A permanent magnet synchronous motor stator slot number analysis method, device, equipment and medium

Abstract

The application discloses a permanent magnet synchronous motor stator slot number analysis method, device, equipment and medium. The method comprises the following steps: determining a target order set and a target amplitude of each order according to a frequency domain order spectrum of a target motor; determining a plurality of candidate stator slot numbers and a target order influence score of each candidate stator slot number; determining a target current influence score of each candidate stator slot number according to a current frequency domain spectrum corresponding to the target motor, based on an upper sideband expression, a lower sideband expression and a preset tolerance of a stator tooth harmonic in the current frequency domain spectrum; performing weighted summation on the target order influence score and the target current influence score to determine a comprehensive influence score corresponding to each candidate stator slot number, and determining a target stator slot number corresponding to the target motor based on the comprehensive influence score corresponding to each candidate stator slot number. The application can realize lossless and efficient analysis of the permanent magnet synchronous motor stator slot number, and reduce the labor and time cost of detection.

Description

Technical Field

[0001] This invention relates to the field of motor analysis technology, and in particular to a method, apparatus, equipment and medium for analyzing the number of stator slots in a permanent magnet synchronous motor. Background Technology

[0002] In the field of performance evaluation and fault diagnosis of permanent magnet synchronous motors, achieving accurate and efficient identification of the core structural parameter of stator slot number is a key technical means to improve the level of reverse analysis of motor design and support on-site operation and maintenance and health management, and has important engineering application and scientific research value.

[0003] Currently, traditional methods for analyzing the stator slot count of permanent magnet synchronous motors generally employ a disassembly and measurement approach. Taking on-site motor evaluation as an example, operators must first remove the motor from the operating system, open the front and rear end covers, and record the slot count one by one through visual counting. If the stator is encapsulated, the detection method becomes even more complex. Therefore, traditional methods for obtaining stator slot counts are highly dependent on operator experience, the analysis process is fragmented and cumbersome, resulting in high labor and time costs, poor repeatability and consistency of results, and the disassembly process requires machine shutdown, severely impacting normal production rhythm and equipment availability. Consequently, the objectivity, efficiency, and robustness of motor parameter analysis are low, failing to meet the needs of industrial sites for rapid surveys of large numbers of motors, long-term condition monitoring, and improved utilization of maintenance resources. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, and medium for analyzing the number of stator slots in a permanent magnet synchronous motor, enabling efficient and non-destructive analysis of the stator slot count of a permanent magnet synchronous motor. This significantly reduces labor costs, improves the repeatability and consistency of analysis results, and minimizes the impact on normal production rhythm and equipment availability. Consequently, it enhances the objectivity, efficiency, and robustness of motor parameter analysis, meeting the needs of industrial sites for rapid surveys of large numbers of motors, long-term condition monitoring, and improved utilization of maintenance resources.

[0005] According to one aspect of the present invention, a method for analyzing the number of stator slots in a permanent magnet synchronous motor is provided, the method comprising: The frequency domain order spectrum of the target motor at different speeds is determined, and the order energy of the frequency domain order spectrum is extracted and normalized to determine the target order set and the target amplitude corresponding to each order in the target order set. Determine multiple candidate stator slot numbers corresponding to the target order set, and based on the target order set, determine the target order influence score corresponding to each candidate stator slot number; Determine the current frequency domain spectrum corresponding to the target motor. Based on the upper sideband expression, lower sideband expression and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, determine the target current influence score corresponding to each candidate stator slot number. The target order influence score and the target current influence score are weighted and summed according to a preset weight combination to determine the comprehensive influence score corresponding to each candidate stator slot number. Based on the comprehensive influence score corresponding to each candidate stator slot number, the target stator slot number corresponding to the target motor is determined.

[0006] According to another aspect of the present invention, a device for analyzing the number of stator slots of a permanent magnet synchronous motor is provided, the device comprising: The target amplitude determination module is used to determine the frequency domain order spectrum of the target motor at different speeds, and to perform order energy extraction and normalization processing on the frequency domain order spectrum to determine the target order set and the target amplitude corresponding to each order in the target order set. The first score determination module is used to determine multiple candidate stator slot numbers corresponding to the target order set, and to determine the target order influence score corresponding to each candidate stator slot number based on the target order set. The second score determination module is used to determine the current frequency domain spectrum corresponding to the target motor. Based on the upper sideband expression, lower sideband expression and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, it determines the target current influence score corresponding to each candidate stator slot number. The target slot number determination module is used to perform a weighted summation of the target order influence score and the target current influence score according to a preset weight combination to determine the comprehensive influence score corresponding to each candidate stator slot number, and to determine the target stator slot number corresponding to the target motor based on the comprehensive influence score corresponding to each candidate stator slot number.

[0007] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the permanent magnet synchronous motor stator slot number analysis method according to any embodiment of the present invention.

[0008] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the permanent magnet synchronous motor stator slot number analysis method according to any embodiment of the present invention.

[0009] The technical solution of this invention determines the frequency domain order spectrum of the target motor at different speeds, and performs order energy extraction and normalization processing on the frequency domain order spectrum to determine the target order set and the target amplitude corresponding to each order in the target order set. This accurately reflects the structural characteristic response of different orders and provides a reliable data foundation for subsequent steps. Multiple candidate stator slot numbers corresponding to the target order set are determined, and based on the target order set, a target order influence score is determined for each candidate stator slot number. This quantifies the degree of matching between the order distribution corresponding to the candidate stator slot numbers and the order distribution observed in the actual vibration order spectrum, providing a reliable indicator for subsequent fusion. The current frequency domain spectrum corresponding to the target motor is determined. Based on the upper sideband expression, lower sideband expression, and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, a target current influence score is determined for each candidate stator slot number. This quantifies the degree of matching between the candidate stator slot numbers and the current frequency domain spectrum, providing reliable data support for subsequent stator slot number analysis. The target order influence score and the target current influence score are weighted and summed according to a preset weight combination to determine the comprehensive influence score corresponding to each candidate stator slot number. Based on the comprehensive influence score corresponding to each candidate stator slot number, the target stator slot number corresponding to the target motor is determined, realizing non-destructive testing of the motor stator slot number and greatly improving the efficiency and reliability of slot number analysis. This invention determines the target order influence score and target current influence score corresponding to each candidate stator slot number, and obtains the target stator slot number by weighted fusion of the target order influence score and target current influence score. This achieves efficient non-destructive testing of the stator slot number of permanent magnet synchronous motors, greatly reducing labor costs, improving the repeatability and consistency of analysis results, and reducing the impact on normal production rhythm and equipment availability. This improves the objectivity, efficiency, and robustness of motor parameter analysis, meeting the task requirements of rapid general survey of large batches of motors, long-term condition monitoring, and improved utilization of operation and maintenance resources in industrial sites.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1This is a flowchart of a method for analyzing the number of stator slots in a permanent magnet synchronous motor according to Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the structure of a permanent magnet synchronous motor stator slot number analysis device provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device that implements the method for analyzing the number of stator slots in a permanent magnet synchronous motor according to an embodiment of the present invention. Detailed Implementation

[0013] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0014] It should be noted that the terms "first," "second," "target," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0015] Example 1 Figure 1 This is a flowchart illustrating a method for analyzing the stator slot count of a permanent magnet synchronous motor according to Embodiment 1 of the present invention. This embodiment is applicable to situations involving the analysis of the stator slot count of a permanent magnet synchronous motor. This method can be executed by a permanent magnet synchronous motor stator slot count analysis device, which can be implemented in hardware and / or software. This permanent magnet synchronous motor stator slot count analysis device can be configured in an electronic device. For example... Figure 1 As shown, the method includes: S110. Determine the frequency domain order spectrum of the target motor at different speeds, and perform order energy extraction and normalization on the frequency domain order spectrum to determine the target order set and the target amplitude corresponding to each order in the target order set.

[0016] The target motor can refer to the specific permanent magnet synchronous motor entity whose stator slot number is to be identified. The frequency domain order spectrum can refer to the spectrum matrix obtained after order tracking of the motor vibration signal, where the X-axis represents the order, the Y-axis represents the rotational speed, and the Z-axis represents the amplitude at the corresponding order (X-axis) and corresponding rotational speed (Y-axis). The target order set can refer to the set of several most representative orders selected from the frequency domain order spectra of multiple rotational speeds after energy extraction and normalization. The target amplitude can refer to the average normalized amplitude corresponding to each order in the target order set.

[0017] Specifically, an accelerometer is placed on the target motor housing to synchronously collect speed pulse signals. The motor is then allowed to operate steadily at multiple different speeds. A Fast Fourier Transform (FFT) is performed on the vibration signal at each speed to obtain a frequency domain order spectrum matrix (X-axis represents the order, Y-axis represents the speed, and Z-axis represents the amplitude at the corresponding order (X-axis) and corresponding speed (Y-axis)). The energy of each order is extracted from the frequency domain order spectrum corresponding to each speed, and the energy of each order is averaged and normalized to obtain the average normalized amplitude of each order. Several orders with higher average amplitudes are selected to form the target order set, and their corresponding average amplitudes are the target amplitudes. By averaging the order energy, occasional interference and speed sensitivity can be suppressed, and normalization eliminates the influence of speed on the absolute value of the amplitude, laying a data foundation for subsequent steps.

[0018] For example, several orders with the most significant energy can be selected from the frequency domain order spectrum as the basis for subsequent matching. The target order set R corresponding to the top N peaks, sorted in descending order of the average amplitude A(r), can be expressed as follows: Where R is the target order set, and r is the order in the target order set.

[0019] Then, normalizing the assignment, it can be expressed as the formula: in, For the target amplitude, The unnormalized average amplitude, The maximum unnormalized magnitude in the target order set.

[0020] For example, S110, "performing order energy extraction and normalization of the frequency domain order spectrum to determine the target order set and the target amplitude corresponding to each order in the target order set," may include: performing order energy extraction on multiple frequency domain order spectra to determine the average amplitude corresponding to each order; sorting the average amplitude corresponding to each order from high to low, selecting the top-ranked preset number of average amplitudes, and determining the target order set based on the multiple orders corresponding to the preset number of average amplitudes; normalizing the average amplitude corresponding to each order in the target order set to determine the target amplitude corresponding to each order in the target order set.

[0021] Specifically, the frequency domain order spectra at multiple speeds are aligned according to order. For each order, its amplitude in each speed spectrum is extracted, and the arithmetic mean is calculated to obtain the average amplitude corresponding to each order. This can suppress occasional interference and load fluctuations at a single speed, making inherent topological features such as tooth harmonics more stable and prominent, and the obtained average amplitude of the order is more representative. The average amplitudes corresponding to all orders are sorted from high to low, and the top 10 or 15 orders are selected to form the target order set. This retains the topologically significant orders with significant energy, filters out low-amplitude noise orders, reduces the computational burden of subsequent candidate slot number matching, and highlights key evidence such as the main tooth harmonic order. The average amplitudes corresponding to each order in the target order set are normalized, such as by dividing by the largest amplitude in the set, to obtain the target amplitude corresponding to each order in the target order set. This eliminates the influence of amplitude dimensions and absolute values, providing a unified scale for the calculation of subsequent order influence scores.

[0022] S120. Determine the number of candidate stator slots corresponding to the target order set, and based on the target order set, determine the target order influence score corresponding to each candidate stator slot number.

[0023] Here, the candidate stator slot number can refer to the possible stator slot number derived from the target order set. The target order influence score can refer to the quantitative value of the degree of matching between the order distribution corresponding to the candidate stator slot number and the order distribution observed in the actual vibration order spectrum.

[0024] Specifically, based on the known number of pole pairs of the target motor and the target order, the number of candidate stator slots corresponding to the target order set can be deduced. For each candidate stator slot number, its theoretically expected characteristic order (theoretical fundamental order) is listed. Considering the possible fluctuation range of the actual order around the theoretical value, the closest matching point of these theoretical orders in the measured target order set is searched. Based on the matching results, the target order influence score of the candidate stator slot number is determined, providing a reliable indicator for subsequent stator slot number analysis.

[0025] For example, the formula for calculating the impact of the target order on the score is shown below: in, Let Z be the target order influence score, Z be the number of candidate stator slots, k be a positive integer not exceeding 10, and m be the error value between the measured order and the theoretical order considering the manufacturing process. The target amplitude corresponding to order r, For higher harmonic attenuation weights, For sideband search weights, Gaussian distance weights B is the difference between the actual detected significant order and the theoretical integer multiple of the order, and B is the order score reward function.

[0026] For example, the formula for calculating the order score reward function is shown below: Where B is the order score reward function, This refers to the number of times that a candidate stator slot number satisfies the preset matching conditions within the scanning range for a significant order.

[0027] For example, for a candidate number of stator slots Z, its theoretical vibration order should be k·Z (k=1,2,3,...10). Actual prototypes may be affected by manufacturing errors and modulation, and the actual order may fluctuate within ±2 orders of the theoretical value, which needs to be taken into account in the algorithm.

[0028] The matching function can be defined as follows: For each candidate stator slot number Z, scan positive integer k (ranging from 1 to 10), calculate the theoretical basis order kZ, and search for the nearest point in the target order set (significant order set) R within its neighborhood [-2, +2]. If a match is found, accumulate the weighted energy, the definition function of which can be expressed as follows: in, is the original order influence score; Z is the candidate stator slot number; k is a positive integer not exceeding 10; m is the error value between the measured order and the theoretical order considering the manufacturing process, ranging from [-2, +2]; The target amplitude corresponding to order r; The weight for attenuation of higher harmonics is physically represented as follows: based on the selection of the frequency band of interest, the order score within the kth harmonic is calculated, but the weight decreases as the harmonic order increases. The sideband search weights take into account the fluctuations of the actual production prototype within the principal order ±2 orders. Gaussian distance weights, = The difference between the actual detected significant order and the theoretical integer multiple of the order is expressed in physical terms as: when r The higher the order score, the closer it is to 0; otherwise, the lower the order score, the lower the order score. r The increase leads to a severe decline.

[0029] Definition of the order score reward function based on the cepstral definition: For a candidate number of stator slots Z, according to the slot frequency harmonic formula, its highlight order often does not simply appear in a single harmonic (such as when k=2); rather, significant orders can be found in kZ (k=1, 2, 3, ... 10). The number of times the following matching condition is satisfied within the scanning range can be counted as follows: in, This represents the number of successful matches corresponding to the number of candidate stator slots. This is the relative error cutoff threshold.

[0030] The relative error cutoff threshold is defined based on the frequency resolution of the discrete Fourier transform and the rotational speed fluctuation. It can be done as follows: The more frequency intervals (i.e., harmonic orders) that are matched, the stronger the score, indicating a higher probability that it represents the actual number of slots Z. Where B is the order score reward function, This refers to the number of times that a candidate stator slot number satisfies the preset matching conditions within the scanning range for a significant order.

[0031] Therefore, the final definition function of the order score can be expressed as follows: in, Let Z be the target order influence score, Z be the number of candidate stator slots, k be a positive integer not exceeding 10, and m be the error value between the measured order and the theoretical order considering the manufacturing process. The target amplitude corresponding to order r, For higher harmonic attenuation weights, For sideband search weights, Gaussian distance weights, = B is the difference between the actual detected significant order and the theoretical integer multiple of the order, and B is the order score reward function.

[0032] S130. Determine the current frequency domain spectrum corresponding to the target motor. Based on the upper sideband expression, lower sideband expression and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, determine the target current influence score corresponding to each candidate stator slot number.

[0033] The current frequency domain spectrum refers to the spectrum obtained by performing a conventional Fast Fourier Transform on the stator phase current signal of the motor, with the horizontal axis representing frequency and the vertical axis representing current amplitude. The upper current sideband expression refers to the formula for calculating the upper sideband frequency of the tooth harmonics in the stator current of a permanent magnet synchronous motor. The lower current sideband expression refers to the formula for calculating the lower sideband frequency of the tooth harmonics in the stator current of a permanent magnet synchronous motor. The preset tolerance refers to the allowed frequency deviation window when searching for sideband peaks in the current frequency domain spectrum. The target current influence score refers to the quantified value of the degree of matching between the candidate stator slot number and the current frequency domain spectrum.

[0034] Specifically, the stator phase current is collected during the steady-state operation of the target motor, and a fast Fourier transform is performed to obtain the current frequency domain spectrum (frequency on the horizontal axis, amplitude on the vertical axis). For each candidate stator slot number, the fundamental frequency is calculated based on the current speed and number of pole pairs, and then the theoretical upper and lower sidebands are calculated. The peak amplitude is searched in the current spectrum with the theoretical sideband as the center and a preset tolerance as the window. The actual upper and lower sideband amplitudes are normalized and then weighted and summed to obtain the target current influence score corresponding to the candidate stator slot number. This provides electrical-side verification for the candidate stator slot number and reliable data support for subsequent stator slot number analysis.

[0035] For example, the step S130, "determining the target current influence score corresponding to each candidate number of stator slots based on the upper and lower sideband expressions and preset tolerances of the stator tooth harmonics in the current frequency domain spectrum," may include: determining the upper and lower sidebands corresponding to each candidate number of stator slots based on the upper and lower sideband expressions and preset tolerances of the stator tooth harmonics in the current frequency domain spectrum; normalizing the upper and lower sidebands based on the current fundamental frequency amplitude; and quantizing the current frequency domain spectrum based on the normalized upper and lower sidebands to determine the target current influence score corresponding to each candidate number of stator slots.

[0036] Specifically, for each candidate number of stator slots, the theoretical frequencies of the upper and lower sidebands are calculated by substituting the expressions for the current upper and lower sidebands. In the current frequency domain spectrum, a window is constructed using these theoretical frequencies and a preset tolerance to search for the maximum amplitude, obtaining the upper and lower sidebands corresponding to that candidate number of stator slots. This ensures reliable capture of peak values ​​near the theoretical sidebands, providing accurate evidence of sideband energy for subsequent scoring. The fundamental frequency amplitude of the current is extracted, and the normalized upper and lower sideband amplitudes are calculated. Based on the normalized upper and lower sidebands, the current frequency domain spectrum is quantized to obtain the target current influence score corresponding to that candidate number of stator slots. This makes the current sideband energy of different candidate slot numbers comparable, providing reliable data support for subsequent stator slot number analysis.

[0037] For example, the formula for calculating the fractional influence of the target current can be as follows: in, Let Z be the target current influence fraction, Z be the number of candidate stator slots, and k be a positive integer not exceeding 10. To integrate weights, This represents the maximum amplitude of the upper sideband after normalization. This is the maximum amplitude of the lower sideband after normalization.

[0038] For example, stator tooth harmonics are determined by both the number of stator slots Z and the number of pole pairs P, as shown in the following equation: in, This represents the theoretical frequency of the upper sideband. This indicates the theoretical frequency of the lower sideband. The fundamental frequency of the current is represented by k, and the harmonic order is represented by k (k=1, 2, 3, ... 10).

[0039] In the actual current spectrum, the maximum amplitude is searched within a window centered on each theoretical frequency and with a preset tolerance tol (considering signal acquisition error) as the radius, as shown in the following formula: in, This is the maximum amplitude value of the upper sideband. This is the maximum amplitude value of the lower sideband. This represents the actual current amplitude.

[0040] The quantization result of the current spectrum can then be expressed as: in, Let Z be the target current influence fraction, Z be the number of candidate stator slots, and k be a positive integer not exceeding 10. To integrate weights, This represents the maximum amplitude of the upper sideband after normalization. This is the maximum amplitude of the lower sideband after normalization.

[0041] in, , , This represents the fundamental frequency amplitude of the current.

[0042] It should be noted that: The physical meaning of weighting is described as follows: setting reasonable weights for low-order and mid-to-high-order harmonics within the frequency band of interest, so that high-order harmonics are not overwhelmed by their amplitude or by other noise, or spectral aliasing occurs with other frequency components.

[0043] S140. The target order influence score and the target current influence score are weighted and summed according to the preset weight combination to determine the comprehensive influence score corresponding to each candidate stator slot number, and the target stator slot number corresponding to the target motor is determined based on the comprehensive influence score corresponding to each candidate stator slot number.

[0044] The preset weight combination can refer to the pre-set fusion weight of the order influence score and the current influence score. The comprehensive influence score can refer to a comprehensive index used to measure the credibility of the candidate stator slot number. The target stator slot number can refer to the final output, non-intrusive estimation result of the target motor stator slot number.

[0045] Specifically, based on a preset weight combination, the target order influence score and the target current influence score corresponding to each candidate stator slot number can be weighted and summed to calculate the comprehensive influence score. The candidate stator slot number with the highest comprehensive influence score is then taken as the target stator slot number, thereby achieving non-destructive analysis of the stator slot number and greatly improving analysis efficiency.

[0046] For example, the weighted summation of the target order influence score and the target current influence score corresponding to each candidate number of stator slots can be performed as follows: in, This represents the comprehensive impact score corresponding to the number of candidate stator slots.

[0047] For example, the preset weight combination includes a first weight corresponding to the target order influence score and a second weight corresponding to the target current influence score, wherein the first weight is greater than the second weight, and the sum of the first weight and the second weight is 1.

[0048] In this embodiment, by determining the frequency domain order spectrum of the target motor at different speeds, and performing order energy extraction and normalization on the frequency domain order spectrum, the target order set and the target amplitude corresponding to each order in the target order set are determined. This eliminates speed sensitivity, accurately reflects the structural response characteristics of different orders, and provides a reliable data foundation for subsequent steps. Multiple candidate stator slot numbers corresponding to the target order set are determined, and based on the target order set, the target order influence score corresponding to each candidate stator slot number is determined. This quantifies the degree of matching between the order distribution corresponding to the candidate stator slot numbers and the order distribution observed in the actual vibration order spectrum, providing a reliable indicator for subsequent fusion. The current frequency domain spectrum corresponding to the target motor is determined. Based on the upper sideband expression, lower sideband expression, and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, the target current influence score corresponding to each candidate stator slot number is determined. This quantifies the degree of matching between the candidate stator slot numbers and the current frequency domain spectrum, providing reliable data support for subsequent stator slot number analysis. The target order influence score and target current influence score are weighted and summed according to a preset weight combination to determine the comprehensive influence score corresponding to each candidate stator slot number. Based on the comprehensive influence score corresponding to each candidate stator slot number, the target stator slot number corresponding to the target motor is determined, realizing non-destructive testing of motor stator slot number and greatly improving the efficiency and reliability of slot number analysis. This invention achieves efficient and non-invasive analysis of permanent magnet synchronous motor stator slot number by determining the target order influence score and target current influence score corresponding to each candidate stator slot number and weighting and fusing the target order influence score and target current influence score to obtain the target stator slot number. This greatly reduces labor costs, improves the repeatability and consistency of analysis results, and reduces the impact on normal production rhythm and equipment availability. As a result, it improves the objectivity, efficiency and robustness of motor parameter analysis, meeting the task requirements of rapid general survey of large batches of motors, long-term condition monitoring and improving the utilization rate of operation and maintenance resources in industrial sites.

[0049] Example 2 Figure 2 This is a schematic diagram of a permanent magnet synchronous motor stator slot number analysis device provided in Embodiment 3 of the present invention. Figure 2 As shown, the device includes: a target amplitude determination module 210, a first fraction determination module 220, a second fraction determination module 230, and a target slot number determination module 240; The target amplitude determination module 210 is used to determine the frequency domain order spectrum of the target motor at different speeds, and to perform order energy extraction and normalization processing on the frequency domain order spectrum to determine the target order set and the target amplitude corresponding to each order in the target order set. The first score determination module 220 is used to determine multiple candidate stator slot numbers corresponding to the target order set, and to determine the target order influence score corresponding to each candidate stator slot number based on the target order set. The second score determination module 230 is used to determine the current frequency domain spectrum corresponding to the target motor. Based on the upper sideband expression, lower sideband expression and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, the target current influence score corresponding to each candidate stator slot number is determined. The target slot number determination module 240 is used to perform a weighted summation of the target order influence score and the target current influence score according to a preset weight combination, determine the comprehensive influence score corresponding to each candidate stator slot number, and determine the target stator slot number corresponding to the target motor based on the comprehensive influence score corresponding to each candidate stator slot number.

[0050] In this embodiment, by determining the frequency domain order spectrum of the target motor at different speeds, and performing order energy extraction and normalization on the frequency domain order spectrum, the target order set and the target amplitude corresponding to each order in the target order set are determined. This eliminates speed sensitivity, accurately reflects the structural characteristics of different orders, and provides a reliable data foundation for subsequent steps. Multiple candidate stator slot numbers corresponding to the target order set are determined, and based on the target order set, the target order influence score corresponding to each candidate stator slot number is determined. This quantifies the degree of matching between the order distribution corresponding to the candidate stator slot numbers and the order distribution observed in the actual vibration order spectrum, providing a reliable indicator for subsequent fusion. The current frequency domain spectrum corresponding to the target motor is determined. Based on the upper sideband expression, lower sideband expression, and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, the target current influence score corresponding to each candidate stator slot number is determined. This quantifies the degree of matching between the candidate stator slot numbers and the current frequency domain spectrum, providing reliable data support for subsequent stator slot number analysis. The target order influence score and the target current influence score are weighted and summed according to a preset weight combination to determine the comprehensive influence score corresponding to each candidate stator slot number. Based on the comprehensive influence score corresponding to each candidate stator slot number, the target stator slot number corresponding to the target motor is determined, realizing non-destructive testing of the motor stator slot number and greatly improving the efficiency and reliability of slot number analysis. This invention determines the target order influence score and target current influence score corresponding to each candidate stator slot number, and obtains the target stator slot number by weighted fusion of the target order influence score and target current influence score. This achieves efficient non-destructive testing of the stator slot number of permanent magnet synchronous motors, greatly reducing labor costs, improving the repeatability and consistency of analysis results, and reducing the impact on normal production rhythm and equipment availability. This improves the objectivity, efficiency, and robustness of motor parameter analysis, meeting the task requirements of rapid general survey of large batches of motors, long-term condition monitoring, and improved utilization of operation and maintenance resources in industrial sites.

[0051] Optionally, the target amplitude determination module 210 is specifically used for: extracting order energy from the frequency domain order spectrum to determine the average amplitude corresponding to each order; sorting the average amplitude corresponding to each order from high to low, selecting a preset number of average amplitudes with the highest ranking, and determining a target order set based on the multiple orders corresponding to the preset number of average amplitudes; normalizing the average amplitude corresponding to each order in the target order set to determine the target amplitude corresponding to each order in the target order set.

[0052] Optionally, the formula for calculating the target order influence score is as follows: in, Let Z be the target order influence score, Z be the number of candidate stator slots, k be a positive integer not exceeding 10, and m represent the error value between the measured order and the theoretical order, considering the manufacturing process. The target amplitude corresponding to order r, For higher harmonic attenuation weights, For sideband search weights, Gaussian distance weights, B is the difference between the actual detected significant order and the theoretical integer multiple of the order, and B is the order score reward function.

[0053] Optionally, the calculation formula for the order score reward function is as follows: Where B is the order score reward function, This refers to the number of times that a candidate stator slot number satisfies the preset matching conditions within the scanning range for a significant order.

[0054] Optionally, the second score determination module 230 is specifically used to: determine the upper and lower sidebands corresponding to each candidate number of stator slots based on the upper sideband expression, lower sideband expression, and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum; normalize the upper and lower sidebands based on the current fundamental frequency amplitude, and quantize the current frequency domain spectrum based on the normalized upper and lower sidebands to determine the target current influence score corresponding to each candidate number of stator slots.

[0055] Optionally, the formula for calculating the target current influence fraction can be as follows: in, Let Z be the target current influence fraction, Z be the number of candidate stator slots, and k be a positive integer not exceeding 10. To integrate weights, This represents the maximum amplitude of the upper sideband after normalization. This is the maximum amplitude of the lower sideband after normalization.

[0056] Optionally, the preset weight combination includes a first weight corresponding to the target order influence score and a second weight corresponding to the target current influence score, wherein the first weight is greater than the second weight, and the sum of the first weight and the second weight is 1.

[0057] The above-described device can execute the permanent magnet synchronous motor stator slot number analysis method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the permanent magnet synchronous motor stator slot number analysis method.

[0058] Example 3 Figure 3 This is a schematic diagram of the structure of an electronic device that implements the method for analyzing the number of stator slots in a permanent magnet synchronous motor according to an embodiment of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0059] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0060] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0061] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the method for analyzing the number of stator slots in a permanent magnet synchronous motor.

[0062] In some embodiments, the permanent magnet synchronous motor stator slot number analysis method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the permanent magnet synchronous motor stator slot number analysis method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the permanent magnet synchronous motor stator slot number analysis method by any other suitable means (e.g., by means of firmware).

[0063] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication unit 19, or installed from storage unit 18, or installed from ROM 12. When the computer program is executed by processor 11, it performs the functions defined in the methods of the embodiments of the present invention.

[0064] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0065] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0066] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0067] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0068] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0069] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0070] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0071] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for analyzing the number of stator slots in a permanent magnet synchronous motor, characterized in that, include: The frequency domain order spectrum of the target motor at different speeds is determined, and the order energy of the frequency domain order spectrum is extracted and normalized to determine the target order set and the target amplitude corresponding to each order in the target order set. Determine multiple candidate stator slot numbers corresponding to the target order set, and based on the target order set, determine the target order influence score corresponding to each candidate stator slot number; Determine the current frequency domain spectrum corresponding to the target motor. Based on the upper sideband expression, lower sideband expression and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, determine the target current influence score corresponding to each candidate stator slot number. The target order influence score and the target current influence score are weighted and summed according to a preset weight combination to determine the comprehensive influence score corresponding to each candidate stator slot number. Based on the comprehensive influence score corresponding to each candidate stator slot number, the target stator slot number corresponding to the target motor is determined.

2. The method according to claim 1, characterized in that, The step of extracting and normalizing the order energy of the frequency domain order spectrum to determine the target order set and the target amplitude corresponding to each order in the target order set includes: The frequency domain order spectrum is subjected to order energy extraction to determine the average amplitude corresponding to each order. The average amplitude corresponding to each order is sorted from high to low, and a preset number of the top-ranked average amplitudes are selected. Based on the multiple orders corresponding to the preset number of average amplitudes, a target order set is determined. The average amplitude corresponding to each order in the target order set is normalized to determine the target amplitude corresponding to each order in the target order set.

3. The method according to claim 1, characterized in that, The formula for calculating the target order influence score is as follows: in, Let Z be the target order influence score, Z be the number of candidate stator slots, k be a positive integer not exceeding 10, and m be the error value between the measured order and the theoretical order, considering the manufacturing process. The target amplitude corresponding to order r, For higher harmonic attenuation weights, For sideband search weights, Gaussian distance weights, B is the difference between the actual detected significant order and the theoretical integer multiple of the order, and B is the order score reward function.

4. The method according to claim 3, characterized in that, The formula for calculating the order score reward function is as follows: Where B is the order score reward function, This refers to the number of times that a candidate stator slot number satisfies the preset matching conditions within the scanning range for a significant order.

5. The method according to claim 1, characterized in that, The determination of the target current influence score for each candidate stator slot number, based on the upper and lower sideband expressions and preset tolerances of the stator tooth harmonics in the current frequency domain spectrum, includes: Based on the upper and lower sideband expressions of the stator tooth harmonics in the current frequency domain spectrum and the preset tolerance, the upper and lower sidebands corresponding to each candidate number of stator slots are determined. The upper and lower sidebands are normalized based on the fundamental frequency amplitude of the current, and the current frequency domain spectrum is quantized based on the normalized upper and lower sidebands to determine the target current influence score corresponding to each candidate stator slot number.

6. The method according to claim 1, characterized in that, The formula for calculating the fractional influence of the target current can be as follows: in, Let Z be the target current influence fraction, Z be the number of candidate stator slots, and k be a positive integer not exceeding 10. To integrate weights, This represents the maximum amplitude of the upper sideband after normalization. This is the maximum amplitude of the lower sideband after normalization.

7. The method according to claim 1, characterized in that, The preset weight combination includes a first weight corresponding to the target order influence score and a second weight corresponding to the target current influence score. The first weight is greater than the second weight, and the sum of the first weight and the second weight is 1.

8. A device for analyzing the number of stator slots in a permanent magnet synchronous motor, characterized in that, include: The target amplitude determination module is used to determine the frequency domain order spectrum of the target motor at different speeds, and to perform order energy extraction and normalization processing on the frequency domain order spectrum to determine the target order set and the target amplitude corresponding to each order in the target order set. The first score determination module is used to determine multiple candidate stator slot numbers corresponding to the target order set, and to determine the target order influence score corresponding to each candidate stator slot number based on the target order set. The second score determination module is used to determine the current frequency domain spectrum corresponding to the target motor. Based on the upper sideband expression, lower sideband expression and preset tolerance of the stator tooth harmonics in the current frequency domain spectrum, it determines the target current influence score corresponding to each candidate stator slot number. The target slot number determination module is used to perform a weighted summation of the target order influence score and the target current influence score according to a preset weight combination to determine the comprehensive influence score corresponding to each candidate stator slot number, and to determine the target stator slot number corresponding to the target motor based on the comprehensive influence score corresponding to each candidate stator slot number.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the permanent magnet synchronous motor stator slot number analysis method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for analyzing the number of stator slots of a permanent magnet synchronous motor as described in any one of claims 1-7.

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