CN114611564A - Steam pipeline drain valve fault detection method based on convolutional neural network and temperature - Google Patents
Steam pipeline drain valve fault detection method based on convolutional neural network and temperature Download PDFInfo
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- 238000012549 training Methods 0.000 claims abstract description 12
- 230000000903 blocking effect Effects 0.000 claims abstract description 7
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- 238000012360 testing method Methods 0.000 claims description 9
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- 239000011159 matrix material Substances 0.000 claims description 3
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- F16K—VALVES; TAPS; COCKS; ACTUATING-FLOATS; DEVICES FOR VENTING OR AERATING
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Abstract
The invention discloses a steam pipeline trap fault detection method based on a convolutional neural network and temperature data, which comprises the steps of firstly, respectively arranging four temperature sensor probes on pipelines at an inlet end and an outlet end of a trap, and acquiring temperature data of a trap in a normal working state, a blocking state and an air leakage state, wherein 10000 groups of data in each state are acquired; 80% of the data is used for training the neural network, and 20% of the data is used for evaluating the error degree of the neural network; and finally, inputting the temperature data of the pipeline into the trained neural network, and automatically mapping the temperature data into the running state of the drain valve by the neural network through internal processing. The invention uses the neural network algorithm, so that the detection system can judge the fault condition of the trap only according to the temperature. Compared with other methods for detecting the drain valve, the method firstly introduces the neural network algorithm to analyze the temperature data of the drain valve, and has the advantages of simple used sensor and detection device, no need of manual participation, high detection accuracy and the like.
Description
Technical Field
The invention relates to the field of pipeline valves, in particular to a fault detection method for a steam pipeline drain valve.
Background
High-temperature steam output by the steam boiler generates certain condensed water in a pipeline, the purity of the steam is reduced due to the existence of the condensed water, the steam containing water can affect the use, and the quality of products needing to be heated and baked by the steam is affected. In order to keep the dryness of the steam and reduce the moisture, a steam trap for blocking and draining the water is arranged on the steam pipeline at intervals to drain the condensed water in the pipeline. Because the aquatic contains impurity, through certain time, impurity is at pipeline wall and the inside deposit of trap to and because the corrosion of high temperature steam environment to pipeline wall and trap, the trap is malfunctioning very easily, makes moisture can not in time discharge and reduces the steam quality, or makes steam leak, causes the loss.
The fault detection method of the drain valve used in the overhaul comprises the following steps: 1. detecting the internal structure of the drain valve by using ultrasonic waves; 2. a small transparent window is arranged at the rear end of the drain valve, and the drainage condition of the drain valve can be seen through the small window; 3. the method comprises the steps of comprehensively acquiring and analyzing signals such as temperature, sound and the like to judge the working condition of the drain valve. The detection methods all need to manually carry detection equipment to check on site, the technology used by the detection equipment is complex, various sensor probes need to be installed on the drain valve, the fault detection accuracy rate needs to be judged according to the experience of people, and the accuracy rate is not high.
With the development and progress of machine learning algorithms such as neural networks, the analysis understanding and learning capability of the algorithms on data are enhanced. The advanced data analysis algorithm is introduced, decision judgment can be made instead of manual work, the data learning and analysis understanding capacity is enhanced, the requirement on data is lowered, and only a single type of signal data is needed to generate an accurate decision through machine learning. In the fault diagnosis of the drain valve, a neural network algorithm is introduced to replace the experience of people to judge the fault condition of the drain valve, the requirement on signal data is reduced, the accuracy of fault diagnosis is improved, and related research is lacked in the aspect.
Disclosure of Invention
The invention mainly aims to provide steam pipeline steam trap fault detection based on a convolutional neural network and temperature data. The invention mainly aims to provide a steam pipeline drain valve fault detection method based on a convolutional neural network and temperature data, which realizes fault diagnosis of a drain valve by analyzing the temperature data of front and rear pipelines of the drain valve, so as to overcome the problems that the existing drain valve detection method needs to manually carry detection equipment to check on site, the detection equipment has complex technology, various sensor probes need to be installed on the drain valve, the fault detection accuracy rate needs to be judged according to the experience of people, the accuracy rate is low, and the like, and the method is very practical.
The invention is realized by adopting the following technical scheme and technical measures.
The invention provides a steam pipeline drain valve fault detection method based on a convolutional neural network and temperature data,
firstly, a plurality of temperature sensors are respectively and uniformly distributed and installed at the front end and the rear end of a steam pipeline drain valve, and the temperature sensors are used for acquiring temperature data of the drain valve in different running states, including a blocking state of the drain valve, an air leakage state of the drain valve, a normal working state of the drain valve and the like. 80% of these data were used to train the neural network and 20% were used to test the error of the neural network.
And secondly, building a convolutional neural network model, using the temperature data of the training sample data as input data of the neural network, using the drain valve operation state code of the training sample data as output data of the neural network, and training the neural network. The test data is used to test the error level of the neural network.
And finally, taking the temperature data of the front end and the rear end of the steam pipeline steam trap as the input of a neural network, and mapping the temperature data into a working state code of the steam trap by the trained neural network.
The pipeline is a steam pipeline output by the steam boiler, the temperature data of the pipeline is the temperature data of a plurality of points at the front end and the rear end of the steam trap measured by temperature sensors, and the data are used as the input of a neural network. The normal and fault states of the trap are represented by working state codes, 1000, 0100, 0010, 0001 represent the normal working, blocking, leakage and semi-blocking states of the trap respectively, and the working state codes are used as the output of the neural network.
The convolutional neural network input is
The output of the neural network is Y =
The convolution layer is output as
Where T represents the transpose of the matrix,
to is that
Representing the temperature of 8 sites as inputs to the neural network,
to
Representing the output value of the neural network,
the value is 0 or 1, and the like,
representing a neural network
The output of the layer(s) is,
represents the Relu activation function, th
The number of the layer convolution kernels is
Wherein
Is as follows
The number of the layers is one of the volume layers,
is a convolution kernel that is a function of the convolution kernel,
is the offset value.
The convolutional neural network pooling layer adopts a maximum pooling method.
The transfer function of the convolutional neural network full-connection layer is a sigmoid function.
And the convolutional neural network obtains a fault type classification result by adopting a softmax classifier.
Compared with the prior art, the invention has at least the following advantages and beneficial effects.
Aiming at steam pipeline drain valve equipment, the fault condition of the drain valve can be judged only through temperature data by the steam pipeline drain valve fault detection method based on the convolutional neural network and the temperature data, and the method has the advantages of replacing human experience to judge the fault condition of the drain valve, reducing the requirements on signal data, reducing the complexity of detection equipment, improving the accuracy of fault diagnosis and the like.
Drawings
FIG. 1 is a diagram of a steam pipeline trap fault detection model based on a convolutional neural network and temperature data.
Fig. 2 is a schematic diagram of a convolutional neural network structure.
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 obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
The convolutional neural network can automatically adjust the weight and bias of the network through training, forward propagation of training data and backward propagation of errors, so that the error between input data and expected output is gradually reduced, and the mapping relation from the input to the output is established.
According to the method, a convolutional neural network algorithm is introduced into the steam pipeline drain valve fault diagnosis process, a plurality of temperature data collected at the front end and the rear end of the steam pipeline drain valve are used as the input of the neural network by utilizing the outstanding advantages of the convolutional neural network, the common fault type of the drain valve is used as the output, and the mapping relation between the input and the output is established. The steam trap fault diagnosis method based on the convolutional neural network and the temperature data, which is provided by the inventor, firstly trains the neural network by using the existing temperature data and the corresponding steam trap fault data, and then uses the trained neural network for analyzing the acquired temperature data in a use stage.
The convolutional neural network is mainly used for processing image data, and the invention uses temperature data of the front end and the rear end of the steam pipeline steam trap and corresponding fault codes. The convolutional neural network of the present invention is a convolutional neural network for processing one-dimensional sequence data, as shown in fig. 2, the number of input layers is 50, the number of output layers is 30, and the network structure sequentially includes 3 convolutional layers, 1 pooling layer, and 200 full-link layers.
In the embodiment of the invention, firstly, a plurality of temperature sensors are respectively and uniformly distributed and installed at the front end and the rear end of the steam pipeline drain valve, and the temperature sensors are used for acquiring temperature data of the drain valve in different running states, including a blocking state of the drain valve, an air leakage state of the drain valve, a normal working state of the drain valve and the like. 80% of these data were used to train the neural network and 20% were used to test the error of the neural network.
And secondly, building a convolutional neural network model, using the temperature data of the training sample data as input data of the neural network, using the drain valve operation state code of the training sample data as output data of the neural network, and training the neural network. The test data is used to test the error level of the neural network.
And finally, taking the temperature data of the front end and the rear end of the steam pipeline steam trap as the input of a neural network, and mapping the temperature data into a working state code of the steam trap by the trained neural network.
The temperature data of the pipeline is the temperature data of a plurality of point positions at the front end and the rear end of the trap measured by the temperature sensor, and the data are used as the input of the neural network. The normal state and the fault state of the drain valve are represented by working state codes, 1000, 0100, 0010 and 0001 respectively represent the normal working state, the blocking state, the gas leakage state and the semi-blocking state of the drain valve, and the working state codes are used as the output of a neural network.
The convolutional neural network input is
The output of the neural network is Y =
The convolution layer is output as
Where T represents the transpose of the matrix,
to is that
Representing the temperature of 8 sites as inputs to the neural network,
to
Representing the output value of the neural network,
the value is 0 or 1, and the like,
representing a neural network
The output of the layer(s) is,
represents the Relu activation function, th
The number of the layer convolution kernels is
Wherein
Is as follows
The number of the layers is one of the volume layers,
is a convolution kernel that is a function of the convolution kernel,
is the offset value.
The convolutional neural network pooling layer adopts a maximum pooling method.
The transfer function of the convolutional neural network fully-connected layer is a sigmoid function.
And the convolutional neural network obtains a fault type classification result by adopting a softmax classifier.
Claims (6)
1. The method for detecting the fault of the steam pipeline drain valve based on the convolutional neural network and the temperature data is characterized in that firstly, a plurality of temperature sensors are uniformly distributed and installed at the front end and the rear end of the steam pipeline drain valve respectively, and the temperature sensors are used for acquiring the temperature data of the drain valve in different running states, including the blocking state of the drain valve, the air leakage state of the drain valve, the normal working state of the drain valve and the like; 80% of these data were used to train the neural network and 20% were used to test the error of the neural network; secondly, building a convolution neural network model, using temperature data of training sample data as input data of the neural network, using drain valve operation state codes of the training sample data as output data of the neural network, training the neural network, and using test data to test the error degree of the neural network; and finally, taking the temperature data of the front end and the rear end of the steam pipeline steam trap as the input of a neural network, and mapping the temperature data into a working state code of the steam trap by the trained neural network.
2. The method of claim 1, wherein the steam pipe is a steam pipe of a steam boiler, the temperature data of the pipe is the temperature data of a plurality of points at the front end and the rear end of the steam trap measured by the temperature sensors, the data are used as the input of the neural network, the normal and fault states of the steam trap are represented by operation state codes, 1000, 0100, 0010, 0001 respectively represent the normal operation, blockage, air leakage, and semi-blockage states of the steam trap, and the operation state codes are used as the output of the neural network.
3. The method of claim 1, wherein the convolutional neural network is used as an input to the convolutional neural network for steam pipe trap fault detection based on convolutional neural network and temperature data
The output of the neural network is Y =
The convolution layer is output as
Where T represents the transpose of the matrix,
to
Representing the temperature of 8 sites as inputs to the neural network,
to
Represents the output value of the neural network and,
the value is 0 or 1, and the like,
representing a neural network
The output of the layer(s) is,
represents the Relu activation function, th
The number of the layer convolution kernels is
Wherein
Is as follows
The number of the layers is one of the volume layers,
is a convolution kernel that is a function of the convolution kernel,
is the offset value.
4. The method of claim 1, wherein the convolutional neural network pooling layer employs a max pooling method.
5. The method of claim 1, wherein the transfer function of the convolutional neural network fully-connected layer is a sigmoid function.
6. The convolutional neural network and temperature data based steam pipe trap fault detection method of claim 1, wherein a softmax classifier is used to derive the fault type classification result.
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- 2020-11-24 CN CN202011335224.XA patent/CN114611564A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1487247A (en) * | 2003-07-31 | 2004-04-07 | 上海交通大学 | Cooling air conditioner unit fault simulating and diagnosing system |
| DE102008008096A1 (en) * | 2008-02-08 | 2009-08-13 | Siemens Aktiengesellschaft | Distributed system for e.g. evaluation of destruction of infrastructure, in power stations, has recording and alarming unit announcing danger situations, where objects, danger situations and extent of announcing are determined |
| CN104712542A (en) * | 2015-01-12 | 2015-06-17 | 北京博华信智科技股份有限公司 | Reciprocating compressor sensitive characteristic extracting and fault diagnosis method based on internet of things |
| CN111191693A (en) * | 2019-12-18 | 2020-05-22 | 广西电网有限责任公司电力科学研究院 | Method for identifying thermal fault state of high-voltage switch cabinet based on convolutional neural network |
| CN111027260A (en) * | 2019-12-24 | 2020-04-17 | 大连圣力来监测技术有限公司 | Reciprocating compressor fault diagnosis method based on neural network |
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