CN115539139A - Method for monitoring safety of steam turbine - Google Patents
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- CN115539139A CN115539139A CN202211078745.0A CN202211078745A CN115539139A CN 115539139 A CN115539139 A CN 115539139A CN 202211078745 A CN202211078745 A CN 202211078745A CN 115539139 A CN115539139 A CN 115539139A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F01—MACHINES OR ENGINES IN GENERAL; ENGINE PLANTS IN GENERAL; STEAM ENGINES
- F01D—NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES
- F01D21/00—Shutting-down of machines or engines, e.g. in emergency; Regulating, controlling, or safety means not otherwise provided for
- F01D21/003—Arrangements for testing or measuring
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Abstract
The invention discloses a method for monitoring the safety of a steam turbine, which comprises the following steps: collecting, storing and preprocessing vibration data of the steam turbine to obtain preprocessed data; converting the preprocessed data into system signals; and comparing the system signal with the fault signal in the database to obtain the running state of the blade, and judging the safety of the steam turbine. The monitoring method of the steam turbine safety of the invention is based on each vibration state parameter in the steam turbine operation process, obtains the change characteristic in the blade vibration real-time operation process according to the monitoring value, compares the real-time change characteristic with the fault characteristic, realizes fault diagnosis, calculates the real-time service life of the steam turbine, and realizes the real-time evaluation of the service life of the steam turbine; the fault diagnosis process and the service life evaluation process do not need to interrupt the operation of the steam turbine.
Description
Technical Field
The invention particularly relates to a method for monitoring the safety of a steam turbine and evaluating the service life of the steam turbine based on blade vibration data.
Background
Steam turbines are important mechanical devices in the power generation industry, and the operation process and the power generation process of the steam turbines are highly integrated. A typical steam turbine consists of a shaft, blades, bearings, bushings, couplings, a casing, and a base. The long blades of the steam turbine are used as key parts of the steam turbine, and the extreme change of the working conditions of the long blades of the steam turbine often enables the long blades of the steam turbine to bear high mechanical load, and is one of main failure sources of the steam turbine.
Because the steam turbine belongs to rotating machinery, and the long blade is the blade with the worst working condition in each stage of blade of the steam turbine, the vibration problem contained in the long blade often causes potential safety hazard, so that a power generation unit faces high fault risk and economic loss risk. Shutdown can cause the downslide of factors such as output, security, environmental protection, customer satisfaction, and along with the continuous improvement of the operation and maintenance level of the power generation industry, people avoid the potential safety hazard of removing the long blades of the steam turbine by utilizing the shutdown time as much as possible.
In recent years, attention has turned to more effective troubleshooting strategies and more aggressive maintenance techniques, one of the more prominent strategies being condition-based maintenance techniques, which have the advantage over other more traditional maintenance strategies (e.g., unscheduled downtime, planned preventative maintenance, etc.) that newly-occurring faults and anomalies can be captured and screened much earlier, thereby avoiding catastrophic events resulting from the faults.
To implement state-based maintenance techniques for fault monitoring and diagnosis of long blades of steam turbines, different monitoring and measurement methods are used to capture and track various operating parameters, such as vibration monitoring, lubrication oil and swarf monitoring, infrared thermal imaging, acoustic emissions, process monitoring, human senses, and the like. Since vibration of long blades of a steam turbine is one of the main causes of failure, understanding the vibration behavior of long blades of a steam turbine under healthy and suspected failure conditions over a certain time interval to analyze irregularities in the vibration characteristics of long blades of a steam turbine is a major technical goal of state-based maintenance techniques.
The existing steam turbine safety monitoring and service life assessment method mostly focuses on the aspects of shafting transverse vibration, torsional vibration, bearing vibration and the like, a real-time monitoring means for a long blade which is one of important components in a steam turbine is lacked, the blade strength of the long blade of the steam turbine in the operation process cannot be obtained in real time only by means of detection during shutdown maintenance, and the safety and service life of the steam turbine cannot be accurately assessed. Once the long blade fails in the operation process, the existing analysis process can only monitor the change when the blade vibrates greatly until the vibration frequency of a shafting is influenced, the frequency change generated when the long blade has microcracks cannot be monitored, the safety monitoring level of the steam turbine cannot be further improved, and the service life of the steam turbine cannot be accurately evaluated.
The above background disclosure is only for the purpose of assisting understanding of the inventive concept and technical solutions of the present invention, and does not necessarily belong to the prior art of the present patent application, and should not be used for evaluating the novelty and inventive step of the present application in the case that there is no clear evidence that the above content has been disclosed before the filing date of the present patent application.
Disclosure of Invention
In view of the above, in order to overcome the defects of the prior art, the present invention provides a method for monitoring the safety of a steam turbine based on blade vibration parameters.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for monitoring the safety of a steam turbine comprises the following steps:
collecting, storing and preprocessing vibration data of a turbine blade to obtain preprocessed data;
converting the preprocessed data into system signals;
and comparing the system signal with the fault signal in the database to obtain the running state of the blade, and judging the safety of the steam turbine.
According to some preferred embodiments of the invention, the vibration data of the steam turbine includes vibration data of the blades and vibration data of other components of the steam turbine reflecting vibration information of the blades; the vibration data of the blade is obtained by using an optical sensor and/or an eddy current sensor. The vibration data of the steam turbine includes shaft torsional vibration data of blade amplitude, instantaneous angular velocity, bearing vibration and casing vibration.
According to some preferred embodiments of the present invention, the blade amplitude is calculated by the following formula:
P＝n·πD·ΔT/60
in the formula, P is the amplitude of the blade, n is the rotating speed, D is the diameter of the blade top monitoring position, and delta T is the time difference between the actual arrival time and the theoretical arrival time of the blade tip.
According to some preferred aspects of the invention, the pre-treatment is carried out as follows: and screening the vibration data by adopting a Lauda rule, removing the data which is obviously out of limit and is measured due to abnormal reading of the sensor, and then carrying out filtering and normalization processing.
According to some preferred embodiments of the invention, a plurality of groups of sensors are arranged in the length extending direction of the steam turbine, one group of sensors corresponds to one group of blades, and each group of sensors is correspondingly arranged in the width direction range of one group of blades.
According to some preferred embodiment aspects of the invention, each set of sensors comprises at least two optical sensors and one eddy current sensor, the eddy current sensor being arranged between the two optical sensors.
According to some preferred embodiments of the present invention, the projection of adjacent sensors on the cross section of the steam turbine corresponds to a central angle of 30 to 60 °; and/or projections of a plurality of sensors on the longitudinal section of the steam turbine are positioned on different horizontal planes and vertical planes. The installation angle of the sensor is over against the long blade.
According to some preferred aspects of the invention, the preprocessed data is converted to system signals using a channel signal conditioner.
According to some preferred embodiment aspects of the invention, the system signal comprises a time domain signal, a frequency domain signal, a short time fourier transform signal, an instantaneous angular velocity signal, a time-synchronous average signal.
According to some preferred embodiments of the present invention, the database stores blade vibration information of the turbine blade in a normal operation state or a failure state.
According to some preferred embodiments of the invention, the fault conditions include blade cracking, high cycle fatigue, blade rubbing, blade root loosening, erosion, creep and corrosion.
According to some preferred implementation aspects of the invention, the comparison is to perform least square calculation on the obtained system signal and the normal operation information and the fault feature information in the database respectively to obtain multiple sets of regression equations, find out the most matched regression equation, and obtain the operation state corresponding to the regression equation according to the database, so as to obtain the operation state of the current long blade and realize the health monitoring and management of the blade.
The method also comprises the step of calculating the total service life loss of the steam turbine during service by collecting the real-time temperature, stress and operation time of the blade under a certain working condition.
According to some preferred embodiments of the invention, the total lifetime loss is obtained using the following formula:
in the formula, τ i Refers to the actual operating time, tau, of the turbine in the operating state i of the blades b,j Meaning the life, τ, of the blade in the operating state i b Which refers to the total life loss of the steam turbine during service.
Due to the adoption of the technical scheme, compared with the prior art, the invention has the beneficial effects that: the monitoring method of the steam turbine safety of the invention is based on each vibration state parameter in the steam turbine operation process, obtains the change characteristic in the blade vibration real-time operation process according to the monitoring value, compares the real-time change characteristic with the fault characteristic, realizes fault diagnosis, calculates the real-time service life of the steam turbine, and realizes the real-time evaluation of the service life of the steam turbine; the fault diagnosis process and the service life evaluation process do not need to interrupt the operation of the steam turbine.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments 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 it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic block diagram of a steam turbine safety monitoring and life assessment system in accordance with a preferred embodiment of the present invention;
FIG. 2 is a schematic illustration of the installation of an attached sensor in a preferred embodiment of the invention;
FIG. 3 is a schematic diagram of the process of obtaining blade vibration data in accordance with the preferred embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not a whole embodiment. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, shall fall within the protection scope of the present invention.
The invention provides a method for monitoring the safety and evaluating the service life of a steam turbine based on blade vibration information, aiming at the problems of poor health monitoring effect, low economy and the like of the current long blade operation process of the steam turbine, so that the real-time acquisition of the long blade operation information of the steam turbine and the analysis of various fault characteristics are realized, and the method has good robustness.
The principle of the invention is as follows: long blades are the components most likely to cause turbine failure, and are subject to various high dynamic loads during turbine operation, including thermal, inertial, and bending loads. Long blade failure modes are typically blade cracking, high cycle fatigue, blade rubbing, blade root loosening, erosion, creep and corrosion. Blade health monitoring may be determined by observing and comparing the vibration levels of the natural frequencies of the blades under normal conditions and under fault operating conditions. As each blade rotates past the sensor, the system records the actual time of arrival of the blade. The theoretical moment at which each turn of the blade reaches the sensor is specified to be the same when the blade is not vibrating. When the natural frequency of the blade is matched with the excitation frequency, blade resonance occurs, if the blade tip deflects due to vibration, the blade tip sweeps across the sensor earlier or later than expected, so that a signal pulse inconsistent with the theoretical arrival time is generated, and the signal pulse is collected to obtain the time difference delta T, so that the amplitude of the blade is obtained:
P＝n·πD·ΔT/60
in the formula, P is the amplitude of the blade, n is the rotating speed, D is the diameter of the blade top monitoring position, and delta T is the time difference between the actual arrival time and the theoretical arrival time of the blade tip.
Example 1 method for turbine safety monitoring and life assessment based on blade vibration parameters
Based on the principle, the method for monitoring the safety and evaluating the service life of the steam turbine based on the blade vibration parameters in the embodiment specifically comprises the following steps:
step 1, acquiring vibration data of a turbine blade based on an additionally-installed sensor, and acquiring vibration data of other components capable of visually reflecting blade vibration information, wherein the vibration data includes but is not limited to vibration related information such as blade amplitude, instantaneous angular velocity, bearing vibration and shaft torsional vibration of shell vibration. As shown in fig. 3, the vibration data is pre-processed (data conditioning) and then stored.
The pretreatment method comprises the following steps: and screening the vibration data by adopting a Lauda rule, eliminating the data which is obviously out of limit and is measured due to abnormal reading of the sensor, and then filtering and normalizing.
The vibration data of the turbine blade and the vibration data of other components capable of visually reflecting the vibration information of the blade in the step 1 are obtained by the following method: wherein the vibration data of the turbine blade is derived from an additional sensor; the vibration data of other parts capable of visually reflecting the blade vibration information is from a monitoring system of the steam turbine system.
The measurement principle, the installation position and the selection of sensor parameters of the added sensor are explained as follows:
the measurement principle is as follows: in the rotating process of the long blade of the steam turbine, besides the existing measuring points and monitoring means of a steam turbine system, a sensor required by non-contact measurement of the long blade of the steam turbine is additionally arranged, and signal pulses generated when the blade passes by the sensor are collected. This signal pulse can be compared in a subsequent step with the arrival time of each blade theoretically rotating to the sensor, and if the blade tip deflects due to vibration, the blade tip will sweep past the sensor earlier or later than expected, thereby generating a signal pulse that is not in accordance with the theoretical arrival time.
Mounting position: the installation angle of the sensor is just opposite to the long blade, and the sensor can select an optical sensor and an eddy current sensor according to the requirement of information acquisition precision. The length extending direction of steam turbine in this embodiment is provided with the multiunit sensor, and a set of sensor corresponds a set of blade, and every sensor of group corresponds the width direction within range that sets up at a set of blade. The number of sensors installed is a set of three: the eddy current sensor and the two optical sensors are arranged at an interval of 45 degrees in the circumferential direction in a staggered mode. The sensor is fixed on the inner cylinder of the steam turbine in a mechanical fixing mode, and the working surface of the probe of the sensor is over against the end surface of the moving blade of the steam turbine. As shown in fig. 2. The left side of fig. 2 is a schematic left-view structure diagram, the width of which is the width of a group of blades, and in the width and the view angle, sensors (black dots in fig. 2) are arranged in a staggered manner in the horizontal direction and the vertical direction, and an eddy current sensor is arranged between two optical sensors.
Sensor parameters: the working temperature range of a sensor required by the non-contact measurement of the long blades of the steam turbine is-25 ℃ to 200 ℃, the measurement number covers 8 to 120 blades in the whole week, and the application range covers most of domestic commercial steam turbines. The application range of the power supply voltage of the eddy current sensor is 5-24V direct current power supply, the output signal is a pulse signal, the pulse signal is rectangular and can be modulated into a digital signal by a signal conditioner, and the maximum measuring frequency is 1000Hz. The maximum measuring frequency of the optical sensor is 500Hz, the range of the induction distance is 0.001-0.1m, and the application range of the power supply voltage is 10-30V direct current power supply.
Step 2, converting the preprocessed data into system signals
And (3) adopting a 4-channel signal conditioner for amplifying the long blade vibration signal obtained in the step (1) and transmitting the data to a data acquisition board. After being conditioned by the signal conditioner, the blade vibration signals are subjected to signal preprocessing and are respectively converted into time domain signals, frequency domain signals, short-time Fourier transform signals, instantaneous angular velocity signals and time synchronization average signals, and the five types of signals are data sources for data analysis in the next step.
The calculation of these five types of signals is briefly described as follows: the time domain signal is a digital signal sampled and amplified by a signal conditioner; the frequency domain signal is a digital signal of the time domain signal after Fourier transformation; the short-time Fourier signal is a two-dimensional digital signal which divides a time domain signal into smaller parts according to a time sequence, independently applies Fourier change to all the parts, and then recombines data into time and frequency; the instantaneous angular velocity signal is extracted from the signal conditioner raw signal, represents the torsional vibration of the blade, and represents the time interval change between the continuous pulses of the signal conditioner; the time synchronization average signal is a frequency spectrum average signal obtained by performing Fast Fourier Transform (FFT) on a time domain signal, and removes any signal component irrelevant to the rotating speed of the blade, so that noise in a complex signal frequency spectrum can be reduced.
Step 3, comparing the system signal with the fault signal in the database to obtain the running state of the blade
The normal operation information and the fault characteristic information in the database are obtained through finite element simulation. The finite element simulation aims at obtaining operation vibration information of different states of a blade, such as no defect, assembly defect, looseness, crack, trembling, fretting wear, high cycle fatigue, low cycle fatigue and the like, and generating a long blade operation database. The assembly defects are generated due to the difference between the geometric characteristics of the blades and can be simulated by setting the geometric characteristic difference; the looseness is generated due to overlarge gaps between the blade roots and the large shaft supporting structure, and can be simulated by setting geometric characteristic differences; the cracks are generated due to foreign matters, manufacturing defects, high-cycle or low-cycle fatigue, resonance fatigue and stress corrosion, and can be simulated by setting different extreme working conditions; the tremble is generated due to the interaction between the aerodynamic force and the vibration displacement of the blades, and can be simulated by calculating the performance of the blades under different working conditions; the high-cycle fatigue and the low-cycle fatigue can be simulated by setting a large-range operation condition and further utilizing a corresponding calculation module in finite element software.
And (3) respectively carrying out least square calculation on the time domain signal, the frequency domain signal, the short-time Fourier transform signal, the instantaneous angular velocity signal and the time synchronization average signal obtained in the step (2) and the normal operation information and the fault characteristic information in the database to obtain five sets of regression equations, wherein the fault signals comprise the time domain signal, the frequency domain signal, the short-time Fourier transform signal, the instantaneous angular velocity signal and the time synchronization average signal in different fault states.
And finding out the most matched regression equation, and obtaining the running state corresponding to the regression equation according to the database so as to obtain the running state of the current long blade and realize the health monitoring and management of the long blade.
Step 4, obtaining the safety of the steam turbine through the operation state of the blades
The operation state of the blade is a key evaluation index of the safety of the steam turbine, and the operation state information of the blade obtained in the step 3 corresponds to the safety of the steam turbine. Namely, the safety of the steam turbine is judged according to the operating state of the long blade obtained in the step 3.
Step 5, obtaining the service life evaluation of the steam turbine through the operation state of the blades or the safety of the steam turbine
When the blade works under a certain specific working condition, the corresponding service life is obtained by collecting the temperature, the stress and the running time at the moment and looking up the endurance strength of the material, and the total service life loss of the steam turbine during service is obtained by using the following formula, so that the service life evaluation of the steam turbine is further carried out.
In the formula, τ i Refers to the actual operating time, tau, of the turbine in the operating state i of the blades b,j Meaning the life, τ, of the blade in the operating state i b Which refers to the total life loss of the steam turbine during service.
Embodiment 2 System for steam turbine safety monitoring and Life evaluation based on blade vibration parameters
In order to match the method in example 1, this example provides a turbine safety monitoring and life evaluation system, which is classified by the implementation function and has four subsystems (a first subsystem, a second subsystem, a third subsystem, and a fourth subsystem). The method comprises the following steps of 1, signal acquisition and preprocessing, step 2, fault screening and fault diagnosis, step 3, data display and human-computer interaction, wherein the signal acquisition and preprocessing layer corresponding to the step 1 in the embodiment 1 is integrated in a first subsystem, the signal conditioning layer corresponding to the step 2 is integrated in a second subsystem, the fault screening and fault diagnosis layer corresponding to the step 3 is integrated in a third subsystem, the data display and human-computer interaction layer is integrated in a fourth subsystem, and a core algorithm is a state-based turbine long blade fault monitoring and diagnosis algorithm.
The first subsystem integrates various sensors for monitoring the operation state of the long blade of the steam turbine, and preprocesses the operation data and monitoring information of the long blade of the steam turbine, thereby facilitating subsequent analysis. The data preprocessed by the first subsystem is introduced into the second subsystem, and the second subsystem expands the data signals after signal conditioning to generate system signals and inputs the system signals to the third subsystem and the fourth subsystem at the same time. And the third subsystem carries out fault monitoring and diagnosis by using the conditioned vibration information and the conditioned running condition information of the long blades of the steam turbine and outputs a corresponding conclusion to the fourth subsystem. The fourth subsystem integrates output signals of the second subsystem and the third subsystem, and outputs health monitoring results of the long blades of the steam turbine in a simple and visual mode through a preset human-computer display interface, so that operation and maintenance personnel and equipment expert analysis of a power plant are facilitated. In particular, the method comprises the following steps of,
1) First subsystem
In the rotating process of the long blade of the steam turbine, besides all existing measuring points and monitoring means of a steam turbine system, a sensor required by non-contact measurement of the long blade of the steam turbine is additionally arranged, and the fault detection of the long blade, including blade fretting wear, cracks, bending, blade looseness and the like, is realized by measuring blade parameters (such as natural frequency, arrival time, arrival angle and the like). The first subsystem preprocesses the acquired signals and introduces the preprocessed data into the second subsystem. See example 1 for sensor settings and parameters.
2) The second subsystem
The apparatus is used to amplify the long blade vibration signal from the first subsystem and transmit the data to a data acquisition board using a 4-channel signal conditioner. The adjusting frequency response range of the second subsystem is between 0.05Hz and 50000Hz, and the data signal conditioning of the first subsystem can be completed. And recording the data output by the second subsystem through LABVIEW software, and backing up and storing the data on the fourth subsystem.
3) Third sub-system
And carrying out fault monitoring and diagnosis by using the conditioned vibration information and the conditioned operating condition information of the long blades of the steam turbine, identifying the current operating state of the blades, and screening the faults of the blades, such as whether the blades have the defects of assembly defects, looseness, cracks, tremor, fretting wear, high cycle fatigue, low cycle fatigue and the like, and outputting corresponding conclusions to a fourth subsystem.
4) The fourth subsystem
The system at least comprises a liquid crystal display, and the displayed content covers units such as the rotating speed value, the fault information, the running state and the like of the long blade of the steam turbine. The hardware part is provided with a keyboard and a mouse, a user can easily change programs in the man-machine interaction interface to meet the operation requirements, and the following operations can be executed according to the needs: (ii) automatic calibration of the sampled information, (ii) fixed or free setting of the range, (iii) graph scaling, (iv) display of operating status and fault information.
The invention provides a technical scheme for diagnosing the fault of the steam turbine based on blade vibration, which realizes the safety monitoring and service life evaluation of the steam turbine and improves the fault monitoring capability of the long blade of the steam turbine in the operation process. The system comprises a first subsystem, a second subsystem, a third subsystem and a fourth subsystem, wherein the first subsystem acquires vibration information of the long blade in a non-contact and non-destructive measuring mode, the second subsystem conditions acquired signals, the third subsystem performs characteristic matching and fault discrimination on state information by a fault diagnosis method based on the vibration information of the blade, the fourth subsystem integrates various information into a human-computer interaction interface, and a fault diagnosis conclusion is obtained after characteristic matching is performed on real-time operation data of the system, so that operation and maintenance personnel and equipment experts of a power plant can analyze the fault diagnosis conclusion, and the accuracy and the portability of safety monitoring and service life evaluation of a steam turbine are improved.
The method of the invention is based on vibration state parameters (vibration related information such as blade vibration amplitude, instantaneous angular velocity, bearing vibration and shaft torsional vibration of shell vibration) in the operation process of the steam turbine, obtains the change characteristic in the real-time operation process of the blade vibration according to the monitoring value, compares the real-time change characteristic with the fault characteristic, realizes fault diagnosis, calculates the real-time service life of the steam turbine, and realizes the real-time evaluation of the service life of the steam turbine; the fault diagnosis process and the life evaluation process do not require interruption of the operation of the steam turbine.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered in the protection scope of the present invention.
Claims (13)
1. A method for monitoring the safety of a steam turbine is characterized by comprising the following steps:
collecting, storing and preprocessing vibration data of the steam turbine to obtain preprocessed data;
converting the preprocessed data into system signals;
and comparing the system signal with the fault signal in the database to obtain the running state of the blade, and judging the safety of the steam turbine.
2. The method of monitoring according to claim 1, wherein the turbine vibration data includes blade vibration data and vibration data of other components of the turbine reflecting blade vibration information; the vibration data of the blade is obtained by using an optical sensor and/or an eddy current sensor.
3. The method of monitoring of claim 2, wherein the blade amplitude is calculated by the formula:
P＝n·πD·ΔT/60
in the formula, P is the amplitude of the blade, n is the rotating speed, D is the diameter of the blade top monitoring position, and delta T is the time difference between the actual arrival time and the theoretical arrival time of the blade tip.
4. The monitoring method according to claim 1, wherein the pre-processing is performed according to the following steps: and screening the vibration data by adopting a Lauda rule, removing the data which is obviously out of limit and is measured due to abnormal reading of the sensor, and then carrying out filtering and normalization processing.
5. The method of monitoring according to claim 2, wherein a plurality of sets of sensors are provided along a length of the steam turbine, one set of sensors corresponding to one set of blades, and each set of sensors corresponding to a width of one set of blades.
6. The method of claim 5, wherein each set of sensors includes at least two optical sensors and one eddy current sensor, the eddy current sensor being disposed between the two optical sensors.
7. The method of claim 6, wherein the projections of adjacent sensors onto the cross-section of the turbine correspond to a central angle of 30-60 °; and/or projections of a plurality of sensors on the longitudinal section of the steam turbine are positioned on different horizontal planes and vertical planes.
8. The monitoring method of claim 1, wherein a channel signal conditioner is employed to convert the preprocessed data into system signals; the system signals comprise time domain signals, frequency domain signals, short-time Fourier transform signals, instantaneous angular velocity signals and time synchronization average signals.
9. The monitoring method according to claim 1, wherein the database stores blade vibration information of the turbine blade in a normal operation state or a failure state.
10. The method of monitoring of claim 9, wherein the fault conditions include blade cracking, high cycle fatigue, blade rubbing, blade root loosening, erosion, creep, and corrosion.
11. The monitoring method according to claim 10, wherein the comparison is to perform least square calculation on the obtained system signal and the normal operation information and the fault feature information in the database respectively to obtain multiple sets of regression equations, find out the most matched regression equation, and obtain the operation state corresponding to the regression equation according to the database, thereby obtaining the operation state of the current long blade and realizing the health monitoring and management of the blade.
12. The method of claim 1, wherein the step of calculating the total life loss of the turbine during service comprises collecting the real-time temperature, stress and operation time of the blades under a certain condition.
13. The method of monitoring of claim 12, wherein the total life loss is calculated using the formula:
in the formula, τ i The actual operation time, tau, of the turbine in the operating state i of the blades b,j Meaning the life, τ, of the blade in the operating state i b Which refers to the total life loss of the steam turbine during service.
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| CN116773374B (en) * | 2023-06-15 | 2024-05-31 | 上海发电设备成套设计研究院有限责任公司 | Cylinder stress corrosion and low cycle fatigue long life monitoring method for nuclear turbine |
| CN116950729A (en) * | 2023-09-19 | 2023-10-27 | 华能山东发电有限公司烟台发电厂 | Turbine blade fault detection method and system |
| CN116950729B (en) * | 2023-09-19 | 2024-02-27 | 华能山东发电有限公司烟台发电厂 | Turbine blade fault detection method and system |
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