layer, a pooling layer, a drop-out layer, a full connection layer and an LSMT layer.
The building of the drop-out layer has the effects of improving the generalization capability of the neural network and preventing overfitting;
in addition, after the CNN-LSMT neural network framework is built, the CNN-LSMT neural network framework is optimized and trained by using the public data set, so that parameters in the CNN-LSMT neural network framework are optimized, and the prediction accuracy and robustness of the neural network are improved.
As an implementable manner, regarding the upper computer display system 3, the upper computer display module 3 is used for displaying the life prediction result processed by the neural network 2 and storing the result in the database; the upper computer display module 3 also has the functions of historical data query and real-time data display; the upper computer display module 3 includes:
the acquisition module is used for acquiring a prediction result from the neural network processing module;
a database for storing the prediction results for a plurality of times;
the real-time data display module is used for displaying the prediction result;
and the historical data query module is used for querying a historical prediction result from the database.
Specifically, the upper computer display system 3 is set up in a TBM master control room, wherein a database is built up by using SQL2014, so as to ensure that the overall upper computer display system 3 can realize operations such as storage, calling and deletion of data after each processing.
In conclusion, in the embodiment, the service life prediction result of the TBM scraper can be effectively checked by the staff in time.
Example 2
Referring to fig. 2, a flow chart of a method for predicting the service life of a TBM scraper based on real-time monitoring according to the present invention is shown, and it can be seen that the overall prediction method includes the following steps:
s1, constructing a lower computer acquisition system, and building and training a neural network processing module; the neural network processing module comprises a CNN-LSMT neural network framework, the CNN-LSMT neural network framework comprises a convolution layer, a pooling layer, a drop-out layer, a full-connection layer and an LSMT layer, and the CNN-LSMT neural network framework completes training based on a public data set;
s2, acquiring eddy current signals and vibration signals based on a lower computer acquisition system, and preprocessing the signals, wherein the preprocessing comprises weight processing, and the weight of the eddy current signals is higher than that of the vibration signals;
s3, acquiring the preprocessed signal based on the neural network processing module, and predicting the service life of the TBM scraper based on the preprocessed signal to obtain a prediction result;
and S4, displaying and storing the prediction result in an upper computer display system.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (9)
1. The utility model provides a TBM scraper life-span prediction system based on real-time supervision which characterized in that: the prediction system comprises a lower computer acquisition system and a neural network processing module;
the lower computer acquisition system is used for acquiring state signals of the TBM scraper and preprocessing the state signals, wherein the state signals comprise eddy current signals and vibration signals; the lower computer acquisition system is also in wireless communication with the neural network processing module so as to transmit the state signal to the neural network processing module;
the neural network processing module is used for receiving the preprocessed state signal and predicting the residual life of the TBM scraper based on the preprocessed state signal so as to obtain a prediction result; the neural network processing module is also in wireless communication with an upper computer display system so as to transmit the prediction result to the upper computer display system;
wherein:
the preprocessing comprises weight processing, and the eddy current signal is weighted higher than the vibration signal;
the neural network processing module comprises a CNN-LSMT neural network framework, the CNN-LSMT neural network framework comprises a convolution layer, a pooling layer, a drop-out layer, a full-connection layer and an LSMT layer, and the CNN-LSMT neural network framework completes training based on a public data set to realize service life prediction of the TBM scraper.
2. The system for predicting the service life of the TBM scraper based on real-time monitoring as claimed in claim 1, wherein the lower computer acquisition system mainly comprises:
the sensor acquisition module is used for acquiring eddy current signals and vibration signals from the TBM scraper;
the data conversion module is used for converting the acquired eddy current signals and vibration signals into transmittable digital signals;
the MCU data preprocessing module is used for preprocessing the digital signal with weight processing;
and the wireless data transmission module transmits the preprocessed signals to the neural network processing module based on a wireless network.
3. The system of claim 2 for predicting the life of the TBM scraper based on real-time monitoring, wherein the system comprises: the sensor acquisition module comprises a vibration sensor and an eddy current sensor, and the vibration sensor and the eddy current sensor are embedded in the TBM scraper.
4. The system of claim 2 for predicting the life of the TBM scraper based on real-time monitoring, wherein the system comprises: the data conversion module comprises an acquisition card and an AD conversion chip, wherein:
the acquisition card comprises an integrated amplification circuit and a filter circuit so as to convert the acquired eddy current signals and vibration signals into analog signals;
the AD conversion chip is used for converting the analog signal into a digital signal.
5. The system of claim 2 for predicting the life of the TBM scraper based on real-time monitoring, wherein the system comprises: the wireless data transmission module adopts one of a Bluetooth module or a WIFI module.
6. A real-time monitoring based TBM scraper life prediction system according to any of claims 1-5, characterized in that: the prediction system further comprises an upper computer display system:
and the upper computer display system is used for acquiring a prediction result from the neural network processing module, and displaying and storing the prediction result.
7. The system for predicting the service life of the TBM scraper based on real-time monitoring as claimed in claim 6, wherein the upper computer display system comprises:
the acquisition module is used for acquiring a prediction result from the neural network processing module;
a database for storing the prediction results a plurality of times;
the real-time data display module is used for displaying the prediction result;
and the historical data query module is used for querying a historical prediction result from the database.
8. A TBM scraper life prediction method based on real-time monitoring is characterized by comprising the following steps:
s1, constructing a lower computer acquisition system, and building and training a neural network processing module; the neural network processing module comprises a CNN-LSMT neural network framework, the CNN-LSMT neural network framework comprises a convolution layer, a pooling layer, a drop-out layer, a full-connection layer and an LSMT layer, and the CNN-LSMT neural network framework completes training based on a public data set;
s2, acquiring eddy current signals and vibration signals based on a lower computer acquisition system, and preprocessing the signals, wherein the preprocessing comprises weight processing, and the weight of the eddy current signals is higher than that of the vibration signals;
and S3, acquiring the preprocessed signal based on the neural network processing module, and predicting the service life of the TBM scraper based on the preprocessed signal to obtain a prediction result.
9. The method for predicting the service life of the TBM scraper based on real-time monitoring according to claim 8, wherein the method comprises the following steps:
when the step S1 is executed, constructing an upper computer display system;
after step S3 is executed, the method further includes: and S4, displaying and storing the prediction result in an upper computer display system.
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