A brushless motor bearing health state online monitoring and early warning method

Abstract

This application provides an online monitoring and early warning method for the health status of brushless motor bearings, comprising the following steps: Step S1, real-time acquisition of electrical parameters and speed parameters of the brushless motor, wherein the electrical parameters include current and voltage; Step S2, calculation of the motor's input power based on the electrical parameters, and calculation of the motor's electromagnetic torque based on the current; Step S3, real-time separation of the load torque component and the bearing friction additional torque component from the electromagnetic torque using a decoupling algorithm; Step S4, calculation of the bearing loss power based on the bearing friction additional torque component and the real-time speed; Step S5, assessment of the bearing's health status based on the changing trend of the bearing loss power relative to a preset health benchmark value, and outputting early warning signals of different levels based on the assessment results, thereby realizing online, quantitative assessment and early warning of the bearing's health status, particularly suitable for fluid machinery where the load varies with the speed.

Description

Technical Field

[0001] This application relates to the field of motor condition monitoring and fault diagnosis technology, and more specifically, to an online monitoring and early warning method for the wear degree of brushless motor bearings based on electrical parameter decoupling. Background Technology

[0002] Brushless motors are widely used in fans, pumps, industrial drives, and electric vehicles due to their high efficiency, long lifespan, and high control precision. Practical applications show that the overall lifespan of a brushless motor is largely limited by the lifespan of its mechanical bearings. When bearings experience abnormal wear, lubrication failure, or cage damage, their frictional torque increases significantly. To overcome this increased frictional torque, the motor needs to consume more electrical energy to maintain the same output load. This extra power consumption is ultimately converted into heat, causing the bearing temperature to rise, further accelerating grease deterioration and wear, creating a vicious cycle until the bearing seizes, leading to equipment downtime.

[0003] Currently, traditional bearing fault diagnosis methods mainly rely on vibration or temperature sensors. While these methods offer high accuracy, they suffer from drawbacks such as high sensor cost, inconvenient installation, and the need for additional data acquisition channels, hindering large-scale application in cost-sensitive or space-constrained environments. Existing brushless motor control systems, although generally equipped with current, voltage, and speed detection functions, typically use these parameters only for overcurrent protection, speed closed-loop control, or basic power calculations, lacking in-depth analysis and decoupling capabilities for internal motor losses, particularly bearing friction losses. Especially under variable load conditions (such as fans and pumps), changes in input power include components caused by load variations as well as those caused by bearing wear. These two factors are coupled and difficult to effectively separate, resulting in high false alarm rates for monitoring methods based on a single power threshold, failing to meet the reliability requirements of practical applications. Summary of the Invention

[0004] The purpose of this application is to overcome the shortcomings of existing technologies and provide a method for online monitoring and early warning of the health status of brushless motor bearings. This method deeply mines the electrical and speed parameters collected by the motor controller itself, combines physical models and algorithms, and decouples the bearing loss power from the input power to achieve online, quantitative assessment and early warning of the bearing's health status. This method is particularly suitable for fluid machinery where the load varies with the speed.

[0005] This application provides an online monitoring and early warning method for the health status of a brushless motor bearing, comprising the following steps: Step S1, real-time acquisition of electrical parameters and speed parameters of the brushless motor, wherein the electrical parameters include current and voltage; Step S2, calculation of the motor's input power based on the electrical parameters, and calculation of the motor's electromagnetic torque based on the current; Step S3, real-time separation of the load torque component and the bearing friction additional torque component from the electromagnetic torque using a decoupling algorithm; Step S4, calculation of the bearing loss power based on the bearing friction additional torque component and the real-time speed; Step S5, assessment of the bearing's health status based on the changing trend of the bearing loss power relative to a preset health benchmark value, and outputting early warning signals of different levels based on the assessment results.

[0006] In some embodiments, the online monitoring and early warning method for the health status of brushless motor bearings further includes the step of establishing a benchmark loss model: when the motor is in a healthy state, by changing the motor speed and collecting data, a benchmark relationship curve is established between the total loss, including copper loss, iron loss and wind friction loss, and the current and speed, and the health benchmark value is determined based on the benchmark relationship curve.

[0007] In some embodiments, in step S3, the decoupling algorithm specifically includes: step S31, for a fan or pump type load, establishing a load model in which the load torque component is proportional to the square of the rotational speed; step S32, obtaining the proportional coefficient of the load model and the total moment of inertia of the motor and the load; step S33, calculating the load torque component and the inertial torque component at the current moment based on the real-time rotational speed; step S34, using the dynamic equation T_em=T_L+T_b+T_J, subtracting the load torque component and the inertial torque component from the electromagnetic torque to obtain the bearing frictional additional torque, where T_em is the electromagnetic torque, T_L is the load torque component, T_b is the bearing frictional additional torque, and T_J is the inertial torque component.

[0008] In some embodiments, the proportional coefficient of the load model is obtained by fitting historical data from the motor's factory calibration test or the initial stage of operation; the total moment of inertia is obtained by identifying motor design parameters or acceleration / deceleration tests.

[0009] In some embodiments, in step S5, the method for assessing the bearing health status based on the changing trend of bearing loss power is as follows: Step S51, calculate the real-time sliding average value of the bearing loss power; Step S52, compare the sliding average value with the initial health benchmark value to obtain the power change rate; Step S53, set multiple sequentially increasing warning thresholds, and when the power change rate exceeds different warning thresholds, output a warning signal of the corresponding level.

[0010] In some embodiments, the online monitoring and early warning method for the health status of brushless motor bearings further includes a speed fluctuation auxiliary diagnosis step: calculating the speed fluctuation amount within a sliding window based on the real-time speed; monitoring whether the speed fluctuation amount exceeds a preset fluctuation threshold, and determining whether its increasing trend is consistent with the increasing trend of the bearing power loss in time; if both are satisfied, then enhancing the confidence of the early warning signal.

[0011] In some embodiments, the current is the quadrature-axis current I_q, and the electromagnetic torque is calculated using the formula T_em=k_t×I_q, where k_t is the torque constant of the motor.

[0012] In some embodiments, the copper loss model in the reference loss model is represented as a function of the square of the current, and the iron loss and wind friction loss models are represented as polynomial functions of the rotational speed.

[0013] In some embodiments, the online monitoring and early warning method for the health status of brushless motor bearings further includes a temperature compensation step: collecting the operating temperature of the motor and using the operating temperature to correct the resistivity in the copper loss model in real time, so as to improve the accuracy of copper loss calculation.

[0014] In some embodiments, the method is executed by the controller of the brushless motor and data acquisition is performed directly using the current sensor, voltage sensor and speed detection module built into the controller.

[0015] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit the disclosure of this application. Attached Figure Description To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating an online monitoring and early warning method for the health status of a brushless motor bearing, as described in this application, is shown.

[0017] Figure 2 The flowchart of step S3 in the online monitoring and early warning method for the health status of a brushless motor bearing of this application is shown.

[0018] Figure 3 This diagram illustrates step S4, setting three threshold levels, in a brushless motor bearing health status online monitoring and early warning method according to this application.

[0019] Figure 4The flowchart of step S5 in the online monitoring and early warning method for the health status of a brushless motor bearing of this application is shown. Detailed Implementation The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain this application, and should not be construed as limiting this application.

[0020] To enable those skilled in the art to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0021] In the embodiments of this application, it should be noted that, in this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations.

[0022] Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] In the description of the embodiments of this application, the words "example" or "for example" are used to indicate exemplification, illustration, or description. Any embodiment or design described as "example" or "for example" in the embodiments of this application is not to be construed as being more preferred or having more advantages than another embodiment or design. The use of the words "example" or "for example" is intended to present relative concepts in a clear manner.

[0024] In addition, "multiple" in the embodiments of this application refers to two or more. Therefore, "multiple" can also be understood as "at least two" in the embodiments of this application. "At least one" can be understood as one or more, such as one, two or more. For example, including at least one means including one, two or more and is not limited to which ones are included. For example, including at least one of A, B and C, then it can be A, B, C, A and B, A and C, B and C, or A and B and C.

[0025] It should be noted that in the embodiments of this application, "and / or" describes the relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. In addition, the character " / ", unless otherwise specified, generally indicates that the associated objects before and after it are in an "or" relationship.

[0026] It should be noted that in the embodiments of this application, "connection" can be understood as electrical connection. The connection between two electrical components can be a direct or indirect connection between the two electrical components. For example, the connection between A and B can be a direct connection between A and B, or an indirect connection between A and B through one or more other electrical components.

[0027] This application provides a method for online monitoring and early warning of the health status of brushless motor bearings, such as... Figure 1 As shown, the online monitoring and early warning method for the health status of a brushless motor bearing includes the following steps: Step S1, real-time acquisition of the electrical parameters and speed parameters of the brushless motor, the electrical parameters including current and voltage; Step S2, calculation of the motor's input power based on the electrical parameters, and calculation of the motor's electromagnetic torque based on the current; Step S3, real-time separation of the load torque component and the bearing friction additional torque component from the electromagnetic torque using a decoupling algorithm; Step S4, calculation of the bearing loss power based on the bearing friction additional torque component and the real-time speed; Step S5, assessment of the bearing's health status based on the changing trend of the bearing loss power relative to a preset health benchmark value, and outputting early warning signals of different levels based on the assessment results.

[0028] Through the above steps, this application achieves online, accurate monitoring and graded early warning of the health status of the brushless motor bearings for water pumps without adding any hardware sensors, effectively ensuring the reliable operation of the equipment.

[0029] Step S1 involves data acquisition and preprocessing. The brushless motor controller acquires the three-phase current and bus voltage of the motor in real time, and calculates the instantaneous input power based on the acquired current and voltage. Simultaneously, the real-time speed of the motor is also acquired.

[0030] This step is the foundation and "sensory system" of the entire monitoring method. Its task is to acquire the raw physical quantities of the motor during operation with high fidelity and convert them into digital signals that can be used for subsequent calculations. Its accuracy directly determines the success or failure of the entire diagnostic system.

[0031] The brushless motor's controller (typically an MCU or DSP) acquires the motor's three-phase currents (i_a, i_b, i_c) and DC bus voltage (U_dc) in real time at a fixed high-frequency sampling period (e.g., Δt = 1ms to 10ms). Simultaneously, the motor's real-time speed n(t) (usually in revolutions per minute, rpm) is acquired synchronously via Hall effect sensors or encoder signals. Preprocessing includes: digital filtering of the current and voltage signals (e.g., moving average filtering or low-pass filtering) to eliminate sampling noise and spike interference introduced by switching devices; and using coordinate transformations (Clark and Park transformations) to convert the currents in the three-phase stationary coordinate system into quadrature-axis current I_q and direct-axis current I_d in the rotating coordinate system, where I_q is the current component that generates torque and is crucial for subsequent calculations. Finally, the instantaneous input power is calculated using the formula P_in(t) = U_dc(t) × I_dc(t) (where I_dc is the DC bus current, which can be obtained through power balancing or direct sampling).

[0032] For example, the motor controller acquires the three-phase currents i_a, i_b, i_c and the DC bus voltage U_dc in real time with a fixed sampling period Δt = 10ms. Through Clark and Park transformations, the three-phase currents are converted into quadrature-axis current I_q and direct-axis current I_d in a rotating coordinate system (for I_d = 0 control mode). Simultaneously, the real-time motor speed n(t) (in rpm) is acquired. The input power P_in can be calculated from the DC bus power, i.e., P_in(t) = U_dc(t) × I_dc(t), where I_dc is the DC bus current.

[0033] Step S2 involves establishing a baseline loss model. Based on the law of conservation of energy in motors, a baseline model is established for the total motor losses and operating parameters. The total losses include copper losses, iron losses, and windage losses. The copper loss model is represented as a function of the square of the current, while the iron loss and windage loss models are represented as polynomial functions of the rotational speed. The coefficients in the models are obtained through factory calibration tests of the motor under healthy conditions, forming a baseline curve.

[0034] This step is the process of establishing a "health baseline." It's equivalent to creating a "factory health checkup file" for the motor, recording the inherent relationship between the motor's internal, unavoidable losses (copper losses, iron losses, and windage losses) and operating parameters (current, speed) when the motor is brand new and in good condition. This baseline serves as a "benchmark" for subsequent judgments on whether bearing losses are abnormal.

[0035] This step is typically performed during factory testing or initial installation and commissioning of the motor. Under healthy conditions, the motor is controlled to traverse its operating speed range, running at multiple stable speed points (e.g., n_1, n_2, ..., n_m). At each speed point n_i, the stable input power P_base(n_i) and the corresponding quadrature-axis current I_(q_base)(n_i) are recorded. Using the health data collected in step S1, we can subtract the calculated copper loss k_1×I_(q_base)^2 from the total input power P_base to obtain the values ​​of "iron loss + windage loss" at different speeds. Then, using fitting algorithms such as the least squares method, the polynomial coefficients a and b are determined. At this point, we have established the baseline power curve P_base(n) of the motor under healthy conditions (or equivalently, the functional relationship of P_(fe+fw)(n)). This curve will be stored in the controller as a "reference" for online decoupling in step S3.

[0036] The online monitoring and early warning method for the health status of brushless motor bearings also includes the step of establishing a benchmark loss model: when the motor is in a healthy state, by changing the motor speed and collecting data, a benchmark relationship curve between the total loss, including copper loss, iron loss and wind friction loss, and current and speed is established, and the health benchmark value is determined based on the benchmark relationship curve.

[0037] The specific process of establishing the reference loss model in step S2 is as follows: Under the healthy condition of the motor at the factory or during initial installation, the motor is controlled to run stably at different speed points, and the input power and current corresponding to each speed point are recorded. Based on the recorded input power, current, and speed, the current coefficient in the copper loss model and the speed polynomial coefficient in the iron loss and wind friction loss model are determined by a fitting algorithm, thereby constructing the reference power curve P_base(n) and the reference current curve I_base(n) under the healthy condition.

[0038] For example, initial calibration is performed when the motor is in a healthy state after factory testing or installation. The motor is controlled to run at multiple stable speed points, such as 1000rpm, 2000rpm, and 3000rpm. At each speed point n_i, the stable input power P_base(n_i) and quadrature-axis current I_(q_base)(n_i) are recorded. A baseline curve is established by fitting using the least squares method. The copper loss model is P_cu=k_1×I_q^2, where k_1 is a constant related to the winding resistance, determined by the motor parameters; here, k_1=0.5 (example value). The sum model of iron loss and windage loss is P_fe+P_fw=a×n+b×n^3. By subtracting the copper loss from the power P_base(n_i)-k_1×I_(q_base)(n_i)^2, and fitting the speed n_i, the coefficients are obtained as a=0.002 and b=1e-8 (example values). Therefore, under healthy conditions, for any rotational speed n, the reference input power P_base(n) and reference current I_(q_base)(n) can be retrieved. The healthy reference value for bearing loss power P_bearing0 is 0 in this stage.

[0039] The online monitoring and early warning method for the health status of brushless motor bearings also includes a temperature compensation step: collecting the operating temperature of the motor and using the operating temperature to correct the resistivity in the copper loss model in real time, so as to improve the accuracy of copper loss calculation.

[0040] Step S3 involves decoupling the bearing friction torque. Based on the motor dynamics equations and load characteristic model, the additional bearing friction torque is decoupled from the electromagnetic torque in real time. For square torque loads such as fans and pumps, a load model is established where the load torque is proportional to the square of the speed. Using the real-time calculated electromagnetic torque, the load torque calculated from the load model, and the inertial torque caused by speed changes, the additional bearing friction torque is calculated in real time through the dynamic balance equations.

[0041] This step is the core and "brain" of the entire method. Its task is to isolate the effects of load and inertia changes from the current measurements in real time and dynamically, and accurately calculate the "culprit" caused by bearing wear—the additional torque T_b due to bearing friction.

[0042] In step S3, as Figure 2As shown, the decoupling algorithm specifically includes: Step S31, for fan or pump type loads, establishing a load model in which the load torque component is proportional to the square of the rotational speed; Step S32, obtaining the proportional coefficient of the load model and the total moment of inertia of the motor and load; Step S33, calculating the load torque component and inertial torque component at the current moment based on the real-time rotational speed; Step S34, using the dynamic equation T_em=T_L+T_b+T_J, subtracting the load torque component and the inertial torque component from the electromagnetic torque to obtain the bearing frictional additional torque, where T_em is the electromagnetic torque, T_L is the load torque component, T_b is the bearing frictional additional torque, and T_J is the inertial torque component.

[0043] The decoupling formula for the bearing frictional additional torque in step S3 is: T_b(k) = T_em(k) - C × n(k)^2 - J(n(k) - n(k-1)) / Δt, where T_b(k) is the bearing frictional additional torque at the current moment, T_em(k) is the electromagnetic torque at the current moment, C is the load torque coefficient obtained through factory calibration, n(k) is the rotational speed at the current moment, J is the moment of inertia of the motor and the load, and Δt is the sampling period. The electromagnetic torque T_em(k) is calculated from the quadrature-axis current I_q(k) of the motor and the torque constant k_t, i.e., T_em(k) = k_t × I_q(k).

[0044] For example, during online runtime, for each sampling time k, the following decoupling calculation is performed: To calculate the electromagnetic torque, based on the quadrature axis current I_q(k) and the torque constant k_t (determined by the back electromotive force coefficient of the motor, in this example k_t=0.1Nm / A), the electromagnetic torque is: T_em(k)=k_t×I_q(k).

[0045] Calculate the load torque. For a centrifugal water pump, the load torque is proportional to the square of the rotational speed. Using a pre-calibrated load torque coefficient C (in this example, C = 5e-6 Nm / rpm^2), calculate the load torque based on the current rotational speed n(k): T_L(k) = C × n(k)^2.

[0046] To calculate the inertial torque, use the speed difference between the two pulses and the moment of inertia J (the sum of the moments of inertia of the motor rotor and the impeller; in this example, J = 0.01 kg × m^2, and unit conversion is required, converting n from rpm to rad / s). Calculate the inertial torque term: T_J(k) = J × (n(k) - n(k-1)) / Δt.

[0047] To decouple the additional torque due to bearing friction, the additional torque due to bearing friction can be solved according to the dynamic balance equation T_em=T_L+T_b+T_J: T_b(k)=T_em(k)-T_L(k)-T_J(k).

[0048] The decoupling algorithm here clearly demonstrates how each input variable ultimately calculates T_b through the physical model.

[0049] As can be seen, to calculate T_L and T_J, we heavily rely on the real-time rotational speed n(k) provided in step S1. Furthermore, to establish an accurate T_L model (i.e., to determine the coefficients C), we rely on the initialization process or online learning process in step S2. Once all terms are available, the final decoupling formula is: T_b(k) = T_em(k) - C × n(k)^2 - J × (ω(k) - ω(k-1)) / Δt.

[0050] Thus, we have successfully separated the bearing frictional additional torque T_b(k) from the complex electromagnetic torque.

[0051] The current is the quadrature axis current I_q, and the electromagnetic torque is calculated using the formula T_em=k_t×I_q, where k_t is the torque constant of the motor.

[0052] Step S4 involves calculating the bearing loss power and assessing its health status. The bearing frictional torque, decoupled in real-time from step S3, is multiplied by the real-time rotational speed to obtain the real-time bearing loss power. The sliding average of this bearing loss power is calculated and compared with a preset health benchmark value to obtain the power change rate. Multiple incremental warning thresholds are set; when the power change rate exceeds different threshold levels, a corresponding bearing health status warning signal is output.

[0053] This step converts the "cause of the disease" (additional torque) obtained in step S3 into a more physically meaningful and intuitive "symptom index" (power loss), and performs quantitative assessment and graded early warning accordingly.

[0054] The method for assessing the bearing health status in step S4 includes: calculating the real-time sliding average value P_bearing of the bearing loss power. This average value is compared with the initial health baseline value P_bearing0, and the rate of change ΔP_b = (P_bearing - P_bearing0) / P_bearing0 × 100%. A mild wear warning threshold (e.g., 15%), a moderate wear alarm threshold (e.g., 30%), and a severe fault alarm threshold (e.g., 50%) are set. When ΔP_b continuously exceeds a certain threshold for a preset number of times, a warning signal of the corresponding level is output.

[0055] For example, the decoupled bearing frictional additional torque can be converted into bearing loss power: P_bearing(k)=T_b(k)×ω(k), where ω(k)=(2πn(k)) / 60 is the mechanical angular velocity.

[0056] To filter out instantaneous fluctuations, a moving average P_bearing is calculated, encompassing the most recent N=100 sampling points (i.e., 1 second). Since P_bearing0 is 0 in a healthy state, we compare its absolute value here. In practical applications, an initial state average can be set as the benchmark P_bearing0 (which may not be 0, representing initial friction). For simplicity, we directly use P_bearing as the evaluation criterion here.

[0057] Set three threshold levels, such as Figure 3 As shown, it includes: The mild wear warning threshold is set when P_bearing > 5W (corresponding to approximately 15% of the rated power increment) and continues for more than 10 sliding windows. In this case, the controller sends a "bearing wear warning, please arrange inspection / lubrication" message to the host computer via the communication bus.

[0058] The moderate wear alarm threshold is set when P_bearing > 10W (corresponding to approximately 30% of the rated power increment) and continues for more than 5 sliding windows, sending the message "Moderate bearing wear, shutdown maintenance needs to be arranged soon".

[0059] The critical fault alarm threshold is set so that when P_bearing > 17W (corresponding to approximately 50% of the rated power increment), or when P_bearing experiences severe and irregular fluctuations, an emergency shutdown signal of "critical bearing fault, please stop the machine immediately" will be sent immediately.

[0060] The output of this step (P_bearing) is the input of step S5. Meanwhile, the health baseline value P_bearing0 in this step is directly derived from step S2.

[0061] Step S5 is a speed fluctuation-assisted diagnosis, which extracts the real-time speed fluctuation. When the speed fluctuation exceeds a preset range and is consistent with the increasing trend of bearing loss power obtained in step S4, the confidence of the fault diagnosis conclusion is enhanced.

[0062] This step is an independent auxiliary diagnostic mechanism that verifies the core criteria. Its role is to improve the confidence of the overall diagnostic conclusion and effectively distinguish between mechanical faults and electrical disturbances.

[0063] When bearings experience severe wear, such as ball surface peeling or cage deformation, in addition to increased frictional torque, it also causes minute, periodic speed disturbances in the rotor during rotation. Even if closed-loop speed control can maintain a stable average speed, this instantaneous speed fluctuation (n_ripple) will still exist. We can calculate the root mean square error of the instantaneous speed relative to the average speed within a sliding window as a quantitative indicator of n_ripple.

[0064] The online monitoring and early warning method for the health status of brushless motor bearings also includes a speed fluctuation auxiliary diagnosis step: calculating the speed fluctuation amount within a sliding window based on the real-time speed; monitoring whether the speed fluctuation amount exceeds a preset fluctuation threshold, and determining whether its increasing trend is consistent with the increasing trend of the bearing power loss in time; if both are satisfied, the confidence of the early warning signal is enhanced.

[0065] In step S5, as Figure 4 As shown, the method for assessing the bearing health status based on the changing trend of bearing loss power is as follows: Step S51, calculate the real-time sliding average value of the bearing loss power; Step S52, compare the sliding average value with the initial health benchmark value to obtain the power change rate; Step S53, set multiple sequentially increasing warning thresholds, and output a warning signal of the corresponding level when the power change rate exceeds different warning thresholds.

[0066] The specific steps for auxiliary diagnosis of speed fluctuation in step S5 are as follows: Calculate the root mean square error between the instantaneous speed and the average speed within a sliding window, which is taken as the speed fluctuation amount n_ripple. When n_ripple continuously exceeds a certain multiple (e.g., 2 times) of its healthy value, and is synchronous with the continuous increase of P_bearing in time, it is determined to be a mechanical failure of the bearing, and a high-confidence fault confirmation signal is output.

[0067] For example, while calculating P_bearing, the rotational speed fluctuation n_ripple within the same sliding window is also calculated, which is the standard deviation of all instantaneous rotational speeds within the window. When n_ripple exceeds twice its baseline value (e.g., 10 rpm) under healthy conditions, reaching more than 20 rpm, and this phenomenon is observed to be highly consistent with the trend of P_bearing continuously increasing in time in step S4, the system determines it as "double confirmation of bearing mechanical failure," further improving the reliability of the early warning and eliminating false alarms caused by electrical reasons such as power grid voltage fluctuations.

[0068] When step S4 detects a continuous increase in P_bearing, the system simultaneously checks the change in n_ripple. If n_ripple also increases significantly (e.g., more than twice its healthy baseline value), it can "double-confirm" that the current anomaly indeed originates from a mechanical failure of the bearing, rather than electrical causes such as mains voltage fluctuations or load changes. If P_bearing increases while n_ripple remains stable, it may indicate that the fault type is simply increased friction due to lubrication failure, rather than structural damage.

[0069] The proportional coefficient of the load model is obtained by fitting historical data from the motor's factory calibration test or the initial stage of operation; the total moment of inertia is obtained by identifying the motor design parameters or acceleration / deceleration tests.

[0070] The method is executed by the controller of the brushless motor, and data is acquired directly using the controller's built-in current sensor, voltage sensor and speed detection module.

[0071] Compared with existing technologies, this invention has the following advantages: It requires no additional sensors, directly utilizing the existing voltage and current sampling of the motor controller for power calculation and data processing; it eliminates the need for additional sensors such as vibration and temperature sensors, resulting in low cost and easy integration into existing systems; it provides clear physical meaning and quantitative assessment, transforming the abstract bearing wear degree into quantifiable bearing loss power or additional torque through a decoupling algorithm based on energy conservation and dynamic principles, achieving a quantitative assessment of the bearing's health status rather than a simple qualitative judgment; it exhibits strong adaptability to variable loads, establishing an accurate load torque model for typical variable load equipment such as fans and pumps, and introducing inertia term compensation, successfully separating the impact of load changes and bearing wear on input power, fundamentally solving the problem of high false alarm rates under varying operating conditions; and it boasts high diagnostic reliability, combining the core criterion of bearing loss power with the auxiliary criterion of speed fluctuation to form a multi-dimensional, mutually corroborating diagnostic logic, effectively distinguishing between load mutations, electromagnetic interference, and genuine mechanical wear, significantly improving the accuracy and robustness of the diagnosis.

[0072] The above are merely preferred embodiments of this application and are not intended to limit this application in any way. Although this application has disclosed preferred embodiments as above, it is not intended to limit this application. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the technical solution of this application. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of this application without departing from the scope of the technical solution of this application shall still fall within the scope of the technical solution of this application.

Claims

1. A method for online monitoring and early warning of the health status of brushless motor bearings, characterized in that, Includes the following steps: Step S1: Real-time acquisition of electrical parameters and speed parameters of the brushless motor, including current and voltage; Step S2: Calculate the input power of the motor based on the electrical parameters, and calculate the electromagnetic torque of the motor based on the current; Step S3: Using a decoupling algorithm, the load torque component and the bearing friction additional torque component are separated from the electromagnetic torque in real time. Step S4: Calculate the bearing loss power based on the additional torque component of bearing friction and the real-time rotational speed; Step S5: Assess the health status of the bearing based on the trend of the bearing loss power relative to the preset health benchmark value, and output different levels of early warning signals based on the assessment results.

2. The method for online monitoring and early warning of the health status of brushless motor bearings according to claim 1, characterized in that: It also includes the step of establishing a benchmark loss model: when the motor is in a healthy state, by changing the motor speed and collecting data, a benchmark relationship curve is established between the total loss, including copper loss, iron loss and wind friction loss, and the current and speed. The healthy benchmark value is determined based on the benchmark relationship curve.

3. The method for online monitoring and early warning of the health status of brushless motor bearings according to claim 1, characterized in that: In step S3, the decoupling algorithm specifically includes: Step S31: For fan or pump type loads, establish a load model in which the load torque component is proportional to the square of the rotational speed. Step S32: Obtain the proportional coefficient of the load model and the total moment of inertia of the motor and the load; Step S33: Calculate the load torque component and inertial torque component at the current moment based on the real-time rotational speed; Step S34: Using the dynamic equation T_em=T_L+T_b+T_J, subtract the load torque component and the inertial torque component from the electromagnetic torque to obtain the bearing frictional additional torque, where T_em is the electromagnetic torque, T_L is the load torque component, T_b is the bearing frictional additional torque, and T_J is the inertial torque component.

4. The method for online monitoring and early warning of the health status of brushless motor bearings according to claim 3, characterized in that: The proportional coefficient of the load model is obtained by fitting historical data from the motor's factory calibration test or the initial stage of operation; the total moment of inertia is obtained by identifying the motor design parameters or acceleration / deceleration tests.

5. The method for online monitoring and early warning of the health status of brushless motor bearings according to claim 1, characterized in that: In step S5, the method for assessing the bearing health status based on the changing trend of bearing loss power is as follows: Step S51: Calculate the real-time sliding average value of the bearing loss power; Step S52: Compare the sliding average value with the initial health baseline value to obtain the power change rate; Step S53: Set multiple sequentially increasing warning thresholds. When the power change rate exceeds different warning thresholds, output a warning signal of the corresponding level.

6. The method for online monitoring and early warning of the health status of brushless motor bearings according to claim 1, characterized in that: It also includes a speed fluctuation auxiliary diagnosis step: calculating the speed fluctuation amount within a sliding window based on the real-time speed; monitoring whether the speed fluctuation amount exceeds a preset fluctuation threshold, and determining whether its increasing trend is consistent with the increasing trend of the bearing loss power in time; if both are satisfied, the confidence of the warning signal is enhanced.

7. The method for online monitoring and early warning of the health status of brushless motor bearings according to claim 1, characterized in that, The current is the quadrature axis current I_q, and the electromagnetic torque is calculated using the formula T_em=k_t×I_q, where k_t is the torque constant of the motor.

8. The method for online monitoring and early warning of the health status of brushless motor bearings according to claim 2, characterized in that, The copper loss model in the reference loss model is expressed as a function of the square of the current, while the iron loss and wind friction loss models are expressed as polynomial functions of the rotational speed.

9. The method for online monitoring and early warning of the health status of brushless motor bearings according to claim 8, characterized in that, It also includes a temperature compensation step: collecting the operating temperature of the motor and using the operating temperature to correct the resistivity in the copper loss model in real time, so as to improve the accuracy of copper loss calculation.

10. The method for online monitoring and early warning of the health status of brushless motor bearings according to any one of claims 1 to 9, characterized in that, The method is executed by the controller of the brushless motor, and data is acquired directly using the controller's built-in current sensor, voltage sensor and speed detection module.

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