TWM659352U - Motor status monitoring system - Google Patents

Abstract

A motor status monitoring system is provided, which includes a vibration sensor and a computing device. The vibration sensor is configured to sense a plurality of vibration signals on multiple axes of a motor device during operation. The computing device is configured to perform the following operations. The vibration signals from the vibration sensor are obtained. A time-domain to frequency-domain processing is performed based on the vibration signals on the axes to obtain frequency domain data for each axis. A data synthesis processing is performed on the frequency domain data to obtain synthesized frequency domain data, and a vibration spectrum characteristic of the motor device is obtained through performing peak detection processing to the frequency domain data and the synthesized frequency domain data.

Description

Motor Status Monitoring System

This novel creation is about a motor status monitoring system.

With the recent popularity of variable frequency drive motors, the operating frequency of motor equipment will change with load changes. Therefore, when the operating frequency of motor equipment is variable, the frequency of the motor equipment's vibration spectrum (also known as the fundamental frequency) is no longer a fixed value, which also increases the difficulty of identifying the vibration frequency of rotating equipment. The traditional method requires fixing the motor speed and using a stroboscope to determine the fundamental frequency of the motor equipment, but this operation is difficult to achieve in actual production due to the limitations of the use conditions of the stroboscope. Alternatively, the tester can read the current output frequency of the inverter and record the vibration spectrum of the motor device, but this requires on-site operation and is not suitable for long-term collection of vibration data for analysis. In addition, for variable-frequency motor equipment, its vibration amount is lower than that of fixed-frequency motor equipment. Therefore, when measuring the vibration of variable-frequency motor equipment, it is easy to be disturbed by the resonance of surrounding equipment with higher speed and larger vibration, which will have a negative impact on the analysis results of the vibration data.

This novel invention proposes a motor state monitoring system, including a vibration sensor and a computing device. The vibration sensor is configured to sense multiple vibration signals in multiple axial directions when the motor device is running. The computing device is configured to perform the following operations. Multiple vibration signals of the motor device are obtained from the vibration sensor. Time domain to frequency domain processing is performed based on the multiple vibration signals in multiple axial directions to obtain multiple frequency domain data corresponding to the multiple axial directions. Data synthesis processing is performed on the multiple frequency domain data to obtain a synthetic frequency domain data. Vibration spectrum characteristics of the motor device are obtained by performing peak detection processing on the multiple frequency domain data and the synthetic frequency domain data.

Based on the above, in an embodiment of the present novel creation, a vibration sensor is used to measure vibration data of a motor device, and the vibration data includes multiple vibration signals corresponding to multiple axes. The computing device can convert these vibration signals into frequency domain data of multiple axes through time domain to frequency domain processing. Afterwards, the computing device can analyze the vibration spectrum characteristics of the motor device based on the peak detection results of these frequency domain data and the synthetic frequency domain data of these frequency domain data. Based on this, by comprehensively considering the frequency domain data of vibration signals in different axes, the present novel creation can obtain more accurate and reliable vibration spectrum characteristics for the motor device, so as to more accurately monitor the motor status in real time.

In order to make the above features and advantages of the present invention more clearly understood, an embodiment is given below and described in detail with reference to the accompanying drawings.

Some embodiments of the present invention will be described in detail below with reference to the accompanying drawings. When the same element symbols appear in different drawings, they will be regarded as the same or similar elements. These embodiments are only a part of the present invention and do not disclose all possible implementations of the present invention. More precisely, these embodiments are only examples of devices within the scope of the patent application of the present invention.

1 and 2 , the motor state monitoring system 10 includes a vibration sensor 100 and a computing device 200. The vibration sensor 100 is disposed on the motor device M1, which may be a mechanical device having a motor such as a pump or a compressor. In some embodiments, the vibration sensor 100 may be disposed on the motor device M1 in a manner that complies with the ISO 10816-3 specification. The vibration sensor 100 is configured to sense multiple vibration signals Sd in multiple axial directions of the motor device M1 during operation, and provide the multiple vibration signals Sd in multiple axial directions to the computing device 200.

In some embodiments, the vibration sensor 100 may be a three-axis acceleration sensor. The three-axis acceleration sensor can be used to measure the vibration state of the motor device M1 in three different axes, that is, the acceleration sensing values in the three axes. These three axes include the first axis, the second axis and the third axis, which are usually referred to as the X axis, the Y axis and the Z axis. The vibration sensor 100 can provide three vibration signals Sd of the motor device M1 in the three axes to the computing device 200.

In some embodiments, the computing device 200 is an electronic device with computing capabilities, which can be implemented as a computer, a server, an industrial control device, a single board computer (SBC), or a sensing device supporting microcontroller computing, etc., and the present case is not limited to this. The operating system running on the computing device 200 can be, for example, Windows, Linux, or Arduino, and the present case is not limited to this. The computing device 200 may include a transceiver 210, a storage device 220, and a processor 230.

The transceiver 210 can transmit and receive data wirelessly or wired. The transceiver 210 can also perform operations such as low noise amplification, impedance matching, mixing, up or down frequency conversion, filtering, amplification, and the like. The computing device 200 can receive data (such as the vibration signal Sd) sent from the vibration sensor 100 through the transceiver 210.

The storage device 220 is used to store data or software modules or instructions for access by the processor 230, and can be, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk or other similar device, integrated circuit or combination thereof.

The processor 230 is electrically connected to the transceiver 210 and the storage device 220, and may be, for example, a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor, a microprocessor, one or more microprocessors combined with a digital signal processor core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), any other type of integrated circuit, a state machine, an advanced RISC machine (ARM) based processor, and the like.

In some embodiments, the processor 230 can access and execute the software module recorded in the storage device 220 to implement various operations in the motor state monitoring method in the embodiment of the present invention. The above-mentioned software module can be broadly interpreted as meaning instructions, instruction sets, codes, program codes, programs, applications, software packages, threads, procedures, functions, etc., regardless of whether it is called software, firmware, middleware, microcode, hardware description language or others.

Please refer to FIG. 1 to FIG. 3 , the method of this embodiment is applicable to the motor state monitoring system 10 in the above-mentioned embodiment. The following is a detailed description of the steps of this embodiment in conjunction with the various components in the motor state monitoring system 10.

In step S310, the computing device 200 obtains a plurality of vibration signals Sd of the motor device M1 from the vibration sensor 100. In some embodiments, when the vibration sensor 100 is a three-axis acceleration sensor, the plurality of vibration signals Sd in the plurality of axes may include a plurality of acceleration time domain data in the plurality of axes.

In some embodiments, the plurality of axes include a first axis (ie, X axis), a second axis (ie, Y axis), and a third axis (ie, Z axis). That is, the plurality of vibration signals Sd may include acceleration time domain data of the first axis, acceleration time domain data of the second axis, and acceleration time domain data of the third axis.

In step S320, the computing device 200 performs time-domain to frequency-domain processing on the multiple vibration signals Sd in multiple axial directions to obtain multiple frequency-domain data corresponding to the multiple axial directions. In some embodiments, when the multiple vibration signals Sd include multiple acceleration time-domain data in multiple axial directions, the computing device 200 may perform an integration processing on the multiple acceleration time-domain data in multiple axial directions to obtain multiple velocity time-domain data in multiple axial directions. Then, the computing device 200 performs time-domain to frequency-domain processing on the multiple velocity time-domain data respectively to obtain multiple frequency-domain data corresponding to the multiple axial directions.

In some embodiments, the time-domain to frequency-domain processing includes Fast Fourier Transform (FFT) processing.

For details, please refer to FIG. 4 , which is a schematic diagram of obtaining frequency domain data according to an embodiment of the present invention. The computing device 200 can obtain acceleration time domain data AX1 in the X-axis direction, acceleration time domain data AY1 in the Y-axis direction, and acceleration time domain data AZ1 in the Z-axis direction from the vibration sensor 100. The computing device 200 can perform integration processing 41 on the acceleration time domain data AX1 in the X-axis direction to generate velocity time domain data VX1 in the X-axis direction. The computing device 200 can perform integration processing 41 on the acceleration time domain data AY1 in the Y-axis direction to generate velocity time domain data VY1 in the Y-axis direction. The calculation device 200 can perform integration processing 41 on the acceleration time domain data AZ1 in the Z axis to generate the velocity time domain data VZ1 in the Z axis. It should be noted that since the vibration signal Sd obtained by the vibration sensor 100 is acceleration data, but the acceleration data also contains noise, the noise becomes relatively small after the integration processing 41.

Afterwards, the computing device 200 may perform FFT processing 42 on the velocity time domain data VX1 in the X-axis direction to generate frequency domain data FX1 in the X-axis direction. The computing device 200 may perform FFT processing 42 on the velocity time domain data VY1 in the Y-axis direction to generate frequency domain data FY1 in the Y-axis direction. The computing device 200 may perform FFT processing 42 on the velocity time domain data VZ1 in the Z-axis direction to generate frequency domain data FZ1 in the Z-axis direction. The frequency domain data FX1, FY1, and FZ1 respectively display the vibration amplitudes of the motor device M1 in different axial directions at different frequencies. Afterwards, the computing device 200 can analyze the vibration spectrum characteristics of the motor device M1 based on the frequency domain data FX1, FY1, and FZ1 in different axial directions.

In step S330, the computing device 200 performs data synthesis processing on a plurality of frequency domain data to obtain a synthesized frequency domain data. In some embodiments, the computing device 200 may perform average processing on a plurality of frequency domain data to obtain the synthesized frequency domain data. Taking FIG. 4 as an example, the computing device 200 may perform average processing on the frequency domain data FX1, FY1, and FZ1 in different axial directions to obtain a synthesized frequency domain data. Specifically, the computing device 200 may calculate the average of three amplitudes corresponding to a certain frequency in the frequency domain data FX1, FY1, and FZ1 to obtain the amplitude corresponding to the frequency in the synthesized frequency domain data.

In step S340, the computing device 200 obtains the vibration spectrum characteristics of the motor device M1 by performing peak detection processing on the plurality of frequency domain data and the synthesized frequency domain data. The peak detection algorithm of the peak detection processing may be, for example, a threshold-based peak detection or a gradient ascent method, which is used to find the local maximum amplitude in the frequency domain data and the synthesized frequency domain data.

That is, the computing device 200 can perform peak detection processing on multiple frequency domain data and synthesized frequency domain data in different axial directions respectively, and obtain multiple peaks of each frequency domain data and multiple peaks of the synthesized frequency domain data. Afterwards, the computing device 200 can identify the vibration spectrum characteristics of the motor device M1 according to the multiple peaks of each frequency domain data and the multiple peaks of the synthesized frequency domain data. In some embodiments, the vibration spectrum characteristics of the motor device M1 may include a target fundamental frequency and multiple target harmonic frequencies.

For details, please refer to FIG. 5 , which is a flow chart of determining a target baseband according to an embodiment of the present invention. In step S341, the computing device 200 performs peak detection processing on the synthesized frequency domain data, and obtains at least one first candidate baseband of the synthesized frequency domain data within a predetermined frequency range. The above-mentioned predetermined frequency range is a frequency range in which the baseband of the motor device M1 may appear, which can be set according to actual applications, and the present case is not limited to this. That is to say, after performing peak detection processing on the synthesized frequency domain data, the computing device 200 can regard the frequency corresponding to the peak value within the predetermined frequency range as the first candidate baseband.

In step S342, the computing device 200 performs peak detection processing on each frequency domain data to obtain at least one second candidate baseband of each frequency domain data within a predetermined frequency range. Similarly, after performing peak detection processing on frequency domain data in different axes, the computing device 200 may regard the frequency corresponding to the peak value within the predetermined frequency range as the second candidate baseband.

Afterwards, the computing device 200 may select a target baseband from a plurality of candidate basebands. The plurality of candidate basebands may include a first candidate baseband obtained from the synthesized frequency domain data and a second candidate baseband obtained from each frequency domain data.

For example, please refer to FIG. 7A to FIG. 7D. In FIG. 7A, the computing device 200 can generate frequency domain data 71X in the X-axis direction according to the vibration signal in the X-axis direction. The computing device 200 can perform peak detection processing on the frequency domain data 71X in the X-axis direction to obtain multiple peaks. Afterwards, the computing device 200 can regard the frequencies corresponding to the peaks P1, P2, and P3 in the predetermined frequency interval Fr1 as multiple candidate basebands (i.e., second candidate basebands).

In FIG. 7B , the computing device 200 can generate frequency domain data 71Y in the Y-axis direction according to the vibration signal in the Y-axis direction. The computing device 200 can perform peak detection processing on the frequency domain data 71Y in the Y-axis direction to obtain multiple peaks. Afterwards, the computing device 200 can regard the frequencies corresponding to the peaks P4 and P5 in the predetermined frequency interval Fr1 as multiple candidate basebands (i.e., second candidate basebands).

In FIG. 7C , the computing device 200 can generate frequency domain data 71Z in the Z-axis direction according to the vibration signal in the Z-axis direction. The computing device 200 can perform peak detection processing on the frequency domain data 71Z in the Z-axis direction to obtain multiple peaks. Afterwards, the computing device 200 can regard the frequencies corresponding to the peaks P6 and P7 in the predetermined frequency interval Fr1 as multiple candidate basebands (i.e., second candidate basebands).

In addition, in FIG. 7D , the computing device 200 may perform average processing on the frequency domain data 71X, 71Y, and 71Z in different axial directions to obtain the synthesized frequency domain data 71C. The computing device 200 may perform peak detection processing on the synthesized frequency domain data 71C to obtain multiple peaks. Thereafter, the computing device 200 may regard the frequencies corresponding to the peaks P8 and P9 in the predetermined frequency interval Fr1 as multiple candidate basebands (i.e., the first candidate baseband).

In addition, in some other embodiments, the computing device 200 may further filter the frequencies corresponding to the peaks P1 to P9, and select a plurality of candidate basebands from the frequencies corresponding to the peaks P1 to P9.

Returning to FIG. 5, in step S343, the computing device 200 calculates the cumulative amplitude of multiple frequencies of each candidate base frequency according to multiple frequency domain data. Taking FIG. 7A as an example, the computing device 200 can calculate the double frequency, triple frequency, quadruple frequency, ..., N times frequency of the candidate base frequency corresponding to the peak value P1. Assuming that the candidate base frequency corresponding to the peak value P1 is f1, the computing device 200 can calculate the double frequency as 2 f1, triple frequency is 3 f1, quadruple frequency is 4 f1, ..., N times the frequency is N f1. Then, based on the frequency domain data 71X, the computing device 200 can obtain these multiple frequencies (i.e., the double frequency 2 f1, triple frequency 3 f1, quadruple frequency 4 f1, ..., N times frequency N f1) and sum up the amplitudes to obtain the cumulative amplitude. In the same way, the calculation device 200 can obtain the cumulative amplitudes of multiple frequencies of each candidate fundamental frequency corresponding to the peaks P1 to P9.

In step S344, the computing device 200 determines the target baseband according to the accumulated amplitude corresponding to each candidate baseband. In some embodiments, the computing device 200 may compare the accumulated amplitude corresponding to each candidate baseband and select the candidate baseband corresponding to the maximum accumulated amplitude as the target baseband. It can be seen that the computing device 200 considers the frequency domain data in different axes and its synthesized frequency domain data to determine the target baseband of the motor device M1, which can effectively improve the accuracy of evaluating the motor baseband.

In some embodiments, after determining the target base frequency of the motor device M1, the computing device 200 may determine multiple target harmonic frequencies based on the target base frequency. It should be noted that the computing device 200 does not simply use multiple frequencies of the target base frequency as multiple target harmonic frequencies, but further comprehensively considers the cumulative amplitudes of multiple candidate harmonics in different axes to determine the multiple target harmonic frequencies. Based on this, these target harmonic frequencies can show the nonlinear behavior of the motor device M1.

For details, please refer to FIG. 6 , which is a flow chart of determining the target harmonic frequency according to an embodiment of the present invention. In step S345, the computing device 200 calculates multiple frequencies of the target base frequency. For example, assuming that the target base frequency is f2, the computing device 200 can calculate multiple multiple frequencies, which are respectively double the frequency 2 f2, triple frequency 3 f2, quadruple frequency 4 f2, ..., N times frequency N f2.

In step S346, the computing device 200 extracts a plurality of candidate harmonic frequencies near each multiple frequency. Specifically, taking FIG. 7A as an example, assuming that the target base frequency is f2. Based on the frequency domain data 71X, the computing device 200 can select a plurality of multiple frequencies (e.g., double frequency 2 f2, triple frequency 3 f2, quadruple frequency 4 f2, ..., N times frequency N f2), and the frequencies corresponding to these peaks are regarded as multiple candidate harmonic frequencies. Similarly, based on the frequency domain data 71Y and 71Z and the synthesized frequency domain data 71C, the computing device 200 can also screen multiple multiple frequencies (for example, double frequency 2 f2, triple frequency 3 f2, quadruple frequency 4 f2, ..., N times frequency N f2), and the frequencies corresponding to these peaks are regarded as multiple candidate harmonic frequencies. In other words, the computing device 200 can use these multiple frequencies of the target base frequency as multiple center frequencies of multiple search ranges, and find multiple peaks in each search range to obtain multiple candidate harmonic frequencies. The frequency ranges of these search ranges can be set according to actual applications.

In step S347, the computing device 200 calculates the cumulative amplitude of each candidate harmonic frequency according to the plurality of frequency domain data. That is, after obtaining the plurality of candidate harmonic frequencies, the computing device 200 can calculate the cumulative amplitude of each candidate harmonic frequency on each frequency domain data and the synthesized frequency domain data. For example, after obtaining a certain candidate harmonic frequency, the computing device 200 can obtain the corresponding amplitude A1 of the frequency domain data 71X, the corresponding amplitude A2 of the frequency domain data 71Y, the corresponding amplitude A3 of the frequency domain data 71Z, and the corresponding amplitude A4 of the synthesized frequency domain data 71C according to the candidate harmonic frequency. The computing device 200 may add the above-mentioned multiple amplitudes (ie, A1+A2+A3+A4) to obtain the cumulative amplitude of the candidate harmonic frequency.

In step S348, the computing device 200 determines a plurality of target harmonic frequencies according to the cumulative amplitudes of the candidate harmonic frequencies. In some embodiments, the computing device 200 may compare the cumulative amplitudes of the candidate harmonic frequencies near the same multiple frequency, and select the candidate harmonic frequency corresponding to the maximum cumulative amplitude as the target harmonic frequency. For example, the computing device 200 may find a plurality of candidate harmonic frequencies according to the double frequency of the target base frequency. Then, the computing device 200 may select the target harmonic frequency from the plurality of candidate harmonic frequencies near the double frequency. In the same manner, the computing device 200 can obtain multiple target harmonic frequencies corresponding to different multiple frequencies. Finally, the computing device 200 can determine the target base frequency and the multiple harmonic frequencies of the motor device M1, and estimate the operating state of the motor device M1 based on the target base frequency and the multiple harmonic frequencies.

In some embodiments, the computing device 200 may calculate the rotational speed of the motor device M1 according to the target base frequency. Further, according to the specification data of the motor device M1, the rated motor rotational speed (unit: rpm) of the motor device M1 is known. The rated motor rotational speed of the motor device M1 is divided by 60 to obtain a predetermined rotational frequency (unit: Hz). This predetermined rotational frequency can be used to determine a predetermined frequency range for estimating the target base frequency. Assuming that according to the calculation method of the aforementioned embodiment, the computing device 200 derives that the target base frequency of the motor device M1 is 24 Hz, the computing device 200 can infer that the actual rotational speed is equal to 24 times 60, that is, 1440 rpm.

In some embodiments, the computing device 200 can combine resonance point scanning and operation parameter adjustment in the process of determining the target baseband and multiple target harmonic frequencies to accurately avoid the resonance points caused by other devices to reduce the adverse effects of resonance interference. In some embodiments, the computing device 200 can perform data pre-processing on the vibration signal provided by the vibration sensor 100, such as data cleaning or other noise filtering processing, to eliminate misleading information to improve the estimation accuracy and reliability of frequency characteristics.

In summary, in the embodiment of the present invention, the vibration sensor is used to sense the vibration data of the motor device, and the vibration data includes multiple vibration signals corresponding to multiple axes. The computing device can convert these vibration signals into frequency domain data of multiple axes through time domain to frequency domain processing. Afterwards, the computing device can analyze the vibration spectrum characteristics of the motor device based on the peak detection results of these frequency domain data and the synthesized frequency domain data of these frequency domain data. Based on this, by analyzing the frequency domain data in different axes, this new invention can effectively improve the estimation accuracy of the vibration spectrum characteristics of the motor equipment, and can monitor the operation status of the motor equipment in real time in a non-invasive way without interfering with the production process of the motor equipment. Therefore, it can effectively prevent the failure of the motor equipment, reduce the downtime of the motor equipment, and extend the user life of the motor equipment.

Although the novel creation has been disclosed as above by way of embodiments, they are not intended to limit the novel creation. Any person with ordinary knowledge in the relevant technical field may make slight changes and modifications without departing from the spirit and scope of the novel creation. Therefore, the protection scope of the novel creation shall be subject to the scope defined in the attached patent application.

10: Motor status monitoring system 100: Vibration sensor 200: Calculation device 220: Storage device 230: Processor 210: Transceiver Sd: Vibration signal 41: Integration processing 42: FFT processing AX1, AY1, AZ1: Acceleration time domain data VX1, VY1, VZ1: Velocity time domain data FX1, FY1, FZ1, 71X, 71Y, 71Z: Frequency domain data 71C: Synthesized frequency domain data P1~P9: Peak value Fr1: Predetermined frequency range S310~S340, S341~S348: Steps

FIG. 1 is a block diagram of a motor state monitoring system according to an embodiment of the present invention. FIG. 2 is a schematic diagram of a motor state monitoring system according to an embodiment of the present invention. FIG. 3 is a flow chart of a motor state monitoring method according to an embodiment of the present invention. FIG. 4 is a schematic diagram of obtaining frequency domain data according to an embodiment of the present invention. FIG. 5 is a flow chart of determining a target baseband according to an embodiment of the present invention. FIG. 6 is a flow chart of determining a target harmonic frequency according to an embodiment of the present invention. FIG. 7A to FIG. 7C are example schematic diagrams of frequency domain data according to an embodiment of the present invention. FIG. 7D is an example schematic diagram of synthesized frequency domain data according to an embodiment of the present invention.

10: Motor status monitoring system

100: Vibration sensor

200: Computing device

220: Storage device

230: Processor

210: Transceiver

Claims (10)

A motor state monitoring system comprises: A vibration sensor configured to sense multiple vibration signals of a motor device in multiple axial directions when the motor device is running; and A computing device configured to: Obtain the multiple vibration signals of the motor device from the vibration sensor; Perform a time domain to frequency domain conversion process on the multiple vibration signals in the multiple axial directions to obtain multiple frequency domain data corresponding to the multiple axial directions; Perform data synthesis processing on the multiple frequency domain data to obtain a synthesized frequency domain data; and Obtain the vibration spectrum characteristics of the motor device by performing peak detection processing on the multiple frequency domain data and the synthesized frequency domain data. A motor state monitoring system as described in claim 1, wherein the vibration sensor includes an acceleration sensor, and the multiple vibration signals in the multiple axial directions include multiple acceleration time domain data in the multiple axial directions. A motor state monitoring system as described in claim 2, wherein the computing device performs an integration process on the multiple acceleration time domain data in the multiple axial directions to obtain multiple velocity time domain data in the multiple axial directions, wherein the computing device performs the time domain to frequency domain process on the multiple velocity time domain data respectively to obtain the multiple frequency domain data corresponding to the multiple axial directions. A motor state monitoring system as described in claim 1, wherein the time domain to frequency domain processing includes fast Fourier transform processing, and the computing device performs average processing on the multiple frequency domain data to obtain the synthetic frequency domain data. A motor status monitoring system as described in claim 1, wherein the multiple axes include a first axis, a second axis and a third axis. A motor status monitoring system as described in claim 1, wherein the vibration spectrum characteristics of the motor device include a target fundamental frequency and a plurality of target harmonic frequencies. The motor state monitoring system as described in claim 6, wherein the computing device selects the target baseband from a plurality of candidate basebands, the plurality of candidate basebands including a first candidate baseband and a second candidate baseband, The computing device performs the peak detection processing on the synthesized frequency domain data to obtain the first candidate baseband of the synthesized frequency domain data within a predetermined frequency range, The computing device performs the peak detection processing on each of the plurality of frequency domain data to obtain the second candidate baseband of each of the plurality of frequency domain data within the predetermined frequency range. A motor state monitoring system as described in claim 7, wherein the calculation device calculates the cumulative amplitude of multiple multiple frequencies of each of the multiple candidate basebands based on the multiple frequency domain data, and determines the target baseband based on the cumulative amplitude corresponding to each of the multiple candidate basebands. A motor state monitoring system as described in claim 6, wherein the computing device calculates multiple multiple frequencies of the target base frequency, extracts multiple candidate harmonic frequencies near each of the multiple multiple frequencies, calculates the cumulative amplitude of each of the multiple candidate harmonic frequencies based on the multiple frequency domain data, and determines the multiple target harmonic frequencies based on the cumulative amplitude of each of the multiple candidate harmonic frequencies. A motor state monitoring system as described in claim 6, wherein the calculation device calculates the rotational speed of the motor device based on the target base frequency.