CN115167366A - Board card fault diagnosis method based on weighted square sum of sensor signal residual sequence - Google Patents
Board card fault diagnosis method based on weighted square sum of sensor signal residual sequence Download PDFInfo
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- CN115167366A CN115167366A CN202210909125.0A CN202210909125A CN115167366A CN 115167366 A CN115167366 A CN 115167366A CN 202210909125 A CN202210909125 A CN 202210909125A CN 115167366 A CN115167366 A CN 115167366A
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- 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
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
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Abstract
The invention provides a board card fault diagnosis method based on a weighted square sum of a sensor signal residual error sequence, and relates to the technical field of electronic controller fault diagnosis. The method comprises the following steps: acquiring original temperature process signals of each temperature sensor from a gas turbine electronic controller system, and performing data preprocessing to form an original data set; inputting an original data set into a residual sequence weighted sum of squares calculation model; selecting the weighted square sum of all sensors associated with the IO board card, and inputting the weighted square sum into an IO board card health degree calculation model to obtain a board card health degree value; and calculating a difference value between the health value of the board card and a preset normal range boundary line, and judging whether the board card breaks down or not according to the difference value. The method can be used for rapidly diagnosing the fault state of the thermocouple IO board card in an engineering mode, provides an engineering applicable means for identifying the IO board card fault of the electronic controller in time, and provides an important support for loop fault diagnosis of the gas turbine control system.
Description
Technical Field
The invention relates to the technical field of electronic controller fault diagnosis, in particular to a board card fault diagnosis method based on a weighted sum of squares of a residual error sequence of a sensor signal.
Background
The control system of the gas turbine is a complex nonlinear dynamic system formed by integrating a large number of parts according to certain modes, functions and requirements. An electronic controller (DCS system) in a gas turbine control system is the core and key of the whole gas turbine control system. How to effectively diagnose faults of an electronic controller of a fuel control system is a difficult problem in the industry. The conventional fault diagnosis method of the electronic controller based on the BIT technology needs to add a large number of redundant diagnosis circuits in the electronic components, which increases the system cost on one hand and also increases new fault points on the other hand. Different manufacturers of gas turbine control systems have a plurality of professional system diagnosis functions built in their respective DCS systems, and these functions must be checked in engineer operation stations with corresponding authorities, and the information acquisition process is passive, requiring operators to find various alarm information at different positions, and then combining various paper data of paper edition, even needing to actually check the hardware indication state in the DCS cabinet, to judge the fault point of the system. Further determination of the cause of the failure is more of the individual abilities and experience of the engineer. The method seriously restricts the quick judgment of the system fault of the gas turbine power plant, and as a result, the unplanned shutdown caused by the false alarm of an electronic controller system frequently occurs, thereby causing great economic loss to the power plant.
At present, when industrial equipment of a factory is maintained, a mode of 'prevention is mainly adopted, planned maintenance is mainly adopted, and temporary first-aid repair is auxiliary' is adopted. Specifically, a factory sets a period of three to five years according to the new and old conditions of industrial equipment, and performs one-time overhaul on all the industrial equipment in the factory, namely, the operation of shutdown, disassembly, maintenance and reinstallation on all the industrial equipment is performed on all the industrial equipment, and whether certain industrial equipment needs to be maintained is not particularly considered, the one-time overhaul period is more than two months, hundreds of professional maintenance personnel are needed, and the total number of all kinds of constructors is nearly thousands of people. During two major repairs, minor repairs are carried out on industrial equipment every two years, namely, the disassembly, maintenance and reinstallation are carried out on auxiliary working equipment in a factory, such as a steam turbine generator unit in a power plant and most of industrial equipment except for other large core equipment, wherein the period is generally dozens of days to two months. Meanwhile, when the factory carries out planned maintenance, the temporary repair is assisted, namely the temporary repair is carried out on the industrial equipment which fails and cannot operate.
In the combustion engine power generation industry, this routine maintenance mode of operation is not suitable for electronic controller systems, represented by DCS. The DCS system is composed of high-precision and high-density electronic components/chips, the working principle and failure mechanism of various components are completely different, the failure mode is often 'sudden', and the problem of fault detection cannot be effectively solved at all only by means of daily checking means and testing means. In the daily operation process of the gas turbine, the temperature of the combustion chamber is the most important control monitoring parameter, and the stability and consistency of the group of parameters (the 9F unit is 31 measuring points which are arranged in a circumferential mode) are directly related to whether the unit can continue to operate or not; once measured value distortion caused by thermocouple IO board card faults occurs, the machine may need to be shut down immediately, and the consequences are very serious.
The problem in the industry is always to study a data-driven fault diagnosis method for an IO board card of an electronic controller, and how to conveniently and quickly judge the IO board card fault of the electronic controller and find a fault diagnosis method which can be realized by engineering.
Disclosure of Invention
The invention aims to provide a board fault diagnosis method based on a weighted sum of squares of a residual sequence of sensor signals, aiming at the defects of the prior art, so as to solve the problem of engineering fault diagnosis of a thermocouple IO board of an electronic controller in a gas turbine control system.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
the invention provides a board fault diagnosis method based on a weighted square sum of a sensor signal residual sequence, which is used for carrying out fault diagnosis on a thermocouple IO board of a gas turbine electronic controller, and comprises the following steps:
acquiring original temperature process signals of each temperature sensor from a gas turbine electronic controller system, and performing data preprocessing to form an original signal data set;
inputting the signals in the original signal data set into a sensor residual sequence weighted square sum calculation model to obtain a signal residual sequence weighted square sum of each temperature sensor;
selecting the weighted square sum of the signal residual sequence of all temperature sensors associated with the thermocouple IO board card, and inputting the selected weighted square sum of the signal residual sequence into a preset thermocouple IO board card health degree calculation model to obtain a health degree value of the thermocouple IO board card;
and calculating a difference value between the health value of the thermocouple IO board card and a preset normal interval range boundary line of the health value, and judging whether the thermocouple IO board card has a fault according to the difference value, so that fault diagnosis of the thermocouple IO board card is realized.
Optionally, raw temperature process signals for the respective temperature sensors are obtained from the gas turbine electronic controller system in OPC UA industrial protocols.
Optionally, real-time temperature process signals of all temperature sensors associated with the thermocouple IO board card are acquired from the gas turbine electronic controller DCS system through an OPC UA industrial protocol interface from the gas turbine electronic controller system, and the data preprocessing includes an operation of removing redundant samples, an operation of removing abnormal samples, and a data normalization operation.
Optionally, the sensor residual error sequence weighted sum of squares calculation model is a sensor residual error sequence weighted sum of squares calculation model based on an unscented Kalman filter,
∑＝(diag(σ)) 2
wherein R is i The method comprises the steps of weighing and summing residual sequences of temperature sensors corresponding to the ith channel of a thermocouple IO board card, establishing filters corresponding to m sensors associated with the thermocouple IO board card based on an unscented Kalman filter, wherein the input of each filter is m-1 measurement parameter values, the input of the ith filter is the measurement parameters of the rest m-1 sensors except the ith sensor, sigma is the standard deviation of the measurement parameters, y is the standard deviation of the measurement parameters, and the filter is used for filtering the measurement parameters of the ith sensor (i) Is the ith oneThe measured parameters of the filter are used to measure,
predicting an output for the i-th filter nonlinear model; m is the total number of temperature sensors associated with the thermocouple IO board.
Optionally, the calculation model of the health degree of the thermocouple IO board card is as follows:
wherein R is IO-card Is the health value, k, of the thermocouple IO board card i Is the weighted weight of the ith channel of the thermocouple IO board card.
Optionally, whether the thermocouple IO board card fails is determined according to the following formula:
F IOcard =1, if R IO-card >R high limit
Wherein, F IOcard Identification bits for the fault judgment of thermocouple IO board cards, at F IOcard If the number is not less than 1, judging that the IO board card of the thermocouple is in fault;
R high limit for the highest limit of the range of the normal interval of the preset health value, the difference value between the health value of the thermocouple IO board card and the boundary line of the range of the preset normal interval of the health value is larger than zero, namely R IO-card >R high limit In the case of (2), F IOcard ＝1。
Alternatively, R high limit Is equal to 3 sigma.
The beneficial effects of the invention include:
the invention provides a board card fault diagnosis method based on a weighted square sum of residual error sequences of sensor signals, which comprises the following steps: acquiring original temperature process signals of each temperature sensor from a gas turbine electronic controller system, and performing data preprocessing to form an original signal data set; inputting the signals in the original signal data set into a sensor residual sequence weighted square sum calculation model to obtain a signal residual sequence weighted square sum of each temperature sensor; selecting the weighted square sum of the signal residual sequence of all temperature sensors associated with the thermocouple IO board card, and inputting the selected weighted square sum of the signal residual sequence into a preset thermocouple IO board card health degree calculation model to obtain a health degree value of the thermocouple IO board card; and calculating a difference value between the health value of the thermocouple IO board card and a preset normal interval range boundary line of the health value, and judging whether the thermocouple IO board card has a fault according to the difference value, so that fault diagnosis of the thermocouple IO board card is realized. The method can be used for rapidly diagnosing the fault state of the thermocouple IO board card in an engineering mode, provides an engineering applicable means for identifying the IO board card fault of the electronic controller in time, and provides an important support for loop fault diagnosis of the gas turbine control system.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 shows a schematic flow diagram of a board fault diagnosis method based on a weighted sum of squares of residual sequences of sensor signals according to an embodiment of the present invention;
fig. 2 shows a flowchart of an actual operation of the board fault diagnosis method based on a weighted sum of squares of residual sequences of sensor signals according to the embodiment 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 obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
The electronic controller in the gas turbine control system is a complex system formed by highly integrated electronic components, and the BIT-based fault diagnosis method is high in implementation cost, and a newly-added fault diagnosis loop increases the complexity of the system on one hand and additionally increases the fault probability of the system on the other hand. Data-based fault diagnosis methods are a recent research hotspot. However, the data-based fault diagnosis method requires a large number of high-quality fault samples for algorithm training and verification. The inability to generate enough fault samples at the industrial site (it is well understood that if the fault samples at the industrial site are abundant, this indicates that the process is not mature and is not capable of wide industrial application), results in that data-based fault diagnosis methods often perform well only in a laboratory environment and cannot be truly used at the industrial site.
In order to solve the problems, the invention provides a convenient and feasible thermocouple IO board card fault diagnosis method.
Fig. 1 shows a schematic flow chart of a board fault diagnosis method based on a weighted sum of squares of residual sequences of sensor signals according to an embodiment of the present invention. As shown in fig. 1, the board fault diagnosis method based on the weighted sum of squares of the residual error sequences of the sensor signals provided by the invention is used for fault diagnosis of a thermocouple IO board of a gas turbine electronic controller, and the method includes:
And 102, inputting the signals in the original signal data set into a sensor residual sequence weighted square sum calculation model to obtain a signal residual sequence weighted square sum of each temperature sensor.
And 103, selecting the weighted square sum of the signal residual sequence of all the temperature sensors associated with the thermocouple IO board card, and inputting the selected weighted square sum of the signal residual sequence into a preset thermocouple IO board card health degree calculation model to obtain a health degree value of the thermocouple IO board card.
And step 104, calculating a difference value between the health value of the thermocouple IO board card and a preset normal interval boundary line of the health value, and judging whether the thermocouple IO board card has a fault according to the difference value, so that fault diagnosis of the thermocouple IO board card is realized.
The method can be used for rapidly diagnosing the fault state of the thermocouple IO board card in an engineering mode, provides an engineering applicable means for identifying the IO board card fault of the electronic controller in time, and provides an important support for loop fault diagnosis of the gas turbine control system.
Optionally, the raw temperature process signals of the respective temperature sensors are obtained from an OPC Server data source connected to the gas turbine electronic controller system in OPC UA industrial protocol from the gas turbine electronic controller system. The gas turbine electronic controller system may be, for example, a 9F unit gas turbine electronic controller system.
Optionally, real-time temperature process signals of all temperature sensors associated with the thermocouple IO board card are acquired from the gas turbine electronic controller DCS system through an OPC UA industrial protocol interface from the gas turbine electronic controller system, and the data preprocessing includes a redundant sample removing operation, an abnormal sample removing operation, and a data normalizing operation, and converts the data into a gaussian distribution form with a mean value of 0 and a variance of 1.
Optionally, the sensor residual error sequence weighted sum-of-squares computation model is a sensor residual error sequence weighted sum-of-squares computation model based on an unscented kalman filter,
∑＝(diag(σ)) 2
wherein R is i The method comprises the steps of weighing and summing residual sequence of temperature sensors corresponding to the ith channel of a thermocouple IO board card, establishing filters corresponding to m sensors associated with the thermocouple IO board card based on an unscented Kalman filter, wherein the input of each filter is m-1 measurement parameter values, and the input of the ith filter is the measurement of the rest m-1 sensors except the ith sensorParameter, σ is the standard deviation of the measured parameter, y (i) For the measured parameter of the i-th filter,
predicting an output for the i-th filter nonlinear model; m is the total number of temperature sensors associated with the thermocouple IO board.
As can be seen from the above formula, the unscented Kalman filter can be based on the measurement parameter y of the gas turbine k And nonlinear model prediction output
The health parameter p is estimated through the residual error of the model, and the health parameter of the nonlinear model is updated, so that the output parameter of the model tracks the output of the real gas turbine, and the gas path fault diagnosis system based on the unscented Kalman filter is established, and the estimation of the health parameter of the gas turbine is realized.
Optionally, the calculation model of the health degree of the thermocouple IO board card is as follows:
wherein R is IO-card Is the health value, k, of the thermocouple IO board card i Is the weighted weight of the ith channel of the thermocouple IO board.
Optionally, whether the thermocouple IO board card fails is determined according to the following formula:
F IOcard =1, if R IO-card >R high limit
Wherein, F IOcard Identification bits for the fault judgment of thermocouple IO board cards, at F IOcard If the number is not less than 1, judging that the IO board card of the thermocouple is in fault;
R high limit the preset health degree value has the highest normal interval rangeAnd limiting, wherein the difference value between the health value of the thermocouple IO board card and the boundary line of the preset normal interval range of the health value is larger than zero, namely R IO-card >R high limit In the case of (A), F IOcard ＝1。
Alternatively, R high limit Is equal to 3 sigma.
After a thermocouple IO board card based on an electronic controller fails, the state of a sensor signal acquired by the board card is abnormal, for example, a numerical value suddenly jumps (step fault), the numerical value returns to normal (pulse fault) after suddenly jumping, the numerical value continuously increases or decreases (time-varying/temperature-drifting fault), the numerical value has periodic interference (often because a strong electric induction signal is mixed into the board card), or the signal stability is deteriorated (white noise interference, often because of poor contact or abnormal grounding). The first reaction that the IO board card breaks down is that the signals of the sensors are abnormal, and when data collected by the sensors are changed abnormally, the weighted square of a residual sequence of the sensors is calculated; the health degree of the IO board card is calculated by using the health degree model of the IO board card, when the health degree exceeds a normal interval range, the board card can be judged to have a fault, and fig. 2 shows an actual operation flow chart of the board card fault diagnosis method based on the weighted sum of squares of the residual error sequences of the sensor signals.
In summary, the invention calculates the weighted sum of squares of the residual error sequences of the sensor signals by using the real-time process sampling signals acquired from the DCS, and then sums the health degree values of all sensors associated with the thermocouple IO board as the health degree index value of the IO board, and determines that the health degree index exceeds the normal range, that is, determines that the IO board has a fault, thereby implementing the engineering fault diagnosis of the IO board.
The above embodiments are merely illustrative of the technical concept and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the content of the present invention and implement the present invention, and not to limit the scope of the present invention, and all equivalent changes or modifications made according to the spirit of the present invention should be covered in the scope of the present invention.
Claims (7)
1. A board fault diagnosis method based on a weighted sum of squares of a residual error sequence of a sensor signal is used for fault diagnosis of a thermocouple IO board of a gas turbine electronic controller, and comprises the following steps:
acquiring original temperature process signals of each temperature sensor from a gas turbine electronic controller system, and performing data preprocessing to form an original signal data set;
inputting the signals in the original signal data set into a sensor residual sequence weighted square sum calculation model to obtain a signal residual sequence weighted square sum of each temperature sensor;
selecting a weighted sum of squares of signal residual sequences of all temperature sensors associated with a thermocouple IO board card, and inputting the selected weighted sum of squares of the signal residual sequences into a preset health degree calculation model of the thermocouple IO board card to obtain a health degree value of the thermocouple IO board card;
and calculating a difference value between the health value of the thermocouple IO board card and a preset normal interval range boundary line of the health value, and judging whether the thermocouple IO board card has a fault according to the difference value, so that fault diagnosis of the thermocouple IO board card is realized.
2. The board fault diagnosis method based on the weighted sum of squares of the residual error sequences of the sensor signals as claimed in claim 1, wherein the raw temperature process signals of the respective temperature sensors are obtained from the gas turbine electronic controller system by OPC UA industrial protocol.
3. The board fault diagnosis method based on the weighted sum of squares of the residual error sequences of the sensor signals as claimed in claim 2, wherein real-time temperature process signals of all temperature sensors associated with thermocouple IO boards are obtained from a gas turbine electronic controller DCS system through an OPC UA industrial protocol interface, and the data preprocessing includes operations of removing redundant samples, removing abnormal samples and normalizing data.
4. The board fault diagnosis method based on the weighted sum-of-squares of the sensor signal residual error sequences as claimed in claim 1, wherein the sensor residual error sequence weighted sum-of-squares calculation model is a sensor residual error sequence weighted sum-of-squares calculation model based on an unscented Kalman filter,
∑＝(diag(σ)) 2
wherein R is i The method comprises the steps of weighting and summing residual sequence of temperature sensors corresponding to the ith channel of a thermocouple IO board card, establishing filters corresponding to m sensors associated with the thermocouple IO board card based on an unscented Kalman filter, wherein the input of each filter is m-1 measurement parameter values, the input of the ith filter is the measurement parameters of the other m-1 sensors except the ith sensor, sigma is the standard deviation of the measurement parameters, y is the standard deviation of the measurement parameters, and the number of the measured parameters is the sum of the residual sequence of the temperature sensors corresponding to the ith channel of the thermocouple IO board card (i) For the measured parameter of the i-th filter,
predicting an output for the i-th filter nonlinear model; m is the total number of temperature sensors associated with the thermocouple IO board.
5. The board fault diagnosis method based on the weighted sum of squares of the residual error sequences of the sensor signals as claimed in claim 4, wherein the thermocouple IO board health calculation model is as follows:
wherein R is IO-card Is the health value, k, of the thermocouple IO board card i Is the weighted weight of the ith channel of the thermocouple IO board card。
6. The board fault diagnosis method based on the weighted sum of squares of the residual error sequences of the sensor signals as claimed in claim 5, wherein whether the thermocouple IO board has a fault is determined according to the following formula:
F IOcard =1, if R IO-card ＞R high limit
Wherein, F IOcard Identification bits for the fault judgment of thermocouple IO board cards, at F IOcard If the number is not less than 1, judging that the IO board card of the thermocouple is in fault;
R high limit for the highest limit of the range of the normal interval of the preset health value, the difference value between the health value of the thermocouple IO board card and the boundary line of the range of the preset normal interval of the health value is larger than zero, namely R IO-card ＞R high limit In the case of (2), F IOcard ＝1。
7. The board fault diagnosis method based on weighted sum of squares of residual sequences of sensor signals as claimed in claim 6, wherein R is high limit Is equal to 3 sigma.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116494493A (en) * | 2023-06-25 | 2023-07-28 | 天津市全福车业有限公司 | Intelligent monitoring method for injection molding centralized feeding system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116494493A (en) * | 2023-06-25 | 2023-07-28 | 天津市全福车业有限公司 | Intelligent monitoring method for injection molding centralized feeding system |
| CN116494493B (en) * | 2023-06-25 | 2023-08-22 | 天津市全福车业有限公司 | Intelligent monitoring method for injection molding centralized feeding system |
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