CN114115197A - Gas turbine state maintenance decision making system, method and storage medium - Google Patents
Gas turbine state maintenance decision making system, method and storage medium Download PDFInfo
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- CN114115197A CN114115197A CN202111408002.0A CN202111408002A CN114115197A CN 114115197 A CN114115197 A CN 114115197A CN 202111408002 A CN202111408002 A CN 202111408002A CN 114115197 A CN114115197 A CN 114115197A
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- 238000012423 maintenance Methods 0.000 title claims abstract description 53
- 238000000034 method Methods 0.000 title claims abstract description 23
- 230000036541 health Effects 0.000 claims abstract description 31
- 230000007613 environmental effect Effects 0.000 claims abstract description 22
- 230000010365 information processing Effects 0.000 claims abstract description 20
- 239000013598 vector Substances 0.000 claims description 9
- 210000002569 neuron Anatomy 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 2
- 230000003862 health status Effects 0.000 claims 6
- 239000007789 gas Substances 0.000 description 43
- 238000002485 combustion reaction Methods 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 239000010687 lubricating oil Substances 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 230000000737 periodic effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
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- 239000003086 colorant Substances 0.000 description 1
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- 238000011005 laboratory method Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
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- 239000000126 substance Substances 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
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Abstract
The invention discloses a gas turbine state maintenance decision-making system, a method and a storage medium, belonging to the related technical field of equipment state maintenance and comprising an information acquisition device, an information processing device, a display device and an early warning device; the information acquisition device is used for acquiring environmental factors, the running state information of the gas turbine and historical running state information; the information processing device trains a prediction model by using the historical running state information and the environmental factors; predicting the health condition of the gas turbine through a prediction model; analyzing the predicted health condition of the gas turbine and outputting a maintenance decision table; the method can realize the prejudgment of the health condition of the gas turbine by introducing the prediction model, and avoids the situation that hidden dangers cannot be eliminated in time because the overhaul time is short when potential unsafe factors exist in the equipment; parts which are possibly failed in the predicted failure can be obtained through the maintenance decision table, and the workload of workers and the power failure time can be reduced.
Description
Technical Field
The invention relates to the technical field related to equipment state maintenance, in particular to a gas turbine state maintenance decision system, a gas turbine state maintenance decision method and a storage medium.
Background
In general, electrical equipment in an electric power system is inspected (or maintained, debugged, and tested) for a predetermined inspection period, and the period of the inspection is a fixed year or several years. Periodic maintenance has two disadvantages: firstly, when potential unsafe factors exist in the equipment, hidden dangers cannot be eliminated in time because the overhaul time is short; secondly, the equipment state is good, but the overhaul time is up, the overhaul is necessary, the overhaul has great blindness, the waste of manpower and material resources is caused, and the overhaul effect is not good.
Therefore, a Condition Based Maintenance (CBM) technology is introduced, which is to judge the abnormality of the equipment according to the equipment Condition information provided by the advanced Condition monitoring and diagnosing technology, predict the fault of the equipment, reasonably arrange Maintenance items and periodic Maintenance modes according to the predicted fault information, namely arrange a Maintenance plan according to the health Condition of the equipment and implement equipment Maintenance. Therefore, how to estimate the health condition of the equipment according to the running state of the equipment is a problem that needs to be solved urgently by the technical personnel in the field.
Disclosure of Invention
In view of the above, the present invention provides a gas turbine state maintenance decision system, method and storage medium, which can predict the health condition of equipment according to the operation state of the equipment to realize on-demand maintenance.
In order to achieve the above purpose, the invention provides the following technical scheme:
a gas turbine state maintenance decision-making system comprises an information acquisition device, an information processing device, a display device and an early warning device;
the information acquisition device is used for acquiring environmental factors, the running state information of the gas turbine and historical running state information;
the information processing device is used for receiving the running state information, the historical running state information and the environmental factors; training a prediction model by using the historical operating state information and the environmental factors; inputting the running state information into the prediction model to predict the health condition of the gas turbine; analyzing the predicted health condition of the gas turbine and outputting a maintenance decision table;
the display device is used for displaying the running state information and/or the predicted health condition of the gas turbine and/or the maintenance decision table;
and the early warning device sends out corresponding prompt when receiving the condition that the health condition of the gas turbine predicted by the information processing device is a fault state condition.
Optionally, the operating state information of the gas turbine includes a thermodynamic parameter and a vibration parameter.
Optionally, when the information processing apparatus predicts the health condition of the gas turbine, the received operating state information and the historical operating state information are divided according to a system structure; selecting running state information and environmental factors of any subsystem as input vectors; the standard value of the running state information of any subsystem is used as hidden layer input; and determining the number of hidden layer neurons and the number of output layer neurons according to the element number of the input vector to obtain the predicted health condition of any subsystem.
The method has the advantages that the state of the gas turbine can be pre-judged through the establishment of the prediction model, the judgment can be made before the gas turbine breaks down, and the condition that hidden dangers cannot be timely eliminated due to the fact that the overhaul time is short when potential unsafe factors exist in equipment is avoided.
Optionally, the information processing apparatus determines the predicted health condition of any subsystem, determines a risk factor of any fault when the predicted health condition of any subsystem is any fault, and calculates the risk factor and the weight of the risk factor to obtain a risk priority number of any fault; and obtaining a risk sequence table of any fault according to the risk priority number of any fault, and generating a maintenance decision table according to the risk sequence table of any fault.
The method has the advantages that the parts which are possibly failed in the predicted failure can be obtained through the maintenance decision table, and the workload of workers and the power failure time can be reduced.
Optionally, the risk factor of the information processing apparatus for any fault is obtained through historical operating state information, operating state information and expert knowledge experience.
Optionally, the information processing apparatus obtains the risk priority number by a fuzzy theory.
Optionally, the display device is a display, and displays the operating state information and/or the predicted health condition of the gas turbine and/or the maintenance decision table at a fixed location.
Optionally, the early warning device may perform early warning in different ways according to different predicted subsystems with faults.
A gas turbine state maintenance decision-making method comprises the following specific steps:
acquiring running state information, historical running state information and environmental factors of the gas turbine;
dividing the running state information and the historical running state information according to subsystems;
training a neural network by using historical running state information and environmental factors to construct a prediction model;
predicting the health condition of the gas turbine through a prediction model;
and analyzing the predicted health condition of the gas turbine and outputting a maintenance decision table.
A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, performs the steps of a gas turbine stateful maintenance decision method as described above.
According to the technical scheme, compared with the prior art, the invention discloses the decision making system and method for the state maintenance of the gas turbine and the storage medium, the prediction of the health condition of the gas turbine can be realized by introducing the prediction model, and the condition that hidden dangers cannot be timely eliminated due to the fact that the overhaul time is short when potential unsafe factors exist in equipment is avoided; parts which are possibly failed in the predicted failure can be obtained through the maintenance decision table, and the workload of workers and the power failure time can be reduced.
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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 embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic structural view of the present invention;
FIG. 2 is a schematic flow chart of the method 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.
The embodiment of the invention discloses a gas turbine state maintenance decision system, a method and a storage medium, wherein the gas turbine state maintenance decision system is structurally shown in figure 1 and comprises an information acquisition device, an information processing device, a display device and an early warning device;
the information acquisition device is used for acquiring the running state information, the historical running state information and the environmental factors of the gas turbine;
wherein collection system includes measuring apparatu and sensor, and the sensor includes: temperature sensors, humidity sensors, pressure sensors, acceleration sensors, photoelectric sensors, thermal resistors, noise sensors, chemical concentration sensors, flame detectors, frequency sensors, and the like.
The collected operating state information includes thermal parameters and vibration parameters.
The information processing device is used for receiving the running state information, the historical running state information and the environmental factors; training a prediction model by using historical running state information and environmental factors; inputting the running state information into a prediction model to predict the health condition of the gas turbine; analyzing the predicted health condition of the gas turbine and outputting a maintenance decision table;
the information processing device adopts MCU, ME8000 series.
A display device for displaying the operating state information of the gas turbine and/or the predicted health condition of the gas turbine and/or a maintenance decision table;
the display device is a display, and the display device internally comprises a video amplification circuit, a field scanning circuit, a line scanning circuit, a switching power supply, a mode identification and control circuit and the like; the display of the system comprises the running state information of the gas turbine, the predicted health condition of the gas turbine and a maintenance decision table; different subsystems are displayed in different areas, so that the fault position is convenient to be determined.
And the early warning device sends out a prompt when receiving the fault state signal of the health condition of the gas turbine predicted by the information processing device.
The early warning method comprises voice reminding and flicker reminding, when the gas turbine is predicted to have a fault, all parts which possibly have the fault flicker, and the factors causing the fault are broadcasted according to the sequence of the maintenance decision list;
when the different subsystems have the predicted faults, different colors are adopted to represent each part with the predicted faults, and the combustion subsystem, the hot channel subsystem and the rotor subsystem are respectively represented by red, yellow and blue.
The method comprises the following steps of failure ignition failure of a gas turbine, flameout of a combustion chamber, low natural gas pressure, high outlet temperature of a gas turbine, low pressure of a gas turbine control oil main pipe, high lubricating oil temperature, high bearing bush temperature, surge of a gas compressor, vibration protection alarm or action, unit overspeed, unit load fluctuation, DCS failure, valve hanging brake, emergency stop and the like; the method can be summarized into instability of a combustion chamber, instability of a gas compressor, instability of a turbine, blade breaking faults, self-excited vibration of a shaft system, failure of a lubricating oil film, test deviation of a sensor and the like.
A method for deciding on the maintenance of a gas turbine, the steps of which are shown in fig. 2, specifically:
s1, acquiring the running state information, the historical running state information and the environmental factors of the gas turbine;
s2, dividing the running state information and the historical running state information according to a combustion subsystem, a hot channel subsystem and a rotor subsystem;
s3, respectively constructing a prediction model by using the historical running state information and the environmental factors of different subsystems, wherein the steps are as follows:
preprocessing the historical running state information and the environmental factors of any subsystem;
taking the preprocessed historical running state information and environmental factors as input vectors, taking corresponding results as expected outputs, and training the neural network;
calculating a difference between the hidden layer and the input layer;
adjusting the link weight between each layer according to the difference;
selecting an optimal value according to the iteration times and the difference value between the hidden layer and the input layer;
and constructing a prediction model of any subsystem according to the optimal value.
The neural network can adopt any one of a BP neural network or an RBF neural network.
S4, predicting the health condition of the gas turbine according to the prediction model, and the specific steps are as follows:
taking the running state information of any subsystem and the environmental factors as input vectors;
taking a standard value of any subsystem operation state information as a hidden layer input;
determining the number of hidden layer neurons and the number of output layer neurons according to the number of elements in the input vector;
the number of cryptic neurons was:
n＝2*m+1；
m is the number of elements in the input vector;
the number of neurons in the output layer was:
l＝m'
m' is the number of target elements in the input vector;
an output value is obtained.
And S5, pre-judging the health state of the gas turbine according to the preset output value.
And (5) continuing to operate in a normal state, and judging whether to repair according to the degree of urgency when a fault early warning occurs.
And S6, analyzing the predicted health condition of the gas turbine, and outputting a maintenance decision table.
Acquiring risk factors through historical operating state information, operating state information and expert knowledge experience according to the acquired fault state;
calculating the risk factors and the weights of the risk factors through a fuzzy number theory to obtain the risk priority number of the fault mode;
defuzzification is carried out on the risk priority number by utilizing a centroid method and an alpha-cut set theory to obtain a defuzzification value of the risk priority number;
according to the ambiguity resolution value, carrying out risk priority ranking on the fault state by adopting a decision test and evaluation laboratory method to obtain a risk sequence table; and generating a maintenance decision table according to the risk sequence table.
And overhauling the equipment according to the sequence of the components in the maintenance decision table.
The embodiment also comprises the calculation of the overhaul interval, and the duration of the overhaul interval is estimated according to the overhaul times and the equipment operation parameters.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
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Cited By (1)
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Application publication date: 20220301 |