Klystron residual life prediction system and method
By monitoring the klystron's operating data in real time and using machine learning algorithms to predict its remaining lifespan and issue early warnings, the problem of equipment downtime and economic losses caused by klystron failure has been solved, achieving more accurate lifespan prediction and improved equipment stability.
Patent Information
- Application Number
- CN202411641983.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-18
- Publication Date
- 2025-11-11
AI Technical Summary
The lack of effective means to predict the remaining life of klystrons in the current technology leads to repairs or replacements only when klystrons suddenly fail, affecting the normal operation of equipment and causing economic losses. In addition, it is difficult to stock and replace klystrons.
Through data acquisition, transmission, storage, and processing units, and by using linear regression or support vector machine learning algorithms, key operating data of the klystron can be monitored in real time, its remaining lifespan can be predicted, and an early warning signal can be issued when it is close to the end of its lifespan.
Accurately predict the remaining lifespan of klystrons, reduce equipment downtime and economic losses, improve the reliability and stability of electron linear accelerators, and are easy to install and applicable to different types of klystrons and accelerators.
Smart Images
Figure CN120928058A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of electron linear accelerator technology, specifically relating to a klystron remaining lifetime prediction system and method. Background Technology
[0002] As a core component of electron linear accelerators, the klystron is not only expensive but also has a limited lifespan. In current industrial applications, due to the lack of effective prediction methods, the remaining lifespan of the klystron is often difficult to accurately determine. Typically, repair or replacement is only carried out when the klystron suddenly fails. However, the process of stocking and replacing klystrons takes a considerable amount of time, which not only affects the normal operation of the equipment but can also cause significant economic losses.
[0003] Specifically, klystrons are affected by various factors during operation, such as temperature, pressure, and current, all of which can cause a gradual decline in their performance. When the performance degrades to a certain level, the klystron will be unable to meet operational requirements, leading to equipment shutdown. Because klystron failures are sudden and unpredictable, effective early warning and maintenance before failure are difficult.
[0004] Furthermore, the preparation and replacement of klystrons also face numerous challenges. As klystrons are high-precision devices, their manufacturing and procurement cycles are lengthy and costly. Therefore, timely replacement and replenishment before klystron failure is crucial. However, due to a lack of effective forecasting methods, it is often difficult to accurately determine the remaining lifespan of klystrons, leading to inappropriate timing for replenishment and replacement, further exacerbating economic losses.
[0005] Therefore, there is a need for a klystron remaining life prediction system and method to solve the problem that existing technologies do not have a way to predict the remaining life of klystrons, and often repair or replace them only when they suddenly fail to work. However, the preparation / replacement of klystrons takes a considerable amount of time, which often causes unpredictable and huge losses. Summary of the Invention
[0006] The purpose of this invention is to provide a system and method for predicting the remaining life of a klystron, so as to solve the problems mentioned in the background art.
[0007] To achieve the above objectives, the present invention provides the following technical solution: a klystron remaining life prediction system, comprising:
[0008] The data acquisition unit is used to acquire various key data of the klystron, including the klystron titanium pump current, klystron high voltage, klystron output microwave power, and klystron operating pulse signal.
[0009] The data transmission unit connects the data collected by the data acquisition unit to the server network indirectly or directly through an interface.
[0010] A data storage unit, deployed on a server, includes sensor data receiving software and a database system for receiving and storing data from the data transmission unit;
[0011] The data processing unit is a software system that analyzes and predicts the remaining lifespan of the klystron using the klystron data stored in the data storage unit.
[0012] The data output unit is used to output the remaining life prediction results of the klystron and issue an early warning signal when the klystron is close to the end of its life.
[0013] It should be noted in the solution that the data acquisition unit includes multiple sensors, each of which is used to collect one or more key data of the klystron.
[0014] It is worth noting that the data transmission unit is connected to the server network via RS232, RS485, or a network interface.
[0015] Furthermore, it should be noted that the data processing unit uses linear regression or support vector machine machine learning algorithms to predict the remaining lifespan of the klystron.
[0016] This invention also provides a technical solution: a method for predicting the remaining life of a klystron, comprising the following steps:
[0017] S1. Collect various key data of the klystron, including the klystron titanium pump current, klystron high voltage, klystron output microwave power, and klystron operating pulse signal.
[0018] S2. Transmit the collected data to the server network and store it on the server;
[0019] S3. Using a software system, analyze and predict the remaining lifespan of the klystron based on the klystron data stored on the server.
[0020] S4 outputs the predicted remaining lifespan of the klystron and issues a warning signal when the klystron is nearing the end of its lifespan.
[0021] As a preferred implementation, when predicting the remaining lifetime of the klystron, an algorithm is used that uses the cumulative microwave power-time product of the klystron as the basis for lifetime prediction. The specific algorithm is as follows:
[0022] L=∑PDt
[0023] Where P is the microwave power of the klystron, D is the duty cycle of the working pulse signal, and t is the working time, all three parameters are acquired by the sensor and stored in the database.
[0024] In a preferred embodiment, the software system uses linear regression and support vector machine learning algorithms to establish a klystron remaining lifetime prediction model, and predicts the klystron performance for a future period based on historical data, thereby updating the remaining lifetime of the klystron.
[0025] Compared with the prior art, the klystron remaining life prediction system and method provided by the present invention has at least the following beneficial effects:
[0026] (1) By collecting and analyzing key operating data of the klystron, the present invention can more accurately predict the remaining life of the klystron, avoiding errors in maintenance or replacement plans due to inaccurate prediction.
[0027] (2) By developing maintenance or replacement plans in advance, the present invention can reduce equipment downtime caused by sudden failure of the klystron, thereby reducing economic losses caused by downtime. At the same time, reasonable spare parts inventory management can also avoid additional costs caused by insufficient spare parts or untimely replacement.
[0028] (3) It can monitor the operating status of the klystron in real time, promptly identify potential problems and take corresponding measures, thereby improving the reliability and stability of the entire electronic linear accelerator.
[0029] (4) It adopts a modular design, which is easy to install and debug on existing equipment. At the same time, its prediction algorithm has high versatility and adaptability, and can be applied to different types of klystrons and electronic linear accelerators. Attached Figure Description
[0030] Figure 1 This is a system structure block diagram of the klystron remaining life prediction system and method of the present invention;
[0031] Figure 2 This is a schematic diagram illustrating the workflow of the klystron remaining life prediction system and method of the present invention. Detailed Implementation
[0032] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.
[0033] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.
[0034] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."
[0035] Please see Figure 1 The present invention provides a klystron remaining life prediction system, comprising:
[0036] The data acquisition unit is used to collect various key data of the klystron, including the klystron titanium pump current, klystron high voltage, klystron output microwave power, and klystron operating pulse signal. These data reflect the current operating status and performance parameters of the klystron.
[0037] The data transmission unit connects the data collected by the data acquisition unit to the server network indirectly or directly through an interface. During the data transmission process, it ensures the integrity and security of the data and prevents data loss or tampering.
[0038] The data storage unit, deployed on a server, includes sensor data receiving software and a database system for receiving and storing data from the data transmission unit. The data receiving software is responsible for receiving data from the data transmission unit and storing it in the database. The database system manages the stored data, including data classification, storage format, indexing, etc., to ensure that the data is easy to query and analyze, and to facilitate subsequent data processing.
[0039] The data processing unit is a software system that analyzes and predicts the remaining lifespan of the klystron using the data storage unit. It uses machine learning algorithms (such as linear regression, support vector machine, etc.) to build a klystron remaining lifespan prediction model, uses historical data as a training set to train and optimize the model, and after training, uses the model to predict the performance of the klystron in the future period, thereby obtaining the remaining lifespan of the klystron.
[0040] The data output unit is used to output the remaining life prediction results of the klystron and issue a warning signal when the klystron is close to the end of its life. The warning signal can be sound, light, text message or email, etc., to remind the user to take timely measures to repair or replace it.
[0041] Specifically, the data acquisition unit is responsible for collecting various key data of the klystron, such as titanium pump current, high voltage, output microwave power, and operating pulse signal; the data transmission unit transmits this data to the server network through an interface; the data storage unit is responsible for storing the received data; the data processing unit analyzes and processes the stored data through a series of algorithms to obtain the remaining life prediction result of the klystron; the data output unit is responsible for outputting the prediction result and issuing an early warning signal when the klystron is about to reach the end of its life. By collecting and analyzing the key operating data of the klystron, this invention can more accurately predict the remaining life of the klystron, avoiding errors in maintenance or replacement plans due to inaccurate predictions.
[0042] Furthermore, it is worth noting that the data acquisition unit includes multiple sensors, each of which is used to acquire one or more key data of the klystron.
[0043] Furthermore, it is worth noting that the data transmission unit is connected to the server network via RS232, RS485, or a network interface.
[0044] Furthermore, it is worth noting that the data processing unit uses linear regression or support vector machine machine learning algorithms to predict the remaining lifespan of the klystron.
[0045] Please see Figure 2 The present invention also provides a technical solution: a method for predicting the remaining life of a klystron, comprising the following steps:
[0046] S1. Collect various key data of the klystron, including the klystron titanium pump current, klystron high voltage, klystron output microwave power, and klystron operating pulse signal.
[0047] S2. Transmit the collected data to the server network and store it on the server;
[0048] S3. Using a software system, analyze and predict the remaining lifespan of the klystron based on the klystron data stored on the server.
[0049] S4 outputs the predicted remaining lifespan of the klystron and issues a warning signal when the klystron is nearing the end of its lifespan.
[0050] Furthermore, it is worth noting that when predicting the remaining lifetime of the klystron, an algorithm based on the cumulative microwave power-time product of the klystron is used. The specific algorithm is as follows:
[0051] L=∑PDt
[0052] Where P is the microwave power of the klystron, D is the duty cycle of the working pulse signal, and t is the working time, all three parameters are acquired by the sensor and stored in the database.
[0053] Furthermore, it is worth noting that the software system uses linear regression and support vector machine machine learning algorithms to establish a klystron remaining lifetime prediction model, and predicts the klystron performance for a future period based on historical data, thereby updating the klystron's remaining lifetime.
[0054] Example 1
[0055] In this embodiment, the lifespan prediction is based on the klystron titanium pump current Ip. As an electro-vacuum device, the vacuum level of the klystron gradually deteriorates during use. When the vacuum level drops to a certain threshold, it can be determined that the klystron has reached the end of its service life. Since the titanium pump current is proportional to the vacuum level, the lifespan of the klystron can be predicted by predicting the titanium pump current value.
[0056] The specific implementation steps are as follows:
[0057] a. Use the data acquisition unit to collect data such as the titanium pump current Ip, high voltage, output microwave power, and working pulse signal of the klystron.
[0058] b. The collected data is transmitted to the data storage unit for storage.
[0059] c. The data processing unit performs calculations and analysis on the stored data. To unify the algorithm under different operating conditions, the lifespan is not based solely on the working time, but rather on the cumulative microwave power-time product L of the klystron (calculated from the factory date). The specific algorithm is as follows:
[0060] L=∑PDt
[0061] Where P is the microwave power of the klystron, D is the duty cycle of the working pulse signal, and t is the working time, all three parameters are acquired by the sensor and stored in the database.
[0062] d. Seek the relationship curve between Ip and L. Since only historical curves for a past period are available in reality, the curve for a future period can be predicted using methods such as linear regression and support vector machines.
[0063] e. Determine the remaining lifespan of the klystron based on the prediction results, and issue a warning signal through the data output unit when the lifespan is about to end.
[0064] For example, the future trend curves of Ip and L can be predicted and updated on a weekly basis, thereby updating the remaining lifespan of the klystron.
[0065] Example 2
[0066] This embodiment uses the electrical performance of the klystron as the standard for lifetime prediction. As a microwave power source, one of the aging characteristics of the klystron is that the high voltage required to generate microwaves of the same power will increase. Therefore, the performance of the klystron can be measured by R=P / V. As the klystron ages, R will gradually increase. Therefore, a threshold Rth can be set based on experience. When R>Rth, it is determined that the klystron has reached the end of its lifetime.
[0067] The specific implementation steps are as follows:
[0068] a. Similarly, the data acquisition unit is used to collect data such as microwave power P, high voltage V, and working pulse signal of the klystron.
[0069] b. The collected data is transmitted to the data storage unit for storage.
[0070] c. The data processing unit is used to perform calculations and analysis on the stored data, and the cumulative microwave power-time product L of the klystron is also used as the basis for the lifetime.
[0071] d. Find the relationship curve between R and L, and predict the curve for a future period of time through methods such as linear regression.
[0072] e. Determine the remaining lifespan of the klystron based on the prediction results, and issue a warning signal through the data output unit when the lifespan is about to end.
[0073] As can be seen from the detailed description of the above embodiments, the klystron remaining life prediction system and method provided by the present invention have the advantages of high accuracy and simple operation. At the same time, the present invention can also be flexibly adjusted and optimized according to actual needs to adapt to different models and specifications of klystrons.
[0074] This solution comprises the following operational processes: First, the system collects data in real time through sensors installed on the klystron, including key information such as titanium pump current, high voltage, output microwave power, and operating pulse signals. This data is transmitted to the server network in real time via a dedicated data transmission interface, ensuring timeliness and accuracy. On the server side, the data storage unit receives and manages this sensor data, including data storage, classification, and indexing, facilitating subsequent data processing. The data processing unit utilizes advanced machine learning algorithms to preprocess the stored data, extract features, and train models to establish a predictive model for the remaining lifespan of the klystron. After the predictive model is established, the system can predict the performance of the klystron in real time and calculate its remaining lifespan. When the prediction results indicate that the klystron is nearing the end of its lifespan, the system automatically issues a warning signal, reminding the user to take timely measures for repair or replacement to avoid potential equipment failures and downtime. In addition, the system also has functions such as real-time monitoring, model updates, and fault diagnosis to ensure the accuracy and reliability of the entire prediction process. By continuously optimizing and updating the predictive model, the system can adapt to changes in the performance parameters of the klystron, improving the accuracy and practicality of the predictions.
[0075] In summary: By collecting and analyzing key operating data of the klystron, this invention can more accurately predict the remaining lifespan of the klystron, avoiding errors in maintenance or replacement plans due to inaccurate predictions; by developing maintenance or replacement plans in advance, this invention can reduce equipment downtime caused by sudden klystron failure, thereby reducing economic losses due to downtime. Simultaneously, reasonable spare parts inventory management can avoid additional costs caused by insufficient spare parts or untimely replacement; this invention can monitor the operating status of the klystron in real time, promptly identify potential problems and take corresponding measures, thereby improving the reliability and stability of the entire electron linear accelerator; this invention adopts a modular design, making it easy to install and debug on existing equipment. Furthermore, its prediction algorithm has high versatility and adaptability, applicable to different types of klystrons and electron linear accelerators.
[0076] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.
[0077] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.
[0078] The preferred embodiments disclosed above are merely illustrative of this specification. The optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described herein. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification. This specification is limited only by the claims and their full scope and equivalents.
Claims
1. A klystron remaining life prediction system, characterized in that: The system includes: The data acquisition unit is used to acquire various key data of the klystron, including the klystron titanium pump current, klystron high voltage, klystron output microwave power, and klystron operating pulse signal. The data transmission unit connects the data collected by the data acquisition unit to the server network indirectly or directly through an interface. A data storage unit, deployed on a server, includes sensor data receiving software and a database system for receiving and storing data from the data transmission unit; The data processing unit is a software system that analyzes and predicts the remaining lifespan of the klystron using the klystron data stored in the data storage unit. The data output unit is used to output the remaining life prediction results of the klystron and issue an early warning signal when the klystron is close to the end of its life.
2. The klystron remaining life prediction system according to claim 1, characterized in that: The data acquisition unit includes multiple sensors, each of which is used to acquire one or more key data from the klystron.
3. The klystron remaining life prediction system according to claim 2, characterized in that: The data transmission unit is connected to the server network via RS232, RS485, or a network interface.
4. The klystron remaining life prediction system according to claim 3, characterized in that: The data processing unit uses linear regression or support vector machine machine learning algorithms to predict the remaining lifespan of the klystron.
5. A method for predicting the remaining life of a klystron, characterized in that: Includes the following steps: S1. Collect various key data of the klystron, including the klystron titanium pump current, klystron high voltage, klystron output microwave power, and klystron operating pulse signal. S2. Transmit the collected data to the server network and store it on the server; S3. Using a software system, analyze and predict the remaining lifespan of the klystron based on the klystron data stored on the server. S4 outputs the predicted remaining lifespan of the klystron and issues a warning signal when the klystron is nearing the end of its lifespan.
6. The method for predicting the remaining life of a klystron according to claim 5, characterized in that: When predicting the remaining lifetime of a klystron, an algorithm is used that uses the cumulative microwave power-time product of the klystron as the basis for lifetime prediction. The specific algorithm is as follows:
7. Among them, P is the microwave power of the klystron, D is the duty cycle of the working pulse signal, and t is the working time. These three parameters are acquired by the sensor and stored in the database.
8. The method for predicting the remaining life of a klystron according to claim 6, characterized in that: The software system uses linear regression and support vector machine learning algorithms to establish a klystron remaining lifetime prediction model, and predicts the klystron performance for a future period based on historical data, thereby updating the remaining lifetime of the klystron.