A gas turbine test cell control and monitoring method

Abstract

The application provides a gas turbine test bed control and monitoring method, and belongs to the technical field of gas turbine power generation, which can at least partially solve the problems of single test bed monitoring means, difficulty in comprehensively perceiving the running state of the test piece, and dependence on manual attendance in the control process in the prior art. The application comprises the following steps: selecting a test type according to test requirements and completing the configuration of a modular test bed, laying out sensors and visual monitoring equipment; importing test piece design parameters and generating preset threshold values; generating control instructions according to the design parameters to start running; generating parameter abnormality early warning signals and visual monitoring abnormality signals; synchronously correlating and analyzing the two types of abnormality signals, and executing graded protection actions according to the abnormality grades; and generating control instructions. The application realizes dual-mode collaborative perception by fusing sensor data monitoring and visual monitoring, combines a graded protection mechanism and automatic control based on design parameters, improves the comprehensiveness and accuracy of test monitoring, and reduces the labor cost.

Description

Technical Field

[0001] This invention relates to the field of gas turbine power generation technology, and more specifically to a method for controlling and monitoring a gas turbine test bench. Background Technology

[0002] Gas turbine testing is an indispensable part of gas turbine research and development, production, and operation and maintenance. Different types of gas turbine components must undergo performance and reliability verification using dedicated test benches. Gas turbine testing involves many types of components, including compressors, combustion chambers, turbines, and rotors. The test conditions and monitoring requirements for different components vary significantly, requiring test benches with strong configuration adaptability.

[0003] In existing technologies, the monitoring methods for gas turbine test benches are relatively simple, typically relying solely on collecting operational parameter data from on-site measuring points. This single monitoring method suffers from insufficient information dimensions: On the one hand, sensor deployment is limited by installation space, making it difficult to cover all operating states of the test specimen; On the other hand, for fault phenomena such as gas leakage, abnormal flame, and local overheating that require visual information to be effectively identified, it is difficult to detect them in a timely manner based solely on sensor data, which can easily lead to missed or misjudged faults.

[0004] In addition, the existing test benches rely heavily on control methods, requiring a large number of test personnel to be on duty throughout the test and manually adjust operating parameters. This not only results in high labor costs but also increases the risk of test errors due to human error.

[0005] Therefore, how to achieve the synergistic integration of sensor data monitoring and visual monitoring, and realize automatic control based on design parameters on the basis of fully perceiving the operating status of the test piece, is a technical problem that urgently needs to be solved in the field of gas turbine test benches. Summary of the Invention

[0006] The present invention aims to solve at least one of the technical problems existing in the prior art, and to provide a method for controlling and monitoring a gas turbine test bench.

[0007] To achieve the above objectives, the present invention provides a method for controlling and monitoring a gas turbine test bench, comprising: Select the corresponding test type according to the test requirements, install the test piece on the modular test bench and complete the configuration corresponding to the test type, and deploy sensors and visual monitoring equipment. The sensors cover the running parts of the test piece and the power components of the modular test bench. The design parameters of the test specimen are imported into the control system, including the operating parameters of the test specimen under different load conditions. Initial control commands are generated based on the design parameters to control the modular test bench to start operation. During the test operation, the operating data of the test piece is collected in real time. The operating data is compared and analyzed with a preset threshold generated based on the design parameters and safety margin coefficient to generate a parameter anomaly warning signal. In addition, the visual monitoring device collects image information of the test area in real time. The image information is analyzed by a visual algorithm to diagnose the operating status of the test piece and generate a visual monitoring anomaly signal. The abnormal parameter warning signal and the abnormal visual monitoring signal are synchronously correlated and analyzed. When either signal triggers an abnormality, the corresponding graded protection action is executed according to the abnormality level. After the test piece enters the stable operation stage, the target load input is received, and control commands are automatically generated according to the design parameters and the target load to control the modular test bench to operate continuously and stably.

[0008] Furthermore, the preset threshold is generated as follows: Based on the design parameters and the safety margin coefficient, the warning threshold, alarm threshold, and emergency shutdown threshold are generated respectively: ; ; ; in, Indicates the warning threshold. Indicates the alarm threshold. Indicates the emergency stop threshold. This indicates the design value of the corresponding parameter in the design parameters. This represents the early warning margin coefficient. Indicates the alarm margin coefficient. This represents the downtime margin coefficient. .

[0009] Furthermore, the graded protection actions include: Level 1 warning: When any of the aforementioned operational data exceeds the warning threshold, or when the visual monitoring anomaly signal indicates a minor leak, an audible and visual alarm will be issued and the monitoring personnel will be notified; Level 2 alarm: When any of the above operating data exceeds the alarm threshold, or when the visual monitoring abnormal signal indicates obvious leakage or flame abnormality, the fuel supply will be automatically reduced to a preset load reduction ratio. Level 3 Emergency Shutdown: When any of the aforementioned operating data exceeds the emergency shutdown threshold, or the fusion confidence exceeds the preset shutdown fusion threshold, the fuel supply is cut off and the bypass valve is opened, and a cooling program of a preset duration is executed before shutdown.

[0010] Furthermore, the analysis of the image information using visual algorithms includes: The monitoring area of ​​the test piece is located using a target detection algorithm; The gas leak profile at the seal was identified using an edge extraction algorithm; The flow state of the leaked gas is determined by a grayscale change analysis algorithm; Abnormal overheating areas of the test specimen were identified using a temperature field analysis algorithm; Based on the above analysis results, the operating status of the test piece is determined and the visual monitoring abnormality signal is generated.

[0011] Furthermore, the synchronous correlation analysis is performed in the following manner: The fusion confidence score is calculated using a weighted fusion method. ; in, This represents the fusion confidence level. This indicates the anomaly confidence level of the abnormality warning signal for the aforementioned parameter. This indicates the confidence level of the abnormality in the visual monitoring signal. This represents the abnormal weighting coefficient of the parameter. Indicates the weighting coefficient for visual anomalies. ; When the fusion confidence level exceeds the preset fusion threshold, it is determined to be an abnormal state and the hierarchical protection action is triggered.

[0012] Furthermore, the deployment location of the visual monitoring equipment is determined according to the type of test: When the test type is a combustion test, cameras are placed at the combustion chamber observation window, fuel line joint, and air intake manifold flange connection, and an infrared thermal imager is added at the combustion chamber observation window; When the test type is a compressor test, the camera is placed at the compressor housing connection, air inlet and exhaust port; When the test type is a turbine test, cameras are placed at the turbine inlet and outlet flanges and bearing end caps, and an infrared thermal imager is added. When the test type is a rotor test, the camera is placed at the support bearings at both ends of the rotor and at the exposed parts of the rotor.

[0013] Furthermore, the generation of the control commands employs an incremental PID control algorithm: ; in, Indicates the first The control command increment per control cycle Represents the proportionality coefficient. Represents the integral coefficient. Denotes the differential coefficient. Indicates the first The deviation value for each control cycle, wherein the deviation value is the difference between the design parameters and the operating data.

[0014] Furthermore, the speed control method during the startup phase is linear rate-of-motion control: ; ; in, express Rotation speed at any given moment Indicates the initial rotational speed. Indicates the rate of increase. Indicates the target rotational speed. Indicates the acceleration time.

[0015] Furthermore, the real-time data acquisition frequency satisfies: ; in, Indicates the sampling frequency. This represents the highest frequency component of the monitored signal; Furthermore, the running data and the image information are stored in a time-synchronized manner, with the time synchronization achieving millisecond-level synchronization accuracy through a unified time server.

[0016] Furthermore, the method also includes a remote monitoring step: The operating data, image information, and execution status of the graded protection actions are transmitted to a remote monitoring center via a communication network. The remote monitoring center monitors the modular test bench in real time and sends control commands to the control system through the remote monitoring center.

[0017] The beneficial effects of this invention are as follows: This invention achieves dual protection through the integration of sensor measurement point data acquisition and visual monitoring technologies, enabling real-time monitoring of test bench operation data and real-time visual monitoring. Sensor measurement point data acquisition obtains the test specimen's operating parameters and compares them with preset thresholds generated based on design parameters. Visual monitoring, through visual algorithms, can diagnose abnormal states such as air leakage and localized overheating in real time. The synchronous correlation analysis of the two monitoring signals comprehensively improves the coverage and accuracy of test status monitoring, reducing the risk of missed fault detection.

[0018] The control system of this invention generates control commands based on the design parameters of the test specimen, realizing automated control of the test process. After the test specimen enters the stable operation stage, only the target load needs to be input to automatically generate control commands, which greatly reduces the need for on-site personnel, lowers labor costs, and avoids the impact of human error on test safety and efficiency.

[0019] This invention introduces a graded protection mechanism, which executes different levels of protection actions according to the anomaly level. This avoids unnecessary downtime caused by over-response while ensuring experimental safety, thereby improving experimental efficiency and economy. Attached Figure Description

[0020] Figure 1 This is a flowchart of the gas turbine test bench control and monitoring method of the present invention; Figure 2 This is a schematic diagram of the modular test bench system architecture of the present invention; Figure 3 This is a schematic diagram of the data fusion and hierarchical protection strategy of the present invention; Figure 4 This is a schematic diagram showing the deployment location of the visual monitoring system according to the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and beneficial effects of this application clearer, the following detailed description, in conjunction with the accompanying drawings and specific embodiments, further illustrates this application. It should be understood that the specific embodiments described in this specification are merely for explaining this application and are not intended to limit it.

[0022] The gas turbine test bench control and monitoring method of the present invention is implemented based on a skid-mounted modular test bench system. (See reference...) Figure 2 The system consists of three parts: a skid-mounted test bench, a monitoring system, and a control system. These three parts are connected by an industrial Ethernet module to form a communication network for data transmission.

[0023] The skid-mounted test bench adopts a modular frame structure design, integrating a power module, test piece mounting module, piping connection module, and auxiliary support module. Each module is detachably connected via standardized flanges, quick couplings, and bolt assemblies, allowing for configuration switching between compressor, combustion, turbine, or rotor tests according to testing requirements. The skid-mounted test bench is equipped with a moving mechanism and a ground-fixing mechanism at its base, enabling it to be moved to different test sites using large forklifts or trailers and positioned securely at the test site.

[0024] The monitoring system is a fusion-based multi-dimensional monitoring unit, with its core consisting of a field measurement point acquisition module, a visual monitoring module, and a data fusion processing submodule. The field measurement point acquisition module includes multiple sensors, a data acquisition card, and a local storage unit. Sensor types include flow sensors, pressure sensors, temperature sensors, combustion pressure pulsation sensors, vibration sensors, and speed sensors. The visual monitoring module includes a camera, an infrared thermal imager, an image acquisition card, and a visual algorithm processing unit. This unit utilizes a GPU-accelerated chip and incorporates target detection algorithms, edge extraction algorithms, grayscale change analysis algorithms, and temperature field analysis algorithms. The data fusion processing submodule synchronously correlates and analyzes abnormal parameter warning signals from the measurement point data with abnormal signals from the visual monitoring.

[0025] The main controller of the control system uses an industrial-grade PLC, which establishes bidirectional signal connections with the data fusion processing submodule of the monitoring system, slave controllers, and the human-machine interface unit. The slave controllers employ distributed I / O modules, with each slave controller responsible for controlling the actuators of its corresponding module. These actuators include hydraulic control valves, electromagnetic control valves, servo motors, igniters, and frequency converters. The human-machine interface unit uses an industrial-grade touchscreen, supporting operations such as test type selection, load command input, test start and stop, and parameter modification, while also possessing remote communication capabilities.

[0026] Example 1: Compressor Test This embodiment uses a gas turbine compressor component as a test piece to specifically illustrate the gas turbine test bench control and monitoring method of the present invention.

[0027] See Figure 1 The control and monitoring method in this embodiment includes the following steps: Step S1: Select the test type according to the test requirements, install the test piece on the modular test bench and complete the configuration, and deploy the sensors and visual monitoring equipment.

[0028] The skid-mounted test bench has a frame dimension of 6000mm long, 2500mm wide, and 2800mm high, with an overall weight of approximately 12 tons. The power module is driven by a 500kW electric motor and connected to the compressor test piece via a speed-increasing gearbox. The test piece mounting module is equipped with a standardized HG20615 mounting flange and is fixed to the compressor housing using M24×80 bolts. The piping connection module includes inlet and exhaust pipes; the inlet pipe has an inner diameter of 300mm, and the exhaust pipe has an inner diameter of 250mm, both connected using quick-connect couplings. Heavy-duty casters with a rated load capacity of 5 tons are installed at the four corners of the bottom as a movement mechanism, and the ground fixing mechanism uses M30 anchor bolts with pre-embedded steel plates for positioning and fixation.

[0029] To meet the monitoring requirements of the compressor test, a vortex flow sensor with a range of 0 to 3000 m³ / h and an accuracy of ±1.0%FS was installed at the compressor inlet. A type K thermocouple temperature sensor with a range of -20 to 500℃ and an accuracy of ±1.5℃ was also installed at the compressor inlet. A diffused silicon pressure sensor with a range of 0 to 1.6 MPa and an accuracy of ±0.25%FS was also installed at the compressor inlet.

[0030] A temperature sensor with a range of 0 to 800°C is installed at the compressor outlet to monitor the temperature of the gas exiting the compressor. A pressure sensor with a range of 0 to 4.0 MPa is installed at the compressor outlet to monitor the total pressure at the compressor outlet. By combining the data from the inlet and outlet pressure sensors, the compressor pressure ratio can be calculated. ; in, This indicates the total pressure at the compressor outlet. This indicates the total pressure at the compressor inlet.

[0031] Four piezoelectric vibration sensors, with a frequency response of 1 to 10000 Hz and a sensitivity of 100 mV / (mm / s), are arranged at 90° intervals radially around the compressor housing. The root mean square velocity of the vibration signal is used to assess the severity of the vibration. ; when Vibration exceeding 4.5 mm / s is considered abnormal, based on ISO 10816 standard. A magnetoelectric speed sensor with a range of 0 to 30,000 rpm is installed at the drive shaft.

[0032] The sampling frequency of the data acquisition card is set to 20kHz, which satisfies the Nyquist sampling theorem. ; The vibration signal has a maximum frequency of 10000Hz, and the sampling frequency of 20kHz is no less than twice the maximum frequency of 20000Hz, satisfying the sampling theorem requirements. All data is transmitted to the control system via industrial Ethernet at a rate of 100Mbps.

[0033] See Figure 4 The visual monitoring module deploys two industrial cameras at the compressor housing connection point, with a resolution of 1920×1080 pixels and a frame rate of 30fps, to monitor for deformation and leakage in the housing. One industrial camera, also with a resolution of 1920×1080 pixels and a frame rate of 30fps, is deployed at the compressor inlet to monitor for foreign object ingress. Due to the relatively low temperature range during compressor testing, no infrared thermal imager was added. The image acquisition card simultaneously acquires image and video data from the three cameras, converts the format, and transmits it to the visual algorithm processing unit.

[0034] The visual algorithm processing unit uses a deep learning model to process image data in real time, with a processing rate of 30 frames per second. The target detection algorithm locates the monitoring areas of the compressor housing connection, air inlet, and exhaust outlet. The edge extraction algorithm performs contour analysis on the image of the monitoring area; when unexpected edge changes are detected at the connection point, it is marked as a potential leak area. The grayscale change analysis algorithm performs a time-series comparison of the grayscale values ​​of the marked areas; when the grayscale change exceeds a preset grayscale threshold, a gas leak is determined, and a visual monitoring anomaly signal is generated.

[0035] Step S2: Import the design parameters of the test specimen into the control system.

[0036] After the monitoring personnel select the "compressor test" mode via the industrial touchscreen, they import the design parameters of the compressor test piece into the parameter storage unit of the control system. The design parameters include: rated speed 15000rpm, design pressure ratio 6.5, design flow rate 25kg / s, 50% load speed 10600rpm, 75% load speed 13000rpm, and 100% load speed 15000rpm.

[0037] The threshold values ​​for each parameter are set based on design parameters and safety margin factors. Taking the compressor outlet temperature as an example, the design outlet temperature is 350℃. The warning threshold is calculated based on a warning margin factor of 5%. ; The alarm threshold is calculated based on an alarm margin factor of 10%. ; The emergency stop threshold is calculated based on a stop margin factor of 15%. .

[0038] Taking the compressor outlet pressure as an example, the designed outlet pressure is 1.04 MPa. The warning threshold is calculated based on a warning margin factor of 5%. ; The alarm threshold is calculated based on an alarm margin factor of 10%. ; The emergency stop threshold is calculated based on a stop margin factor of 15%. .

[0039] The threshold values ​​for the remaining parameters are calculated based on their respective design values ​​and margin coefficients using the method described above.

[0040] Step S3: Generate initial control commands based on design parameters to start the test bench.

[0041] After the test program is initiated, the command generation unit of the control system issues initial control commands to the actuator based on the design parameters. The control system controls the motor to gradually increase its speed to the target speed at a rate of 300 rpm / min. During the speed increase process, the speed is controlled at a linear rate of increase. ; ; Initial rotation speed 0 rpm, target speed The acceleration time is 15000 rpm. If the time is 50 minutes, then the rate of increase is... The speed is 300 rpm / min.

[0042] The control system employs an incremental PID control algorithm to perform closed-loop adjustment of the test bench's operating parameters. The formula for calculating the incremental control command is as follows: ; The proportionality coefficient The integral coefficient is 1.2. The differential coefficient is 0.05. The deviation value is 0.3. The above PID parameters were determined using the Ziegler-Nichols tuning method. This refers to the difference between the design parameter values ​​and the actual collected operational data.

[0043] Step S4: Dual-modal real-time monitoring.

[0044] During the test run, the on-site measurement point acquisition module and the visual monitoring module worked simultaneously.

[0045] The data acquisition card collects data from each sensor in real time at a sampling frequency of 20kHz. The data fusion processing submodule compares the collected data with preset thresholds for each parameter in real time. When any parameter exceeds the warning threshold, a corresponding parameter anomaly warning signal is generated.

[0046] The visual algorithm processing unit analyzes the images at a processing rate of 30 frames per second. The target detection algorithm first locates monitoring areas such as the compressor housing connection, air inlet, and exhaust outlet. The edge extraction algorithm extracts edge features of the connection seals within the monitoring areas, and marks potential leak areas when edge changes exceed a preset edge threshold. The grayscale change analysis algorithm performs time-series analysis on the grayscale values ​​of the marked areas to determine the flow state of the leaking gas. When an anomaly is detected, a visual monitoring anomaly signal is generated.

[0047] Step S5: Synchronously analyze the abnormal parameter warning signal and the abnormal visual monitoring signal, and execute graded protection actions according to the level of abnormality.

[0048] See Figure 3 The data fusion processing submodule performs synchronous correlation analysis between the parameter anomaly warning signal and the visual monitoring anomaly signal. A weighted fusion method is used to calculate the fusion confidence level. ; Among them, the abnormal weight coefficient of the parameter The visual anomaly weighting coefficient is set to 0.6. Set to 0.4. When the fusion confidence level... When the value exceeds 0.7, it is determined to be an abnormal state and the corresponding level of graded protection action is triggered. When any module sends an abnormal signal, the alarm mechanism of the monitoring system is immediately triggered, and the fused abnormal information is transmitted to the control system.

[0049] This embodiment adopts a three-level hierarchical protection strategy: Level 1 Warning: When any operational data exceeds the warning threshold (±5% of the design value), or when the vision system detects a minor leak, the system will issue an audible and visual alarm to notify monitoring personnel. Level 1 warnings do not interfere with trial operations; monitoring personnel will decide whether to take action based on the actual situation.

[0050] Level 2 alarm: When any operating data exceeds the alarm threshold, i.e., exceeds the design value by ±10%, or when the vision system detects a significant leak, the control system automatically reduces the motor speed to 80% of the current speed. The deceleration process also adopts linear deceleration control, with the deceleration rate set at 200 rpm / min to ensure a smooth deceleration process.

[0051] Level 3 Emergency Shutdown: When any operational data exceeds the emergency shutdown threshold, i.e., exceeds the design value by ±15%, or the fusion confidence level... When the value exceeds 0.9, the control system immediately executes the emergency shutdown procedure. The emergency shutdown procedure includes: disconnecting the motor power supply, activating the braking device to decelerate the rotor, opening the exhaust bypass valve to release pipeline pressure, executing a 120-second cooling procedure, and then completely shutting down the machine.

[0052] When step S5 determines that the test piece has not experienced any abnormalities and has entered a stable operating state, the process proceeds to step S6. When step S5 determines that the test piece has an abnormality but has not yet reached the shutdown condition, a first-level early warning or second-level alarm action is executed, and then the process returns to step S4 to continue monitoring.

[0053] Step S6: Receive the target load input, automatically generate control commands based on the design parameters, and control the test bench to operate continuously and stably.

[0054] After the test specimen enters the normal operation phase, the monitoring personnel input the target load through the human-machine interface unit. The control system automatically generates control commands based on the design parameters and load requirements. Taking 50% load operation as an example, with a target speed of 10600 rpm, the control system automatically adjusts the motor speed to the target speed according to the PID algorithm and maintains stable operation.

[0055] During the stable operation phase, monitoring personnel do not need to manually adjust operating parameters. The control system continuously generates control commands based on the deviation between the real-time collected operating data and the design parameters, automatically correcting the operating status. Only 1 to 2 monitoring personnel are required to be on duty throughout the entire stable operation phase.

[0056] During the aforementioned stable operation phase, all sensor data was stored in a 4TB local storage unit at a sampling frequency of 20kHz. Visual images were stored synchronously at a frame rate of 30fps. Data and images were synchronized at the millisecond level via an NTP time server, facilitating data traceability and analysis after the experiment.

[0057] This embodiment achieves dual assurance of real-time monitoring of compressor test bench operation data and real-time visual monitoring by integrating sensor measurement point data acquisition and visual monitoring technologies. Sensor measurement point data acquisition obtains operating parameters of the compressor such as flow rate, temperature, pressure, vibration, and speed, and compares them with preset thresholds. Visual monitoring, through target detection and edge extraction algorithms, diagnoses leaks and foreign object conditions at the casing connection points and air inlet in real time. The synchronous correlation analysis of the two monitoring signals comprehensively improves the coverage and accuracy of compressor test monitoring. The control system automatically generates control commands based on design parameters and PID algorithms. During stable operation, only 1 to 2 monitoring personnel are needed to input load commands to complete the operation control, significantly reducing labor costs.

[0058] Example 2: Combustion Test This embodiment uses a gas turbine combustion chamber component as the test specimen. The control and monitoring method of this embodiment also includes steps S1 to S6. The following details the differences between each step and those in Embodiment 1.

[0059] Step S1: Select the test type according to the test requirements, install the test piece on the modular test bench and complete the configuration, and deploy the sensors and visual monitoring equipment.

[0060] Based on the skid-mounted test bench main body of Example 1, the test piece mounting module is switched to a combustion chamber mounting configuration. The piping connection module is supplemented with a fuel supply pipe and a high-pressure air intake pipe. The fuel supply pipe has an inner diameter of 50mm and is made of 316L stainless steel. The high-pressure air intake pipe has an inner diameter of 200mm. The power module is equipped with an air compressor with a flow rate of 30m³ / min and a maximum pressure of 2.0MPa; and a fuel supply pump with a flow rate of 500L / h and a supply pressure of 3.0MPa. All other configuration parameters are consistent with Example 1; the frame dimensions, bottom moving mechanism, and ground fixing mechanism are all the same in terms of setup and specifications.

[0061] An air flow sensor with a range of 0 to 5000 m³ / h and an accuracy of ±1.0%FS is installed at the combustion chamber inlet. A type K thermocouple temperature sensor with a range of -20 to 500℃ and an accuracy of ±1.5℃ is installed at the combustion chamber inlet. A diffused silicon pressure sensor with a range of 0 to 2.5 MPa and an accuracy of ±0.25%FS is installed at the combustion chamber inlet.

[0062] An exhaust temperature sensor, using a type B platinum-rhodium thermocouple, is installed at the combustion chamber outlet, with a range of 0 to 1800°C. A pressure sensor, with a range of 0 to 2.5 MPa, is also installed at the combustion chamber outlet. A combustion pressure pulsation sensor, with a frequency response of 0.5 to 20000 Hz and a sensitivity of 50 mV / kPa, is added to monitor pressure fluctuations inside the combustion chamber.

[0063] Four piezoelectric vibration sensors are arranged at 90° radial intervals in the combustion chamber shell, with frequency response and sensitivity parameters consistent with those in Example 1. The sampling frequency of the data acquisition card is set to 40kHz. Since the highest frequency component of the combustion pressure pulsation sensor is 20000Hz, a sampling frequency of 40kHz satisfies the Nyquist sampling theorem requirement. All data is transmitted to the control system via industrial Ethernet at a rate of 100Mbps.

[0064] See Figure 4 The combustion test falls under a high-temperature testing scenario. The visual monitoring module is equipped with two industrial cameras with a resolution of 1920×1080 pixels and a frame rate of 30fps, positioned within the combustion chamber observation window. Simultaneously, an infrared thermal imager with a temperature measurement range of 200 to 2000℃ and a thermal sensitivity of 50mK is also deployed within the combustion chamber observation window to monitor flame morphology and combustion chamber wall temperature distribution.

[0065] An industrial camera is installed at the fuel line joint to monitor for leaks. Another industrial camera is installed at the air intake manifold flange connection to monitor the air line's sealing condition. Image and video data from these five vision devices are simultaneously acquired by an image acquisition card and then transmitted to the vision algorithm processing unit.

[0066] In addition to running the same target detection algorithm, edge extraction algorithm, and grayscale change analysis algorithm as in Embodiment 1, the visual algorithm processing unit adds a temperature field analysis algorithm. This algorithm analyzes the thermal images acquired by the infrared thermal imager to identify abnormally overheated areas on the combustion chamber wall. When the temperature of a certain area exceeds the preset temperature deviation of the wall temperature design value, a visual monitoring anomaly signal is generated. A camera at the combustion chamber observation window captures the flame pattern inside the combustion chamber in real time, judging the combustion state by changes in flame color and shape. Similarly, a visual monitoring anomaly signal is generated when the flame pattern exhibits a preset abnormal pattern.

[0067] Step S2: Import the design parameters of the test specimen into the control system.

[0068] After selecting the "combustion test" mode via the industrial touchscreen, the monitoring personnel imported the design parameters of the combustion chamber test piece. The design parameters included: design inlet air temperature 400℃, design inlet air pressure 1.8MPa, design outlet temperature 1200℃, design fuel flow rate 300L / h, design air-fuel ratio 25:1, 50% load fuel flow rate 150L / h, 75% load fuel flow rate 225L / h, and 100% load fuel flow rate 300L / h.

[0069] Taking the combustion chamber outlet temperature as an example, the designed outlet temperature is 1200℃. The warning threshold is calculated based on a warning margin factor of 3%. ; The alarm threshold is calculated based on an alarm margin factor of 6%. ; The emergency stop threshold is calculated based on a stop margin factor of 10%. .

[0070] The combustion test involves a wider range of temperatures and pressures than the compressor test, so a more conservative value was used for the safety margin factor.

[0071] Step S3: Generate initial control commands based on design parameters to start the test bench.

[0072] After the test procedure is started, the control system executes the initial control commands in the following order: First, the air compressor is started to gradually increase the intake pressure to the design inlet pressure of 1.8MPa; then the fuel supply pump is started to gradually increase the fuel flow rate to 150L / h corresponding to 50% load; then the igniter is triggered to ignite the fuel, and the flame is confirmed to be successfully established by the camera in the combustion chamber observation window.

[0073] The control system employs an incremental PID control algorithm to perform closed-loop regulation of the fuel supply flow rate and air intake pressure. The PID parameter for the fuel supply flow rate is: proportional coefficient. The integral coefficient is 0.8. The differential coefficient is 0.03. The value is 0.2. The PID parameter for the air intake pressure is: proportional coefficient. The integral coefficient is 1.0. The differential coefficient is 0.04. The value is 0.25. All PID parameters were determined using the Ziegler-Nichols tuning method.

[0074] Step S4: Dual-modal real-time monitoring.

[0075] The data acquisition card collects data from each sensor in real time at a sampling frequency of 40kHz. The data fusion processing submodule compares the collected data with preset thresholds for each parameter in real time.

[0076] The visual algorithm processing unit analyzes the images at a processing rate of 30 frames per second. At the combustion chamber observation window, the target detection algorithm locates the flame area, while the grayscale change analysis algorithm performs time-series analysis on the changes in flame color and shape. The temperature field analysis algorithm analyzes the wall temperature distribution of the thermal images acquired by the infrared thermal imager to identify abnormally overheated areas. At the fuel line joints and air line flange connections, the edge extraction algorithm and the grayscale change analysis algorithm work together to detect leaks.

[0077] Step S5: Synchronously analyze the abnormal parameter warning signal and the abnormal visual monitoring signal, and execute graded protection actions according to the level of abnormality.

[0078] The data fusion processing submodule performs synchronous correlation analysis between the parameter anomaly warning signal and the visual monitoring anomaly signal. The calculation method for the fusion confidence level is consistent with that in Example 1. Since visual monitoring has greater diagnostic value for flame state and wall temperature distribution in combustion tests, the parameter anomaly weighting coefficient... The visual anomaly weighting coefficient is set to 0.5. Set to 0.5. A fusion confidence level exceeding 0.7 is considered an abnormal state.

[0079] The graded protection strategy for combustion tests has been adjusted based on the characteristics of combustion tests, according to Example 1: Level 1 warning: When any operational data exceeds the warning threshold, i.e., exceeds the design value by ±3%, or when the vision system detects a slight leak, the system will issue an audible and visual alarm to notify the monitoring personnel to pay attention.

[0080] Level 2 alarm: When any operating data exceeds the alarm threshold, i.e., exceeds the design value by ±6%, or when a significant leak or abnormal flame is detected, the control system automatically reduces the fuel supply to 70% of the current flow rate.

[0081] Level 3 Emergency Shutdown: When any operational data exceeds the emergency shutdown threshold, i.e., exceeds the design value by ±10%, or the fusion confidence level... When the value exceeds 0.9, the control system immediately executes the emergency shutdown procedure. The emergency shutdown procedure includes: cutting off the fuel supply, shutting off the fuel supply pump, opening the air bypass valve to maintain airflow to cool the combustion chamber, executing a 180-second cooling procedure, shutting off the air compressor, and completely shutting down the engine.

[0082] Step S6: Receive the target load input, automatically generate control commands based on the design parameters, and control the test bench to operate continuously and stably.

[0083] After the test specimen entered the normal operation phase, the monitoring personnel input the target load. Taking 75% load as an example, the target fuel flow rate is 225 L / h. The control system automatically adjusts the fuel supply pump output flow rate to 225 L / h according to the PID algorithm, while maintaining the inlet air pressure at 1.8 MPa. During the stable operation phase, the control system continuously adjusts the fuel flow rate and air pressure based on the deviation between the real-time collected outlet temperature, pressure, and combustion pressure pulsation data and the design parameters, automatically maintaining a stable combustion state.

[0084] During the aforementioned stable operation phase, all sensor data is stored in an 8TB local storage unit at a sampling frequency of 40kHz. Visual images are stored synchronously at a frame rate of 30fps. Data and images are synchronized at the millisecond level via an NTP time server.

[0085] This embodiment achieves real-time visual monitoring of flame state and wall temperature distribution during combustion tests by adding an infrared thermal imager and temperature field analysis algorithm to the existing compressor test monitoring. This complements the parameter monitoring of sensor measurement data. The graded protection strategy adopts a more conservative margin coefficient based on the temperature and pressure characteristics of the combustion test, and an air cooling step is added to the emergency shutdown procedure to ensure safe cooling of the combustion chamber.

[0086] Example 3: Turbine Test This embodiment uses a gas turbine component as the test specimen. The control and monitoring method in this embodiment also includes steps S1 to S6. The following details the differences between each step and those in Embodiment 1.

[0087] Step S1: Select the test type according to the test requirements, install the test piece on the modular test bench and complete the configuration, and deploy the sensors and visual monitoring equipment.

[0088] Based on the skid-mounted test bench main body of Example 1, the test piece mounting module is switched to a turbine mounting configuration. The piping connection module is equipped with high-temperature gas inlet and exhaust pipes. The high-temperature gas inlet pipe has an inner diameter of 250mm and is made of Inconel 718 high-temperature alloy. The exhaust pipe has an inner diameter of 350mm. The power module uses a 500kW electric motor to drive the turbine rotation, and is equipped with a gas heating device to simulate the actual turbine inlet conditions, with a heating power of 200kW, which can heat the inlet gas temperature to the design temperature. The remaining configuration parameters are consistent with Example 1, and the frame dimensions, bottom moving mechanism, and ground fixing mechanism settings and specifications are all the same as in Example 1.

[0089] A flow sensor with a range of 0 to 4000 m³ / h and an accuracy of ±1.0%FS is installed at the turbine inlet. A type B platinum-rhodium thermocouple temperature sensor with a range of 0 to 1600℃ is installed at the turbine inlet. A pressure sensor with a range of 0 to 3.0 MPa and an accuracy of ±0.25%FS is installed at the turbine outlet. A type K thermocouple temperature sensor with a range of 0 to 800℃ and an accuracy of ±1.5℃ is installed at the turbine outlet. A pressure sensor with a range of 0 to 1.0 MPa is also installed at the turbine outlet.

[0090] Four piezoelectric vibration sensors are arranged at 90° intervals along the radial direction of the turbine housing, with parameters consistent with those in Example 1. A magnetoelectric speed sensor with a range of 0 to 20,000 rpm is installed at the turbine shaft. The sampling frequency of the data acquisition card is set to 20 kHz to meet the Nyquist sampling theorem requirements.

[0091] See Figure 4 The turbine test falls under a high-temperature testing scenario. The visual monitoring module deploys one industrial camera and one infrared thermal imager at the turbine inlet flange. The infrared thermal imager has a temperature measurement range of 200 to 1800℃ and a thermal sensitivity of 50mK, used to monitor the temperature distribution in the turbine inlet area. One industrial camera is deployed at the turbine outlet flange. Another industrial camera is deployed at the bearing end cover to monitor the sealing status of the bearing during turbine operation.

[0092] The visual algorithm processing unit runs the same target detection algorithm and edge extraction algorithm as in Example 1, and simultaneously runs the temperature field analysis algorithm to analyze the thermal images of the infrared thermal imager to identify abnormal overheating areas of the turbine blades and shell.

[0093] Step S2: Import the design parameters of the test specimen into the control system.

[0094] After selecting the "Turbine Test" mode via the industrial touchscreen, the monitoring personnel imported the design parameters of the turbine test specimen. The design parameters included: design inlet temperature 1100℃, design inlet pressure 1.5MPa, design outlet temperature 550℃, rated speed 12000rpm, 50% load speed 8500rpm, 75% load speed 10400rpm, and 100% load speed 12000rpm.

[0095] Taking turbine inlet temperature as an example, the designed inlet temperature is 1100℃. The warning threshold is calculated based on a warning margin factor of 3%. ; The alarm threshold is calculated based on an alarm margin factor of 6%. ; The emergency stop threshold is calculated based on a stop margin factor of 10%. .

[0096] Step S3: Generate initial control commands based on design parameters to start the test bench.

[0097] After the test procedure is started, the control system executes the initial control commands in the following order: First, start the motor and gradually increase the speed to 8500 rpm at a rate of 200 rpm / min to 50% load speed; then start the gas heating device and gradually raise the inlet gas temperature to 50% of the design inlet temperature, i.e., 550℃; after the temperature and speed are stable, gradually and synchronously increase the speed and temperature to the target load.

[0098] The linear acceleration control formula for the acceleration process is consistent with that in Example 1. The PID parameters for the turbine test are: proportional coefficient... The integral coefficient is 1.0. The differential coefficient is 0.04. It is 0.25.

[0099] Step S4: Dual-modal real-time monitoring.

[0100] The data acquisition card collects data from each sensor in real time at a sampling frequency of 20kHz. The data fusion processing submodule compares the collected data with the preset thresholds of each parameter in real time.

[0101] The visual algorithm processing unit analyzes the images at a processing rate of 30 frames per second. The temperature field analysis algorithm performs temperature distribution analysis on the thermal images of the turbine inlet area acquired by the infrared thermal imager. The edge extraction algorithm monitors the sealing status of images at the outlet flange and bearing end cover.

[0102] Step S5: Synchronously analyze the abnormal parameter warning signal and the abnormal visual monitoring signal, and execute graded protection actions according to the level of abnormality.

[0103] The calculation method for the fusion confidence score is consistent with that in Example 1, with the parameter anomaly weighting coefficient... The visual anomaly weighting coefficient is set to 0.55. Set to 0.45.

[0104] The graded protection strategy for the turbine test is similar to that in Example 2, with a warning margin of 3%, an alarm margin of 6%, and a shutdown margin of 10%. The emergency shutdown procedure includes: disconnecting the power supply to the heating device, opening the cooling air passage for cooling, disconnecting the power supply to the motor and activating the braking device, executing a 180-second cooling procedure, and then completely shutting down the turbine.

[0105] Step S6: Receive the target load input, automatically generate control commands based on the design parameters, and control the test bench to operate continuously and stably.

[0106] The control method during the stable operation phase is the same as in Example 1. The control system automatically maintains stable speed and temperature based on the PID algorithm, requiring only 1 to 2 monitoring personnel. During the stable operation phase, data is stored at a sampling frequency of 20kHz, and images are synchronously stored at a frame rate of 30fps, achieving millisecond-level time synchronization through an NTP time server.

[0107] Example 4: Rotor Test This embodiment uses a gas turbine rotor as the test specimen. The control and monitoring method in this embodiment also includes steps S1 to S6. The following details the differences between each step and those in Embodiment 1.

[0108] Step S1: Select the test type according to the test requirements, install the test piece on the modular test bench and complete the configuration, and deploy the sensors and visual monitoring equipment.

[0109] Based on the skid-mounted test bench body of Example 1, the test specimen mounting module is switched to a rotor mounting configuration. The rotor test specimen is fixed to the test bench body via end-support bearings, which are adjustable tilting pad bearings. The power module is driven by a 500kW electric motor and connected to the rotor via a flexible coupling. The piping module is equipped with a lubricating oil supply pipeline with an inner diameter of 30mm, a lubricating oil pump flow rate of 50L / min, and a supply pressure of 0.5MPa. The remaining configuration parameters are consistent with those of Example 1.

[0110] Two eddy current displacement sensors are installed at each of the rotor's end bearing support locations, one horizontally and one vertically, with a range of 0 to 2 mm and a sensitivity of 8 mV / μm, to monitor the rotor shaft trajectory and bearing clearance changes. One piezoelectric vibration sensor with parameters consistent with Example 1 is installed at each of the rotor's end bearing support locations. A magnetoelectric speed sensor with a range of 0 to 30000 rpm is installed at the drive shaft. One K-type thermocouple temperature sensor with a range of 0 to 300°C is installed at each of the end bearings to monitor bearing temperature. The data acquisition card's sampling frequency is set to 20 kHz.

[0111] See Figure 4 The visual monitoring module has one industrial camera at each of the support bearings at both ends of the rotor, with a resolution of 1920×1080 pixels and a frame rate of 30fps. Another industrial camera is installed on the exposed part of the rotor to monitor its operation and identify any visual characteristics corresponding to abnormal noises. Since the rotor test had a low temperature range, no infrared thermal imager was added.

[0112] The visual algorithm processing unit uses a target detection algorithm to locate the monitoring area of ​​the rotor and bearing, an edge extraction algorithm to monitor the sealing status and oil film leakage at the bearing end cover, and a grayscale change analysis algorithm to perform time-series analysis on the operating status of the exposed parts of the rotor.

[0113] Step S2: Import the design parameters of the test specimen into the control system.

[0114] After selecting the "Rotor Test" mode via the industrial touchscreen, the monitoring personnel imported the design parameters of the rotor test piece. The design parameters included: rated speed 18000 rpm, first critical speed 6500 rpm, second critical speed 15000 rpm, design bearing temperature 70℃, design bearing vibration value 2.8 mm / s, 50% load speed 9000 rpm, 75% load speed 13500 rpm, and 100% load speed 18000 rpm.

[0115] Taking bearing vibration as an example, the designed vibration value is 2.8 mm / s. The warning threshold is calculated based on a warning margin factor of 30%. ; The alarm threshold is calculated based on an alarm margin factor of 60%. ; The emergency stop threshold is set at 7.1 mm / s, in accordance with ISO 10816 standard.

[0116] Step S3: Generate initial control commands based on design parameters to start the test bench.

[0117] After the test program is started, the control system gradually increases the motor speed at a rate of 500 rpm / min. During the speed increase, when the speed approaches the first-order critical speed of 6500 rpm, the rate of increase is reduced to 100 rpm / min to smoothly pass through the critical speed region. After passing through the first-order critical speed region, the rate of increase is restored to 500 rpm / min. When the speed approaches the second-order critical speed of 15000 rpm, the rate of increase is reduced again to 100 rpm / min. After passing through the second-order critical speed region, the normal rate of increase is restored until the target speed is reached.

[0118] The control system adopts an incremental PID control algorithm, and the PID parameters are: proportional coefficient. The integral coefficient is 1.5. The differential coefficient is 0.06. It is 0.35.

[0119] Step S4: Dual-modal real-time monitoring.

[0120] The data acquisition card collects vibration, displacement, rotational speed, and bearing temperature data in real time at a sampling frequency of 20kHz. The data fusion processing submodule compares the collected data with preset thresholds in real time. The root mean square velocity of the vibration signal is calculated using the following formula: ; The visual algorithm processing unit analyzes images of the bearing and exposed parts of the rotor. The edge extraction algorithm monitors lubricating oil leakage at the bearing end cover, and the grayscale change analysis algorithm performs time-series analysis on the operating status of the rotor surface. When abnormal vibration marks or surface changes are detected, a visual monitoring anomaly signal is generated.

[0121] Step S5: Synchronously analyze the abnormal parameter warning signal and the abnormal visual monitoring signal, and execute graded protection actions according to the level of abnormality.

[0122] The calculation method for the fusion confidence score is consistent with that in Example 1, with the parameter anomaly weighting coefficient... The visual anomaly weighting coefficient is set to 0.7. The value is set to 0.3. In rotor tests, sensor data more directly reflects vibration and displacement anomalies, therefore the parameter anomaly weighting coefficient is set too high.

[0123] The graded protection strategy for rotor testing is as follows: Level 1 warning: When the bearing vibration value exceeds 3.64 mm / s, or the bearing temperature exceeds 91℃, or the vision system detects lubricating oil leakage, an audible and visual alarm will be issued.

[0124] Level 2 alarm: When the bearing vibration value exceeds 4.48 mm / s, or the bearing temperature exceeds 112℃, or the vision system detects obvious leakage, the control system automatically reduces the speed to 70% of the current speed.

[0125] Level 3 Emergency Stop: When the bearing vibration value exceeds 7.1 mm / s, or the fusion confidence level... When the value exceeds 0.9, the control system immediately executes the emergency stop procedure. The emergency stop procedure includes: disconnecting the motor power supply, activating the braking device to decelerate the rotor, maintaining the lubricating oil supply until the rotor completely stops rotating, and achieving a complete shutdown.

[0126] Step S6: Receive the target load input, automatically generate control commands based on the design parameters, and control the test bench to operate continuously and stably.

[0127] The control method during the stable operation phase is consistent with that in Example 1. The control system automatically maintains stable rotational speed based on the PID algorithm and continuously monitors vibration and displacement data to ensure the rotor operates in a safe state. Only 1 to 2 monitoring personnel are required. During the stable operation phase described above, data is stored at a sampling frequency of 20kHz, and images are synchronously stored at a frame rate of 30fps, achieving millisecond-level time synchronization through an NTP time server.

[0128] This embodiment achieves comprehensive monitoring coverage of the rotor test by deploying eddy current displacement sensors and vibration sensors at the support bearings at both ends of the rotor, combined with visual monitoring for lubricating oil leakage detection and rotor operating status analysis. During acceleration, the acceleration rate is automatically reduced when passing through the critical speed region, ensuring the rotor safely passes through the resonance zone. The graded protection strategy uses vibration value as the core monitoring parameter, tailored to the characteristics of the rotor test, and sets a shutdown threshold according to the ISO 10816 standard.

[0129] In summary, the embodiments of the present invention have at least the following technical effects: This invention achieves dual protection through the integration of sensor measurement point data acquisition and visual monitoring technologies, enabling real-time monitoring of test bench operation data and real-time visual monitoring. Sensor measurement point data acquisition obtains the test specimen's operating parameters and compares them with preset thresholds generated based on design parameters. Visual monitoring, through visual algorithms, can diagnose abnormal states such as air leakage and localized overheating in real time. The synchronous correlation analysis of the two monitoring signals comprehensively improves the coverage and accuracy of test status monitoring, reducing the risk of missed fault detection.

[0130] The control system of this invention generates control commands based on the design parameters of the test specimen, realizing automated control of the test process. After the test specimen enters the stable operation stage, only the target load needs to be input to automatically generate control commands, which greatly reduces the need for on-site personnel, lowers labor costs, and avoids the impact of human error on test safety and efficiency.

[0131] This invention introduces a graded protection mechanism, which executes different levels of protection actions according to the anomaly level. This avoids unnecessary downtime caused by over-response while ensuring experimental safety, thereby improving experimental efficiency and economy.

[0132] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for controlling and monitoring a gas turbine test bench, characterized in that, include: Select the corresponding test type according to the test requirements, install the test piece on the modular test bench and complete the configuration corresponding to the test type, and deploy sensors and visual monitoring equipment. The sensors cover the running parts of the test piece and the power components of the modular test bench. The design parameters of the test specimen are imported into the control system, including the operating parameters of the test specimen under different load conditions. Initial control commands are generated based on the design parameters to control the modular test bench to start operation. During the test operation, the operating data of the test piece is collected in real time. The operating data is compared and analyzed with a preset threshold generated based on the design parameters and safety margin coefficient to generate a parameter anomaly warning signal. In addition, the visual monitoring device collects image information of the test area in real time. The image information is analyzed by a visual algorithm to diagnose the operating status of the test piece and generate a visual monitoring anomaly signal. The abnormal parameter warning signal and the abnormal visual monitoring signal are synchronously correlated and analyzed. When either signal triggers an abnormality, the corresponding graded protection action is executed according to the abnormality level. After the test piece enters the stable operation stage, the target load input is received, and control commands are automatically generated according to the design parameters and the target load to control the modular test bench to operate continuously and stably.

2. The gas turbine test bench control and monitoring method according to claim 1, characterized in that, The preset threshold is generated as follows: Based on the design parameters and the safety margin coefficient, the warning threshold, alarm threshold, and emergency shutdown threshold are generated respectively: ； ； ； in, Indicates the warning threshold. Indicates the alarm threshold. Indicates the emergency stop threshold. This indicates the design value of the corresponding parameter in the design parameters. This represents the early warning margin coefficient. Indicates the alarm margin coefficient. This represents the downtime margin coefficient. .

3. The gas turbine test bench control and monitoring method according to claim 2, characterized in that, The graded protection actions include: Level 1 warning: When any of the aforementioned operational data exceeds the warning threshold, or when the visual monitoring anomaly signal indicates a minor leak, an audible and visual alarm will be issued and the monitoring personnel will be notified; Level 2 alarm: When any of the above operating data exceeds the alarm threshold, or when the visual monitoring abnormal signal indicates obvious leakage or flame abnormality, the fuel supply will be automatically reduced to a preset load reduction ratio. Level 3 Emergency Shutdown: When any of the aforementioned operating data exceeds the emergency shutdown threshold, or the fusion confidence exceeds the preset shutdown fusion threshold, the fuel supply is cut off and the bypass valve is opened, and a cooling program of a preset duration is executed before shutdown.

4. The gas turbine test bench control and monitoring method according to claim 1, characterized in that, The analysis of the image information using visual algorithms includes: The monitoring area of ​​the test piece is located using a target detection algorithm; The gas leak profile at the seal was identified using an edge extraction algorithm; The flow state of the leaked gas is determined by a grayscale change analysis algorithm; Abnormal overheating areas of the test specimen were identified using a temperature field analysis algorithm; Based on the above analysis results, the operating status of the test piece is determined and the visual monitoring abnormality signal is generated.

5. The gas turbine test bench control and monitoring method according to claim 1, characterized in that, The synchronous correlation analysis method is as follows: The fusion confidence score is calculated using a weighted fusion method. ； in, This represents the fusion confidence level. This indicates the anomaly confidence level of the abnormality warning signal for the aforementioned parameter. This indicates the confidence level of the abnormality in the visual monitoring signal. This represents the abnormal weighting coefficient of the parameter. Indicates the weighting coefficient for visual anomalies. ; When the fusion confidence level exceeds the preset fusion threshold, it is determined to be an abnormal state and the hierarchical protection action is triggered.

6. The gas turbine test bench control and monitoring method according to claim 1, characterized in that, The deployment location of the visual monitoring equipment is determined according to the type of test: When the test type is a combustion test, cameras are placed at the combustion chamber observation window, fuel line joint, and air intake manifold flange connection, and an infrared thermal imager is added at the combustion chamber observation window; When the test type is a compressor test, the camera is placed at the compressor housing connection, air inlet and exhaust port; When the test type is a turbine test, cameras are placed at the turbine inlet and outlet flanges and bearing end caps, and an infrared thermal imager is added. When the test type is a rotor test, the camera is placed at the support bearings at both ends of the rotor and at the exposed parts of the rotor.

7. The gas turbine test bench control and monitoring method according to claim 1, characterized in that, The control commands are generated using an incremental PID control algorithm: ； in, Indicates the first The control command increment per control cycle Represents the proportionality coefficient. Represents the integral coefficient. Denotes the differential coefficient. Indicates the first The deviation value for each control cycle, wherein the deviation value is the difference between the design parameters and the operating data.

8. The gas turbine test bench control and monitoring method according to claim 1, characterized in that, The speed control method during the startup phase is linear acceleration rate control: ； ； in, express Rotation speed at any given moment Indicates the initial rotational speed. Indicates the rate of increase. Indicates the target rotational speed. Indicates the acceleration time.

9. The gas turbine test bench control and monitoring method according to claim 1, characterized in that, The real-time data acquisition frequency satisfies: ； in, Indicates the sampling frequency. This represents the highest frequency component of the monitored signal; Furthermore, the running data and the image information are stored in a time-synchronized manner, with the time synchronization achieving millisecond-level synchronization accuracy through a unified time server.

10. The gas turbine test bench control and monitoring method according to any one of claims 1 to 9, characterized in that, It also includes remote monitoring steps: The operating data, image information, and execution status of the graded protection actions are transmitted to a remote monitoring center via a communication network. The remote monitoring center monitors the modular test bench in real time and sends control commands to the control system through the remote monitoring center.

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