Accurate support adjusting system for shaft support based on shock absorption positioning

Abstract

The application discloses a kind of based on shock absorption positioning's axle support precise support adjustment system, belong to support regulation technical field, to solve the technical problem of passive shock absorption, static calibration, insufficient precision, poor working condition adaptability, low intelligent degree of existing axle support support regulation.It is comprised of multi-source data real-time sensing unit, vibration grading discrimination and tracing unit, positioning deviation dynamic calculation unit, self-adaptive precise support adjustment unit, dynamic shock absorption compensation regulation unit, full-closed-loop precision calibration unit and data self-learning and intelligent operation and maintenance unit, and constructs the full-closed-loop intelligent regulation mechanism of "sensing-discrimination-calculation-regulation-calibration-iteration".

Description

Technical Field

[0001] This invention belongs to the field of bearing adjustment technology, and particularly relates to a precise support adjustment system for shaft bearings based on vibration damping positioning. Background Technology

[0002] Shaft supports are core load-bearing components in precision equipment such as rotating shaft systems. The support accuracy, positioning stability, and vibration suppression capabilities of shaft supports directly determine the overall transmission accuracy, operational smoothness, operating noise, and service life of the machine. Currently, most existing shaft support control schemes adopt a fixed support matching and manual periodic calibration control mode. The overall approach is mainly static and passive control, lacking a systematic, dynamic, and closed-loop intelligent control system, making it difficult to adapt to the high-precision, long-cycle, and variable-condition industrial operation requirements.

[0003] The systemic defects of existing technologies are as follows: First, existing support systems rely solely on the support structure itself for passive vibration reduction, failing to collect and analyze multi-dimensional vibration data and operating load data of the shaft system in real time. They cannot effectively distinguish between inherent equipment vibration, operating condition disturbance vibration, and abnormal vibration from support offset, and cannot implement differentiated vibration reduction control for different vibration sources. During long-term operation, this leads to problems such as vibration energy accumulation, slight shaft offset, and uneven stress on support points, continuously exacerbating shaft wear. Second, existing positioning and calibration methods rely on manual periodic calibration and fixed parameter adjustments, lacking real-time monitoring and dynamic correction mechanisms. They cannot identify shaft support level deviation, shaft coaxiality deviation, support clearance deviation, and support force offset in real time. They are significantly affected by equipment thermal deformation, operating load fluctuations, and changes in environmental temperature and humidity, causing support deviations to accumulate continuously and resulting in a continuous decline in equipment transmission accuracy. Third, existing control logic is singular and rigid, employing a uniform parameter adjustment mode across the entire domain. It fails to set hierarchical and zoned control strategies for different operating speeds, load conditions, and vibration amplitudes. Static matching parameters cannot adapt to the dynamic operating conditions of the equipment, resulting in poor precision support. Finally, existing technologies lack fault tracing and adaptive compensation mechanisms, making it impossible to trace the source of data and predict trends for abnormal problems such as support deviation, vibration damping failure, and positioning offset. This results in delayed operation and maintenance intervention, which can easily lead to abnormal equipment vibration, precision failure, or even production shutdowns. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies, such as passive and lagging shaft support adjustment, low calibration accuracy, poor adaptability to working conditions, and lack of closed-loop iterative mechanism, and to provide a shaft support precision support adjustment system based on vibration damping positioning.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: a shaft support precision support adjustment system based on vibration reduction and positioning, comprising a multi-source data real-time sensing unit with mutual communication and linkage, a vibration classification and source tracing unit, a positioning deviation dynamic calculation unit, an adaptive precision support adjustment unit, a dynamic vibration reduction compensation control unit, a full closed-loop precision calibration unit, and a data self-learning and intelligent operation and maintenance unit.

[0006] The multi-source data real-time sensing unit is used to acquire multi-dimensional operating data, working condition data and environmental data of the shaft system and shaft support in real time at a millisecond-level acquisition frequency, and to preprocess the raw acquired data to output standardized synchronous data.

[0007] The vibration grading identification and tracing unit is communicatively connected to the multi-source data real-time sensing unit, used to receive standardized data, combine it with pre-stored vibration feature maps to complete vibration level grading, and trace and locate the causes of vibration anomalies and fault locations.

[0008] The positioning deviation dynamic measurement unit is connected to the multi-source data real-time sensing unit and is used to build a deviation measurement model based on standardized data, quantify various support deviation parameters of the shaft support in real time, and generate a deviation level rating.

[0009] The adaptive precision support adjustment unit is communicatively connected to the vibration grading identification and tracing unit and the positioning deviation dynamic calculation unit, respectively, and is used to match the corresponding grading adjustment strategy and dynamically correct the shaft support parameters according to the vibration level, deviation level and real-time working condition parameters.

[0010] The dynamic vibration reduction compensation control unit is connected to the vibration classification and source tracing unit and the adaptive precision support adjustment unit, respectively. It is used to combine the vibration identification results and support adjustment parameters to implement differentiated active vibration reduction compensation and suppress vibration transmission and accumulation.

[0011] The full closed-loop precision calibration unit is connected to the adaptive precision support adjustment unit, the dynamic shock absorption compensation control unit and the multi-source data real-time sensing unit respectively, and is used to perform real-time verification and iterative correction of each control result to form a closed-loop control mechanism.

[0012] The data self-learning and intelligent operation and maintenance unit is connected to all other functional units for storing system operation data, iteratively optimizing algorithm models, predicting abnormal equipment operation trends, and outputting operation and maintenance data reports.

[0013] Furthermore, the data collected by the multi-source real-time sensing unit includes three-dimensional vibration data, positioning attitude data, working condition load data, environmental parameter data, and equipment operating parameter data. The three-dimensional vibration data includes the vibration amplitude, vibration frequency, vibration phase, and vibration acceleration of the X, Y, and Z axes. The positioning attitude data includes the levelness of the support surface, the coaxiality of the shaft system, the support gap, the offset of the support point, and the tilt angle deviation of the support. The working condition load data includes the shaft system operating speed, real-time load, torque fluctuation, and the force distribution at the support point. The environmental parameter data includes the ambient temperature, ambient humidity, external vibration interference, and the equipment thermal deformation coefficient. The equipment operating parameters include the equipment running time, start-stop status, historical adjustment parameters, and historical deviation records.

[0014] Furthermore, the multi-source data real-time sensing unit has a built-in data preprocessing submodule, which is used to perform Kalman filtering noise reduction, abnormal mutation data removal, multi-dimensional data normalization, and cross-time series data alignment processing on the raw collected data, and output standardized data with time synchronization and uniform accuracy.

[0015] Furthermore, the vibration classification and tracing unit incorporates a vibration feature database and a time-frequency joint analysis algorithm. The vibration feature database pre-stores the inherent vibration characteristics of the device, the standard vibration threshold for variable load, and various abnormal vibration fault characteristics. The vibration classification and tracing unit extracts the time-domain and frequency-domain feature parameters of real-time vibration data and compares and matches them with the vibration feature database to classify vibration into three levels: Level 1 inherent vibration, Level 2 operational condition disturbance vibration, and Level 3 structural abnormal vibration. Simultaneously, based on the vibration phase, amplitude, and axial distribution characteristics, it traces and locates the deviation causes and fault locations corresponding to the vibration anomalies.

[0016] Furthermore, the positioning deviation dynamic calculation unit has a built-in high-precision deviation calculation model. The deviation calculation model is used to calculate the levelness deviation, coaxiality deviation, support gap deviation, and support force deviation. It generates a comprehensive deviation level of support accuracy through multi-parameter weighted calculation. The dynamic update frequency of the deviation calculation model is at the millisecond level, and the static calculation accuracy can reach 0.001mm.

[0017] Furthermore, the adaptive precision support adjustment unit is configured with a three-level adaptive control strategy, specifically including:

[0018] First-level fine-tuning mode: corresponding to the disturbance vibration and slight positioning deviation of the second-level working condition, it slightly corrects the support gap and support level parameters, optimizes the support force distribution, and achieves rapid correction without stopping the machine;

[0019] Level 2 fine-tuning mode: For abnormal vibration and moderate positioning deviation of the three-level structure, it specifically corrects the support parameters of the offset point, corrects the coaxiality of the shaft system and the balance of the support, and matches the real-time working condition parameters to eliminate the vibration causes;

[0020] Three-level reconfiguration adjustment mode: corresponding to severe deviations and multi-dimensional abnormal vibrations across the entire domain, refitting the support reference surface, calibrating the support posture across the entire domain, and eliminating structural support imbalances and cumulative deviations.

[0021] Furthermore, the dynamic vibration reduction compensation control unit is configured with differentiated vibration reduction logic and thermal deformation pre-compensation logic. The differentiated vibration reduction logic matches the corresponding damping coefficient and response threshold for different levels of vibration, respectively achieving the maintenance of inherent vibration stability, absorption of disturbance vibration energy, and suppression of abnormal vibration sources. The thermal deformation pre-compensation logic predicts the thermal offset trend of the support based on the ambient temperature and thermal deformation data, and pre-adjusts the support and vibration reduction parameters in advance to offset the accuracy loss and vibration interference caused by thermal deformation.

[0022] Furthermore, the fully closed-loop precision calibration unit executes a closed-loop iterative process of "adjustment-acquisition-comparison-correction". After each adjustment parameter update, it collects the latest support status data and vibration data in real time and compares them with the preset precision threshold. If there is a residual deviation, it automatically calculates the correction amount and iterates the adjustment process until the parameters meet the standard. At the same time, the fully closed-loop precision calibration unit supports dual modes of timed full-domain calibration and dynamic on-demand calibration to eliminate the accumulation of small deviations over long-term operation.

[0023] Furthermore, the data self-learning and intelligent operation and maintenance unit has the functions of data traceability and storage, model parameter self-learning, operation trend prediction, and operation and maintenance report generation. The data traceability and storage is used to record the entire process operation data and control records. The model parameter self-learning is based on historical control data and iteratively optimizes the identification threshold, calculates the model and adjustment strategy through machine learning. The operation trend prediction is based on time series data analysis to predict the accuracy decay and vibration abnormality trend and output early warning signals. The operation and maintenance report generation is used to automatically count the control frequency, accuracy qualification rate and abnormal fault data.

[0024] Furthermore, the overall operation flow of the system includes:

[0025] S1. System initialization: Load reference parameters, vibration characteristic spectrum, algorithm model and control strategy, and complete the self-test of the whole domain unit and communication linkage.

[0026] S2. The multi-source data real-time sensing unit collects multi-dimensional raw data and performs preprocessing, outputting standardized synchronous data;

[0027] S3, the vibration classification and source tracing unit completes the vibration level classification and cause tracing, and the positioning deviation dynamic calculation unit simultaneously quantifies the support deviation and generates the deviation level.

[0028] S4. The system combines vibration level, deviation level and real-time operating conditions to match the corresponding support adjustment strategy and vibration reduction compensation strategy.

[0029] S5. The adaptive precision support adjustment unit and the dynamic damping compensation control unit work together to execute control commands and complete the correction of support parameters and vibration energy compensation.

[0030] S6. The full closed-loop precision calibration unit verifies the control effect and iteratively corrects residual deviations until the preset high-precision standard is met.

[0031] S7, the data self-learning and intelligent operation and maintenance unit, stores operating data, iteratively optimizes model parameters, and predicts operating trends to achieve continuous intelligent control of the system.

[0032] Furthermore,

[0033] The beneficial effects of the present invention after adopting the above structure are as follows:

[0034] (1) The present invention constructs a vibration classification identification and source tracing system, which breaks through the technical limitations of traditional passive vibration reduction. It can accurately distinguish between inherent vibration, working condition disturbance vibration and structural abnormal vibration, accurately trace the vibration fault location and cause, realize differentiated active vibration reduction control, suppress vibration energy accumulation from the source, significantly reduce shaft wear, and extend equipment service life.

[0035] (2) The present invention establishes a millisecond-level dynamic positioning deviation calculation model, which can accurately quantify multi-dimensional deviation parameters such as shaft support levelness, coaxiality, support gap, and support force. It solves the problems of traditional technology lacking quantitative basis and poor accuracy of manual experience adjustment. The support control accuracy can reach the level of 0.001mm, which significantly improves the shaft transmission accuracy and support stability.

[0036] (3) The present invention sets up a three-level adaptive control strategy, abandoning the traditional uniform static parameter adjustment mode, which can adapt to the dynamic operation requirements of equipment with different speeds, different loads and different environmental conditions, and achieve precise matching of deviation degree and control strength, greatly improving the adaptability of working conditions and the accuracy of control.

[0037] (4) The present invention constructs a fully closed-loop intelligent control system of "sensing-identification-calculation-regulation-calibration-iteration", which automatically completes data collection, analysis, regulation, verification and optimization throughout the entire process without human intervention. It completely solves the problems of easy accumulation of deviation and lagging calibration in traditional technology, and ensures long-term high-precision and stable operation of equipment.

[0038] (5) The present invention is equipped with a data self-learning and trend prediction mechanism, which can autonomously iterate and optimize the algorithm model and control parameters based on long-term equipment operation data, adapt to dynamic changes such as equipment aging, working condition upgrades, and environmental fluctuations, and has long-term autonomous optimization capabilities. At the same time, it can realize fault trend prediction and early intervention, greatly reduce equipment downtime failure rate, and improve production continuity. Attached Figure Description

[0039] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0040] Figure 1 This is a block diagram of the architecture logic of the present invention;

[0041] Figure 2 This is a flowchart illustrating the system operation steps of the present invention.

[0042] Figure 3 This is a schematic diagram of the logic of the three-level adaptive support adjustment strategy of the present invention;

[0043] Figure 4 This is the logic diagram for the vibration grading identification and deviation calculation linkage of the present invention. Detailed Implementation

[0044] The specific implementation process of the shaft support precision support and adjustment system based on vibration damping positioning is as follows:

[0045] System initialization and parameter configuration implementation

[0046] After the system is powered on, it first completes a self-test of all functional units, verifying the communication status and operational stability of each module, including multi-source data sensing, algorithm calculation, control output, and closed-loop calibration, to ensure that data transmission of each unit is synchronized and command response is normal. After the self-test is completed, the system loads the equipment's dedicated benchmark parameter library, including the standard levelness threshold of the shaft support, the standard coaxiality error range, the standard support gap interval, the three-level vibration amplitude threshold, and the working condition load matching parameters. At the same time, it loads the pre-trained vibration characteristic spectrum, the high-precision deviation calculation model, and the three-level graded adjustment strategy database. After the parameters are initialized, the system enters the millisecond-level real-time monitoring standby state and starts the full-dimensional data acquisition and dynamic calculation process.

[0047] Multi-source data perception and preprocessing implementation

[0048] During system operation, the multi-source data real-time sensing unit continuously and frequently collects X, Y, and Z axis vibration parameters, support multi-point support attitude parameters, shaft speed and load parameters, ambient temperature and humidity, and equipment thermal deformation parameters. To address issues such as pulse interference, high-frequency noise, and instantaneous data loss in the original collected data, the data preprocessing submodule uses a Kalman filter algorithm to reduce noise and automatically remove abrupt abnormal data. It performs time-series alignment and normalization on heterogeneous data with different collection frequencies and dimensions, unifies data operation standards, and ensures that the data input to the algorithm model is accurate, stable, and synchronized. The preprocessed standardized data is then synchronously transmitted to the vibration discrimination unit and deviation measurement unit in real time.

[0049] Vibration identification, source tracing, and positioning deviation calculation implementation

[0050] After receiving standardized data, the vibration classification and tracing unit accurately extracts the core characteristic parameters of vibration amplitude, frequency, and phase through time-domain waveform analysis and frequency-domain feature extraction technology. It then accurately compares and matches the real-time characteristic parameters with the built-in vibration characteristic spectrum. If the vibration parameters are within the inherent vibration threshold range of the equipment, it is determined to be Level 1 normal vibration, and no control command needs to be triggered. If the vibration parameters exceed the inherent threshold but do not reach the fault threshold, it is determined to be Level 2 operating condition disturbance vibration, and the cause is identified as load fluctuation or slight environmental interference. If the vibration parameters exceed the fault threshold, it is determined to be Level 3 structural abnormal vibration. Combining the spatial distribution characteristics of the vibration, it accurately traces the cause of the fault to be support gap deviation, levelness offset, uneven support stress, or thermal deformation offset, and pinpoints the specific abnormal support points.

[0051] At the same time, the positioning deviation dynamic measurement unit synchronously performs quantitative calculations of horizontal deviation, coaxiality deviation, support gap deviation, and support force deviation in real time based on attitude data, working load data, and thermal deformation data. It calculates the comprehensive deviation level of support accuracy through a multi-parameter weighted algorithm and generates a standardized multi-dimensional deviation report, providing a quantitative basis for subsequent graded and precise control.

[0052] Adaptive support adjustment and dynamic damping compensation implementation

[0053] Based on vibration level assessment results and comprehensive deviation level reports, combined with real-time equipment operating conditions, the system intelligently matches corresponding graded control strategies. When level two disturbance vibration and slight positioning deviation are detected, the system activates the level one fine-tuning mode, slightly optimizing support gaps and support force distribution parameters, and simultaneously fine-tuning the damping coefficient to quickly absorb instantaneous vibration energy and suppress vibration propagation. This process requires no equipment shutdown and does not affect production operations. When level three abnormal vibration and moderate structural deviation are detected, the system activates the level two fine-tuning mode to accurately locate the offset support points, specifically correcting the support attitude parameters at those points, rectifying shaft coaxiality and support balance, eliminating vibration causes at their source, and simultaneously matching current speed and load parameters to ensure precise adaptation of control parameters to dynamic operating conditions. When severe cumulative deviation and multi-dimensional abnormal vibration are detected across the entire domain, the system activates the level three parameter reconstruction mode to refit the support reference surface, calibrating all support point parameters across the entire domain, and completely eliminating structural support imbalance problems.

[0054] While the support parameters are being adjusted, the dynamic damping compensation control unit operates synchronously to implement differentiated damping compensation. To address the accuracy deviation and vibration interference caused by thermal deformation, the system predicts the thermal offset in advance based on the temperature change trend and pre-adjusts the support and damping parameters to offset the operating errors caused by thermal deformation. To address the local vibration caused by uneven support stress, a zoned differentiated damping configuration is adopted to achieve precise damping compensation at different support points, eliminating the problem of poor adaptability of traditional uniform damping parameters.

[0055] Full closed-loop accuracy calibration implementation

[0056] After each control command is executed and system parameters are updated, the full closed-loop precision calibration unit immediately initiates the closed-loop verification process. Through the multi-source data real-time sensing unit, it re-collects core parameters such as support posture, shaft coaxiality, vibration amplitude, and support clearance. The real-time operating parameters are compared and verified with the preset high-precision benchmark thresholds in all aspects. If all parameters fall within the qualified precision range, the control is considered complete, and the current system operating parameters are locked. If there are slight residual deviations, the system automatically calculates the secondary correction amount and iteratively executes the fine-tuning control process until all parameters meet the precision requirements. At the same time, the system is configured with a timed calibration mechanism, which automatically performs a full-domain precision calibration once a day and completes a full-dimensional parameter review once a week to continuously eliminate the accumulation of slight deviations caused by long-term operation of the equipment and stably maintain the high-precision support state of the shaft support.

[0057] Data self-learning and intelligent operation and maintenance implementation

[0058] The system stores all equipment operating condition data, vibration characteristic data, support deviation data, control execution records, and parameter iteration data throughout the entire process, building a dedicated operating database for the equipment. This enables full-process traceability of all abnormal issues and control operations. Based on massive historical operating data, the system continuously iterates and optimizes vibration identification thresholds, deviation calculation model coefficients, and hierarchical control strategy parameters through machine learning algorithms. It autonomously adapts to long-term dynamic changes such as equipment aging, operating condition upgrades, and environmental changes, continuously improving the system's control accuracy and adaptability. At the same time, the system uses time-series big data analysis technology to predict the trend of support accuracy decay and vibration anomaly evolution in real time, outputting early maintenance warning signals to achieve early intervention in faults. The system automatically generates maintenance reports on a daily, weekly, and monthly basis, statistically analyzing core data such as control frequency, accuracy pass rate, and number of abnormal faults, providing comprehensive data support for equipment inspection, maintenance, and parameter optimization.

[0059] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents. In conclusion, if those skilled in the art, inspired by this description, design similar structural methods and embodiments without departing from the inventive spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A shaft support precision support adjustment system based on vibration reduction and positioning, including a multi-source data real-time sensing unit with mutual communication and linkage, a vibration classification and source tracing unit, a positioning deviation dynamic calculation unit, an adaptive precision support adjustment unit, a dynamic vibration reduction compensation control unit, a full closed-loop precision calibration unit, and a data self-learning and intelligent operation and maintenance unit.

2. The shaft support precision support and adjustment system based on vibration damping and positioning according to claim 1, characterized in that: The data collected by the multi-source real-time sensing unit includes three-dimensional vibration data, positioning attitude data, working condition load data, environmental parameter data, and equipment operating parameter data.

3. The shaft support precision support and adjustment system based on vibration damping and positioning according to claim 1, characterized in that: The multi-source data real-time sensing unit has a built-in data preprocessing submodule. The data preprocessing submodule is used to filter and reduce noise in the raw collected data, remove abnormal and sudden data, normalize multi-dimensional data, and align cross-time series data to eliminate interference and time series deviations in the raw data and output standardized synchronous data, providing an accurate and stable data foundation for the system algorithm operation.

4. The shaft support precision support and adjustment system based on vibration damping and positioning according to claim 1, characterized in that: The vibration classification, identification, and source tracing unit incorporates a vibration feature database and a time-frequency joint analysis algorithm.

5. The shaft support precision support and adjustment system based on vibration damping and positioning according to claim 1, characterized in that: The positioning deviation dynamic measurement unit has a built-in high-precision deviation measurement model, which is used to achieve multi-dimensional accurate measurement of horizontal deviation, coaxiality deviation, support gap deviation, and support force deviation.

6. The shaft support precision support and adjustment system based on vibration damping and positioning according to claim 1, characterized in that: The adaptive precision support adjustment unit is configured with a three-level adaptive control strategy to adapt to different deviation conditions, including a first-level fine-tuning mode, a second-level fine-tuning mode, and a third-level reconfiguration adjustment mode.

7. The shaft support precision support and adjustment system based on vibration damping and positioning according to claim 6, characterized in that: The dynamic damping compensation control unit is configured with differentiated damping logic and thermal deformation pre-compensation logic.

8. The shaft support precision support and adjustment system based on vibration damping and positioning according to claim 7, characterized in that: The full closed-loop accuracy calibration unit executes a closed-loop iterative process of "adjustment-acquisition-comparison-correction" and supports dual working modes of timed full-domain calibration and dynamic on-demand calibration.

9. The shaft support precision support and adjustment system based on vibration damping positioning according to any one of claims 1-8, characterized in that, The overall operation process of the system includes the following steps: S1. System initialization: Load reference parameters, vibration characteristic spectrum, algorithm model and control strategy, and complete the self-test of the whole domain unit and communication linkage. S2. The multi-source data real-time sensing unit collects multi-dimensional raw data and performs preprocessing, outputting standardized synchronous data; S3, the vibration classification and source tracing unit completes the vibration level classification and cause tracing, and the positioning deviation dynamic calculation unit simultaneously quantifies the support deviation and generates the deviation level. S4. The system combines vibration level, deviation level and real-time operating conditions to match the corresponding support adjustment strategy and vibration reduction compensation strategy. S5. The adaptive precision support adjustment unit and the dynamic damping compensation control unit work together to execute control commands and complete the correction of support parameters and vibration energy compensation. S6. The full closed-loop precision calibration unit verifies the control effect and iteratively corrects residual deviations until the preset high-precision standard is met. S7, the data self-learning and intelligent operation and maintenance unit, stores operating data, iteratively optimizes model parameters, and predicts operating trends to achieve continuous intelligent control of the system.

Images