CN117908520A - Board card fault diagnosis method based on sensor signal autoregressive moving average model - Google Patents
Board card fault diagnosis method based on sensor signal autoregressive moving average model Download PDFInfo
- Publication number
- CN117908520A CN117908520A CN202410113730.6A CN202410113730A CN117908520A CN 117908520 A CN117908520 A CN 117908520A CN 202410113730 A CN202410113730 A CN 202410113730A CN 117908520 A CN117908520 A CN 117908520A
- Authority
- CN
- China
- Prior art keywords
- board card
- sensor
- card
- value
- moving average
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000003745 diagnosis Methods 0.000 title claims abstract description 34
- 230000036541 health Effects 0.000 claims abstract description 57
- 230000008569 process Effects 0.000 claims abstract description 28
- 238000007781 pre-processing Methods 0.000 claims abstract description 8
- 230000002159 abnormal effect Effects 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000004422 calculation algorithm Methods 0.000 claims description 4
- 239000007789 gas Substances 0.000 description 23
- 238000012423 maintenance Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000002485 combustion reaction Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006698 induction Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 230000007246 mechanism Effects 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
- 238000005312 nonlinear dynamic Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Abstract
The invention provides a board card fault diagnosis method based on a sensor signal autoregressive moving average model, which comprises the following steps: acquiring an original sensor process signal from a gas turbine electronic controller system, and preprocessing to form an original signal data set; inputting the health degree values into a sensor autoregressive moving average model to obtain health degree values of each sensor; the health degree values of all sensors associated with the analog IO board card are selected and input into an autoregressive moving average model of the analog IO board card, and the health degree values of the analog IO board card are obtained; and calculating the difference value between the health degree value of the analog quantity IO board card and the boundary line of the normal interval range of the preset health degree value to judge whether the analog quantity IO board card fails. The method can diagnose the fault state of the analog quantity IO board card in an engineering mode, provides an engineering application means for immediately distinguishing the fault of the analog quantity IO board card of the electronic controller, and provides important support for the fault diagnosis of a loop of a gas turbine control system.
Description
Technical Field
The invention relates to the technical field of fault diagnosis of electronic controllers, in particular to a board fault diagnosis method based on a sensor signal autoregressive moving average model.
Background
The gas turbine control system is a complex nonlinear dynamic system formed by integrating a large number of parts according to a certain mode, function and requirement. The electronic controller (DCS system) in the 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 engine control system is always a difficult problem in the industry. In the traditional electronic controller fault diagnosis method based on BIT technology, a large number of redundant diagnosis circuits are needed to be added in the electronic component, so that the system cost is increased, and a new electronic fault point is also added.
The manufacturers of different gas engine control systems have built in a plurality of professional system diagnosis functions in respective DCS systems, the functions can be seen only in engineer operation stations with corresponding authorities, the information acquisition process is passive, operators are required to find various alarm information from different positions, and then various drawing data of paper edition are combined, and even hardware indication states are required to be actually checked in a DCS cabinet, so that fault points of the system can be judged. Further judgment of the cause of the failure is more of the engineer's personal ability and experience. This severely restricts the quick judgment of the system failure in the gas turbine power plant, and as a result, unplanned shutdown caused by false alarms of the electronic controller system occurs frequently, which causes great economic loss to the power plant.
Currently, when industrial equipment of a factory is maintained, a mode of 'preventing mainly, planning maintenance mainly and temporarily repairing mainly' is adopted. Specifically, the factory is set to be in a period of three to five years according to the new and old conditions of industrial equipment, all industrial equipment in the factory is subjected to primary overhaul, namely, the operations of shutdown, disassembly, maintenance and refitting are carried out on all industrial equipment, whether certain industrial equipment needs to be maintained or not is not considered, the primary overhaul is generally carried out for more than two months, more than one hundred of professional maintenance personnel are needed, and the sum of all kinds of constructors is nearly thousands. During the two overhauls, the industrial equipment is overhauled once every one to two years, namely, most industrial equipment except auxiliary working equipment in a factory, such as a turbo generator set and other large core equipment in the power plant is disassembled, maintained and reinstalled, and the period is generally tens of days to two months. Meanwhile, the factory is subjected to 'scheduled maintenance' and is assisted with 'temporary rush repair', namely, the industrial equipment which fails to operate due to failure is subjected to temporary rush repair.
In the power generation industry of the gas turbine, the working mode of routine maintenance is not suitable for an electronic controller system represented by DCS. The DCS system has high-precision and high-density electronic components/chips, the operating principles and failure mechanisms of various components are completely different, and the failure modes are often 'abrupt', so that the problem of fault detection cannot be effectively solved only by means of daily inspection and test. 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 circumferentially arranged) are directly related to whether the unit can continue to operate; once the measured value is distorted due to the fault of the analog I0 board card, the measured value may need to be stopped immediately, and the result is very serious.
It is a difficult problem in industry to research how to conveniently and quickly judge the fault of the IO board card 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 card fault diagnosis method based on a sensor signal autoregressive moving average model to solve the problem of engineering fault diagnosis of an analog quantity IO board card of an electronic controller in a gas turbine control system.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
the invention provides a board card fault diagnosis method based on a sensor signal autoregressive moving average model, which comprises the following steps:
Step A, acquiring an original sensor process signal from a gas turbine electronic controller system, and performing data preprocessing to form an original signal data set, wherein the sensor process signal comprises a temperature process signal, a pressure process signal and a flow process signal;
step B, inputting signals in the original signal data set into a sensor signal autoregressive moving average model to obtain signal predicted values of all sensors;
step C, acquiring the latest signal value of the sensor from the gas turbine control system again, and calculating the health value of the sensor by using the latest signal value and the predicted value;
Step D, selecting the latest signal values, predicted values and health values of all sensors associated with the analog quantity IO board card, and inputting the selected values into the analog quantity IO board card health calculation model to obtain the health values of the analog quantity IO board card;
And E, calculating a difference value between the health degree value of the analog quantity IO board card and a boundary line of a normal interval range of the preset health degree value, and judging whether the analog quantity IO board card fails or not according to the difference value, so that fault diagnosis of the analog quantity IO board card is realized.
Optionally, in step a, raw process signals for the individual sensors are acquired from the gas turbine electronic controller system using OPC UA industrial protocol.
Optionally, in the step a, the data preprocessing includes redundant sample removing operation, abnormal sample removing operation, and abnormal working condition removing data of the gas turbine.
Optionally, in said step B, the sensor signal autoregressive moving average model is as follows:
the time sequence X t-1,Xt-2,…,Xt-p of any signal is input to the algorithm:
Pt＝c+φ1Xt-1+φ2Xt-2+…+φpXt-p+∈t-θ1∈t-1-θ2∈t-2-…-θq∈t-q
P t is the predicted value of the time series at time t;
c is a constant term;
p is the order of the autoregressive portion;
q is the order of the moving average portion;
Phi 1,φ2,…,φp is the autoregressive coefficient;
epsilon t,∈t-1,…∈t-q is the white noise error term;
θ 1,θ2,…,θq is a moving average coefficient;
the health degree value of the sensor is as follows:
H t is the health value of the sensor at time t;
X t is the observed value of the sensor at time t;
For this purpose, the average value of the sensor history value;
k is a preset health coefficient.
Optionally, in the step D, the analog quantity IO board health calculation model is as follows:
wherein H io-card is the health value of the analog IO board card, The health degree value of the sensor corresponding to the ith channel of the analog IO board card is k i, the weighting weight of the ith channel is k i, and m is the total number of sensors associated with the analog IO board card.
Optionally, in the step E, whether the analog quantity IO board card fails is determined according to the following formula:
F io-card =1, if H io-card>Hio-card-high-limit;
Wherein, F io-card is a fault judgment identification bit of the analog quantity IO board card, and when F io-card =1, the analog quantity IO board card is judged to have a fault; h io-card-high-limit is the highest limit of the health degree values of the preset analog quantity board card, and when the difference between the health degree value of the analog quantity IO board card and the boundary line of the normal interval range of the preset IO board card is greater than 0, that is, H io-card>Hio-card-high-limit, F io-card =1.
The beneficial effects of the invention include:
The invention provides a board card fault diagnosis method based on a sensor signal autoregressive moving average model, which comprises the following steps: acquiring an original sensor process signal from a gas turbine electronic controller system, and performing data preprocessing to form an original signal data set, wherein the sensor process signal comprises a temperature process signal, a pressure process signal and a flow process signal; inputting signals in the original signal data set into a sensor autoregressive moving average model to obtain health degree values of each sensor; the health degree values of all sensors associated with the analog IO board card are selected, and the health degree values of the selected sensors are input into an autoregressive moving average model of the analog IO board card so as to obtain the health degree values of the analog IO board card; and calculating the difference value between the health degree value of the analog IO board card and the boundary line of the normal interval range of the preset health degree value, and judging whether the analog board card fails or not according to the difference value. The method can diagnose the fault state of the analog quantity IO board card in an engineering mode, provides an engineering application means for immediately distinguishing the fault of the analog quantity IO board card of the electronic controller, and provides important support for the fault diagnosis of a loop of a gas turbine control system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments or the description of the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows a flow chart of a board card fault diagnosis method based on a sensor signal autoregressive moving average model according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating an actual operation flow of a board fault diagnosis method based on an autoregressive moving average model of sensor signals according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the 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 fault diagnosis loop is newly added, so that on one hand, the complexity of the system is increased, and on the other hand, the probability of the system fault is additionally increased. Data-based fault diagnosis methods are the most recent research focus. However, the data-based fault diagnosis method requires a large number of high-quality fault samples for algorithm training and verification. The inability of an industrial site to generate sufficient fault samples (which is readily understood to indicate that the process is not mature and cannot find wide industrial application if the fault samples are abundant) results in data-based fault diagnostic methods that often perform well only in laboratory environments and cannot be truly used on an industrial site.
In order to solve the problems, the invention provides a convenient and feasible fault diagnosis method for the analog IO board card.
Fig. 1 shows a flow chart of a board card fault diagnosis method based on a sensor signal autoregressive moving average model provided by an embodiment of the present invention, and as shown in fig. 1, the board card fault diagnosis method based on the sensor signal autoregressive moving average model provided by the present invention is used for performing fault diagnosis on an analog quantity IO board card of an electronic controller of a gas turbine, and the method includes:
And step A, acquiring an original sensor process signal from the electronic controller system of the gas turbine, and performing data preprocessing to form an original signal data set. The sensor process signals include temperature process signals, pressure process signals, and flow process signals. For example, raw sensor process signals are obtained from a 9F unit gas turbine electronic controller system using natural gas fuel.
And B, inputting signals in the original signal data set into a sensor autoregressive moving average model to obtain signal predicted values of all the sensors.
And step C, acquiring the latest signal value of the sensor from the gas turbine control system again, and calculating the health value of the sensor by using the latest signal value and the predicted value.
And D, selecting the latest signal values, predicted values and health values of all sensors associated with the analog IO board card, and inputting the selected values into the analog IO board card health calculation model to obtain the health values of the analog IO board card.
And E, calculating a difference value between the health degree value of the analog quantity IO board card and a boundary line of a normal interval range of the preset health degree value, and judging whether the analog quantity IO board card fails or not according to the difference value.
After the analog I0 board card of the electronic controller fails, the state of the sensor signals acquired by the board card is abnormal, and the first response of the failure of the IO board card is that the sensor signals are abnormal. The method can rapidly diagnose the fault state of the analog I0 board card in an engineering way, provides an engineering application means for timely identifying the analog IO board card fault of the electronic controller, and provides important support for the loop fault diagnosis of the gas turbine control system.
Optionally, the real-time temperature process signals of all the temperature sensors associated with the thermocouple IO board card are acquired from the gas turbine electronic controller DCS system by taking OPC UA industrial protocol as an interface, and the data preprocessing comprises redundant sample removing operation, abnormal sample removing operation and data normalization operation.
Optionally, the sensor signal autoregressive moving average calculation model is as follows:
The time sequence X t-1,Xt-2,…,Xt-q of any signal is input to the algorithm:
Pt＝c+φ1Xt-1+φ2Xt-2+…+φpXt-p+∈t-θ1∈t-1-θ2∈t-2-…-θq∈t-q
P t is the predicted value of the time series at time t;
c is a constant term;
p is the order of the autoregressive portion (autoregressive order);
q is the order of the moving average portion (moving average order);
Phi 1,φ2,…,φp is the autoregressive coefficient;
epsilon t,∈t-1,…∈t-q is the white noise error term;
θ 1,θ2,…,θq is a moving average coefficient.
The health degree value of the sensor is as follows:
H t is the health value of the sensor at time t;
X t is the observed value of the sensor at time t;
For this purpose, the average value of the sensor history value;
k is a preset health coefficient.
Optionally, the analog IO board card health value is calculated as follows:
wherein H io-card is the health value of the analog IO board card, The health degree value of the sensor corresponding to the ith channel of the analog IO board card is k i, the weighting weight of the ith channel is k i, and m is the total number of sensors associated with the analog IO board card.
Optionally, whether the analog quantity IO board card fails is judged according to the following formula:
f io-card =1, if H io-card>Hio-card-high-limit
Wherein, F io-card is a fault judgment identification bit of the analog quantity IO board card, and when F io-card =1, the analog quantity IO board card is judged to have a fault;
H io-card-high-limit is the highest limit of the health degree values of the preset analog quantity board card, and when the difference between the health degree value of the analog quantity IO board card and the boundary line of the normal interval range of the preset IO board card is greater than 0, that is, if H io-card>Hio-card-high-limit, F io-card =1.
Fig. 2 is a schematic diagram of an actual operation flow of a board fault diagnosis method based on an autoregressive moving average model of sensor signals according to an embodiment of the invention. The specific fault diagnosis flow of the analog IO board card of the actual gas turbine control system is shown in fig. 2.
After the board card breaks down based on the analog quantity I0 of the electronic controller, the state of the sensor signal collected by the board card can be abnormal, such as sudden jump of the numerical value (step fault), restoration of the numerical value to be normal (pulse fault) after sudden jump of the numerical value, continuous increase or decrease of the numerical value (time-varying/temperature-drift fault), periodic interference in the numerical value (often because strong electric induction signals are strung in the board card), or poor signal stability (white noise interference, often caused by poor contact or abnormal grounding). The first response of the faults of the I0 board card is that the signals of the sensors are abnormal, when the collected data of the sensors are abnormally changed, the difference between the predicted value and the actual value of the sensors is correspondingly abnormally changed, the corresponding health degree value is obviously changed, so that the health degree value of the IO board card is also abnormally hopped, and when the health degree value of the board card exceeds the normal interval range, the board card can be judged to be faulty. The method can rapidly diagnose the fault state of the analog I0 board card in an engineering way, provides an engineering application means for timely identifying the fault of the analog I0 board card of the electronic controller, and provides important support for the fault diagnosis of a loop of a gas turbine control system.
The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement the same, but not limit the scope of the present invention, and all equivalent changes or modifications made according to the spirit of the present invention should be included in the scope of the present invention.
Claims (6)
1. The board card fault diagnosis method based on the sensor signal autoregressive moving average model is characterized by comprising the following steps of:
Step A, acquiring an original sensor process signal from a gas turbine electronic controller system, and performing data preprocessing to form an original signal data set, wherein the sensor process signal comprises a temperature process signal, a pressure process signal and a flow process signal;
step B, inputting signals in the original signal data set into a sensor signal autoregressive moving average model to obtain signal predicted values of all sensors;
step C, acquiring the latest signal value of the sensor from the gas turbine control system again, and calculating the health value of the sensor by using the latest signal value and the predicted value;
Step D, selecting the latest signal values, predicted values and health values of all sensors associated with the analog quantity IO board card, and inputting the selected values into the analog quantity IO board card health calculation model to obtain the health values of the analog quantity IO board card;
And E, calculating a difference value between the health degree value of the analog quantity IO board card and a boundary line of a normal interval range of the preset health degree value, and judging whether the analog quantity IO board card fails or not according to the difference value, so that fault diagnosis of the analog quantity IO board card is realized.
2. The method for board fault diagnosis based on the autoregressive moving average model of sensor signals according to claim 1, wherein in said step a, the original process signals of each sensor are obtained from the gas turbine electronic controller system under OPC UA industrial protocol.
3. The method for diagnosing board card faults based on the autoregressive moving average model of sensor signals according to claim 2, wherein in the step A, the data preprocessing comprises redundant sample removing operation, abnormal sample removing operation and abnormal working condition data of the gas turbine.
4. The board fault diagnosis method based on the sensor signal autoregressive moving average model according to claim 1, wherein in the step B, the sensor signal autoregressive moving average model is as follows:
the time sequence X t-1,Xt-2,…,Xt-p of any signal is input to the algorithm:
Pt＝c+φ1Xt-1+φ2Xt-2+…+φpXt-p+∈t-θ1∈t-1-θ2∈t-2-…-θq∈t-q
P t is the predicted value of the time series at time t;
c is a constant term;
p is the order of the autoregressive portion;
q is the order of the moving average portion;
Phi 1,φ2,…,φp is the autoregressive coefficient;
epsilon t,∈t-1,…∈t-q is the white noise error term;
θ 1,θ2,…,θq is a moving average coefficient;
the health degree value of the sensor is as follows:
H t is the health value of the sensor at time t;
X t is the observed value of the sensor at time t;
For this purpose, the average value of the sensor history value;
k is a preset health coefficient.
5. The method for diagnosing board faults based on the autoregressive moving average model of sensor signals as claimed in claim 4, wherein in said step D, the analog IO board health calculation model is as follows:
wherein H io-card is the health value of the analog IO board card, The health degree value of the sensor corresponding to the ith channel of the analog IO board card is k i, the weighting weight of the ith channel is k i, and m is the total number of sensors associated with the analog IO board card.
6. The method for diagnosing a board card failure based on an autoregressive moving average model of sensor signals according to claim 5, wherein in said step E, it is determined whether the analog quantity IO board card fails according to the following formula:
F io-card =1, if H io-card>Hio-card-high-limit;
Wherein, F io-card is a fault judgment identification bit of the analog quantity IO board card, and when F io-card =1, the analog quantity IO board card is judged to have a fault; h io-card-high-limit is the highest limit of the health degree values of the preset analog quantity board card, and when the difference between the health degree value of the analog quantity IO board card and the boundary line of the normal interval range of the preset IO board card is greater than 0, that is, H io-card>Hio-card-high-limit, F io-card =1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410113730.6A CN117908520A (en) | 2024-01-26 | 2024-01-26 | Board card fault diagnosis method based on sensor signal autoregressive moving average model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202410113730.6A CN117908520A (en) | 2024-01-26 | 2024-01-26 | Board card fault diagnosis method based on sensor signal autoregressive moving average model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117908520A true CN117908520A (en) | 2024-04-19 |
Family
ID=90697391
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202410113730.6A Pending CN117908520A (en) | 2024-01-26 | 2024-01-26 | Board card fault diagnosis method based on sensor signal autoregressive moving average model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117908520A (en) |
-
2024
- 2024-01-26 CN CN202410113730.6A patent/CN117908520A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US5625574A (en) | Method and data processing system for monitoring operating states of a technical plant | |
| CN108255649B (en) | Diagnosis strategy design method based on modeling simulation cooperative analysis | |
| CN104390657B (en) | A kind of Generator Unit Operating Parameters measurement sensor fault diagnosis method and system | |
| CN111639921B (en) | Intelligent equipment fault judgment and auxiliary disposal method based on expert system | |
| CN112487910A (en) | Fault early warning method and system for nuclear turbine system | |
| CN107291991A (en) | A kind of Wind turbines early defect method for early warning based on dynamic network mark | |
| CN112633611A (en) | Submarine cable state maintenance strategy optimization method and system based on big data analysis | |
| CN114519382A (en) | Nuclear power plant key operation parameter extraction and abnormity monitoring method | |
| CN115167366A (en) | Board card fault diagnosis method based on weighted square sum of sensor signal residual sequence | |
| CN103498706A (en) | Turboset performance monitoring and diagnosing method based on general logic table | |
| CN117908520A (en) | Board card fault diagnosis method based on sensor signal autoregressive moving average model | |
| US11339763B2 (en) | Method for windmill farm monitoring | |
| CN110794683A (en) | Wind power gear box state evaluation method based on deep neural network and kurtosis characteristics | |
| CN110889646A (en) | Method for checking correctness of operating parameters of generator set adapting to working condition change | |
| CN115167365A (en) | Board card fault diagnosis method based on sensor signal wavelet energy spectrum entropy calculation | |
| CN107083601B (en) | A kind of electronic jacquard machine Mechanical Running Condition detection and analysis method | |
| Fumagalli et al. | Agile diagnostic tool based on electrical signature analysis | |
| Tutiv'en et al. | Wind turbine main bearing condition monitoring via convolutional autoencoder neural networks | |
| CN114687930A (en) | Wind turbine generator fault early warning closed-loop management and control system and method | |
| CN114442543A (en) | Computer monitoring method suitable for early warning of hydropower station fault | |
| Sun et al. | Research on condition monitoring of key components in wind turbine based on cointegration analysis | |
| CN117930818A (en) | Board fault diagnosis method based on wavelet band subdivision and multi-level clustering | |
| CN117687390A (en) | Thermocouple bus network fault diagnosis method for electronic controller of gas turbine | |
| CN115167181A (en) | Sensor signal processing method for fault simulation of gas turbine electronic controller board card | |
| Li et al. | Research and application on rapid recognition of CNC machine tools’ running state |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication |