CN111768022A - Equipment detection method and device for coal machine production equipment - Google Patents
Equipment detection method and device for coal machine production equipment Download PDFInfo
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Abstract
The invention provides a device detection method and a device of coal production equipment, wherein the method comprises the steps of acquiring device operation data of the coal production equipment, which is acquired by a sensor; inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment; carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values; according to the method, the device operation data acquired by the sensor is acquired, and the device operation data is detected by combining the prediction model to obtain the abnormal characteristic value of the device, so that the operation state of the device can be subjected to early warning, fault report and the like, the problem of low accuracy caused by the lack of the device operation data in the current fault analysis is solved, the personnel safety problem and the production economic loss caused by the fault of the coal machine device are avoided, and the early warning is favorable for improving the production benefit of a coal mine enterprise.
Description
Technical Field
The invention belongs to the field of deep learning, and particularly relates to a method and a device for detecting equipment of coal machine production equipment.
Background
The coal mine early warning system developed based on the GIS technology is a relatively wide early warning design scheme used by coal mine enterprises in the market at present. The GIS is called as a geographic information system, and can collect, store, manage, calculate, analyze, display and describe geographic data related to the whole coal mine space under the support of a computer hardware and software system, so that the GIS can analyze the whole geographic information of the whole coal mine in a relatively detailed manner. The early warning system developed based on the GIS technology utilizes a GIS information acquisition platform to effectively acquire and process the information of the hazard source, utilizes a developed risk early warning model to carry out data mining analysis and the like on the acquired information to obtain a risk early warning conclusion, and carries out graphical display. The main early warning information can be concentrated on the analysis and evaluation of mineral resources, the management of large-scale buildings of coal mines and the like.
The method adopted by the mainstream system in the market at present only aims at partial underground environment parameters, such as gas and carbon dioxide concentration, cannot collect data about the coal machine sensing equipment, and cannot perform comprehensive analysis and early warning on the service condition and safety of the coal machine production equipment.
Disclosure of Invention
In order to solve at least one of the above problems in the current binocular stereo vision technology, the invention provides an equipment detection method for coal production equipment, which comprises the following steps:
acquiring equipment operation data of coal production equipment acquired by a sensor;
inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment;
carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values;
wherein the prediction model is obtained by training historical equipment operation data of the coal production equipment.
In certain embodiments, the predictive model comprises a convolutional network model, wherein the step of building the convolutional network model comprises:
setting an input layer, a convolution layer, a pooling layer, a full-connection layer and a convolution network output layer;
the input of the convolutional layer is equipment operation data, and the output of the convolutional layer is a plurality of first eigenvectors of each coal production equipment; the first characteristic vector is obtained by convolving a vector matrix formed by the equipment operation data according to a set convolution kernel and a set convolution function;
the input of the pooling layer is the output of the convolutional layer, and the output is a plurality of second eigenvectors formed by randomly connecting at least a plurality of first eigenvectors;
the input of the full-connection layer is the output of the pooling layer, and the output is a full-connection feature vector formed by connecting all the second feature vectors;
and the input of the convolution network output layer is the output of the full connection layer.
In some embodiments, the predictive model further comprises a support vector machine model, wherein the step of establishing the support vector machine model comprises:
establishing a linear model expression of a support vector machine, wherein the input of the linear model expression is the output of the convolution network model;
and setting a kernel function of the support vector machine.
In some embodiments, performing fault reporting or early warning on the coal production equipment according to the characteristic parameter value and the corresponding preset fault threshold includes:
judging whether the characteristic parameter value output by the prediction model is higher than a preset fault threshold value of corresponding coal production equipment, and if so, performing fault reporting on the coal production equipment;
and if not, judging whether the ratio of the characteristic parameter value output by the prediction model to the preset fault threshold value is higher than a set threshold value, and if so, early warning the coal machine production equipment.
In some embodiments, performing fault reporting or early warning on the coal production equipment according to the characteristic parameter value and the corresponding preset fault threshold value, further includes:
and if the currently output characteristic parameter value is in a normal range, calculating the ratio of the characteristic parameter values output in the set historical time period, which is higher than a preset fault threshold value, and if the ratio is in the set range, performing inspection and maintenance reminding on the coal machine production equipment.
In another aspect, the present invention provides an apparatus detecting device for a coal production apparatus, including:
the acquisition module is used for acquiring equipment operation data of the coal production equipment, which is acquired by the sensor;
the data input module is used for inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment;
the processing module is used for carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values;
wherein the prediction model is obtained by training historical equipment operation data of the coal production equipment.
In certain embodiments, the processing module comprises:
the fault reporting unit is used for judging whether the characteristic parameter value output by the prediction model is higher than a preset fault threshold value of corresponding coal production equipment or not, and if so, carrying out fault reporting on the coal production equipment;
and the early warning unit is used for judging whether the ratio of the characteristic parameter value output by the prediction model to the preset fault threshold value is higher than a set threshold value or not if the fault report output is negative, and early warning the coal production equipment if the ratio is positive.
In some embodiments, the processing module further comprises:
and the inspection and maintenance reminding unit is used for calculating the proportion of the characteristic parameter values output in the set historical time period, which are higher than the preset fault threshold value, if the current output characteristic parameter values are in the normal range, and performing inspection and maintenance reminding on the coal machine production equipment if the proportion is in the set range.
In certain embodiments, the predictive model includes a convolutional network model and a vector machine model.
In certain embodiments, the equipment operating data includes temperature, humidity, voltage, and current of the coal production equipment.
In yet another embodiment of the present invention, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above method when executing the program.
A further embodiment of the invention provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as described above.
The invention has the following beneficial effects:
the invention provides a device detection method and a device of coal production equipment, which are used for detecting the device operation data by acquiring the device operation data acquired by a sensor and combining a prediction model to obtain the abnormal characteristic value of the device, further carrying out early warning, fault report and the like on the operation state of the device, solving the problem of low accuracy caused by the lack of the device operation data in the current fault analysis, avoiding personnel safety problems and production economic losses caused by faults generated by the coal production equipment, and being beneficial to the improvement of the production benefits of coal mine enterprises due to early warning.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart illustrating an apparatus detection method of a coal production apparatus according to an embodiment of the present invention.
Fig. 2 shows a schematic diagram of a convolutional network feature value extraction process.
Fig. 3 is a schematic structural diagram of a deep learning neural network in the embodiment of the present invention.
Fig. 4 shows a specific flowchart of step S3.
Fig. 5 is a schematic structural diagram of an apparatus detection device of a coal production apparatus according to an embodiment of the present invention.
FIG. 6 shows a schematic structural diagram of an electronic device suitable for use in implementing embodiments of 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.
Fig. 1 illustrates an apparatus detection method of a coal production apparatus according to an embodiment of the present invention, as shown in fig. 1, including:
s1: acquiring equipment operation data of coal production equipment acquired by a sensor;
s2: inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment;
s3: carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values;
wherein the prediction model is obtained by training historical equipment operation data of the coal production equipment.
The invention provides an equipment detection method of coal production equipment, which is characterized in that equipment operation data acquired by a sensor is acquired, and the equipment operation data is detected by combining a prediction model to obtain an abnormal characteristic value of the equipment, so that early warning, fault report and the like can be carried out on the operation state of the equipment, the problem of low accuracy caused by the lack of the equipment operation data in the current fault analysis is solved, the personnel safety problem and the production economic loss caused by the fault of the coal production equipment are avoided, and the early warning is favorable for improving the production benefit of coal mine enterprises.
The equipment operation data in the invention comprises the temperature, humidity, voltage, current and the like of the coal mining machine production equipment, and the data are various data collected by the coal mining machine (element temperature, humidity, voltage, current and the like), a hydraulic support, a pump station and the like according to a sensor.
Specifically, in step S1, the device operation data is collected by a sensor, but the methods adopted by the mainstream systems in the market at present only aim at some downhole environmental parameters, such as gas and carbon dioxide concentration, and cannot collect data about the coal machine sensing device, and cannot perform comprehensive analysis and early warning on the use condition and safety of the coal machine production device.
In a preferred embodiment, the present invention uses a convolution network model in combination with a support vector machine model, and the feature vector input value output by the convolution network model is used in the support vector machine model, which is described in detail below.
In some embodiments, the invention includes a step of building a convolutional network model and a step of building a support vector machine model.
The step of establishing the convolutional network model comprises:
setting an input layer, a convolution layer, a pooling layer, a full-connection layer and a convolution network output layer;
the input of the convolutional layer is equipment operation data, and the output of the convolutional layer is a plurality of first eigenvectors of each coal production equipment; the first characteristic vector is obtained by convolving a vector matrix formed by the equipment operation data according to a set convolution kernel and a set convolution function;
the input of the pooling layer is the output of the convolutional layer, and the output is a plurality of second eigenvectors formed by randomly connecting at least a plurality of first eigenvectors;
the input of the full-connection layer is the output of the pooling layer, and the output is a full-connection feature vector formed by connecting all the second feature vectors;
and the input of the convolution network output layer is the output of the full connection layer.
Specifically, the convolutional network is composed of a plurality of layers, each layer is a two-dimensional plane, and a plurality of independent neurons are basic constituent units forming the two-dimensional plane. And the convolutional neural network divides it into a wide convolution and a narrow convolution according to whether a zero padding method is adopted or not. The wide convolution is suitable for being used when the difference between the filter and the input vector is large, and the narrow convolution is short in running time and high in efficiency, and is more suitable for being used in places such as coal mines where analysis results need to be displayed in real time.
The process of extracting the eigenvalue by the convolutional neural network is shown in fig. 2, firstly converting the equipment operation data into a vector matrix, then carrying out convolution operation on the vector matrix by using a convolution kernel with a set size to obtain a plurality of first eigenvectors, carrying out deconvolution (pooling) on the first eigenvectors to form a plurality of second eigenvectors through connection, and then forming fully-connected eigenvectors in a fully-connected layer.
In some embodiments, as shown in figure 3,
the CNN structure and parameters adopt a first convolution kernel with the size of 5x5, a first pooling layer with the size of 2x2 maximal pooling outputs 32 dimensions, a second convolution kernel with the size of 5x5, a second pooling layer with the size of 2x2 maximal pooling outputs 64 dimensions, and a full-connection layer outputs 256-dimensional feature vectors.
In some embodiments, the output layer ultimately employs a Softmax function with cross entropy as the optimization target.
The Softmax function can "compress" a K-dimensional vector z containing arbitrary real numbers into another K-dimensional real vector σ (z) such that each element ranges between (0,1) and the sum of all elements is 1:
where the sum x is the column vector
The cross thetai entropy is:
wherein p represents the distribution of the real markers, q is the distribution of the predicted markers of the trained model, and the similarity of p and q can be measured by the cross entropy loss function.
In some embodiments, the step of establishing the support vector machine model comprises:
establishing a linear model expression of a support vector machine, wherein the input of the linear model expression is the output of the convolution network model;
and setting a kernel function of the support vector machine.
Specifically, the linear model expression of the SVM is:
hθ＝θ0+θ1x1+θ2x2+θ3x3+L+θnxn(2-1)
wherein x is1:xnI.e. n features, as input to the model, theta0:θnIs the n +1 parameters of the linear model.
Expressing x in linear model expressioniBy substitution of fiAn expression of the SVM model is obtained:
hθ＝θ0+θ1f1+θ2f2+θ3f3+L+θnfn(2-2)
wherein f isiIs xiIs the kernel function of (i.e. x)iA non-linear polynomial term of (2).
The SVM adopts an RBF kernel, and the SVM model kernel has two very important parameters C and gamma.
The RBF formula is:
where C is a penalty factor, i.e., tolerance to errors. The higher C indicates that the error is less tolerable and is easily overfitted. The smaller C, the easier it is to under-fit. When C is too large or too small, the generalization ability is deteriorated gamma is a parameter of the RBF function after the RBF function is selected as the kernel. The distribution of the data after being mapped to a new feature space is determined implicitly, the larger the gamma is, the fewer the support vectors are, and the smaller the gamma value is, the more the support vectors are. The number of support vectors affects the speed of training and prediction.
In some embodiments, the present invention further includes a step of training the prediction model, specifically, by dividing the historical operating data of the device into a training set and a test set, and inputting the training set into the established model, for example, the original sample is randomly divided into two halves, where one is a training amount and the other is a test amount, and then repeating this process ten times to obtain ten training sets and ten corresponding test sets. And then, taking one of the ten training sets and a corresponding test set, substituting the training set and the test set into the CNN and the SVM for training, then sequentially removing the training set and the test set to obtain all results, and comprehensively evaluating the ten performances.
In some embodiments, step S3 specifically includes:
s31: judging whether the characteristic parameter value output by the prediction model is higher than a preset fault threshold value of corresponding coal production equipment, and if so, performing fault reporting on the coal production equipment;
s32: and if not, judging whether the ratio of the characteristic parameter value output by the prediction model to the preset fault threshold value is higher than a set threshold value, and if so, early warning the coal machine production equipment.
In a preferred embodiment, step S3 further includes:
s33: and if the currently output characteristic parameter value is in a normal range, calculating the ratio of the characteristic parameter values output in the set historical time period, which is higher than a preset fault threshold value, and if the ratio is in the set range, performing inspection and maintenance reminding on the coal machine production equipment.
Specifically, as shown in fig. 4, data obtained after passing through a convolutional neural network and a support vector machine are compared with an early warning setting value, classification early warning is performed according to different preset grade data, so that accurate and real-time early warning is achieved, a feature value obtained after analysis is compared with an early warning preset value, so that corresponding grade display can be performed in a system, wherein the condition of grade one is that the value reaches a preset fault value, the fact that the equipment is in a fault at the moment is proved, the display is performed, and the equipment part are refined; the condition of the second grade is that the numerical value reaches more than 90% of the preset fault numerical value, at the moment, fault early warning is carried out, and information of equipment, equipment parts and the like which are about to break down is displayed; the condition of grade three is that the characteristic value of the abnormal value is over-reached, but only accounts for 30% -50% of the total, the preset equipment may have problems and needs to be checked and maintained routinely; and the grade four is a normal range, and the normal operation numerical value of the equipment is displayed to display the normal operation of the equipment.
Based on the above discussion, the convolutional neural network and the support vector machine are used for carrying out feature extraction, classification and comparative analysis on key data according to various types of data collected by sensors, such as coal mining machines (element temperature, humidity, voltage, current and the like), hydraulic supports, pump stations and the like, on the key data, visually and real-timely displaying on a page is carried out, the personnel safety problem and the production economic loss caused by faults of the coal mining machines are avoided, and early warning is favorable for improving the production benefit of coal mine enterprises.
The invention makes up the defects of the coal early warning design on the coal machine equipment in the market. More faults of traditional coal equipment are detected and early-warned, or a professional technician is used to observe, collect, judge and analyze the faults on site, so that a conclusion can be obtained, more faults are influenced by subjective factors such as experience of the technician, and a real-time, quick and accurate result cannot be obtained. According to the technical scheme, professional related elements and environment data influencing equipment are collected through the sensors, unified storage is carried out, extraction, calculation and analysis are carried out according to an algorithm, a conclusion is drawn, early warning is given, a modular automation process is formed, accuracy and real-time performance of data are guaranteed, construction of an intelligent mine is accelerated, safety of workers of a coal mine enterprise is guaranteed, economic loss caused by equipment failure of a coal machine is reduced, and production benefits of coal mine production are improved.
Based on the same inventive concept, an embodiment of the present invention further provides an apparatus detecting device for a coal production apparatus, as shown in fig. 5, including:
the acquisition module 1 is used for acquiring equipment operation data of coal production equipment acquired by a sensor;
the data input module 2 is used for inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment;
the processing module 3 is used for carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values;
wherein the prediction model is obtained by training historical equipment operation data of the coal production equipment.
In certain embodiments, the processing module comprises:
the fault reporting unit is used for judging whether the characteristic parameter value output by the prediction model is higher than a preset fault threshold value of corresponding coal production equipment or not, and if so, carrying out fault reporting on the coal production equipment;
and the early warning unit is used for judging whether the ratio of the characteristic parameter value output by the prediction model to the preset fault threshold value is higher than a set threshold value or not if the fault report output is negative, and early warning the coal production equipment if the ratio is positive.
In some embodiments, the processing module further comprises:
and the inspection and maintenance reminding unit is used for calculating the proportion of the characteristic parameter values output in the set historical time period, which are higher than the preset fault threshold value, if the current output characteristic parameter values are in the normal range, and performing inspection and maintenance reminding on the coal machine production equipment if the proportion is in the set range.
In certain embodiments, the predictive model includes a convolutional network model and a vector machine model.
In certain embodiments, the equipment operating data includes temperature, humidity, voltage, and current of the coal production equipment.
The invention provides an equipment detection device of coal production equipment, which is used for detecting equipment operation data by acquiring the equipment operation data acquired by a sensor and combining a prediction model to obtain an abnormal characteristic value of the equipment, further carrying out early warning, fault report and the like on the operation state of the equipment, solving the problem of low accuracy caused by the lack of the equipment operation data in the current fault analysis, avoiding personnel safety problems and production economic losses caused by faults generated by the coal equipment, and being beneficial to the improvement of the production benefits of coal mine enterprises due to early warning.
An embodiment of the present invention further provides a specific implementation manner of an electronic device, which is capable of implementing all steps in the method in the foregoing embodiment, and referring to fig. 6, the electronic device specifically includes the following contents:
a processor (processor)601, a memory (memory)602, a communication interface (communications interface)603, and a bus 604;
the processor 601, the memory 602 and the communication interface 603 complete mutual communication through the bus 604;
the processor 601 is configured to call the computer program in the memory 602, and the processor executes the computer program to implement all the steps of the method in the above embodiments, for example, when the processor executes the computer program, the processor implements the following steps:
s1: acquiring equipment operation data of coal production equipment acquired by a sensor;
s2: inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment;
s3: and carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values, wherein the prediction model is obtained through training of historical equipment operation data of the coal machine production equipment.
From the above description, the electronic device provided by the invention obtains the device operation data acquired by the sensor, and detects the device operation data by combining the prediction model to obtain the abnormal characteristic value of the device, so that the operation state of the device can be pre-warned and fault reported, the problem of low accuracy caused by lack of the device operation data in the current fault analysis is solved, the personnel safety problem and the production economic loss caused by the fault of the coal equipment are avoided, and the early warning is favorable for improving the production benefit of coal mine enterprises.
An embodiment of the present invention further provides a computer-readable storage medium capable of implementing all the steps of the method in the above embodiments, wherein the computer-readable storage medium stores thereon a computer program, which when executed by a processor implements all the steps of the method in the above embodiments, for example, the processor implements the following steps when executing the computer program:
s1: acquiring equipment operation data of coal production equipment acquired by a sensor;
s2: inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment;
s3: and carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values, wherein the prediction model is obtained through training of historical equipment operation data of the coal machine production equipment.
From the above description, the computer-readable storage medium provided by the invention can detect the equipment operation data by acquiring the equipment operation data acquired by the sensor and combining the prediction model to obtain the characteristic value of the equipment abnormality, and further can perform early warning, fault report and the like on the operation state of the equipment, so that the problem of low accuracy caused by lack of the equipment operation data in the current fault analysis is solved, the personnel safety problem and the production economic loss caused by the fault of the coal machine equipment are avoided, and the early warning is favorable for improving the production benefit of coal mine enterprises.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment. Although embodiments of the present description provide method steps as described in embodiments or flowcharts, more or fewer steps may be included based on conventional or non-inventive means. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or end product executes, it may execute sequentially or in parallel (e.g., parallel processors or multi-threaded environments, or even distributed data processing environments) according to the method shown in the embodiment or the figures. The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the presence of additional identical or equivalent elements in a process, method, article, or apparatus that comprises the recited elements is not excluded. For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, in implementing the embodiments of the present description, the functions of each module may be implemented in one or more software and/or hardware, or a module implementing the same function may be implemented by a combination of multiple sub-modules or sub-units, and the like. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein. The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction. The above description is only an example of the embodiments of the present disclosure, and is not intended to limit the embodiments of the present disclosure. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.
Claims (10)
1. An equipment detection method for coal machine production equipment is characterized by comprising the following steps:
acquiring equipment operation data of coal production equipment acquired by a sensor;
inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment;
carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values;
wherein the prediction model is obtained by training historical equipment operation data of the coal production equipment.
2. The device detection method of claim 1, wherein the predictive model comprises a convolutional network model, and wherein the step of building the convolutional network model comprises:
setting an input layer, a plurality of convolutional layers, a pooling layer corresponding to the number of the convolutional layers, a full-connection layer and a convolutional network output layer;
the input of the convolutional layer is equipment operation data, and the output of the convolutional layer is a plurality of first eigenvectors of each coal production equipment; the first characteristic vector is obtained by convolving a vector matrix formed by the equipment operation data according to a set convolution kernel and a set convolution function;
the input of the pooling layer is the output of the convolutional layer, and the output is a plurality of second eigenvectors formed by randomly connecting at least a plurality of first eigenvectors;
the input of the full-connection layer is the output of the pooling layer, and the output is a full-connection feature vector formed by connecting all the second feature vectors;
and the input of the convolution network output layer is the output of the full connection layer.
3. The device detection method of claim 2, wherein the predictive model further comprises a support vector machine model, wherein the step of establishing the support vector machine model comprises:
establishing a linear model expression of a support vector machine, wherein the input of the linear model expression is the output of the convolution network model;
and setting a kernel function of the support vector machine.
4. The equipment detection method of claim 1, wherein performing fault reporting or early warning on the coal production equipment according to the characteristic parameter values and corresponding preset fault thresholds comprises:
judging whether the characteristic parameter value output by the prediction model is higher than a preset fault threshold value of corresponding coal production equipment, and if so, performing fault reporting on the coal production equipment;
and if not, judging whether the ratio of the characteristic parameter value output by the prediction model to the preset fault threshold value is higher than a set threshold value, and if so, early warning the coal machine production equipment.
5. The equipment detection method of claim 4, wherein performing fault reporting or early warning on the coal production equipment according to the characteristic parameter values and corresponding preset fault thresholds further comprises:
and if the currently output characteristic parameter value is in a normal range, calculating the ratio of the characteristic parameter values output in the set historical time period, which is higher than a preset fault threshold value, and if the ratio is in the set range, performing inspection and maintenance reminding on the coal machine production equipment.
6. The utility model provides an equipment detection device of coal machine production facility which characterized in that includes:
the acquisition module is used for acquiring equipment operation data of the coal production equipment, which is acquired by the sensor;
the data input module is used for inputting the equipment operation data into a preset prediction model to generate characteristic parameter values of the coal production equipment;
the processing module is used for carrying out fault report or early warning on the coal machine production equipment according to the characteristic parameter values and the corresponding preset fault threshold values;
wherein the prediction model is obtained by training historical equipment operation data of the coal production equipment.
7. The device detection apparatus of claim 6, wherein the processing module comprises:
the fault reporting unit is used for judging whether the characteristic parameter value output by the prediction model is higher than a preset fault threshold value of corresponding coal production equipment or not, and if so, carrying out fault reporting on the coal production equipment;
and the early warning unit is used for judging whether the ratio of the characteristic parameter value output by the prediction model to the preset fault threshold value is higher than a set threshold value or not if the fault report output is negative, and early warning the coal production equipment if the ratio is positive.
8. The device detection apparatus of claim 6, wherein the processing module further comprises:
and the inspection and maintenance reminding unit is used for calculating the proportion of the characteristic parameter values output in the set historical time period, which are higher than the preset fault threshold value, if the current output characteristic parameter values are in the normal range, and performing inspection and maintenance reminding on the coal machine production equipment if the proportion is in the set range.
9. The device detection apparatus of claim 6, wherein the predictive model comprises a convolutional network model and a vector machine model.
10. The equipment detection device of claim 6, wherein the equipment operational data includes temperature, humidity, voltage, and current of coal production equipment.
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