A method and system for identifying inter-turn short circuit faults in brushless DC motors

Abstract

The present invention discloses a method and system for identifying inter-turn short-circuit faults in brushless DC motors, belonging to the field of brushless DC motors. The method mainly comprises: S1, obtaining all peak points of phase current using a peak detection algorithm; S2, determining the peak points of any phase current at two commutation moments based on the commutation law of the phase current, and estimating the commutation peak difference of any phase current; S3, comparing the commutation peak differences between the phase currents in the horizontal and vertical directions, and determining whether an inter-turn short-circuit fault has occurred in the brushless DC motor based on the comparison results. The present invention can accurately detect inter-turn short circuits and locate the faulty phase in a brushless DC motor.

Description

Technical Field

[0001] The present invention relates to the field of brushless DC motors, and in particular to a method and system for identifying an inter-turn short circuit fault in a brushless DC motor. Background Art

[0002] Brushless DC motors eliminate the issues associated with brushes and offer advantages such as compact structure, high efficiency, low cost, easy maintenance, a wide speed adjustment range, and a long service life. They are widely used in fields such as industrial automation and aerospace. However, over long periods of operation, harsh operating environments and frequent electromagnetic, magnetic, thermal, and mechanical shocks can easily lead to various motor failures. Among these, interturn short circuits in the stator winding are a common fault, accounting for approximately 30% of all motor failures. If interturn short circuits are not promptly addressed in their early stages, the excessive short-circuit current can cause localized overheating, damaging the winding insulation. In severe cases, this can lead to motor burnout and potentially safety accidents. Therefore, accurately identifying interturn short circuits in brushless DC motors and arranging targeted maintenance can help ensure safe and stable motor operation.

[0003] There are three common identification methods for motor turn-to-turn short circuit faults:

[0004] 1) Analytical model-based method: Construct an equivalent model of the motor under inter-turn short-circuit fault, evaluate the key parameter indicators during motor operation, and then realize motor fault prediction and diagnosis.

[0005] 2) Signal analysis-based method: By analyzing the multi-dimensional physical signals such as "electrical-magnetic-thermal-mechanical" under the inter-turn short circuit of the motor, the correlation between the characteristic signals and the fault characteristics is established, thereby realizing the prediction and diagnosis of motor faults.

[0006] 3) Data-driven approach: Utilize a large amount of motor operation data and use big data methods such as machine learning and neural networks to build and train the optimal decision-making model to achieve motor fault prediction and diagnosis.

[0007] The above three types of motor turn-to-turn short circuit fault identification methods each have their own advantages and disadvantages:

[0008] 1) Analytical model-based methods are highly dependent on the accuracy of the motor model. Accurately capturing the motor's key parameters allows for precise modeling and fault diagnosis. However, motor parameters can exhibit nonlinear variations under different operating conditions. Furthermore, factors such as motor load, uneven rotor permanent magnet material, and operating environment all pose challenges to the accurate construction of analytical models.

[0009] 2) Signal analysis-based methods use signal analysis algorithms such as Fourier analysis, wavelet transform, and empirical mode decomposition to extract time-frequency domain fault characteristics of physical signals such as voltage, current, and vibration. These characteristics are then used to accurately identify the fault state. However, the key to this type of method is how to select highly correlated and sensitive feature information based on multidimensional physical signals, while also considering the difficulty of obtaining feature information.

[0010] 3) Data-driven approaches build artificial intelligence (AI) motor diagnostic models by training and learning from differentiated motor operating data, thereby completing motor fault diagnosis. This approach has strong adaptability and nonlinear learning capabilities and does not rely on precise motor modeling and feature extraction. However, it is highly dependent on the quality of the training data, and the variability of operating data from different motors can affect the method's versatility.

[0011] Currently, these three types of fault identification methods are widely used to diagnose interturn short-circuit faults in permanent magnet synchronous motors or asynchronous motors. However, the mechanical structure and drive method of brushless DC motors are different from those of permanent magnet synchronous motors. Conventional fault identification methods are difficult to directly apply to brushless DC motors, and research on interturn short-circuit fault identification methods for brushless DC motors is rarely reported. Therefore, a turn-to-turn short-circuit fault identification method for brushless DC motors is urgently needed. Summary of the Invention

[0012] The purpose of the present invention is to overcome the technical problems existing in the prior art and provide a method and system for identifying inter-turn short-circuit faults in a brushless DC motor. Based on the mapping relationship between the driving current peak value and the inter-turn short-circuit fault, a peak detection algorithm is used to accurately extract the phase current peak points during the commutation of the brushless DC motor. The peak differences between the phase currents are then compared horizontally and vertically to complete the inter-turn short-circuit detection and fault phase location of the brushless DC motor.

[0013] The object of the present invention is achieved through the following technical solutions:

[0014] In a first aspect, a method for identifying a turn-to-turn short circuit fault in a brushless DC motor is provided, comprising the following steps:

[0015] S1. Obtain all peak points of phase current using peak detection algorithm;

[0016] S2. According to the commutation law of the phase current, determine the peak points of the two commutation moments of any phase current, and estimate the commutation peak difference of any phase current;

[0017] S3. Compare the commutation peak differences between the phase currents in the horizontal and vertical directions, and determine whether a turn-to-turn short circuit fault occurs in the brushless DC motor based on the comparison results.

[0018] In some embodiments, step S1 specifically includes:

[0019] S11. Obtaining a period of a motor drive phase current based on the speed and pole pair number of the brushless DC motor;

[0020] S12, using a current sensor to obtain a discrete sampling sequence of phase current of any phase of the brushless DC motor;

[0021] S13. Using the discrete sampling values ​​of the phase current of any phase, obtain a peak detection matrix of the corresponding phase;

[0022] S14, summing the peak detection matrix row by row to obtain a summed matrix;

[0023] S15, obtaining the position of the minimum element value of the summed matrix to obtain a new peak detection matrix;

[0024] S16, respectively calculating the standard deviation of each column element of the new peak detection matrix;

[0025] S17. Find the positions of the phase current peak points and their current amplitudes corresponding to all standard deviation values, and establish a peak point matrix of the corresponding phase currents.

[0026] In some embodiments, step S12 specifically includes:

[0027] The sampling period is set to T and the sampling frequency is f s , the discrete sampling sequence of phase current of any phase Wherein, T=k·T0, k is a positive integer greater than 2, T0 is the period of the motor drive phase current, N=T×f s .

[0028] In some embodiments, step S13 specifically includes:

[0029] Using the discrete sampling sequence x of the phase current of phase i i , obtain the peak detection matrix M i :

[0030]

[0031] The matrix elements are calculated as follows:

[0032] Set the maximum sliding window length L = [N / 2] - 1, where [N / 2] represents the smallest integer not greater than N / 2;

[0033] For each sliding window w k (k=1,2,…,L), calculate the elements in sequence value:

[0034] When j=k+1,k+2,…,Nk,

[0035]

[0036] When j=1,2,…,k or j=N-k+1,…,N,

[0037]

[0038] In the above formula, the coefficient r is a uniformly distributed random number in [0,1], and the coefficient α=1.

[0039] Preferably, the standard deviation is calculated as follows:

[0040]

[0041] Among them, λ i Indicates the position of the minimum element value.

[0042] Preferably, the peak point matrix of the corresponding phase current is as follows:

[0043]

[0044] In the above formula, is the peak value of phase i current in the discrete sampling sequence x i The position in is the corresponding peak current amplitude, is the number of peak current points of phase i.

[0045] In some embodiments, step S2 specifically includes:

[0046] According to the peak point matrix of any phase current, find the current amplitude of the peak point position at two commutation moments that meet certain conditions, and calculate the difference between the peak point current amplitudes at the two commutation moments.

[0047] Preferably, the peak point positions of the two switching moments are At the same time:

[0048] has the maximum element value;

[0049] The difference between the peak current amplitudes at the two commutation moments:

[0050]

[0051] Among them, h1,

[0052] Preferably, the step S3 specifically includes:

[0053] The difference in peak current amplitude at the moment of commutation of the Fth phase current (F∈{a,b,c}) If the following two relations are met, it is considered that a turn-to-turn short circuit fault has occurred in phase F:

[0054] ③ (i=a,b,candi≠F);

[0055] ④ (i=a,b,candi≠F);

[0056] In the above formula, the coefficient Lim ranges from 0.1 to 0.5.

[0057] In a second aspect, a brushless DC motor turn-to-turn short circuit fault identification system is provided, comprising:

[0058] A peak point calculation module is used to obtain all peak points of phase current using a peak detection algorithm;

[0059] The commutation peak difference calculation module is used to determine the peak points of the two commutation moments of any phase current according to the commutation law of the phase current, and estimate the commutation peak difference of any phase current;

[0060] The inter-turn short circuit fault judgment module is used to compare the commutation peak difference between the phase currents in the horizontal and vertical directions, and judge whether an inter-turn short circuit fault occurs in the brushless DC motor based on the comparison results.

[0061] It should be further explained that the technical features corresponding to the above options can be combined or replaced with each other to form a new technical solution if there is no conflict.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] (1) The present invention uses a peak detection algorithm to extract the phase current peak point of the brushless DC motor at the commutation moment, and then compares the peak difference between the phase currents horizontally and vertically, which can realize the inter-turn short circuit detection and fault phase location of the brushless DC motor.

[0064] (2) The present invention only relies on the phase current sensor of the brushless DC motor, and does not require the addition of other sensors or measurement points. It is simple to operate, highly feasible, and does not affect the normal operation of the brushless DC motor.

[0065] (3) The present invention inverts the health status of the brushless DC motor by detecting the change in the peak value of the phase current at the commutation moment. The characteristics of the current peak at the commutation moment are obvious and the fault differentiation is high, which is conducive to improving the accuracy of identifying the inter-turn short-circuit fault of the brushless DC motor.

[0066] (4) The present invention can change the fault recognition threshold and sensitivity by adjusting preset parameters, and can be applied to various types of brushless DC motors, with good versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A simplified flow chart of a method for identifying a turn-to-turn short-circuit fault in a brushless DC motor according to an embodiment of the present invention;

[0068] Figure 2 This is a specific fault identification process shown in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The technical solutions of the present invention are described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings herein can be arranged and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0070] It should be noted that the defects existing in the solutions in the above-mentioned prior art are the results obtained by the inventor after practice and careful research. Therefore, the discovery process of the above-mentioned problems and the solutions proposed in the embodiments of this application below for the above-mentioned problems should be the contributions made by the inventor to this application in the process of invention and creation, and should not be understood as technical contents known to technical personnel in this field.

[0071] In response to the technical problems pointed out in the background technology, the embodiments provided by the present invention are as follows:

[0072] Reference Figure 1-Figure 2 A method for identifying inter-turn short-circuit faults in brushless DC motors uses a peak detection algorithm to obtain all peak points of phase current. Based on the commutation pattern of the phase current, the peak points at two commutation moments of any phase current are determined. The difference between the two commutation moments of any phase current is then estimated. The peak difference is then compared horizontally and vertically, thereby enabling inter-turn short-circuit detection and fault phase location in the brushless DC motor. The specific steps of the method are as follows:

[0073] S1. Obtain the motor drive phase current period T0 based on the brushless DC motor's speed n and pole pair number P:

[0074] T0=60 / Pn

[0075] Use the current sensor to obtain the discrete sampling sequence of the phase current of the i-th phase (i = a, b, c) of the brushless DC motor The sampling period is set to T (usually T = k·T0, the coefficient k is a positive integer greater than 2), and the sampling frequency is f s , so N = T × f s .

[0076] Using the discrete sampling value x of the phase current of phase ii , obtain the peak detection matrix M i .

[0077]

[0078] Specifically, the calculation method of the above matrix elements is as follows:

[0079] ① Set the maximum sliding window length L = [N / 2] - 1, where [N / 2] represents the smallest integer not greater than N / 2;

[0080] ②For each sliding window w k (k=1,2,…,L), calculate the elements in sequence value:

[0081] When j=k+1,k+2,…,Nk,

[0082]

[0083] When j=1,2,…,k or j=N-k+1,…,N,

[0084]

[0085] In the above formula, the coefficient r is a uniformly distributed random number in [0,1], and the coefficient α=1.

[0086] For the peak detection matrix M i Sum by row to get the matrix

[0087]

[0088] Get the matrix γ i The position of the minimum element value λ i , and obtain a new peak detection matrix based on this

[0089]

[0090] Calculate the new peak detection matrix separately The standard deviation of each column element

[0091]

[0092] Find all standard deviation values (j=1,2,…,N) corresponds to the phase current peak point position and current amplitude, and establishes the peak point matrix PEAK of phase i current i .

[0093]

[0094] In the above formula, is the peak value of phase i current in the discrete sampling sequence x i The position in is the corresponding peak current amplitude, is the number of peak current points of phase i.

[0095] S2. Based on the peak point matrix PEAK of phase i current i , find the peak position of the two commutation moments that simultaneously meet the following conditions and its current amplitude Among them h1,

[0096] ① The peak position difference between the two commutation moments satisfies:

[0097] ② Among all combinations that meet condition ①, Has the maximum element value.

[0098] S9. Calculate the difference in peak current amplitude at the commutation moment of phase i (i=a,b,c) current

[0099]

[0100] S3. If there is a difference in the peak current amplitude at the commutation moment of the Fth phase current (F∈{a,b,c}) If the following two relations are met, it is considered that a turn-to-turn short circuit fault has occurred in phase F.

[0101] ⑤ (i=a,b,candi≠F);

[0102] ⑥ (i=a,b,candi≠F).

[0103] In the above formula, the coefficient Lim generally ranges from 0.1 to 0.5. The specific value is set according to the motor parameters. By adjusting the preset parameters, the fault recognition threshold and sensitivity can be changed. It can be applied to various types of brushless DC motors and has good versatility.

[0104] In summary, all the peak points of the phase current are extracted through the peak detection algorithm, and then the phase current peak value at the commutation moment is accurately obtained in combination with the commutation law of the brushless DC motor. The commutation peak difference between the phase currents is then compared horizontally and vertically. The health status of the brushless DC motor is inverted by detecting the change in the peak value of the phase current at the commutation moment. The characteristics of the current peak at the commutation moment are obvious and the fault differentiation is high, which is conducive to improving the accuracy of identifying the inter-turn short-circuit fault of the brushless DC motor.

[0105] In another exemplary embodiment, based on the same inventive concept as the above method, a brushless DC motor turn-to-turn short circuit fault identification system is provided, comprising:

[0106] A peak point calculation module is used to obtain all peak points of phase current using a peak detection algorithm;

[0107] The commutation peak difference calculation module is used to determine the peak points of the two commutation moments of any phase current according to the commutation law of the phase current, and estimate the commutation peak difference of any phase current;

[0108] The inter-turn short circuit fault judgment module is used to compare the commutation peak difference between the phase currents in the horizontal and vertical directions, and judge whether an inter-turn short circuit fault occurs in the brushless DC motor based on the comparison results.

[0109] It should be noted that each module in the system implements functions corresponding to each step in the method, which will not be described in detail here.

[0110] The above specific implementation methods are detailed descriptions of the present invention. It cannot be considered that the specific implementation methods of the present invention are limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, they can make several simple deductions and substitutions without departing from the concept of the present invention, which should be regarded as falling within the scope of protection of the present invention.

Claims

1. A method for identifying a turn-to-turn short-circuit fault in a brushless DC motor, characterized in that: The following steps are involved: S1. Obtain all peak points of phase current using peak detection algorithm; S2. According to the commutation law of the phase current, determine the peak points of the two commutation moments of any phase current, and estimate the commutation peak difference of any phase current; S3. Compare the commutation peak differences between the phase currents in the horizontal and vertical directions, and determine whether a turn-to-turn short circuit fault occurs in the brushless DC motor based on the comparison results.

2. The method for identifying a turn-to-turn short circuit fault of a brushless DC motor according to claim 1, wherein: The step S1 specifically includes: S11. Obtaining a period of a motor drive phase current based on the speed and pole pair number of the brushless DC motor; S12, using a current sensor to obtain a discrete sampling sequence of phase current of any phase of the brushless DC motor; S13. Using the discrete sampling values ​​of the phase current of any phase, obtain a peak detection matrix of the corresponding phase; S14, summing the peak detection matrix row by row to obtain a summed matrix; S15, obtaining the position of the minimum element value of the summed matrix to obtain a new peak detection matrix; S16, respectively calculating the standard deviation of each column element of the new peak detection matrix; S17. Find the positions of the phase current peak points and their current amplitudes corresponding to all standard deviation values, and establish a peak point matrix of the corresponding phase currents.

3. The method for identifying a turn-to-turn short circuit fault of a brushless DC motor according to claim 2, wherein: The step S12 specifically includes: The sampling period is set to T and the sampling frequency is f s , the discrete sampling sequence of phase current of any phase Wherein, T=k·T0, k is a positive integer greater than 2, T0 represents the period of the motor drive phase current, N=T×f s .

4. The method for identifying a turn-to-turn short circuit fault of a brushless DC motor according to claim 3, wherein: The step S13 specifically includes: Using the discrete sampling sequence x of the phase current of phase i i , obtain the peak detection matrix M i : The matrix elements are calculated as follows: Set the maximum sliding window length L = [N / 2] - 1, where [N / 2] represents the smallest integer not greater than N / 2; For each sliding window w k (k=1,2,…,L), calculate the elements in sequence value: When j=k+1,k+2,…,Nk, When j=1,2,…,k or j=N-k+1,…,N, In the above formula, the coefficient r is a uniformly distributed random number in [0,1], and the coefficient α=1.

5. The method for identifying a turn-to-turn short circuit fault of a brushless DC motor according to claim 4, wherein: The standard deviation is calculated as follows: Among them, λ i Indicates the position of the minimum element value.

6. The method for identifying a turn-to-turn short-circuit fault in a brushless DC motor according to claim 5, wherein: The peak point matrix of the corresponding phase current is as follows: In the above formula, is the peak value of phase i current in the discrete sampling sequence x i The position in is the corresponding peak current amplitude, is the number of peak current points of phase i.

7. The method for identifying a turn-to-turn short-circuit fault in a brushless DC motor according to claim 6, wherein: The step S2 specifically includes: According to the peak point matrix of any phase current, find the current amplitude of the peak point position at two commutation moments that meet certain conditions, and calculate the difference between the peak point current amplitudes at the two commutation moments.

8. The method for identifying a turn-to-turn short circuit fault in a brushless DC motor according to claim 7, wherein: Peak point positions of the two commutation moments At the same time: has the maximum element value; The difference between the peak current amplitudes at the two commutation moments: in, 9. The method for identifying a turn-to-turn short circuit fault of a brushless DC motor according to claim 8, wherein: The step S3 specifically includes: The difference in peak current amplitude at the moment of commutation of the Fth phase current (F∈{a,b,c}) If the following two relations are met, it is considered that a turn-to-turn short circuit fault has occurred in phase F: ① ② In the above formula, the coefficient Lim ranges from 0.1 to 0.

5.

10. A brushless DC motor turn-to-turn short circuit fault identification system, characterized in that: include: A peak point calculation module is used to obtain all peak points of phase current using a peak detection algorithm; The commutation peak difference calculation module is used to determine the peak points of the two commutation moments of any phase current according to the commutation law of the phase current, and estimate the commutation peak difference of any phase current; The inter-turn short circuit fault judgment module is used to compare the commutation peak difference between the phase currents in the horizontal and vertical directions, and judge whether an inter-turn short circuit fault occurs in the brushless DC motor based on the comparison results.

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