CN112763091B - Intelligent detection device and test method for temperature signals of subway bolts - Google Patents
Intelligent detection device and test method for temperature signals of subway bolts Download PDFInfo
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- G01K7/00—Measuring temperature based on the use of electric or magnetic elements directly sensitive to heat ; Power supply therefor, e.g. using thermoelectric elements
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Abstract
The invention discloses an intelligent detection device and a test method for a temperature signal of a subway bolt, which solve the problem that the temperature detection device in the prior art cannot detect the temperature of the subway bolt constantly, so that the acquisition of the temperature signal of the subway bolt is not real-time; measuring temperature signals of subway bolts of a plurality of different sections by using a plurality of temperature sensors, transmitting the temperature signals to a computer in groups through a single chip microcomputer, and judging the health degree of the subway through analysis of a bp neural network on the computer; has real-time performance. The temperature of the subway bolt can not be detected within a certain time period any more, the temperature of the subway bolt can be detected in real time through the temperature sensor, the temperature signal is collected for analysis, and the instantaneity of the temperature signal collection of the subway bolt is ensured.
Description
Technical Field
The invention relates to the technical field of subway temperature detection, in particular to an intelligent temperature signal detection device and a detection method for a subway bolt.
Background
With the continuous development of urban economy and society and the continuous increase of urban traffic volume, people seek faster and faster convenient transportation modes, wherein subways play more and more important roles in urban transportation because of the advantages of no influence of ground traffic conditions, high running speed, relatively fixed time and the like, and are also travel modes generally selected by people at present. The health condition of the subway is an important index of whether a subway train can run safely, the health condition of the subway can be detected by measuring the temperature of a subway bolt, and the temperature of the subway bolt cannot be detected constantly by the conventional temperature detection device, so that the acquisition of a temperature signal of the subway bolt is not real-time.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides an intelligent detection device and a test method for a temperature signal of a subway bolt, and solves the problem that the temperature detection device in the prior art cannot detect the temperature of the subway bolt constantly, so that the acquisition of the temperature signal of the subway bolt is not real-time.
In order to achieve the purpose of the invention, the technical scheme adopted by the invention is as follows:
the intelligent temperature signal detection device for the subway bolt comprises a power supply, a single chip microcomputer connected with the power supply and a plurality of temperature sensors respectively arranged on the subway bolt, wherein the plurality of temperature sensors are electrically connected with the single chip microcomputer; a neural network prediction model is arranged on the computer; the neural network prediction model is used for judging the health degree of the subway according to the temperature signal of the single chip microcomputer, and the display is used for displaying temperature data and the health condition of the subway.
The method comprises the following steps that a plurality of temperature sensors are respectively arranged on subway bolts and used for measuring the temperature of the subway bolts in real time, the plurality of temperature sensors transmit acquired subway bolt temperature signals to a single chip microcomputer, the single chip microcomputer calculates and outputs temperature values of the plurality of subway bolts and uploads the temperature values to a computer through a communication module, a neural network prediction model on the computer can comprehensively judge the health degree of the subway according to the temperature value signals received by the computer, and a display displays the temperature data of the subway bolts and the health state of the subway; if the temperature sensor detects that the temperature of the subway bolt is abnormal, the display displays the temperature abnormality and the bolt position of the temperature abnormality, and the temperature abnormality and the bolt position are checked and processed by a subway department, so that the safe operation of a subway train is ensured.
Furthermore, the communication module is a wireless network communication module, the wireless network communication module is more beneficial to site construction, installation is convenient, construction difficulty is reduced, a large number of transmission lines are not required to be arranged, the transmission lines can be saved, and the site is tidy.
Furthermore, the plurality of temperature sensors are PT100 temperature sensors, the measuring temperature range of the plurality of temperature sensors is-200 ℃ to 850 ℃, and the display accuracy is +/-0.1 ℃. The PT100 temperature sensor can convert temperature variable into a transmittable instrument of standardized output signals, so that the intelligent detection device for the temperature signals of the whole subway bolt has better compatibility and more stable and convenient temperature data transmission,
furthermore, a plurality of temperature sensors and the singlechip are provided with universal sensor interfaces. The universal sensor interface can use a UTI method, a plurality of temperature sensors are electrically connected with the single chip microcomputer, a universal sensor interface chip is used, only one reference resistor insensitive to temperature is needed, the Pt100 temperature sensor is connected with a circuit of the UTI, the proportion of the Pt100 temperature sensor and the reference resistor can be obtained through the single chip microcomputer, therefore, the resistance value and the temperature of the Pt100 temperature sensor are obtained, and the detection result of the intelligent temperature signal detection device of the subway bolt is more accurate.
Furthermore, the neural network prediction model is a bp neural network prediction model, and the bp neural network prediction model is a calculation model based on an error back propagation algorithm and generally comprises an input and output model, an action function model, an error calculation model and a self-learning model; the bp neural network prediction model can judge the health state of the subway according to the temperature signal of the subway bolt of the singlechip received by the computer;
the invention also provides a test method of the intelligent temperature signal detection device for the subway bolt, which comprises the following steps:
step 1: grouping subways and subway bolts, acquiring temperature signal data of the subway bolts through a plurality of temperature sensors, and grouping the acquired temperature signal data; the specific grouping process is as follows: the temperature of n groups of subway bolts is obtained through a plurality of temperature sensors, the temperature of the subway bolts is calculated through a single chip microcomputer, the temperature offset of the n groups of subway bolts is obtained through an operation temperature report of the subway bolts, and the temperature offset is expressed in a mode of { [ Delta ] Tn, [ Delta ] Tn-1, … … [ Delta ] T1 ], wherein [ Delta ] T is expressed as the temperature offset of the n groups of subway bolts, and the temperature offset is expressed as: the temperature sensor obtains the difference value between the temperature of the subway bolt during the operation of the subway and the temperature value during the normal operation of the subway; the temperature offset of the n groups of subway bolts comprises abnormal temperature values of maintained subway bolts as a fault data sample group, and the temperature values of the bolts when the subway can normally run are used as a normal data sample group;
step 2: according to the temperature offset of the n groups of subway bolts obtained in the step 1, establishing a subway bolt temperature offset model and solving the temperature offset;
and step 3: determining a multi-core function of the neural network of the support vector machine and judging the health condition of the subway according to the solving result of the temperature offset obtained in the step two;
the invention has the beneficial effects that: the invention utilizes a plurality of temperature sensors to measure temperature signals of subway bolts in a plurality of different sections, and transmits the temperature signals to a computer in groups through a singlechip, and judges the health degree of the subway through the analysis of a bp neural network on the computer; has real-time performance. The temperature of the subway bolt can not be detected within a certain time period any more, the temperature of the subway bolt can be detected in real time through the temperature sensor, the temperature signal is collected for analysis, and the instantaneity of the temperature signal collection of the subway bolt is ensured.
Drawings
FIG. 1 is a functional structure schematic diagram of an intelligent temperature signal detection device for a subway bolt.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in figure 1, the intelligent detection device for the temperature signal of the subway bolt comprises a power supply, a single chip microcomputer connected with the power supply and a plurality of temperature sensors respectively arranged on the subway bolt, wherein the plurality of temperature sensors are PT100 temperature sensors, the measurement temperature range of the plurality of temperature sensors is-200-850 ℃, and the display precision is +/-0.1 ℃. The PT100 temperature sensor can convert temperature variables into transmittable standardized output signals, so that the intelligent temperature signal detection device for the whole subway bolt has better compatibility and more stable and convenient temperature data transmission;
the temperature sensors are electrically connected with the single chip microcomputer, the single chip microcomputer is used for acquiring temperature signals of the temperature sensors, the single chip microcomputer is electrically connected with a computer with a display, a communication module is electrically connected between the computer and the single chip microcomputer and used for transmitting the temperature signals in the single chip microcomputer to the computer, the communication module is a wireless network communication module, the wireless network communication module is more beneficial to site construction and convenient to install, the construction difficulty is reduced, a large number of transmission lines are not required to be arranged, the transmission lines can be saved, and the site is clean;
a neural network prediction model is arranged on the computer; the neural network prediction model is a bp neural network prediction model, and the bp neural network prediction model is a calculation model based on an error back propagation algorithm and generally comprises an input/output model, an action function model, an error calculation model and a self-learning model; the bp neural network prediction model can judge the health state of the subway according to the temperature signal of the subway bolt of the singlechip received by the computer; the display is used for displaying temperature data and subway health conditions.
And universal sensor interfaces are arranged between the plurality of temperature sensors and the single chip microcomputer. The universal sensor interface can use a UTI method, a plurality of temperature sensors are electrically connected with the single chip microcomputer, a universal sensor interface chip is used, only one reference resistor insensitive to temperature is needed, the Pt100 temperature sensor is connected with a circuit of the UTI, the proportion of the Pt100 temperature sensor and the reference resistor can be obtained through the single chip microcomputer, therefore, the resistance value and the temperature of the Pt100 temperature sensor are obtained, and the detection result of the intelligent temperature signal detection device of the subway bolt is more accurate.
The system comprises a plurality of PT100 temperature sensors, a singlechip, a wireless network communication module, a bp neural network prediction model, a display and a controller, wherein the PT100 temperature sensors are respectively arranged on subway bolts and used for measuring the temperature of the subway bolts in real time, the PT100 temperature sensors transmit acquired subway bolt temperature signals to the singlechip, the singlechip calculates and outputs temperature values of the subway bolts and uploads the temperature values to the computer through the wireless network communication module, the bp neural network prediction model on the computer can comprehensively judge the health degree of the subway according to the temperature value signals received by the computer, and the display displays the temperature data of the subway bolts and the health state of the subway; if the temperature sensor detects that the temperature of the subway bolt is abnormal, the display displays the temperature abnormality and the bolt position of the temperature abnormality, and the temperature abnormality and the bolt position are checked and processed by a subway department, so that the safe operation of a subway train is ensured.
The invention also provides a test method of the intelligent detection device for the temperature signal of the subway bolt, which can judge the health degree of the subway according to the offset of the temperature of the subway bolt;
the method comprises the following steps:
step 1: grouping subways and subway bolts, acquiring temperature signal data of the subway bolts through a plurality of PT100 temperature sensors, and grouping the acquired temperature signal data; if the PT100 temperature sensor detects that the temperature of the subway bolt is abnormal, the display displays the temperature abnormality and the bolt position of the temperature abnormality;
the specific grouping method of the temperature signals of the subway bolts comprises the following steps: the temperature of n groups of subway bolts is obtained through a plurality of temperature sensors, the temperature of the subway bolts is calculated through a single chip microcomputer, the temperature offset of the n groups of subway bolts is obtained through an operation temperature report of the subway bolts, and the temperature offset is expressed in a mode of { [ Delta ] Tn, [ Delta ] Tn-1, … … [ Delta ] T1 ], wherein [ Delta ] T is expressed as the temperature offset of the n groups of subway bolts, and the temperature offset is expressed as: the temperature sensor obtains the difference value between the temperature of the subway bolt during the operation of the subway and the temperature value during the normal operation of the subway; the temperature offset of the n groups of subway bolts comprises abnormal temperature values of maintained subway bolts as a fault data sample group, and the temperature values of the bolts when the subway can normally run are used as a normal data sample group; and judging the health of the subway by using the temperature offset delta T of the n groups of subway bolts.
Step 2: according to the temperature offset of the n groups of subway bolts obtained in the step 1, establishing a subway bolt temperature offset model and solving the temperature offset;
the concrete method for establishing the subway bolt temperature offset model and solving the temperature offset comprises the following steps: establishing a subway bolt temperature offset calculation model by adopting a bp neural network, taking the temperature offset of each normal sample group as model input, taking a subway health index value 1 as output expectation of the normal sample group, and training the subway bolt temperature offset neural network; when the temperature offset of the subway bolt for fault maintenance is solved, a fault sample group is used as the input of a trained offset neural network model, and the output quantity obtained through the model is the temperature offset of the subway bolt; taking n groups of temperature offset delta T parameters as a background parameter set of the subway health state;
and step 3: determining a multi-core function of the neural network of the support vector machine and judging the health condition of the subway according to the solving result of the temperature offset obtained in the step two;
the specific method for determining the multi-core function of the neural network comprises the following steps: constructing a multi-kernel function using a convex combination of a plurality of basic kernel functions in the form of
Wherein Q is the combined multi-core function, betaiAs a weight coefficient of each kernel, QiM is the total number of the basic kernel functions;
in the multi-core support vector machine, the sample parameters of an original sample space R are mapped into a combined space Z by a multi-core function;
weighting coefficient beta of each kernel function in multi-kernel function by adopting particle swarm optimizationiDetermining;
the multi-core function of the support vector machine is written in the following form:
Q＝β1Q1+β2Q2+β3Q3wherein Q is1、Q2、Q3Is a kernel function, beta1、β2、β3A weight coefficient representing a kernel function;
in the particle swarm optimization algorithm, a weight coefficient space { beta is subjected to1,β2,β3When searching, the fitness function is F ═ P/N, where P is kernel function weight coefficient
The accurate number of the time-classified data,
is the weight coefficient of the kernel function in the a-th iteration, N is the total amount of samples, F is the weight coefficient of the kernel function
Temporal classification accuracy;
the particle optimization algorithm comprises the following steps:
(1) initializing a basic kernel function coefficient beta;
(2) calculating the classification accuracy F corresponding to the coefficient;
(3) updating individual extremum p according to classification accuracyi,jAnd a global extremum pg,j；
(4) Updating coefficients according to the combined space Z
The velocity and position of the constituent particle space;
(5) judging whether an iteration termination condition is reached, if so, executing the step (6), otherwise, returning to the step (2) for iteration execution;
(6) obtaining an optimal basic kernel function coefficient;
(7) finishing;
the specific method for judging the health condition of the subway comprises the following steps: after the multi-core function is determined, classifying the multi-core support vector machine by using the temperature value of the subway bolt and the health condition of the corresponding subway bolt to obtain a subway health condition judgment model; and (3) after obtaining the model, processing the temperature data of the subway bolt to be judged according to the first step and the second step, and judging the health condition of the subway by using the multi-core support vector machine obtained in the step (3) after obtaining the temperature offset.
Claims (4)
1. The testing method of the intelligent temperature signal detection device for the metro bolts is characterized in that the intelligent temperature signal detection device for the metro bolts comprises a power supply, a single chip microcomputer connected with the power supply and a plurality of temperature sensors respectively arranged on the metro bolts, the plurality of temperature sensors are all electrically connected with the single chip microcomputer, the single chip microcomputer is used for obtaining temperature signals of the temperature sensors, the single chip microcomputer is electrically connected with a computer with a display, a communication module is electrically connected between the computer and the single chip microcomputer, and the communication module is used for transmitting the temperature signals in the single chip microcomputer to the computer;
a neural network prediction model is arranged on the computer; the neural network prediction model is used for judging the health degree of the subway according to the temperature signal of the single chip microcomputer, and the display is used for displaying temperature data and the health condition of the subway; the communication module is a wireless network communication module; the plurality of temperature sensors are PT100 temperature sensors, the measurement temperature range of the plurality of temperature sensors is-200-850 ℃, and the display precision is +/-0.1 ℃; a universal sensor interface is arranged between each of the temperature sensors and the single chip microcomputer;
the neural network prediction model is a bp neural network prediction model;
the testing method comprises the following steps:
step 1: grouping subways and subway bolts, acquiring temperature signal data of the subway bolts through a plurality of temperature sensors, and grouping the acquired temperature signal data; the specific grouping method in step 1 is as follows: the temperature of n groups of subway bolts is obtained through a plurality of temperature sensors, the temperature of the subway bolts is calculated through a single chip microcomputer, the temperature offset of the n groups of subway bolts is obtained through an operation temperature report of the subway bolts, and the temperature offset is expressed in a mode of { [ Delta ] Tn, [ Delta ] Tn-1, … … [ Delta ] T1 ], wherein [ Delta ] T is expressed as the temperature offset of the n groups of subway bolts, and the temperature offset is expressed as: the temperature sensor obtains the difference value between the temperature of the subway bolt during the operation of the subway and the temperature value during the normal operation of the subway; the temperature offset of the n groups of subway bolts comprises abnormal temperature values of maintained subway bolts as a fault data sample group, and the temperature values of the bolts when the subway can normally run are used as a normal data sample group;
step 2: according to the temperature offset of the n groups of subway bolts obtained in the step 1, establishing a subway bolt temperature offset model and solving the temperature offset;
and step 3: and (4) determining a multi-core function of the neural network of the support vector machine and judging the health condition of the subway according to the solving result of the temperature offset obtained in the step (2).
2. The method for testing the intelligent temperature signal detection device for the metro bolts according to claim 1, wherein the concrete method for establishing the metro bolt temperature offset model and solving the temperature offset in the step 2 is as follows: establishing a subway bolt temperature offset calculation model by adopting a bp neural network, taking the temperature offset of each normal sample group as model input, taking a subway health index value 1 as output expectation of the normal sample group, and training the subway bolt temperature offset neural network; when the fault temperature offset for maintenance is solved, a fault sample group is used as the input of a trained offset neural network model, and the output quantity obtained through the model is the temperature offset of the subway bolt; and taking n groups of temperature offset delta T parameters as a background parameter set of the subway health state.
3. The method for testing the intelligent temperature signal detection device for the metro bolts according to claim 1, wherein the specific method for determining the neural network multi-core function in the step 3 is as follows: constructing a multi-kernel function using a convex combination of a plurality of basic kernel functions in the form of
Wherein Q is the combined multi-core function, betaiAs a weight coefficient of each kernel, QiM is the total number of the basic kernel functions;
in the multi-core support vector machine, the sample parameters of the original sample space are mapped into a combined space by a multi-core function;
weighting coefficient beta of each kernel function in multi-kernel function by adopting particle swarm optimization algorithmiDetermining;
the multi-core function of the support vector machine is written in the following form:
Q＝β1Q1+β2Q2+β3Q3wherein Q is1、Q2、Q3Is a kernel function, beta1、β2、β3A weight coefficient representing a kernel function;
in the particle swarm optimization algorithm, a weight coefficient space { beta is subjected to1,β2,β3When searching, the fitness function is F ═ P/N, where P is kernel function weight coefficient
The accurate number of the time-classified data,
is the weight coefficient of the kernel function in the a-th iteration, N is the total amount of samples, F is the weight coefficient of the kernel function
The accuracy of the classification of the time.
4. The test method of the intelligent temperature signal detection device for the metro bolt according to claim 3, wherein the particle optimization algorithm comprises the following steps:
(1) weight coefficient beta of initialized basic kernel functioni；
(2) Calculating the classification accuracy F corresponding to the coefficient;
(3) updating individual extremum p according to classification accuracyi,jAnd a global extremum pg,j；
(4) Updating the weight coefficients of the post-kernel function according to the combined space
The velocity and position of the constituent particle space;
(5) judging whether an iteration termination condition is reached, if so, executing the step (6), otherwise, returning to the step (2) for iteration execution;
(6) obtaining a weight coefficient of the optimal basic kernel function;
(7) finishing;
the specific method for judging the health condition of the subway comprises the following steps: after the multi-core function is determined, classifying the multi-core support vector machine by using the temperature value of the subway bolt and the health condition of the corresponding subway bolt to obtain a subway health condition judgment model; and (3) after obtaining the model, processing the temperature data of the subway bolt to be judged according to the steps 1 and 2, and judging the health condition of the subway by using the multi-core support vector machine after the step 3 after obtaining the temperature offset.
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