Axial flow hydro-generator bolt detection method and system

Abstract

The present invention discloses a method and system for detecting bolts of an axial-flow hydro-turbine generator set, and belongs to the technical field of hydro-turbine generator detection. In conventional bolt crack detection, bolt preload analysis and evaluation and historical data analysis and evaluation are introduced. Through the bolt preload analysis and evaluation, it can be evaluated whether the bolt preload under the current working condition meets the resistance to fatigue load. When the bolt preload evaluation is abnormal, it indicates that the bolt has a potential fatigue risk. At the same time, when no abnormality is found in the bolt preload and bolt crack detection, the fatigue trend of the bolt can be analyzed and estimated by comparing and analyzing the detection data under the current working condition with the historical data, thereby realizing the estimated analysis of the fatigue of the bolt and preventing it before it happens. The present invention has good versatility and can be applied to the detection of bolts of different positions and specifications in axial-flow hydro-turbine generator sets.

Description

Technical Field

[0001] The invention belongs to the technical field of hydro-generator detection, and in particular relates to a method and system for detecting bolts of an axial-flow hydro-generator set. Background Art

[0002] A hydroelectric generator, a type of generator that uses a turbine as its prime mover to convert water energy into electrical energy, is widely used in hydropower generation. Axial-flow hydroelectric generator sets utilize extensive coupling bolts. Due to the high speeds and frequent starts and stops of hydroelectric generators during operation, these coupling bolts are susceptible to fatigue cracking. If not promptly repaired, these bolts can break, potentially causing operational failures of the entire hydroelectric generator set.

[0003] Coupling bolts are all large bolt structures that are inconvenient to disassemble and assemble. Therefore, the current conventional inspection of coupling bolts generally adopts direct observation method or ultrasonic detection method for inspection. Among them, the direct observation method is that the inspector directly observes the coupling bolts with the naked eye to see if fatigue cracks occur. Its detection efficiency is not only low, but also has very poor detection accuracy. Only obvious cracks can be observed. Therefore, it is gradually being eliminated in practical applications. The ultrasonic detection method is a coupling bolt inspection method commonly used in existing medium and large hydropower stations. It transmits ultrasonic pulses to the coupling bolts and receives reflected pulses, and analyzes and detects based on the reflected pulses. However, the ultrasonic detection method cannot predict and analyze the fatigue of the coupling bolts, and cannot take preventive measures. Moreover, when conducting multi-bolt inspections, the data is relatively messy and the post-processing is time-consuming. Summary of the Invention

[0004] In order to solve the defects existing in the above-mentioned prior art, the purpose of the present invention is to provide an axial flow turbine generator set bolt detection method and system, which can accurately predict and analyze the fatigue state of the bolts and is suitable for bolt detection of various specifications.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for detecting bolts of an axial-flow hydro-generator set, comprising:

[0007] S1: Match the stored historical bolt data according to the inspected bolt information;

[0008] S2: Detect the preload force of the inspected bolt and evaluate it based on the historical bolt data matched in S1; if the evaluation result is abnormal, go to S3; if the evaluation result is normal, go to S4;

[0009] S3: Detect cracks in the inspected bolts and output the detection waveform. If the detection waveform is abnormal, the detection abnormality result is output; if the detection waveform is normal, go to S4;

[0010] S4: Compare and analyze the inspection data of the inspected bolt under the current working condition with the historical bolt data. If the analysis result is abnormal, a warning message is output; otherwise, a normal inspection message is output.

[0011] Preferably, in S1, the bolt information includes the installation position, specification, model and preset preload force of the bolt.

[0012] Preferably, in S1, the historical bolt data includes bolt pre-tightening force data, bolt temperature data, and bolt crack detection waveform data under various operating conditions of the axial-flow hydro-generator set.

[0013] Preferably, in S2, the pre-tightening force of the inspected bolt is detected by ultrasonic stress detection and temperature compensation is performed.

[0014] Further preferably, the ultrasonic stress detection uses a piezoelectric ultrasonic sensor to measure the flight time of the ultrasonic wave in the screw; the temperature compensation uses a temperature sensor to measure the temperature of the inspected bolt to compensate the preload force detection result.

[0015] Preferably, in S3, crack detection of the inspected bolt includes performing oblique detection on the end face of the inspected bolt using ultrasonic longitudinal waves and performing axial detection on the non-threaded portion of the inspected bolt using ultrasonic shear waves.

[0016] Further preferably, the ultrasonic longitudinal wave oblique detection uses a small-angle longitudinal wave straight probe, and the ultrasonic shear wave axial detection uses a shear wave oblique probe.

[0017] The present invention discloses a bolt detection system for an axial flow hydro-generator set, comprising:

[0018] Bolt information entry subsystem, which enters and stores bolt information of bolts in different positions;

[0019] Bolt information matching subsystem, matching the inspected bolt information based on the stored historical bolt data;

[0020] Bolt preload force detection subsystem, detects and analyzes the preload force of the inspected bolts;

[0021] Bolt crack detection subsystem, detects cracks in the inspected bolts and outputs detection waveforms;

[0022] The historical data analysis subsystem stores historical bolt data and compares and analyzes the inspection data of the inspected bolts under the current working conditions with the historical bolt data;

[0023] The detection result output subsystem outputs warning information or normal detection information.

[0024] Preferably, the bolt preload force detection subsystem includes:

[0025] Ultrasonic stress detection module, which uses ultrasonic waves to detect the stress of the inspected bolts;

[0026] Temperature compensation module, using temperature sensor to measure the temperature of the inspected bolt

[0027] The bolt preload force evaluation module evaluates the preload force of the inspected bolts.

[0028] Preferably, the bolt crack detection subsystem includes:

[0029] Ultrasonic longitudinal wave oblique detection module, which uses ultrasonic longitudinal waves to perform oblique detection on the end face of the inspected bolt;

[0030] The ultrasonic shear wave axial detection module uses ultrasonic shear waves to perform axial detection on the non-threaded part of the inspected bolt.

[0031] Compared with the prior art, the present invention has the following beneficial technical effects:

[0032] One of the most effective ways to increase the fatigue life of a bolt pair is to tighten the bolt to a preset preload. Generally, a properly tightened bolt only carries about 5% of the dynamic load. Therefore, a properly tightened bolt pair is very resistant to fatigue loads. The alternating stress generated within the bolt is very small and far below the bolt's tolerance limit. When a bolt fails due to fatigue, it is often due to the applied preload not reaching the designed value, exposing the bolt to bending moment stress, which leads to premature failure.

[0033] The present invention discloses a method for detecting bolts of axial-flow hydro-turbine generator sets. The method introduces bolt preload analysis and evaluation and historical data analysis and evaluation into conventional bolt crack detection. The bolt preload analysis and evaluation can assess whether the bolt preload under the current working conditions meets the requirements for fatigue load resistance. When the bolt preload evaluation is abnormal, it indicates that the bolt has a potential fatigue risk. At the same time, when no abnormalities are found in the bolt preload and bolt crack detection, the fatigue trend of the bolt can be analyzed and estimated by comparing and analyzing the detection data under the current working conditions with the historical data. This method can thus achieve an estimated analysis of the bolt fatigue and take preventive measures. The method has good versatility and can be applied to the detection of bolts of different positions and specifications in axial-flow hydro-turbine generator sets.

[0034] The disclosed axial-flow hydro-generator bolt detection system features simple construction, a high degree of automation, and a wide range of applications. Through a bolt information entry subsystem and a bolt information matching subsystem, it enables classified storage and matching of bolt information for different installation positions, specifications, and models, effectively avoiding the issues of data clutter and time-consuming post-processing during multi-bolt detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a structural block diagram of the detection system of the present invention;

[0036] Figure 2 Schematic diagram of the position of bolts detected by ultrasonic testing. DETAILED DESCRIPTION

[0037] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, which are intended to explain the present invention rather than to limit it.

[0038] like Figure 1 As shown, this embodiment provides an axial-flow hydro-turbine generator bolt detection system. This detection system is different from the existing conventional ultrasonic detection waveform in that it introduces bolt preload analysis and evaluation and historical data analysis and evaluation into conventional bolt crack detection. By combining the bolt preload analysis and evaluation with the historical data analysis and evaluation, a predictive evaluation of bolt fatigue is achieved.

[0039] In this embodiment, the axial flow turbine generator bolt detection system includes: a bolt information entry subsystem for entering and storing bolt information of bolts in different positions; a bolt information matching subsystem for matching the bolt information of the bolt to be detected based on the stored bolt information; a bolt preload detection subsystem for detecting and analyzing the bolt preload; a bolt crack detection subsystem for detecting bolt cracks; and a historical data analysis subsystem for storing historical detection and analysis data and comparing and analyzing the real-time detection and analysis data with the historical detection and analysis data. The bolt information entry subsystem and the bolt information matching subsystem are mainly used for multi-bolt detection, pre-storing and matching data according to the bolt installation position, specification, model, and preset preload of different bolts. Bolt information entry can be performed using external hardware (e.g., a keyboard) or voice input. Bolt information matching can be performed by directly reading the label set on the bolt and then calling the pre-stored data for matching. Alternatively, the staff can directly input the information at the detection site and then call the pre-stored data for matching.

[0040] In this embodiment, the bolt crack detection subsystem includes an ultrasonic longitudinal wave oblique detection module for detecting the bolt end face and an ultrasonic shear wave axial detection module for detecting the unthreaded portion of the bolt. Both the ultrasonic longitudinal wave oblique detection module and the ultrasonic shear wave axial detection module can utilize mature ultrasonic detectors, and the output data of the ultrasonic detectors is transmitted to the detection system. In this embodiment, the ultrasonic longitudinal wave oblique detection module and the ultrasonic shear wave axial detection module utilize different probe configurations. The ultrasonic longitudinal wave oblique detection module utilizes a small-angle longitudinal wave straight probe with a specification of 5 MHz and an angle of 15°, while the shear wave axial detection module utilizes a shear wave oblique probe with a specification of 2.5 MHz.

[0041] The ultrasonic longitudinal wave oblique detection module sets the probe Figure 2 The left end of the bolt (screw end face) is detected by using the longitudinal wave diffusion angle beam. In order to achieve the best reflection condition of the main beam, the main beam should be refracted at a certain angle. Therefore, a small angle longitudinal wave straight probe is used. This has a good detection accuracy for the detection of bolt root cracks and the output waveform is clear. The ultrasonic shear wave axial detection module sets the probe at Figure 2 At the upper right position (non-threaded area), a primary wave is used. During detection, the main sound beam is perpendicular to the tooth surface. The ultrasonic shear wave axial detection module realizes thread detection by combining longitudinal wave direct detection and shear wave oblique detection. The detection accuracy is higher, which can avoid the problem of small cracks and tooth roots that are difficult to detect or have poor detection accuracy at a distance.

[0042] The bolt preload detection subsystem includes an ultrasonic stress detection module and a temperature compensation module for detecting the current bolt preload. The ultrasonic stress detection module uses a piezoelectric ultrasonic sensor to measure the flight time of the ultrasonic wave in the screw. It mainly uses the difference in the flight time of the ultrasonic wave in the bolt when the bolt is free and tightened to calculate the current bolt preload. The temperature compensation module uses a temperature sensor to measure the bolt temperature and compensate for the bolt elongation caused by temperature changes. The bolt preload calculation principle formula is as follows: Among them, F is the preload force of the bolt, E is the elastic modulus of the bolt material, and S is the cross-sectional area of the bolt; ΔL is the deformation of the bolt, L is the clamping length of the bolt pair, and when the bolt is in a free state, the time difference between the transmission and reception of the electrical signal is T0, and when the bolt is in a tightened state, the time difference between the transmission and reception of the electrical signal is T1. Based on the relationship between the time difference between the transmission and reception of the electrical signal and the deformation of the bolt, the deformation of the bolt ΔL is obtained (ultrasonic flight time = bolt length). The system calculates the preload force F of the current smart bolt based on ΔL. The formula for calculating the temperature compensation amount of the bolt deformation is as follows: ΔL 补 =L·α·ΔT, where L is the original length of the bolt, α is the expansion coefficient, and ΔT is the difference between the temperature of the bolt in the free state and the current detection temperature. When temperature compensation is involved, the actual deformation of the bolt is equal to the sum of the temperature compensation for the bolt deformation and the above-mentioned bolt deformation ΔL, thereby avoiding the influence of temperature on detection and improving detection accuracy.

[0043] The bolt preload force assessment module assesses the bolt preload force based on the stored preset bolt preload force information and the detected current bolt preload force information; wherein, the preset bolt preload force information is a range value, the upper limit of which is the maximum design preload force and the lower limit is the minimum design preload force. When the detected bolt preload force exceeds this range value, the bolt preload force is assessed to be abnormal.

[0044] The historical data analysis subsystem collects historical inspection and analysis data, including bolt preload and temperature data for various operating conditions of the axial-flow turbine generator set, and inspection waveform data from the bolt crack detection subsystem. If the bolt preload assessment and bolt crack detection waveforms are normal, historical inspection data for the current operating condition is retrieved and compared with historical data. Trend analysis is typically used to predict future inspection trend results for the current operating condition, thereby enabling an early assessment of bolt fatigue.

[0045] The working method of the above-mentioned axial flow turbine generator set bolt detection system is as follows:

[0046] In the initial state, the bolt information of bolts in different positions is entered and stored, and the detection performs the following steps: Step 1, matching the stored bolt information according to the installation position, specification and model of the bolt to be detected; Step 2, detecting the bolt preload information, and calling the bolt information in Step 1; Step 3, analyzing and evaluating the detected preload information and the preset preload, and outputting the evaluation result. If there is no abnormality in the evaluation result, step 5 is executed. If the evaluation is abnormal, the detection abnormality result is output, wherein the preset preload is a range value, the upper limit of which is the maximum design preload, and the lower limit of which is the minimum design preload; Step 4, detecting bolt cracks, outputting the detection waveform data, if there is no abnormality in the output waveform, step 5 is executed. If the output waveform is abnormal, the detection abnormality result is output; Step 5, comparing and analyzing the detection data under the current working condition with the historical data, and outputting the analysis result. If the analysis result is abnormal, a warning message is output, otherwise a normal detection message is output; for the detection evaluation data, the detection data under the current working condition is classified and stored according to the installation position, specification and model of the bolt.

[0047] It should be noted that the above is only part of the embodiments of the present invention. Equivalent changes made to the system described in the present invention are all included in the scope of protection of the present invention. Those skilled in the art of the present invention may make similar substitutions for the specific examples described, as long as they do not deviate from the structure of the present invention or exceed the scope defined by the claims, and all such substitutions are within the scope of protection of the present invention.

Claims

1. A method for detecting bolts of an axial flow hydro-generator set, characterized in that: include: S1: Match the stored historical bolt data according to the inspected bolt information; S2: Detect the preload of the inspected bolt and evaluate it based on the historical bolt data matched in S1. The preload of the inspected bolt is detected using ultrasonic stress testing and temperature compensation. Ultrasonic stress testing uses a piezoelectric ultrasonic sensor to measure the flight time of the ultrasonic wave in the screw. Temperature compensation uses a temperature sensor to measure the temperature of the inspected bolt and compensate the preload test result. If the evaluation result is abnormal, go to S3; if the evaluation result is normal, go to S4. S3: Detect cracks in the inspected bolts and output the detection waveform. If the detection waveform is abnormal, the detection abnormality result is output. The crack detection of the inspected bolts includes using ultrasonic longitudinal waves in an oblique direction on the end face of the inspected bolts and ultrasonic shear waves in an axial direction on the unthreaded part of the inspected bolts. The ultrasonic longitudinal wave oblique detection uses a small-angle longitudinal wave straight probe, and the ultrasonic shear wave axial detection uses a shear wave oblique probe. If the detection waveform is normal, go to S4. S4: Compare and analyze the inspection data of the inspected bolt under the current working condition with the historical bolt data. If the analysis result is abnormal, a warning message is output; otherwise, a normal inspection message is output.

2. The method for detecting bolts of an axial-flow hydro-generator set according to claim 1, wherein: In S1, the bolt information includes the installation position, specification, model and preset preload of the bolt.

3. The method for detecting bolts of an axial-flow hydro-generator set according to claim 1, wherein: In S1, the historical bolt data includes bolt preload data, bolt temperature data, and bolt crack detection waveform data under various operating conditions of the axial flow turbine generator set.

4. A bolt detection system for an axial-flow hydro-generator set, used to implement the bolt detection method for an axial-flow hydro-generator set as claimed in claim 1, characterized in that: include: Bolt information entry subsystem, which enters and stores bolt information of bolts in different positions; Bolt information matching subsystem, matching the inspected bolt information based on the stored historical bolt data; Bolt preload force detection subsystem, detects and analyzes the preload force of the inspected bolts; The bolt crack detection subsystem detects cracks in the inspected bolts and outputs the detection waveform. The bolt crack detection subsystem includes an ultrasonic longitudinal wave oblique detection module, which uses ultrasonic longitudinal waves to perform oblique detection on the end face of the inspected bolt; and an ultrasonic shear wave axial detection module, which uses ultrasonic shear waves to perform axial detection on the unthreaded part of the inspected bolt. The historical data analysis subsystem stores historical bolt data and compares and analyzes the inspection data of the inspected bolts under the current working conditions with the historical bolt data; The detection result output subsystem outputs warning information or normal detection information.

5. The axial flow turbine generator set bolt detection system according to claim 4, characterized in that: The bolt preload detection subsystem includes: Ultrasonic stress detection module, which uses ultrasonic waves to detect the stress of the inspected bolts; Temperature compensation module, using temperature sensor to measure the temperature of the inspected bolt The bolt preload force evaluation module evaluates the preload force of the inspected bolts.

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