Adaptive cooperative control system of multi-machine parallel magnetic suspension bearing and compressor

Abstract

The present application relates to the technical field of refrigeration compressor, and more particularly to a self-adaptive cooperative control system of multiple parallel magnetic suspension bearings and a compressor, which comprises a working condition sensing module, a cooperative analysis module, a self-adaptive decision module and a cooperative control module. By acquiring the rotor displacement, rotation speed, running instruction of the local rotor and the vibration spectrum data of the parallel other rotor in real time, a bearing coupling relationship matrix is constructed to quantify the vibration coupling characteristics between the rotors, and the working deviation degree calculated is combined to intelligently identify abnormal modes such as resonance risk and coupled vibration, thereby generating and executing a hierarchical control strategy, and verifying the control effect in a closed loop and iterating the strategy. When multiple magnetic suspension compressors are operated in parallel, the vibration interference caused by the self or other rotors can be effectively distinguished and suppressed, and the operation stability, control accuracy and overall reliability of the unit are significantly improved, realizing a fundamental leap from single machine control to multi-machine intelligent cooperation.

Description

Technical Field

[0001] This invention relates to the field of refrigeration compressor technology, and in particular to an adaptive cooperative control system and compressor for multi-machine parallel magnetic levitation bearings. Background Technology

[0002] In the field of magnetic levitation compressors and chillers, magnetic bearings are widely used due to their advantages of being frictionless, highly precise, and efficient. However, in actual operation, especially under high speed and variable load conditions, magnetic levitation rotor systems still face the risk of dynamic instability, such as abnormal vibrations caused by mass imbalance, component loosening, or misalignment. Traditional single-unit independent control strategies are usually based on fixed parameters or limited feedback, making it difficult to quickly and accurately identify and suppress complex and variable vibration sources. Furthermore, when multiple magnetic levitation compressors are tightly integrated in parallel in a modular form within a chiller unit, significant structural vibration coupling and electromagnetic interference occur between the compressor rotors through a common base and piping, forming a complex multibody dynamic system.

[0003] Chinese Patent Publication No. CN208720551U discloses a modular evaporative condensing magnetic levitation chiller unit, which achieves flexible capacity expansion and compact layout by connecting multiple independent refrigeration modules containing magnetic levitation compressors in parallel. However, the modular evaporative condensing magnetic levitation chiller unit has the following problems: multiple high-speed rotating magnetic levitation rotors will generate complex vibration coupling and electromagnetic interference through common foundations, connecting pipelines and other structures. Traditional single-unit independent magnetic levitation bearing control systems lack inter-machine state perception and coordination mechanisms, and cannot effectively distinguish between rotor instability and external coupling excitation, making it difficult to implement precise coordinated vibration suppression. This not only limits the further improvement of the modular unit's performance, but may also affect its long-term reliability and stability. Summary of the Invention

[0004] To address this, the present invention provides an adaptive cooperative control system for multi-machine parallel magnetic levitation bearings, which overcomes the problems in the prior art where magnetic levitation bearing control systems typically only independently control the operating state of a single machine, and cannot sense and quantify the complex coupled vibrations transmitted through the structure when multiple machines are connected in parallel, leading to vibration amplification, control instability, and decreased reliability of modular units during parallel operation.

[0005] To achieve the above objectives, on the one hand, the present invention provides an adaptive cooperative control system for multi-machine parallel magnetic levitation bearings, comprising: The operating condition sensing module is used to acquire the rotor displacement signal, phase data, and rotor speed signal of the magnetic levitation bearing in real time; acquire the current operating mode command and system load parameters of the unit; and acquire the real-time vibration spectrum characteristic data and operating status identifier of other magnetic levitation bearings operating in parallel through the inter-machine communication network. The collaborative analysis module is used to extract the dominant vibration frequency components and harmonic distribution of the current magnetic levitation rotor based on the rotor displacement signal, and construct the bearing coupling relationship matrix by combining the vibration spectrum feature information of other magnetic levitation rotors; and calculate the working deviation of the current vibration state based on the operating mode command and rotor speed signal to identify the abnormal vibration mode of the bearing. An adaptive decision-making module is used to determine the current operating condition classification of the corresponding magnetic levitation bearing based on the vibration anomaly mode and the bearing coupling relationship matrix, and to generate a graded bearing control strategy based on the operating condition classification; wherein, the operating condition classification includes resonance risk condition, coupled vibration condition and stable operating condition, and the graded bearing control strategy includes bearing control parameter adaptive adjustment strategy, bearing vibration active suppression strategy and bearing cooperative operation optimization strategy. The collaborative control module receives the operating condition classification and corresponding graded bearing control strategies, executes differentiated bearing control, and detects and monitors the rotor displacement response signal and vibration energy attenuation rate of the magnetic levitation bearing after executing the graded bearing control strategies. It verifies the effectiveness of the control strategies based on preset bearing stability conditions and iteratively optimizes the operating condition classification rule base according to the verification results. When the verification fails, it triggers an adjustment to the bearing control strategy and generates a system-level operation degradation suggestion instruction.

[0006] As a preferred technical solution for the adaptive cooperative control system of multi-machine parallel magnetic levitation bearings, the working condition sensing module includes: The displacement sensing unit includes a plurality of eddy current displacement sensors arranged radially and axially along the magnetic levitation bearing to acquire the rotor displacement signal and phase data. A rotational speed acquisition unit is communicatively connected to the motor drive unit of the magnetic levitation bearing to acquire the rotor speed signal calculated by the motor drive unit. The instruction receiving unit is connected to the unit's main controller via the system control bus to receive the current operating mode instruction and system load parameters; The inter-machine data exchange unit establishes a communication link with the adaptive and coordinated control system of other parallel multi-machine magnetic levitation bearings through the inter-machine communication network, in order to exchange status information packets including vibration spectrum characteristic data and operating status identifiers in real time. The vibration spectrum characteristic data includes the dominant frequency amplitude and phase information obtained by frequency domain transformation of the time domain signal collected by the eddy current displacement sensor.

[0007] As a preferred technical solution for the adaptive cooperative control system of multi-machine parallel magnetic levitation bearings, the cooperative analysis module extracts the dominant frequency component, its harmonic components, and the amplitude and phase of each component of the current rotor vibration to obtain the local vibration spectrum containing the harmonic distribution. The dominant frequency component and its harmonic components in the vibration spectrum of the machine are matched and associated with the corresponding frequencies and their harmonic components in the vibration spectrum feature information of other magnetic levitation rotors to obtain several matching frequency component pairs. Based on all the matched frequency component pairs, the amplitude ratio and phase difference between each pair of components are calculated, and the bearing coupling relationship matrix is ​​constructed according to the amplitude ratio and phase difference of all frequency component pairs.

[0008] As a preferred technical solution for the adaptive cooperative control system of multi-machine parallel magnetic levitation bearings, the cooperative analysis module calls the pre-stored reference vibration spectrum according to the operation mode command and the rotor speed signal, and compares the measured amplitude and phase of each frequency component in the vibration spectrum of the machine with the reference amplitude range and reference phase interval of the corresponding frequency component in the reference vibration spectrum. Based on the degree to which the measured amplitude of each frequency component deviates from its reference amplitude range, and the degree to which the measured phase deviates from its reference phase interval, a weighted fusion calculation is performed to obtain the working deviation degree, which comprehensively characterizes the difference between the current vibration state and the reference state. The reference vibration spectrum includes the reference amplitude range and reference phase interval of each characteristic frequency component corresponding to the operating conditions.

[0009] As a preferred technical solution for the adaptive cooperative control system of multi-machine parallel magnetic levitation bearings, the cooperative analysis module determines the abnormal vibration mode of the bearing based on the working deviation. If the deviation of the operation does not exceed the first deviation threshold, the abnormal vibration mode is determined to be a normal, non-abnormal mode. If the deviation exceeds the first deviation threshold, the analysis is performed based on the bearing coupling relationship matrix. The coupling strength is determined by all or part of the elements characterizing the coupling influence of other bearings on the current bearing, and it is determined whether the preset coupling strength threshold is exceeded to determine the abnormal vibration mode: if it is not exceeded, it is determined to be the self-abnormal resonance risk mode; if it is exceeded, it is determined to be the vibration mode coupled by other machines.

[0010] As a preferred technical solution for the adaptive cooperative control system of multi-machine parallel magnetic levitation bearings, the adaptive decision module determines the operating condition classification based on the vibration anomaly mode, including: If the vibration abnormal mode is a self-abnormal resonance risk mode, then the operating condition is classified as a resonance risk condition. If the abnormal vibration mode is a coupled vibration mode caused by other machines, then the operating condition is classified as a coupled vibration condition. If the abnormal vibration mode is a non-abnormal mode, then the operating condition is classified as a stable operating condition.

[0011] As a preferred technical solution for the adaptive and coordinated control system of multi-machine parallel magnetic levitation bearings, the adaptive decision module has the following graded bearing control strategy for the resonance risk condition: call and execute the adaptive adjustment strategy of the bearing control parameters; if the working deviation exceeds the second deviation threshold, then assist in calling the active bearing vibration suppression strategy. Furthermore, for the coupled vibration condition, the corresponding graded bearing control strategy is as follows: call and execute the bearing vibration active suppression strategy; if the maximum coupling strength represented by the bearing coupling relationship matrix exceeds the preset strengthening and coordination threshold, then assist in calling the bearing cooperative operation optimization strategy. Furthermore, for the aforementioned stable operating condition, the corresponding graded bearing control strategy is to invoke and execute the aforementioned bearing collaborative operation optimization strategy.

[0012] As a preferred technical solution for the adaptive cooperative control system of multi-machine parallel magnetic levitation bearings, the cooperative control module monitors the rotor displacement response signal after the control is executed and calculates the vibration energy attenuation rate to verify the effectiveness of the graded bearing control strategy. Furthermore, based on the verification results, the operating condition classification rule base is iteratively optimized, and if the verification fails, the adjustment of the bearing control strategy is triggered and the system-level operation degradation suggestion instruction is generated.

[0013] On the other hand, the present invention also provides a processing system, a compressor, comprising: a magnetic levitation bearing for supporting a rotor and providing non-contact levitation; a stator assembly having an electromagnetic coil and a drive unit therein for the magnetic levitation bearing; and a rotor assembly supported by the magnetic levitation bearing and equipped with an impeller, wherein the compressor employs an adaptive cooperative control system of multiple parallel magnetic levitation bearings. The eddy current displacement sensor included in the displacement sensing unit of the working condition sensing module is installed on the stator side of the magnetic levitation bearing and arranged towards the rotor assembly, and is used to collect the displacement signal of the rotor. The rotational speed acquisition unit is communicatively connected to the drive unit to obtain the rotational speed signal of the rotor.

[0014] The compressor is configured to establish a communication connection with at least one other similar compressor through the inter-machine communication network, operate in parallel, and achieve multi-machine collaborative vibration suppression and operation optimization based on the adaptive collaborative control system of the multi-machine parallel magnetic levitation bearing.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: By real-time acquisition and fusion of rotor displacement, speed, operating mode, and vibration spectrum data of the compressor itself and other parallel compressors, this invention constructs a bearing coupling relationship matrix to accurately quantify the vibration coupling strength and characteristics between machines. Based on the calculated operating deviation and coupling analysis results, it intelligently identifies abnormal modes such as resonance risk and coupled vibration, and then generates and executes hierarchical parameter adaptive, active suppression, and collaborative optimization strategies, possessing the ability to verify strategy effectiveness and iteratively optimize the rule base. This invention enables multiple magnetic levitation compressors to effectively distinguish and suppress coupled vibrations caused by their own dynamic instability or transmitted through the structure by other machines when operating in parallel, significantly improving the unit's operational stability, control accuracy, and overall reliability under complex operating conditions. Simultaneously, collaborative optimization enhances system energy efficiency, achieving a fundamental improvement from independent single-machine control to intelligent multi-machine collaboration. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the adaptive cooperative control system for multi-machine parallel magnetic levitation bearings according to an embodiment of the present invention; Figure 2 A logic diagram of the collaborative analysis module for determining abnormal bearing vibration modes in an embodiment of the present invention; Figure 3 The following is a logic diagram for verifying the graded bearing control strategy in the collaborative control module of this embodiment of the invention. Detailed Implementation

[0017] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0018] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0019] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0020] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0021] Please see Figures 1-3 As shown, the present invention provides an adaptive cooperative control system for multi-machine parallel magnetic levitation bearings, including a working condition sensing module, a cooperative analysis module, an adaptive decision-making module, and a cooperative control module.

[0022] Specifically, the operating condition perception module includes a displacement sensing unit, a speed acquisition unit, a command receiving unit, and an inter-machine data exchange unit. Through dedicated sensing, acquisition, and communication units, it synchronously acquires key data from four dimensions: the physical layer, the drive layer, the system layer, and the network layer. This allows it to construct a complete digital mapping of the bearing and its operating environment, laying a reliable data foundation for subsequent intelligent analysis and decision-making.

[0023] The displacement sensing unit directly monitors the rotor's mechanical state. Specifically, within the stator housing of the magnetic levitation bearing, four standard-sized (e.g., 8mm diameter) non-contact eddy current displacement sensors are arranged radially and circumferentially in a uniform and orthogonal pattern to monitor the rotor's translational displacement in two orthogonal directions. Simultaneously, one eddy current displacement sensor is arranged axially to monitor the rotor's axial movement. In practice, five eddy current displacement sensors are typically used. Based on the rotor dynamics model, to fully monitor and control the rotor's five degrees of freedom (two radial translations, two radial tilts, and one axial translation), at least five independent displacement measurement signals are required. The time-domain voltage signals acquired by the sensors directly correspond to the rotor's instantaneous position and phase, providing the most basic physical quantities for vibration analysis.

[0024] The speed acquisition unit acquires the rotor's real-time speed information with high responsiveness and connects to the high-speed permanent magnet synchronous motor drive unit built into the magnetic levitation compressor via a standard communication interface (such as CAN or EtherCAT). It calculates and outputs the rotor speed value in real time. By utilizing the existing high-bandwidth sensing resources of the drive system, the need for an additional speed sensor is avoided, simplifying the structure and ensuring signal source consistency.

[0025] The command receiving unit acts as a bridge connecting the bearing control system and the upper-level unit management system. It communicates with the unit's main controller via a shared system control bus, such as an industrial Ethernet network. This unit listens to the bus at a fixed communication cycle of 10 milliseconds, receiving and parsing global operating mode commands broadcast by the main controller, such as cooling, heating, and natural cooling, as well as real-time system load parameters, such as the percentage load calculated from suction pressure and condensing temperature. This allows the bearing control system to promptly understand the overall machine's operating intentions and load conditions.

[0026] The inter-bearing data exchange unit is a key network infrastructure for achieving multi-bearing collaborative operation. Each bearing control system is configured with an independent network controller and interconnected with other parallel units via a low-latency, highly deterministic inter-bearing communication network, typically Ethernet based on a time-sensitive network. This unit packages and broadcasts local status information packets according to a preset synchronization period of 2 milliseconds, while simultaneously receiving data packets from other units. The information packets mainly contain vibration spectrum characteristic data and operating status indicators. The vibration spectrum characteristic data is obtained from the raw time-domain signal of the displacement sensing unit, after undergoing a Fast Fourier Transform (FFT) by the local processor, primarily extracting and encapsulating the amplitude and phase information of the first three dominant frequencies. The communication period threshold in real-time exchange is typically determined initially through control system simulation calculations based on the sampling theorem in control system stability theory and the highest frequency of vibration coupling, and then fine-tuned through multi-bearing parallel experiments to ensure that the transmission delay of coupled vibration information is much smaller than the vibration period itself, meeting the timeliness requirements of collaborative control. This unit also has a data buffer area, which performs moving average or latest value retention processing on the vibration spectrum characteristic data acquired within a 2-millisecond period, and provides synchronously updated data to the collaborative analysis module at the beginning of each 10-millisecond instruction receiving cycle to ensure that the vibration data on which the analysis decision is based is aligned with the system operation instructions in time.

[0027] In this implementation, multi-scale data fusion from local mechanical state to global system information was achieved, providing high-precision and high-reliability multi-dimensional sensing input for upper-level control algorithms. This enables the control system to not only respond to the rotor's own offset, but also to sense the overall system operation mode, load changes, and mutual influence between parallel units, thus providing an indispensable sensing layer support for achieving true adaptive and cooperative control.

[0028] Specifically, the collaborative analysis module performs a Fast Fourier Transform on the received rotor displacement signal. This process decomposes the time-domain signal into a frequency-domain representation, thereby extracting the distribution of vibration energy at different frequencies. The frequency component with the highest amplitude is identified as the dominant frequency component, which typically corresponds to the fundamental frequency of rotor rotation, while components with frequency values ​​that are integer multiples of this dominant frequency are identified as harmonic components.

[0029] For each identified frequency component, including the dominant frequency and its harmonics, its corresponding amplitude and phase are recorded simultaneously. The amplitude represents the vibration intensity, and the phase corresponds to the vibration timing position. This enables refined analysis of complex vibration states, deconstructing the original, comprehensive displacement waveform into a set of frequency, amplitude, and phase parameters with clear physical meaning, i.e., a structured local vibration spectrum.

[0030] Understandably, to achieve cross-device vibration correlation analysis, it is necessary to match and correlate the vibration spectrum of the local machine with the spectral characteristics of other machines. Specifically, each target frequency component in the local machine's spectrum, including the dominant frequency F0 and its harmonics, such as 2F0, 3F0, etc., is compared with all frequency components present in the spectral characteristic information of other magnetic levitation rotors. The module will synchronously record and analyze these integer and non-integer multiples of characteristic frequency components and their amplitude and phase information.

[0031] When the absolute value of the difference between the frequency values ​​of two frequency components is less than a preset matching frequency difference threshold Δf, they are determined to be corresponding frequency components from the same or related excitation sources, thus forming a matched frequency component pair. The preset matching frequency difference threshold Δf also applies to the pairing of these non-integer multiple components. For example, if the 1.5f0 and 2.7f0 components found in the local spectrum have a difference within Δf in the corresponding frequency range of another machine's spectrum, they will also be identified as a matched frequency component pair and used to calculate the amplitude ratio and phase difference in the coupling relationship matrix. In addition, the matching frequency difference threshold Δf is generally set to 2Hz. The determination of this threshold is usually based on the characteristics of the equipment itself, and the main considerations include the steady-state accuracy of the rotor speed control system, which determines the fluctuation range of the fundamental frequency, the resolution of the spectrum analysis, and the minimum interval required to avoid mismatches caused by noise. In the preliminary design, an initial threshold is set based on a multiple of the speed control accuracy. Then, by conducting offline statistical analysis on a large amount of vibration spectrum data collected from multiple devices under typical operating conditions, the actual distribution dispersion of the same source vibration frequencies is observed. This threshold is then calibrated and optimized so that it can reliably identify the true correspondence under most operating conditions, while effectively eliminating irrelevant frequency interference.

[0032] Based on all obtained matching frequency component pairs, a bearing coupling matrix is ​​constructed. For each pair of matching frequency components, the amplitude ratio and phase difference are calculated. The amplitude ratio, typically the ratio of the amplitude of the other component to the amplitude of the local component, is used to quantify the attenuation or amplification of vibration transmission, while the phase difference quantifies the time delay or phase inversion characteristics of vibration transmission. Then, an N×N square matrix is ​​constructed as the bearing coupling matrix, where N is the total number of bearings in the system. The element in the i-th row and j-th column of the matrix represents the coupling effect of bearing j on bearing i. This element is itself a data structure or vector, containing the calculated amplitude ratios and phase differences for all matching frequency component pairs from bearing j to bearing i.

[0033] It is understood that the coupling described in this embodiment specifically refers to the dynamic physical interactions transmitted through the physical structure of the unit, such as the common base and connecting pipelines, rather than merely the statistical correlation or trend similarity of the operating states of multiple devices. The core purpose of constructing the bearing coupling relationship matrix is ​​to quantify the strength and characteristics of this physical interaction.

[0034] Specifically, the amplitude ratio stored in each element of the matrix, calculated from the matched frequency component pairs, quantifies the degree of attenuation or amplification of vibrational energy as it is transmitted from the source bearing to the target bearing via the structural path. For example, a significantly greater than zero amplitude ratio indicates that an observable portion of the vibration in the target bearing originates directly from the excitation of the source bearing. The phase difference, on the other hand, characterizes the time delay or phase shift of the vibrational signal during transmission. This is determined by the length of the transmission path and the dynamic characteristics of the structure, and is crucial evidence for demonstrating dynamic causality.

[0035] Therefore, this matrix is ​​not a coefficient table reflecting statistical correlation, but a physical model describing the dynamic force network within a multi-bearing system. Based on this model, the control system can distinguish between increased vibration caused by its own rotor imbalance, which manifests as high operational deviation but low coupling strength due to the influence of other machines, and forced response caused by vibration transmitted through the structure from neighboring machines, which manifests as high coupling strength and stable phase difference at specific frequencies. This distinction forms the logical basis for subsequent targeted collaborative control rather than simple global gain adjustment, thereby fundamentally solving the mutual interference problem in multi-machine parallel operation.

[0036] In one embodiment, four parallel magnetic levitation compressors operate collaboratively, with their corresponding bearings labeled as bearing A, bearing B, bearing C, and bearing D, respectively. Based on this, the bearing coupling relationship matrix constructed by the collaborative analysis module is a 4x4 square matrix.

[0037] Source bearings: ABCD Target bearing A [M aa M ab M ac M ad ] B [M ba M bb M bc M bd ] C [M ca M cb M cc M cd ] D [M da M db M dc M dd ] Each row of this matrix corresponds to a target bearing acting as the vibration receiver, and each column corresponds to a source bearing acting as a potential vibration source. Taking the element in the first row and second column as an example, it specifically characterizes the vibration coupling effect of source bearing B on target bearing A. This element is not a single numerical value, but a structured dataset encapsulating the analysis results of all matching frequency component pairs from bearing B to bearing A. This includes the dynamic correlation between the vibration signals of bearing B and bearing A at the three characteristic frequencies: the rotor rotation fundamental frequency, the second harmonic, and the third harmonic. For the fundamental frequency component pair, this element stores the amplitude ratio of the vibration transmitted from bearing B to bearing A, for example, 0.65, indicating that the intensity attenuates to 65% of the source vibration after transmission, and the phase difference between the two signals, for example, 15 degrees, indicating that the vibration waveform of bearing A lags behind the vibration waveform of bearing B by 15 degrees. Similarly, for the second and third harmonic component pairs, their corresponding amplitude ratios and phase difference data are also stored.

[0038] In this invention, by quantitatively describing the complex dynamic interference relationship between multiple bearings, the control system's understanding of vibration problems is improved from perceiving anomalies at a single point to understanding the interaction of the entire network, laying an indispensable analytical foundation for subsequent precise collaborative vibration suppression and operation optimization.

[0039] Specifically, the collaborative analysis module calls up the pre-stored reference vibration spectrum according to the operating mode command and rotor speed signal. It compares the measured amplitude and phase of each frequency component in the machine's vibration spectrum with the reference amplitude range and reference phase interval of the corresponding frequency component in the reference vibration spectrum. The reference vibration spectrum includes the reference amplitude range and reference phase interval of each characteristic frequency component corresponding to the operating condition.

[0040] Based on the degree to which the measured amplitude of each frequency component deviates from its reference amplitude range, and the degree to which the measured phase deviates from its reference phase interval, a weighted fusion calculation is performed to obtain the working deviation degree that comprehensively represents the difference between the current vibration state and the reference state.

[0041] In practice, the pre-stored reference vibration spectrum is established by collecting a large amount of historical vibration data under different operating modes and load points during the unit commissioning and healthy operation phases. For each defined operating mode-speed range combination, its vibration spectrum data is extracted, and statistical methods are used to determine the reference amplitude range and reference phase range of each characteristic frequency component by taking the 95th percentile, including the fundamental frequency, major integer harmonics, and fractional harmonics. This constitutes the reference vibration spectrum under that operating condition, ensuring that the reference can cover the normal fluctuation range under that operating condition.

[0042] When calculating the operational deviation in real time, based on the current operating mode command and rotor speed signal, the corresponding reference vibration spectrum is invoked. Then, the measured amplitude and phase of each characteristic frequency component in the machine's vibration spectrum are compared with the reference amplitude range and reference phase interval of the corresponding component in the reference spectrum. For amplitude, the degree to which the measured value deviates from the boundary of the reference range is calculated; for phase, the angle difference between the measured value and the nearest boundary of the reference interval is calculated.

[0043] It is understandable that different frequency components have varying importance to the overall system stability and fault characterization, therefore their weights in the overall assessment should differ. The determination of weights is mainly based on the statistical analysis of historical fault data. By analyzing the unit's historical operating data and maintenance records, the frequency and amplitude changes of each frequency component during various typical faults are statistically analyzed. Components with stronger correlation and greater sensitivity are assigned higher weights. Engineering knowledge of the current operating conditions is also considered. For example, under high-speed operating conditions, higher-order harmonics may become more important due to their proximity to the structure's natural frequency, and their weights can be dynamically adjusted accordingly.

[0044] In practice, historical operating data of the unit under healthy and typical fault conditions are collected to form a training dataset containing vibration spectrum characteristics under various operating conditions. The data is then analyzed, and for each characteristic frequency component, its sensitivity under different fault modes and its stability under healthy conditions are evaluated using statistical methods. Sensitivity characterizes the significance and consistency of the component's deviation when a specific fault occurs; stability characterizes the fluctuation range of the component's deviation from the baseline under healthy operating conditions. Based on the above analysis, a comprehensive score is calculated for each frequency component, which comprehensively considers its ability to identify key faults and its own noise level. Finally, the scores of all characteristic frequency components are normalized to obtain the final weight coefficients for each component, which are then stored.

[0045] For example, under a typical full-load cooling condition, the weight of the fundamental frequency component is set to 0.5, the second harmonic to 0.3, the third harmonic to 0.15, and other components share the remaining 0.05. The final operating deviation is calculated by multiplying the amplitude deviation and phase deviation of all frequency components by their respective weights and then summing them. Normalization is required, and the calculation result is dimensionless.

[0046] In this implementation, scattered and multidimensional abnormal information on the spectrum is fused into a comprehensive and focused evaluation value through a set of evidence-based weighting rules. This provides a clear and reliable threshold comparison object for the subsequent identification of abnormal vibration modes. At the same time, it enables the system to more accurately perceive the overall degree of state deterioration and provides direct quantitative input for the adaptive decision-making module to judge the urgency of the working condition and select the intensity level of the control strategy.

[0047] Specifically, if the collaborative analysis module determines that the deviation exceeds the first deviation threshold, it will perform analysis based on the bearing coupling relationship matrix, determine the coupling strength based on all or part of the elements characterizing the coupling influence of other bearings on the current bearing, and determine whether it exceeds the preset coupling strength threshold to determine the vibration abnormal mode: if it does not exceed the threshold, it is determined to be its own abnormal resonance risk mode; if it exceeds the threshold, it is determined to be the vibration mode coupled by other machines.

[0048] If the collaborative analysis module determines that the deviation of the work does not exceed the first deviation threshold, it is considered to be in an abnormal mode.

[0049] The process for determining coupling strength is as follows: For any element in the bearing coupling relationship matrix that represents the coupling influence of other bearings on the current bearing, the amplitude ratio of each component is extracted from all matching frequency component pairs contained in that element. These multiple amplitude ratio values ​​are then aggregated into a single value representing the overall coupling level of that element using a predetermined data aggregation rule. In implementation, the data aggregation rule can be a method such as taking the maximum value, taking the average value, or calculating a weighted average. A preferred implementation is to take the maximum value among the amplitude ratios of all matching frequency components as the element coupling strength corresponding to that element. Subsequently, the system iterates through all relevant elements to obtain a set of element coupling strength values, and selects the maximum value from these as the final determination coupling strength used for comparison with a preset coupling strength threshold.

[0050] The first deviation threshold is based on statistical analysis of a large amount of historical health data accumulated during the establishment of the reference vibration spectrum. In implementation, the distribution of working deviation corresponding to all health condition samples can be calculated, and the statistically high percentile value is taken as the candidate value of the threshold, preferably 95%.

[0051] When the real-time calculated operating deviation does not exceed this first deviation threshold, the system is determined to be in a healthy operating range. The abnormal vibration mode is defined as a normal operating mode without abnormalities. If the operating deviation exceeds this threshold, it indicates that the overall vibration state of the system has significantly deviated from the healthy baseline, and the root cause analysis process needs to be initiated.

[0052] At this point, the system performs in-depth analysis based on the bearing coupling relationship matrix. A preset coupling strength threshold is used to quantitatively determine whether the influence from other machines constitutes the main cause of the current vibration anomaly. This threshold is determined based on the understanding and testing of the vibration transmission characteristics of the unit's physical structure. Through theoretical analysis, finite element simulation, and actual measurement, the typical attenuation coefficient range when vibration is transmitted through paths such as the common foundation can be estimated. Based on this, for each possible bearing transmission path, a limited number of transmission characteristic calibration tests are conducted by injecting known vibration and measuring the response to obtain the typical amplitude transmission ratio range of that path within the frequency band of interest. The coupling strength threshold can be set as the upper limit of this typical transmission ratio range. For example, a value of 0.3 indicates that if the coupling influence from a certain source bearing exceeds 30% of the typical maximum level that its vibration energy may reach when transmitted to the unit, then the external coupling influence is considered significant and sufficient to dominate the current abnormal vibration state.

[0053] During the judgment process, the system queries all elements in the bearing coupling relationship matrix that characterize the coupling influence of other bearings on the current bearing, and checks whether any element has a coupling strength (i.e., amplitude ratio) exceeding the aforementioned coupling strength threshold at one or more characteristic frequency components. If the coupling strength of all relevant elements does not exceed the threshold, the current anomaly is determined to be mainly caused by its own factors, and the vibration anomaly mode is defined as a self-abnormal resonance risk mode; conversely, if there is a coupling path exceeding the threshold, it is determined to be a vibration mode coupled by other factors.

[0054] The effectiveness of this implementation lies in its construction of a stable and reliable vibration root cause diagnosis mechanism through two-level threshold judgment. It not only sensitively detects the occurrence of anomalies but also effectively distinguishes between internal and external causes, thereby providing highly targeted fault type input to the adaptive decision-making module. This enables the module to accurately invoke a differentiated control strategy library corresponding to resonance risk or coupled vibration, achieving closed-loop intelligence from perception and diagnosis to decision-making, fundamentally improving the ability of multi-machine parallel systems to cope with complex vibration problems.

[0055] Specifically, the adaptive decision-making module determines the operating condition classification based on the vibration anomaly mode, including: if the vibration anomaly mode is a self-abnormal resonance risk mode, then the operating condition is classified as a resonance risk mode; if the vibration anomaly mode is a coupled vibration mode influenced by other machines, then the operating condition is classified as a coupled vibration mode; if the vibration anomaly mode is a no-anomaly mode, then the operating condition is classified as a stable operating condition.

[0056] Specifically, when the collaborative analysis module identifies the abnormal vibration pattern as a self-abnormal resonance risk mode, it indicates that the current abnormal vibration of the bearing mainly originates from the dynamic problems of its own rotor-bearing system, such as imbalance, misalignment, or loose components, rather than from external disturbances. Based on this, the adaptive decision-making module classifies the current operating condition as a resonance risk condition, focusing its optimization efforts on adjusting and optimizing the internal parameters of the bearing's own control system to correct its dynamic characteristics and restore stability.

[0057] Correspondingly, when an abnormal vibration mode is identified as a coupled vibration mode influenced by other machines, it indicates that the current abnormal vibration of the bearing mainly originates from vibration excitation transmitted through the structure from other parallel devices. Based on this, the adaptive decision module classifies the operating condition as a coupled vibration condition. The optimization direction focuses on expanding the control action from a single machine perspective to the system as a whole, counteracting or isolating externally transmitted vibrations, and reducing the output of interference sources through coordination.

[0058] Specifically, for a known resonance risk condition, the basic strategy of the adaptive decision-making module is to call and execute an adaptive adjustment strategy for bearing control parameters. By adjusting the gain or integral time constant of the PID controller online, the stiffness and damping characteristics of the bearing are changed in an attempt to make the system avoid the resonance point.

[0059] The second deviation threshold is typically set to a value significantly higher than the first deviation threshold. It is determined based on in-depth analysis of historical failure cases, statistically identifying deviation levels at which parameter adjustments alone are insufficient to effectively suppress vibration, necessitating more direct force control. For example, analysis of a case database might reveal that the success rate of parameter adjustments significantly decreases when the operational deviation exceeds 0.25, thus setting the second deviation threshold to 0.25. If the real-time operational deviation exceeds this threshold, stronger intervention is deemed necessary, thereby invoking an active bearing vibration suppression strategy by superimposing a specific frequency active damping signal into the control commands.

[0060] For coupled vibration conditions, the basic strategy is to prioritize and execute the active bearing vibration suppression strategy, which generates and applies an antiphase canceling force based on the phase information of the coupling matrix. A enhanced coordination threshold is used to determine whether the coupling tightness has reached a level requiring source intervention. This threshold is determined primarily based on calibration test results of the unit structure's transmission path. Typical amplitude ratios of vibration transmission between different devices are experimentally measured, and the transmission intensity at which local cancellation becomes inefficient or places excessive demands on actuators. For example, when the amplitude transmission ratio consistently exceeds 0.5, the energy required for local cancellation increases sharply; in this case, source coordination is more energy-efficient. Therefore, the enhanced coordination threshold can be set to 0.5. If the maximum coupling intensity represented by the bearing coupling matrix exceeds this threshold, the bearing coordinated operation optimization strategy is invoked, simultaneously suggesting that the operating speed of the interference source equipment be fine-tuned to avoid sensitive frequencies.

[0061] For stable operating conditions, the strategy is to invoke and execute the bearing collaborative operation optimization strategy. At this time, the focus shifts from vibration suppression to energy efficiency optimization and preventive maintenance, such as performing minimum current optimization or optimizing load distribution among multiple machines to delay equipment aging.

[0062] The bearing control parameter adaptive adjustment strategy, bearing vibration active suppression strategy, and bearing cooperative operation optimization strategy mentioned in the graded bearing control strategy can all be implemented based on mature control methods known in the field. The purpose is to achieve the control objectives set by each strategy, rather than being limited to a specific algorithm or implementation path.

[0063] The purpose of an adaptive adjustment strategy for bearing control parameters is to adjust the bearing controller parameters based on real-time identified resonance risks to alter the system's dynamic characteristics and avoid resonance. This can be achieved through online system identification combined with real-time controller parameter tuning techniques, such as parameter identification based on the least squares method combined with PID parameter self-tuning. The core of its implementation lies in dynamically adjusting control parameters based on vibration state feedback.

[0064] The purpose of an active bearing vibration suppression strategy is to generate and apply a force signal with the same amplitude but opposite phase as the target vibration component to actively cancel the vibration. This can be achieved through existing active vibration control methods such as filter-based adaptive feedforward control, displacement / velocity feedback-based active damping injection, or harmonic suppressors based on frequency domain analysis. The core of its implementation lies in generating precise cancellation control quantities based on the frequency, amplitude, and phase information provided by the coupling matrix.

[0065] The purpose of bearing collaborative operation optimization strategies is to coordinate the operation of multiple machines to optimize energy efficiency or suppress coupling at the system level. This can be achieved through distributed optimization algorithms to optimize load or speed allocation, or through upper-level coordinators issuing operating point adjustment commands. When used to assist in suppressing coupled vibration, collaborative commands can be sent through inter-machine communication, utilizing existing speed control functions to achieve slight shifts in the operating point.

[0066] The specific technical implementations of the above strategies are all well-known means in the relevant technical fields. This embodiment aims to intelligently select and combine these strategies according to specific working condition classification and grading conditions, thereby forming an adaptive collaborative control scheme for a multi-machine parallel magnetic levitation bearing system.

[0067] Specifically, the collaborative control module receives the operating condition classification and the corresponding graded bearing control strategy, executes differentiated bearing control, monitors the rotor displacement response signal after the control is executed, calculates the vibration energy attenuation rate, verifies the effectiveness of the graded bearing control strategy, and iteratively optimizes the operating condition classification rule base based on the verification results. If the verification fails, it triggers the adjustment of the bearing control strategy and generates a system-level operation degradation suggestion instruction.

[0068] Specifically, after initiating differentiated bearing control, the collaborative control module immediately begins continuous monitoring of the rotor displacement response signal. This response signal directly reflects the effect of the magnetic force applied to the rotor by the control system. Based on this signal, the module calculates the vibration energy attenuation rate, which quantifies the rate at which the vibration amplitude decreases over time and is a key parameter for measuring the damping effect of the control strategy. To determine the effectiveness of the control, the module compares the monitored rotor displacement peak value and the calculated vibration energy attenuation rate with preset bearing stability conditions. The preset bearing stability conditions include the range within which the rotor displacement stably converges (e.g., requiring the displacement peak value to decrease to within ± a few micrometers within a few milliseconds after control initiation) and the minimum standard that the vibration energy attenuation rate must reach (e.g., requiring attenuation of no less than a certain percentage per minute). The determination of these two threshold requirements is based on statistical analysis of the response data of a large number of healthy samples after effective control was applied. For example, by collecting the distribution of displacement stability and attenuation rate after control takes effect in historical successful cases, the lower limit of the statistically high confidence interval is taken. Generally, the lower limit of 99% confidence is selected as the criterion threshold to ensure the strictness and reliability of the standard. Within 100ms after the control strategy is implemented, the peak value of rotor radial displacement is continuously less than ±5μm; and the attenuation rate of vibration energy within 50ms is not less than 30%.

[0069] If the monitored indicators meet the above stability criteria, the verification is successful, indicating that the current graded bearing control strategy is effective. The system will use the data from this successful control process to fine-tune the baseline operating condition model used by the collaborative analysis module, update the normal range of vibration characteristics under the corresponding operating condition, or optimize the operating condition classification rules used by the adaptive decision module, strengthening the weight of the feature conditions that led to this successful decision. Optimization methods include, but are not limited to: fine-tuning the weight coefficients in the calculation of the operating deviation under the corresponding operating condition based on continuously successful control cases, making it more focused on key frequency components; or creating new sub-class operating conditions and corresponding control strategy mapping relationships in the rule base based on new and recurring vibration characteristic patterns.

[0070] If the monitored indicators fail to meet the stability criteria within the preset time window, the verification fails. In this case, the module first triggers a bearing control strategy adjustment. Under resonance risk conditions, if the current parameter adjustment strategy is ineffective, it automatically upgrades to a stronger combination of parameter adjustment and active suppression strategies and tries again. If the upgraded strategy still fails continuously in subsequent verifications, it is determined that the current local control resources are insufficient to solve the problem. The module will then generate a system-level operational degradation suggestion command, such as suggesting that the unit's main controller reduce the load or speed of the compressor to ensure the safe and stable operation of the entire unit.

[0071] In this implementation, the system can not only execute preset strategies, but also objectively evaluate the effectiveness of the strategies and optimize them. When encountering unexpected anomalies, it can attempt self-rescue through strategy upgrades and proactively request system-level intervention when necessary. This achieves a leap from passive execution to proactive adaptation, and from single control to closed-loop evolution, greatly improving the intelligence, robustness, and long-term reliability of the entire control system.

[0072] This invention acquires and integrates real-time data on rotor displacement, speed, operating mode, and vibration spectrum from other parallel units. It constructs a bearing coupling matrix to accurately quantify the vibration coupling strength and characteristics between units. Based on calculated operating deviations and coupling analysis results, it intelligently identifies abnormal modes such as resonance risk and coupled vibration. This leads to the generation and execution of tiered parameter adaptation, active suppression, and collaborative optimization strategies, with the capability to verify strategy effectiveness and iteratively optimize the rule base. This invention enables multiple magnetic levitation compressors operating in parallel to effectively distinguish and suppress coupled vibrations caused by their own dynamic instability or those transmitted through the structure from other units. This significantly improves the unit's operational stability, control accuracy, and overall reliability under complex operating conditions. Furthermore, collaborative optimization enhances system energy efficiency, achieving a fundamental improvement from independent single-unit control to intelligent multi-unit collaboration.

[0073] This embodiment also provides a compressor, including: a magnetic levitation bearing for supporting the rotor and providing non-contact levitation; a stator assembly having an electromagnetic coil and drive unit of the magnetic levitation bearing inside; and a rotor assembly supported by the magnetic levitation bearing and equipped with an impeller. The compressor adopts an adaptive cooperative control system of multiple parallel magnetic levitation bearings. The eddy current displacement sensor included in the displacement sensing unit of the working condition sensing module is installed on the stator side of the magnetic levitation bearing and arranged towards the rotor assembly, and is used to collect the displacement signal of the rotor. The rotational speed acquisition unit is communicatively connected to the drive unit to obtain the rotational speed signal of the rotor.

[0074] Specifically, the compressor comprises core mechanical and electromagnetic components such as magnetic levitation bearings, stator assemblies, rotor assemblies, and drive units, and integrates a complete adaptive and coordinated control system for multiple parallel magnetic levitation bearings. The displacement sensing unit of the operating condition sensing module contains several high-precision eddy current displacement sensors, which are directly mounted on the stator side of the magnetic levitation bearings, such as stator silicon steel sheets or bearing housings. The sensor probes are precisely aligned with the rotor journal or thrust disk surface, enabling real-time, non-contact acquisition of the rotor's radial and axial micro-displacement signals and their phase changes. Its speed acquisition unit is directly connected to the compressor's drive unit via hardware circuitry or an internal communication protocol, directly obtaining high-precision, low-delay speed signals calculated by the rotor position sensors from the drive's control closed loop.

[0075] Through this integrated design, the compressor not only possesses independent magnetic levitation support functionality but also gains powerful state perception, intelligent analysis, and collaborative control capabilities. When multiple such compressors are connected in parallel to form a unit, the control systems built into each compressor are interconnected through an inter-machine communication network interface. This allows for the automatic execution of multi-machine vibration spectrum coupling analysis, anomaly root cause diagnosis, hierarchical collaborative control, and effect verification optimization, as described earlier. This transforms the compressor from a simple actuator that merely receives external commands into an active node capable of sensing its own and neighboring machine states, making intelligent decisions, and participating in system-level optimization. This improves the overall system's dynamic response speed and control accuracy, fundamentally solving the challenges of mutual interference and collaborative control that are difficult to overcome when multiple compressors operate in parallel. It provides a core equipment-level foundation for highly reliable and energy-efficient magnetic levitation compressor units.

[0076] Specifically, the compressor is configured to establish a communication connection with at least one other similar compressor through the inter-machine communication network, operate in parallel, and achieve multi-machine collaborative vibration suppression and operation optimization based on the adaptive collaborative control system of the multi-machine parallel magnetic levitation bearing.

[0077] In implementation, when multiple compressors operate in parallel, each compressor is equipped with an adaptive collaborative control system for multi-compressor parallel magnetic levitation bearings. The adaptive collaborative control systems of the multi-compressor parallel magnetic levitation bearings of each compressor establish a communication connection through an inter-compressor data exchange unit to realize the real-time exchange of vibration spectrum characteristic data and operating status indicators between the compressors. The adaptive collaborative control systems of the multi-compressor parallel magnetic levitation bearings quantify the vibration coupling effect between multiple compressors by constructing a bearing coupling relationship matrix, and combine it with a hierarchical bearing control strategy to achieve inter-compressor collaborative control, specifically suppress multi-compressor coupled vibration, and improve the overall operational stability and reliability of the unit.

[0078] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0079] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An adaptive cooperative control system for multi-machine parallel magnetic levitation bearings, characterized in that, include: The operating condition sensing module is used to acquire the rotor displacement signal, phase data, and rotor speed signal of the magnetic levitation bearing in real time, acquire the current operating mode command and system load parameters of the unit, and acquire the real-time vibration spectrum characteristic data and operating status identifier of other magnetic levitation bearings operating in parallel. The collaborative analysis module is used to extract the dominant vibration frequency components and harmonic distribution of the current magnetic levitation rotor based on the rotor displacement signal, and to construct the bearing coupling relationship matrix by combining the vibration spectrum feature information of other magnetic levitation rotors; and to calculate the working deviation of the current vibration state based on the operating mode command and rotor speed signal in order to identify the abnormal vibration mode of the bearing. An adaptive decision-making module is used to determine the current operating condition classification of the corresponding magnetic levitation bearing based on the vibration anomaly mode and the bearing coupling relationship matrix, and to generate a graded bearing control strategy based on the operating condition classification. The collaborative control module is used to receive the operating condition classification and execute the corresponding graded bearing control strategy, detect and monitor the rotor displacement response signal and vibration energy attenuation rate of the magnetic levitation bearing after the graded bearing control strategy is executed to determine the effectiveness of the control strategy, and iteratively optimize the operating condition classification rule base according to the verification results.

2. The adaptive cooperative control system for multi-machine parallel magnetic levitation bearings according to claim 1, characterized in that, The operating condition sensing module includes: The displacement sensing unit includes a plurality of eddy current displacement sensors arranged radially and axially along the magnetic levitation bearing to acquire the rotor displacement signal and phase data. A rotational speed acquisition unit is communicatively connected to the motor drive unit of the magnetic levitation bearing to acquire the rotor speed signal calculated by the motor drive unit. The instruction receiving unit is connected to the unit's main controller via the system control bus to receive the current operating mode instruction and system load parameters; The inter-machine data exchange unit establishes a communication link with the adaptive and coordinated control system of other parallel multi-machine magnetic levitation bearings through the inter-machine communication network, in order to exchange status information packets including vibration spectrum characteristic data and operating status identifiers in real time. The vibration spectrum characteristic data includes the dominant frequency amplitude and phase information obtained by frequency domain transformation of the time domain signal collected by the eddy current displacement sensor.

3. The adaptive cooperative control system for multi-machine parallel magnetic levitation bearings according to claim 2, characterized in that, The collaborative analysis module extracts the dominant frequency component, harmonic components, amplitude and phase of the current rotor vibration to obtain the machine vibration spectrum including the harmonic distribution. The dominant frequency component and its harmonic components in the vibration spectrum of the machine are matched and associated with the corresponding frequencies and their harmonic components in the vibration spectrum feature information of other magnetic levitation rotors to obtain several matching frequency component pairs. Based on all the matched frequency component pairs, the amplitude ratio and phase difference between each pair of components are calculated, and the bearing coupling relationship matrix is ​​constructed according to the amplitude ratio and phase difference of all frequency component pairs.

4. The adaptive cooperative control system for multi-machine parallel magnetic levitation bearings according to claim 3, characterized in that, The collaborative analysis module, based on the operating mode command and the rotor speed signal, calls up the pre-stored reference vibration spectrum and compares the measured amplitude and phase of each frequency component in the machine vibration spectrum with the reference amplitude range and reference phase interval of the corresponding frequency component in the reference vibration spectrum. Based on the degree to which the measured amplitude of each frequency component deviates from its reference amplitude range, and the degree to which the measured phase deviates from its reference phase interval, a weighted fusion calculation is performed to obtain the working deviation degree, which comprehensively characterizes the difference between the current vibration state and the reference state. The reference vibration spectrum includes the reference amplitude range and reference phase interval of each characteristic frequency component corresponding to the operating conditions.

5. The adaptive cooperative control system for multi-machine parallel magnetic levitation bearings according to claim 4, characterized in that, The collaborative analysis module determines the abnormal vibration mode of the bearing based on the working deviation. If the deviation of the operation does not exceed the first deviation threshold, the abnormal vibration mode is determined to be a normal, non-abnormal mode. If the working deviation exceeds the first deviation threshold, the analysis is performed based on the bearing coupling relationship matrix. The coupling strength is determined according to all or part of the elements characterizing the coupling influence of other bearings on the current bearing, and it is determined whether the preset coupling strength threshold is exceeded to determine the vibration abnormal mode. If it is not exceeded, it is determined to be its own abnormal resonance risk mode. If it exceeds the limit, it is determined to be a coupled vibration mode influenced by other machines.

6. The adaptive cooperative control system for multi-machine parallel magnetic levitation bearings according to claim 5, characterized in that, The adaptive decision-making module determines the operating condition classification based on the vibration anomaly mode, including: If the vibration abnormal mode is a self-abnormal resonance risk mode, then the operating condition is classified as a resonance risk condition. If the abnormal vibration mode is a coupled vibration mode caused by other machines, then the operating condition is classified as a coupled vibration condition. If the abnormal vibration mode is a non-abnormal mode, then the operating condition is classified as a stable operating condition.

7. The adaptive cooperative control system for multi-machine parallel magnetic levitation bearings according to claim 6, characterized in that, For the resonance risk condition, the adaptive decision-making module has the following graded bearing control strategy: call and execute the bearing control parameter adaptive adjustment strategy; if the working deviation exceeds the second deviation threshold, then assist in calling the bearing vibration active suppression strategy. Furthermore, for the coupled vibration condition, the corresponding graded bearing control strategy is as follows: call and execute the bearing vibration active suppression strategy; if the maximum coupling strength represented by the bearing coupling relationship matrix exceeds the preset strengthening and coordination threshold, then assist in calling the bearing cooperative operation optimization strategy. Furthermore, for the aforementioned stable operating condition, the corresponding graded bearing control strategy is to invoke and execute the aforementioned bearing collaborative operation optimization strategy.

8. The adaptive cooperative control system for multi-machine parallel magnetic levitation bearings according to claim 7, characterized in that, The collaborative control module monitors the rotor displacement response signal after the control is executed and calculates the vibration energy attenuation rate to verify the effectiveness of the graded bearing control strategy. Furthermore, based on the verification results, the operating condition classification rule base is iteratively optimized, and if the verification fails, the adjustment of the bearing control strategy is triggered and the system-level operation degradation suggestion instruction is generated.

9. A compressor, comprising: Magnetic levitation bearings are used to support the rotor and provide non-contact levitation. The stator assembly contains the electromagnetic coil and drive unit of the magnetic levitation bearing. A rotor assembly, which is supported by the magnetic levitation bearing and equipped with an impeller, is characterized in that the compressor adopts an adaptive cooperative control system of multi-machine parallel magnetic levitation bearings as described in any one of claims 1-8; The eddy current displacement sensor included in the displacement sensing unit of the working condition sensing module is installed on the stator side of the magnetic levitation bearing and arranged towards the rotor assembly, and is used to collect the displacement signal of the rotor. The rotational speed acquisition unit is communicatively connected to the drive unit to obtain the rotational speed signal of the rotor.

10. The compressor according to claim 9, characterized in that, The compressor is configured to establish a communication connection with at least one other similar compressor through the inter-machine communication network, operate in parallel, and achieve multi-machine collaborative vibration suppression and operation optimization based on the adaptive collaborative control system of the multi-machine parallel magnetic levitation bearing.

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