Rotor unbalance adjusting method in variable speed process

Abstract

The invention discloses a rotor unbalance adjusting method in a variable rotating speed process, and belongs to the technical field of rotor dynamic balance. According to the method, a rotor dynamics prediction model based on unbalance is established by obtaining structure parameters and operation parameters of a rotor system, a database is established to store historical data of vibration signals, time domain features and frequency domain features of the vibration signals are extracted, weight coefficients are corrected and fitted through the historical data, and the rotor dynamics prediction result is obtained. And the model is optimized to improve the generalization ability when the rotating speed changes. And collecting a vibration signal at the current rotating speed in real time, calculating a dynamic correction coefficient to further optimize the model, outputting a prediction result to a human-computer interaction interface or a control system, finally generating an adjustment scheme according to the prediction result, driving the automatic balancing head to move to a specified phase angle, and if the unbalance amount after continuous adjustment still exceeds a safety threshold value, stopping the automatic balancing head. If yes, the rotor is limited to enter a dangerous rotating speed area, and a fault traceability report is generated to facilitate shutdown inspection and repair. The method effectively improves the operation stability and safety margin of the rotor system.

Description

Technical field:

[0001] The invention relates to the technical field of rotor dynamic balancing, and in particular to a rotor imbalance adjustment method in a variable speed process. Background technology:

[0002] As industrial equipment develops towards high speed and high precision, the dynamic balance performance of the rotor system, as the core component of rotating machinery, directly affects the operating stability, reliability and service life of the equipment. Under variable speed conditions, the imbalance problem of the rotor system is particularly prominent. Due to the complex and changeable inertial force, centrifugal force and vibration characteristics caused by speed changes, traditional dynamic balancing adjustment methods are difficult to adapt to speed changes in real time and accurately, resulting in excessive vibration, component wear and even equipment failure during the operation of the rotor system.

[0003] Existing rotor dynamic balancing technologies mostly rely on offline balancing or balancing adjustment at a fixed speed, which is difficult to meet the real-time adjustment requirements under variable speed conditions. Traditional methods are usually based on static or quasi-static models, lacking an accurate description of the dynamic characteristics during speed changes, resulting in insufficient model generalization and limited adjustment accuracy. In addition, the existing technology for the collection and analysis of vibration signals is mostly limited to single features in the time domain or frequency domain, failing to fully utilize the comprehensive information of historical data and real-time data, making it difficult to achieve dynamic correction and optimization. At the same time, traditional methods lack an effective fault tracing mechanism after adjustment failure, and are unable to quickly locate the cause of the imbalance, increasing the difficulty and cost of equipment maintenance.

[0004] In order to overcome the above technical bottlenecks and improve the dynamic balance adjustment accuracy and stability of the rotor system under variable speed conditions, there is an urgent need for a rotor imbalance adjustment method that can monitor in real time, dynamically correct and respond quickly. This type of method not only requires the establishment of a high-precision rotor dynamics prediction model, but also requires the dynamic optimization of the model in combination with historical data and real-time data, while having fault tracing and automatic adjustment functions, thereby effectively reducing the vibration level and improving the safety and reliability of equipment operation. The present invention aims to solve the above problems and proposes a new method for rotor imbalance adjustment in a variable speed process. By establishing a rotor dynamics prediction model based on structural parameters and operating parameters, combined with feature extraction and dynamic correction of historical data and real-time data, accurate prediction and adjustment of the rotor imbalance can be achieved, while providing a fault tracing function to provide a reliable basis for equipment maintenance. Summary of the invention:

[0005] In order to solve the above problems, the purpose of the present invention is to provide a rotor imbalance adjustment method in a variable speed process, which is used to accurately predict and adjust the rotor imbalance and improve the dynamic balance adjustment accuracy and stability of the rotor system under variable speed conditions.

[0006] S1. Obtaining structural parameters and operating parameters of the rotor system;

[0007] S2. Establishing a rotor dynamics prediction model for imbalance based on the obtained relevant structural parameters and operating parameters;

[0008] S3. Establish a database for storing historical data of vibration signals and extracting their features;

[0009] S4. Weight coefficient k through historical data α Perform correction fitting and optimize the model to ensure the generalization ability of the model when the speed changes;

[0010] S5. Collect the vibration signal at the current speed in real time to obtain the dynamic correction coefficient k β To optimize the model;

[0011] S6. Output the prediction results to the human-machine interface or control system for rotor dynamic balancing adjustment;

[0012] The rotor system structural parameters obtained in S1 include rotor mass distribution, bearing stiffness coefficient, damping coefficient, rotor geometric dimensions, etc.; the operating parameters include speed change range, speed change rate, etc.;

[0013] The rotor dynamics prediction model for the unbalance in S2 is:

[0014]

[0015] Among them U p (ω) is the predicted value of the imbalance at the current speed;

[0016] m is the mass distribution of the rotor system;

[0017] r is the eccentricity of the rotor during rotation;

[0018] ω is the rotation speed;

[0019] ω 0 is the measured speed error value;

[0020] k α is the weight coefficient based on historical data;

[0021] k β It is the dynamic correction coefficient of the model obtained by real-time data collection;

[0022] The database in S3 contains all the historical data of vibration signals extracted during the operation of the rotor. The extracted features include time domain features such as vibration peak-to-peak value, kurtosis, and pulse factor, and frequency domain features such as rotation frequency amplitude and phase, and the proportion of resonance energy near the critical speed.

[0023] The weight coefficient in S4 is calculated by formula (2).

[0024]

[0025] Among them A a It is the average value of 1 times the fundamental frequency signal amplitude of the historical vibration signal;

[0026] A r is the reference amplitude set according to the rated operating condition of the rotor;

[0027] is the speed change rate;

[0028] The dynamic correction coefficient in S5 is calculated by equation (3).

[0029]

[0030] Where V p is the real-time peak-to-peak value of vibration;

[0031] V t Vibration threshold;

[0032] ω max is the maximum value within the speed range;

[0033] ω min The minimum value within the speed range;

[0034] In S6, an adjustment plan is generated based on the prediction results, and the automatic balancing head is driven to rotate the corresponding angle at the specified position of the rotor. If the imbalance is still higher than the safety threshold after three consecutive adjustments, a speed lock command is sent to the control system to limit the rotor from entering the dangerous speed range, and a fault tracing report is generated to mark the components that may cause imbalance, so as to facilitate shutdown inspection and repair.

[0035] The present invention establishes a rotor imbalance prediction model for a variable speed process, especially a rotor dynamics prediction model based on the imbalance amount, and then establishes a database to store historical data of vibration signals and extracts their time domain and frequency domain characteristics. The weight coefficient and the dynamic correction coefficient are fitted and corrected through the historical data and real-time data respectively, and the prediction result is output. An adjustment plan is generated and a fault tracing report is formed, so that adjustments are performed quickly and accurately to improve the stability and safety of the rotor system. Description of the drawings:

[0036] Figure 1 , rotor unbalance adjustment flow chart of variable speed process; Specific implementation method:

[0037] The technical solutions in the embodiments of the present invention are clearly and completely described below in conjunction with specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] S1. Obtaining structural parameters and operating parameters of the rotor system;

[0039] S2. Establishing a rotor dynamics prediction model for imbalance based on the obtained relevant structural parameters and operating parameters;

[0040] S3. Establish a database for storing historical data of vibration signals and extracting their features;

[0041] S4. Weight coefficient k through historical data α Perform correction fitting and optimize the model to ensure the generalization ability of the model when the speed changes;

[0042] S5. Collect the vibration signal at the current speed in real time to obtain the dynamic correction coefficient k β To optimize the model;

[0043] S6. Output the prediction results to the human-machine interface or control system for rotor dynamic balancing adjustment;

[0044] The rotor system structural parameters obtained in S1 include the mass distribution of the rotor mass of 50kg; the operating parameters include the speed range of [2000rpm, 3000rpm], the real-time vibration peak-to-peak value of 15mm / s, the eccentricity of the rotor rotation process of 0.05m, the rotation speed of 261.8rad / s, the vibration threshold of 10mm / s, the measured speed error of 2.5rad / s, etc.;

[0045] The rotor dynamics prediction model for the unbalance in S2 is:

[0046]

[0047] Among them U p (ω) is the predicted value of the imbalance at the current speed;

[0048] m is the mass distribution of the rotor system;

[0049] r is the eccentricity of the rotor during rotation;

[0050] ω is the rotation speed;

[0051] ω 0 is the measured speed error value;

[0052] kα is the weight coefficient based on historical data;

[0053] k β It is the dynamic correction coefficient of the model obtained by real-time data collection;

[0054] The database in S3 contains all the historical data of the vibration signals extracted during the operation of the rotor. The average value of the 1-fold fundamental frequency signal amplitude of the extracted characteristic historical vibration signal is 8 mm / s, the reference amplitude set for the rated working condition of the rotor is 10 mm / s, and the reference speed change rate is 120 rad / s. 2 ;

[0055] The historical weight coefficient in S4 is calculated by formula (2), and we can get k α is 0.241:

[0056]

[0057] Among them A a It is the average value of 1 times the fundamental frequency signal amplitude of the historical vibration signal;

[0058] A r is the reference amplitude set according to the rated operating condition of the rotor;

[0059] is the speed change rate;

[0060] The dynamic correction coefficient in S5 is calculated by formula (3), and we can get k β is 0.75:

[0061]

[0062] Where V p is the real-time peak-to-peak value of vibration;

[0063] V t Vibration threshold;

[0064] ω max is the maximum value within the speed range;

[0065] ω min The minimum value within the speed range;

[0066] In S6, an adjustment plan is generated according to the prediction result 3.2 g·mm, and the automatic balancing head is driven to rotate the corresponding angle at the specified position of the rotor.

[0067] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for adjusting rotor imbalance in a variable speed process, characterized in that: The steps include: S1. Obtaining structural parameters and operating parameters of the rotor system; S2. Establishing a rotor dynamics prediction model for imbalance based on the obtained relevant structural parameters and operating parameters; S3. Establish a database for storing historical data of vibration signals and extracting their features; S4. Weight coefficient k through historical data α Perform correction fitting and optimize the model to ensure the generalization ability of the model when the speed changes; S5. Collect the vibration signal at the current speed in real time to obtain the dynamic correction coefficient k β To optimize the model; S6. Output the prediction results to the human-machine interface or control system for rotor dynamic balancing adjustment.

2. A rotor imbalance adjustment method in a variable speed process as claimed in claim 1, characterized in that: The rotor system structural parameters obtained in S1 include the mass distribution of the rotor mass, operating parameters, real-time vibration peak-to-peak value, eccentricity of the rotor during rotation, rotation speed, etc.

3. The rotor imbalance adjustment method in a variable speed process according to claim 1, characterized in that: The rotor dynamics prediction model for the unbalance in S2 is: Among them U p (ω) is the predicted value of the imbalance at the current speed; m is the mass distribution of the rotor system; r is the eccentricity of the rotor during rotation; ω is the rotation speed; ω0 is the measured speed error value; k α is the weight coefficient based on historical data; k β It is the dynamic correction coefficient of the model obtained by real-time data collection.

4. The rotor imbalance adjustment method in a variable speed process according to claim 1, characterized in that: The database in S3 contains all historical data of vibration signals extracted during the operation of the rotor. The extracted features include time domain features such as vibration peak-to-peak value, kurtosis, and pulse factor, and frequency domain features such as rotational amplitude, phase, and resonance energy ratio near the critical speed.

5. The rotor imbalance adjustment method in a variable speed process according to claim 1, characterized in that: The weight coefficient in S4 is calculated by formula (2): Among them A a It is the average value of 1 times the fundamental frequency signal amplitude of the historical vibration signal; A r is the reference amplitude set according to the rated operating condition of the rotor; is the speed change rate.

6. The rotor imbalance adjustment method in a variable speed process according to claim 1, characterized in that: The dynamic correction coefficient in S5 is calculated by formula (3): Where V p is the real-time peak-to-peak value of vibration; V t Vibration threshold; ω max is the maximum value within the speed range; ω min Minimum value within the speed range.

7. The rotor imbalance adjustment method in a variable speed process according to claim 1, characterized in that: In S6, an adjustment plan is generated based on the prediction results, and the automatic balancing head is driven to rotate the corresponding angle at the specified position of the rotor. If the imbalance is still higher than the safety threshold after three consecutive adjustments, a speed lock command is sent to the control system to limit the rotor from entering the dangerous speed range, and a fault tracing report is generated to mark the components that may cause imbalance, so as to facilitate shutdown inspection and repair.

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