CN117707089B - An abnormal monitoring system for production workshops based on the Internet of Things - Google Patents
An abnormal monitoring system for production workshops based on the Internet of Things Download PDFInfo
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- G—PHYSICS
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- 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]
- G05B19/41875—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 quality surveillance of production
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention discloses a production workshop abnormity monitoring system based on the Internet of things, and belongs to the technical field of production prevention and control. The system comprises a data acquisition device for acquiring operation information of different devices of a production workshop, wherein the operation information comprises current information, voltage information, temperature information and infrared image information, an Internet of things transmission device is used for uploading the operation information of the devices to an Internet of things monitoring platform according to preset frequency, the Internet of things monitoring platform is used for displaying the uploaded operation information of the devices in real time, analyzing and predicting whether the devices of the production workshop have faults or not based on the operation information of the devices, and if yes, early warning prompt is carried out based on a prediction result and an early warning report is sent to the mobile terminal. And the failure result of the production equipment is obtained through analysis and prediction of the monitoring data of the real-time production workshop equipment, so that the maintenance efficiency is improved.
Description
Technical Field
The invention relates to the technical field of data monitoring of the Internet of things, in particular to a production workshop anomaly monitoring system based on the Internet of things.
Background
Along with the rapid development of the internet of things technology, more and more enterprises begin to apply the internet of things technology to production workshops so as to improve production efficiency, reduce cost and improve product quality.
However, conventional monitoring systems for production shop anomalies are generally based on manual inspection and simple sensor monitoring, and cannot realize real-time and comprehensive anomaly monitoring and early warning. The traditional production workshop anomaly monitoring system only judges the quality and the bad of the operation equipment through a threshold value, and lacks the mining analysis and the fault trend prediction of the operation data of the equipment by adopting an intelligent analysis technology, and because the operation and maintenance equipment of the production workshop is more in variety and more complex, the key influencing factors causing the operation faults of the equipment are not easy to find, so that the equipment maintenance efficiency is lower.
Disclosure of Invention
In view of the above, the present invention provides a production shop anomaly monitoring system based on the internet of things for solving the above-mentioned problems of the prior art
In order to achieve the above object, the present invention provides the following technical solutions:
the production workshop abnormity monitoring system based on the Internet of things comprises data acquisition equipment, internet of things transmission equipment, an Internet of things monitoring platform and a mobile terminal;
the data acquisition equipment acquires operation information of different equipment of the production workshop, wherein the operation information comprises current information, voltage information, temperature information and infrared image information;
the internet of things transmission equipment uploads equipment operation information to the internet of things monitoring platform according to a preset frequency;
And the Internet of things monitoring platform displays the uploaded equipment operation information in real time, analyzes and predicts whether the production workshop equipment fails or not based on the equipment operation information, if so, performs early warning prompt and sends an early warning report to the mobile terminal.
Preferably, the data acquisition device includes:
the current sensor, the voltage sensor, the temperature sensor and the infrared camera are used for respectively acquiring current information, voltage information, temperature information and infrared image information.
Preferably, the internet of things transmission device includes:
The storage module is used for caching the equipment operation information;
the frequency adjustment module is used for sending a driving signal according to a preset frequency, wherein the preset frequency comprises the following steps:
default upload frequency:
Wherein, sigma is a time standard deviation, omega is a preset standard deviation weight, sigma 0 is a preset standard deviation reference value, and f 0 is a standard uploading frequency of an uploading module used;
When the cached equipment information is judged to be more than 80 percent of the total cache capacity, the uploading frequency is increased:
fb=Wlog2(1+S/N);
wherein W is the maximum channel bandwidth of an uploading module used by a channel, S is the average power of signals transmitted in the channel, and N is Gaussian noise power in the channel;
And the uploading module is used for uploading the equipment operation information to the monitoring and control platform of the Internet of things based on the driving signal.
Preferably, the internet of things monitoring platform includes:
the display module is used for displaying the uploaded equipment operation information in real time;
the prediction module is used for analyzing and predicting whether the production workshop equipment fails or not based on the equipment operation information and generating a corresponding early warning report;
and the early warning module is used for carrying out early warning prompt and sending an early warning report to the mobile terminal when the production workshop equipment fails.
Preferably, the prediction module includes:
Preprocessing the equipment operation information, including acquiring historical voltage information, historical current information and historical temperature information of different equipment in a production workshop, performing feature extraction to generate a spectrogram feature sample data set, acquiring historical infrared image information of the different equipment in the production workshop, performing feature extraction to generate an infrared image feature sample data set, and dividing the spectrogram feature sample data set and the infrared image feature sample data set into a training set, a test set and a prediction set;
constructing a neural network model, and inputting training set and test set data to perform learning training;
preprocessing equipment operation information, and calling a trained neural network model to perform multi-scale fault identification to obtain a prediction result.
Preferably, the data format of the generated spectrogram feature sample data set is:
α=[(x1,y1),(x2,y2),…,(xn,yn)];
where x 1,x2…xn is the device runtime and y 1,y2…yn is the spectrogram of the corresponding runtime.
Preferably, the data format of the generated infrared map feature sample data set is:
β=[(u1,w1),(u2,w2),…,(um,wm)];
Where u 1,u2…um is the device runtime and w 1,w2…wm is the infrared image corresponding to the runtime.
Preferably, the method comprises the steps of constructing a neural network model, inputting training set data and test set data for learning training, wherein the training set data comprises a convolution layer, a pooling layer and a full-connection layer, the convolution layer iteratively extracts characteristics of training sets of different layers, a prediction result is obtained through the full-connection layer after passing through the pooling layer, a calculation loss function is output according to the prediction result, parameter weight of the neural network model is updated, and an Adam algorithm is adopted for optimization training, so that a trained neural network model is obtained.
Preferably, the preprocessing the equipment operation information and calling the trained neural network model to perform multi-scale fault recognition to obtain a prediction result includes:
carrying out data cleaning, wavelet packet noise reduction and normalization processing on the equipment operation information:
Extracting spectrogram characteristics of voltage information, current information and temperature information in equipment operation information, and obtaining a spectrogram characteristic input set with the same data format as that of a spectrogram characteristic sample data set;
extracting infrared image characteristics in equipment operation information, and obtaining an infrared image characteristic input set with the same data format as the infrared image characteristic sample data set;
And respectively inputting the spectrogram characteristic input set and the infrared image characteristic input set into the trained neural network model to obtain a prediction result.
Preferably, the Internet of things monitoring platform further comprises a control module for controlling the start and stop of corresponding equipment based on the prediction result.
Through the technical scheme, compared with the prior art, the invention has the following beneficial effects:
1) The invention realizes low-power consumption uploading in the transmission equipment of the Internet of things through different uploading frequencies, and improves the reliability of data acquisition through buffering;
2) The neural network model is constructed by collecting and processing multi-dimensional data and adopting deep learning, and the change rule of various monitoring data is intelligently identified rapidly, accurately and in batches through the spectrogram characteristic data and the infrared image characteristic data collected in real time, so that the neural network model is particularly suitable for rapid batch management of a large amount of monitoring data in a production workshop, and the equipment maintenance efficiency is improved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
FIG. 1 is a schematic diagram of a system architecture of the present invention;
fig. 2 is a schematic structural diagram of an internet of things transmission device according to the present invention;
fig. 3 is a schematic structural diagram of an internet of things monitoring platform according to the present invention.
Detailed Description
The invention further provides a production workshop abnormality monitoring system based on the Internet of things, which is described with reference to the accompanying drawings and the specific embodiment.
Referring to fig. 1, the embodiment of the invention discloses a production workshop anomaly monitoring system based on the internet of things, which comprises data acquisition equipment, internet of things transmission equipment, an internet of things monitoring platform and a mobile terminal;
The data acquisition equipment acquires operation information of different equipment in a production workshop, and the data acquisition equipment comprises:
the current sensor, the voltage sensor, the temperature sensor and the infrared camera are used for respectively acquiring current information, voltage information, temperature information and infrared image information.
Specifically, the internet of things transmission device uploads device operation information to the internet of things monitoring platform according to a preset frequency, referring to fig. 2, the internet of things transmission device specifically includes:
The storage module is used for caching the equipment operation information;
the frequency adjustment module is used for sending a driving signal according to a preset frequency, wherein the preset frequency comprises the following steps:
default upload frequency:
Wherein, sigma is a time standard deviation, omega is a preset standard deviation weight, sigma 0 is a preset standard deviation reference value, and f 0 is a standard uploading frequency of an uploading module used;
When the cached equipment information is judged to be more than 80 percent of the total cache capacity, the uploading frequency is increased:
fb=Wlog2(1+S/N);
wherein W is the maximum channel bandwidth of an uploading module used by a channel, S is the average power of signals transmitted in the channel, and N is Gaussian noise power in the channel;
And the uploading module is used for uploading the equipment operation information to the monitoring and control platform of the Internet of things based on the driving signal.
Specifically, the internet of things monitoring platform displays the uploaded equipment operation information in real time, analyzes and predicts whether the production workshop equipment fails based on the equipment operation information, if yes, performs early warning prompt and sends an early warning report to the mobile terminal, and please refer to fig. 3, the internet of things monitoring platform specifically includes:
the display module is used for displaying the uploaded equipment operation information in real time;
the prediction module is used for analyzing and predicting whether the production workshop equipment fails or not based on the equipment operation information and generating a corresponding early warning report;
and the early warning module is used for carrying out early warning prompt and sending an early warning report to the mobile terminal when the production workshop equipment fails.
Specifically, the prediction module includes:
Preprocessing the equipment operation information, including acquiring historical voltage information, historical current information and historical temperature information of different equipment in a production workshop, performing feature extraction to generate a spectrogram feature sample data set, acquiring historical infrared image information of the different equipment in the production workshop, performing feature extraction to generate an infrared image feature sample data set, and dividing the spectrogram feature sample data set and the infrared image feature sample data set into a training set, a test set and a prediction set;
constructing a neural network model, and inputting training set and test set data to perform learning training;
preprocessing equipment operation information, and calling a trained neural network model to perform multi-scale fault identification to obtain a prediction result.
Specifically, the data format of the generated spectrogram characteristic sample data set is as follows:
α=[(x1,y1),(x2,y2),…,(xn,yn)];
where x 1,x2…xn is the device runtime and y 1,y2…yn is the spectrogram of the corresponding runtime.
Specifically, the data format of the generated infrared map feature sample data set is as follows:
β=[(u1,w1),(u2,w2),…,(um,wm)];
Where u 1,u2…um is the device runtime and w 1,w2…wm is the infrared image corresponding to the runtime.
The method comprises the steps of constructing a neural network model, inputting training set and test set data for learning training, wherein the training set comprises a convolution layer, a pooling layer and a full-connection layer, characteristics of different layers are extracted through characteristic iteration of the convolution layer, network parameters are reduced and overfitting is reduced through the pooling layer, after three groups of convolution and pooling, a network of the full-connection layer is input to obtain classified output, a loss function is calculated according to the classified output, an update gradient is calculated according to the loss function, and after the network weight is updated, optimization training is carried out by adopting an Adam algorithm, so that a trained neural network model is obtained.
Specifically, preprocessing equipment operation information, and calling a trained neural network model to perform multi-scale fault identification to obtain a prediction result, wherein the method comprises the following steps:
carrying out data cleaning, wavelet packet noise reduction and normalization processing on the equipment operation information:
Extracting spectrogram characteristics of voltage information, current information and temperature information in equipment operation information, and obtaining a spectrogram characteristic input set with the same data format as that of a spectrogram characteristic sample data set;
extracting infrared image characteristics in equipment operation information, and obtaining an infrared image characteristic input set with the same data format as the infrared image characteristic sample data set;
And respectively inputting the spectrogram characteristic input set and the infrared image characteristic input set into the trained neural network model to obtain a prediction result.
Specifically, the monitoring platform of the Internet of things further comprises a control module, and corresponding equipment start and stop are controlled based on the prediction result.
More specifically, key influencing factors influencing the operation faults of the equipment are analyzed according to the prediction result, and the preventive maintenance of the equipment is guided to be carried out by combining with the equipment maintenance plan.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (7)
1. The production workshop abnormity monitoring system based on the Internet of things is characterized by comprising data acquisition equipment, internet of things transmission equipment, an Internet of things monitoring platform and a mobile terminal;
the data acquisition equipment acquires operation information of different equipment of the production workshop, wherein the operation information comprises current information, voltage information, temperature information and infrared image information;
the internet of things transmission equipment uploads equipment operation information to the internet of things monitoring platform according to a preset frequency;
The Internet of things monitoring platform displays the uploaded equipment operation information in real time, analyzes and predicts whether the production workshop equipment fails or not based on the equipment operation information, if so, performs early warning prompt based on a prediction result and sends an early warning report to the mobile terminal;
wherein, thing networking monitor platform includes:
the display module is used for displaying the uploaded equipment operation information in real time;
the prediction module is used for analyzing and predicting whether the production workshop equipment fails or not based on the equipment operation information and generating a corresponding early warning report;
The early warning module carries out early warning prompt based on the prediction result and sends an early warning report to the mobile terminal;
the prediction module includes:
Acquiring historical voltage information, historical current information and historical temperature information of different equipment in a production workshop, performing feature extraction to generate a spectrogram feature sample data set, acquiring historical infrared image information of the different equipment in the production workshop, performing feature extraction to generate an infrared image feature sample data set, and dividing the spectrogram feature sample data set and the infrared image feature sample data set into a training set, a test set and a prediction set respectively;
constructing a neural network model, and inputting training set and test set data to perform learning training;
preprocessing equipment operation information, and calling a trained neural network model to perform multi-scale fault identification to obtain a prediction result;
The internet of things transmission device comprises:
The storage module is used for caching the equipment operation information;
the frequency adjustment module is used for sending a driving signal according to a preset frequency, wherein the preset frequency comprises the following steps:
default upload frequency:
In the formula, The time standard deviation is used as a reference time standard,For the preset standard deviation weight value,For the preset standard deviation reference value,Uploading frequency for the used uploading module standard;
When the cached equipment information is judged to be more than 80 percent of the total cache capacity, the uploading frequency is increased:
In the formula, The maximum channel bandwidth of the uploading module used for the channel is S, the average power of the signal transmitted in the channel is S, and N is Gaussian noise power in the channel;
And the uploading module is used for uploading the equipment operation information to the Internet of things monitoring platform based on the driving signal.
2. The internet of things-based production plant anomaly monitoring system of claim 1, wherein the data acquisition device comprises:
the current sensor, the voltage sensor, the temperature sensor and the infrared camera are used for respectively acquiring current information, voltage information, temperature information and infrared image information.
3. The production workshop anomaly monitoring system based on the internet of things according to claim 1, wherein the generated spectrogram characteristic sample data set has a data format of:
In the formula, For the run-time of the device,Is a spectrogram corresponding to the running time.
4. The production workshop anomaly monitoring system based on the internet of things according to claim 1, wherein the data format of the generated infrared map feature sample data set is:
In the formula, For the run-time of the device,Is an infrared image corresponding to the run time.
5. The production workshop anomaly monitoring system based on the Internet of things, which is characterized in that the system comprises a convolution layer, a pooling layer and a full-connection layer, wherein the convolution layer iteratively extracts the characteristics of training sets of different layers, obtains a prediction result through the full-connection layer after passing through the pooling layer, outputs a calculation loss function according to the prediction result, updates the parameter weight of the neural network model, and adopts an Adam algorithm to perform optimization training to obtain a trained neural network model.
6. The system for monitoring the abnormality of the production plant based on the internet of things according to claim 1, wherein the preprocessing the equipment operation information and calling the trained neural network model to perform multi-scale fault recognition to obtain the prediction result comprises:
carrying out data cleaning, wavelet packet noise reduction and normalization processing on the equipment operation information:
Extracting spectrogram characteristics of voltage information, current information and temperature information in equipment operation information, and obtaining a spectrogram characteristic input set with the same data format as that of a spectrogram characteristic sample data set;
extracting infrared image characteristics in equipment operation information, and obtaining an infrared image characteristic input set with the same data format as the infrared image characteristic sample data set;
And respectively inputting the spectrogram characteristic input set and the infrared image characteristic input set into the trained neural network model to obtain a prediction result.
7. The system for monitoring the abnormality of the production plant based on the Internet of things according to claim 1, wherein the platform for monitoring the Internet of things further comprises a control module for controlling the start and stop of corresponding equipment based on a prediction result.
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