Motor fault detection method and system, extractor hood and storage medium

Abstract

The disclosure provides a motor fault detection method, system, range hood and storage medium. The motor fault detection method comprises: in the running process of the motor, sampling the phase current of the motor in a first preset period to obtain first sampling data; reconstructing the phase current based on the first sampling data to obtain reconstructed data of the phase current; and detecting whether the motor is in a fault state according to the first sampling data and the reconstructed data. The method can more accurately and timely detect the existence of direct current bias fault of the motor by collecting the first sampling data in real time during the running process of the motor, reconstructing the phase current according to the first sampling data to obtain the reconstructed data, i.e. the standard sinusoidal current of the phase current, and then detecting whether the motor has a direct current bias fault according to the first sampling data and the reconstructed data.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of motor control, and particularly relates to a motor fault detection method and system, a range hood and a storage medium. BACKGROUND

[0002] For a permanent magnet synchronous motor, selecting a suitable static operating point can prevent the circuit from producing nonlinear distortion and ensure better signal amplification effect.

[0003] The maximum torque current ratio control and field weakening control algorithms of the range hood using the permanent magnet synchronous motor all depend on the current values of the three-phase currents. In order to ensure that accurate current values of the three-phase currents can be obtained, before the motor starts, when the three-phase currents of the motor are all zero, the direct current bias value in the ideal state is calculated according to the phase current values fed back by each sampling resistor, and the static operating point is set according to the direct current bias value.

[0004] However, during the operation of the motor, the temperature will continue to rise, and the motor body and the driving board will be affected by the temperature change, causing the static operating point required by the amplification circuit to change. The static operating point set according to the direct current bias value in the ideal state cannot meet the requirements. Therefore, as the temperature rises due to the operation of the motor, the amplified signal will be severely distorted, which will further cause the motor to malfunction. SUMMARY

[0005] In order to solve the above technical problems and detect the direct current bias fault of the motor in time, the present disclosure provides a motor fault detection method and system, a range hood and a storage medium.

[0006] In a first aspect, the present disclosure provides a motor fault detection method, comprising:

[0007] During the operation of the motor, the phase current of the motor is sampled in a first preset period to obtain first sampling data;

[0008] The phase current is reconstructed based on the first sampling data to obtain reconstructed data of the phase current;

[0009] Whether the motor is in a fault state is detected according to the first sampling data and the reconstructed data.

[0010] Optionally, the step of reconstructing the phase current based on the first sampling data comprises:

[0011] A first direct current bias value is determined according to the first sampling data;

[0012] If the first direct current bias value meets a first preset range, the phase current is reconstructed based on the first sampling data.

[0013] Optionally, the step of reconstructing the phase current based on the first sampling data comprises:

[0014] reconstructing the phase current based on a variance of the first sampling data and the first DC bias value.

[0015] Optionally, the step of reconstructing the phase current based on the first sampling data comprises:

[0016] reconstructing the phase current based on a maximum value and a minimum value in the first sampling data.

[0017] Optionally, the first DC bias value is an average value of the first sampling data.

[0018] Optionally, the step of detecting whether the motor is in the fault state according to the first sampling data and the reconstructed data comprises:

[0019] obtaining error data of the phase current according to the first sampling data and the reconstructed data;

[0020] calculating a first fault evaluation value according to the error data; wherein the first fault evaluation value is used to measure a dispersion degree of the error data;

[0021] if the first fault evaluation value is greater than a first preset threshold, detecting that the motor is in the fault state.

[0022] Optionally, the motor fault detection method further comprises:

[0023] sampling a phase current of the motor in a second preset period before starting the motor to obtain second sampling data;

[0024] detecting whether the motor is in a fault state according to the second sampling data.

[0025] Optionally, the step of detecting whether the motor is in the fault state according to the second sampling data comprises:

[0026] determining an average value of the second sampling data as a second DC bias value;

[0027] if the second DC bias value satisfies a second preset range, obtaining a second fault evaluation value according to the second sampling data; wherein the second fault evaluation value is used to measure a dispersion degree of the second sampling data;

[0028] if the second fault evaluation value is greater than a second preset threshold, detecting that the motor is in the fault state.

[0029] Optionally, the sampling of the phase current of the motor in the first preset period comprises:

[0030] After the motor is started, it is determined whether the D-axis current and the Q-axis current of the motor are stable;

[0031] If stable, the phase current of the motor is sampled in the first preset period.

[0032] In a second aspect, the present disclosure provides a motor fault detection system, comprising:

[0033] A first sampling module is configured to sample the phase current of the motor in a first preset period during the operation of the motor to obtain first sampling data;

[0034] A phase current reconstruction module is configured to reconstruct the phase current based on the first sampling data to obtain reconstructed data of the phase current;

[0035] A first fault detection module is configured to detect whether the motor is in a fault state according to the first sampling data and the reconstructed data.

[0036] Optionally, the phase current reconstruction module comprises:

[0037] A first DC bias calculation unit is configured to determine a first DC bias value according to the first sampling data;

[0038] A first fault detection unit is configured to reconstruct the phase current based on the first sampling data when the first DC bias value meets a first preset range.

[0039] Optionally, the phase current reconstruction module comprises:

[0040] A first reconstruction unit is configured to reconstruct the phase current based on the variance of the first sampling data and the first DC bias value.

[0041] Optionally, the phase current reconstruction module comprises:

[0042] A second reconstruction unit is configured to reconstruct the phase current based on the maximum value and the minimum value in the first sampling data.

[0043] Optionally, the first DC bias value is the average value of the first sampling data.

[0044] Optionally, the first fault detection module comprises:

[0045] An error statistical unit is configured to obtain error data of the phase current according to the first sampling data and the reconstructed data;

[0046] The first evaluation unit is configured to calculate a first fault evaluation value according to the error data, wherein the first fault evaluation value is used to measure a dispersion degree of the error data.

[0047] The second fault detection unit is configured to detect that the motor is in the fault state when the first fault evaluation value is greater than a first preset threshold.

[0048] Optionally, the motor fault detection system further comprises:

[0049] The second sampling module is configured to sample phase currents of the motor in a second preset period before starting the motor to obtain second sampling data.

[0050] The second fault detection module is configured to detect whether the motor is in a fault state according to the second sampling data.

[0051] Optionally, the second fault detection module comprises:

[0052] The second DC bias calculation unit is configured to determine an average value of the second sampling data as a second DC bias value.

[0053] The second evaluation unit is configured to obtain a second fault evaluation value according to the second sampling data when the second DC bias value satisfies a second preset range, wherein the second fault evaluation value is used to measure a dispersion degree of the second sampling data.

[0054] The fourth fault detection unit is configured to detect that the motor is in the fault state when the second fault evaluation value is greater than a second preset threshold.

[0055] Optionally, the first sampling module is further configured to determine whether D-axis currents and Q-axis currents of the motor are stable after the motor is started, and sample the phase currents of the motor in a first preset period if the D-axis currents and the Q-axis currents are stable.

[0056] In a third aspect, the disclosure provides an extractor hood, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the motor fault detection method of the first aspect when executing the computer program.

[0057] In a fourth aspect, the disclosure provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the motor fault detection method of the first aspect.

[0058] The positive progress effect of the disclosure is that:

[0059] The motor fault detection method, system, range hood and storage medium provided by the present disclosure can obtain first sampling data of phase current during operation of the motor, reconstruct the phase current according to the first sampling data to obtain reconstruction data, the reconstruction data being the phase current of a standard sine wave, and then detect whether the motor has a direct current bias fault according to the first sampling data and the reconstruction data.

[0060] Because the first sampling data is obtained by real-time collection of the phase current during operation of the motor, the direct current bias fault of the motor can be found more accurately and timely, and the motor can be prevented from malfunctioning.

[0061] Further, second sampling data of the phase current is obtained before the motor starts, and whether the motor has a direct current bias fault is detected according to the second sampling data, so that the reliability of the motor start can be ensured, and the motor can be prevented from being damaged. BRIEF DESCRIPTION OF DRAWINGS

[0062] Figure 1 A flowchart of a motor fault detection method provided for Embodiment 1 of the present disclosure is shown.

[0063] Figure 2 A comparison diagram of first sampling data and reconstruction data provided for Embodiment 1 of the present disclosure is shown.

[0064] Figure 3 A current sampling circuit diagram provided for Embodiment 1 of the present disclosure is shown.

[0065] Figure 4 A differential amplification circuit diagram provided for Embodiment 1 of the present disclosure is shown.

[0066] Figure 5 A flowchart of motor fault detection provided for Embodiment 1 of the present disclosure is shown.

[0067] Figure 6 A module diagram of a motor fault detection system provided for Embodiment 2 of the present disclosure is shown.

[0068] Figure 7 A structure diagram of a range hood provided for Embodiment 3 of the present disclosure is shown. DETAILED DESCRIPTION

[0069] The present disclosure will be further described by way of examples without limiting the present disclosure to the described examples.

[0070] It should be noted that if the embodiments of this disclosure involve descriptions such as "first" and "second," such descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" and "second" may explicitly or implicitly include at least one of those features.

[0071] Furthermore, the technical solutions of various implementation methods can be combined with each other, but this must be based on the ability of a person skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed in this disclosure.

[0072] Example 1

[0073] This embodiment provides a method for detecting motor faults.

[0074] like Figure 1 As shown, the motor fault detection method includes steps S1-S3:

[0075] Step S1: During the operation of the motor, the phase current of the motor is sampled within the first preset cycle to obtain the first sampled data;

[0076] Step S2: Reconstruct the phase current based on the first sampled data to obtain the reconstructed phase current data;

[0077] Step S3: Detect whether the motor is in a fault state based on the first sampled data and the reconstructed data.

[0078] In step S1, since the motors used in practical applications are usually three-phase motors, such as permanent magnet synchronous motors, the currents of the three-phase motors are independent of each other. Therefore, the phase current in this embodiment is any one of the three-phase currents.

[0079] In one embodiment, step S1 specifically includes:

[0080] After the motor starts, determine whether the D-axis current and Q-axis current of the motor are stable;

[0081] If stable, the phase current of the motor is sampled within the first preset period;

[0082] If the current is unstable, you can restart the motor until the D-axis current and Q-axis current of the motor can be stabilized.

[0083] In practical applications, factors such as unstable power supply voltage, unstable load, and poor contact in the circuit may lead to unstable current. In order to ensure stable operation of the motor, it is necessary to first determine whether the D-axis current and Q-axis current are stable.

[0084] Exemplarily, the D-axis current is sampled in a period of time, and whether the current is stable can be determined according to the variance or average value of the D-axis current. The same is true for the Q-axis current.

[0085] In step S2, the first sampling data obtained by actually sampling the phase current during the operation of the motor is usually not a standard sine wave, and the reconstructed data obtained by reconstructing the phase current is a standard sine wave. The first sampling data is compared with the phase current of the standard sine wave, and the direct current bias fault can be determined according to the comparison result.

[0086] Exemplarily, the first sampling data and the reconstructed data are as shown in Figure 2 It can be seen that there is a certain deviation between the part of the sampling current values in the first sampling data and the reconstructed current values in the reconstructed data.

[0087] In an embodiment, step S2 specifically comprises:

[0088] determining a first direct current bias value according to the first sampling data;

[0089] if the first direct current bias value meets a first preset range, reconstructing the phase current based on the first sampling data;

[0090] if the first direct current bias value does not meet the first preset range, detecting that the motor is in a fault state.

[0091] That is, the phase current is reconstructed only when the first direct current bias value meets the first preset range. If the first direct current bias value does not meet the first preset range, it indicates that the motor has failed, and the phase current no longer needs to be reconstructed.

[0092] The first preset range is determined according to a preset direct current bias value of the motor.

[0093] Exemplarily, the sampling range of the phase current is-5A to 5A, and the preset direct current bias value of the motor is 5A. Therefore, the first preset range can be 4.85A to 5.15A. The first preset range can be further reduced to 4.9A to 5.1A.

[0094] Specifically, the first direct current bias value can be the average value of the first sampling data.

[0095] Exemplarily, the calculation method of the first direct current bias value is:

[0096]

[0097] wherein, i bias1 is the first direct current bias value, i is the first sampling data at time t, T1 is a first preset period, and t is time.

[0098] Specifically, the embodiment provides two reconstruction manners of phase current.

[0099] One reconstruction manner of phase current is to reconstruct the phase current based on the variance of the first sampling data and the first DC bias value.

[0100] For example, the calculation manner of the reconstructed data is as follows:

[0101]

[0102] wherein, is the reconstructed data, i is the first sampling data at time t, i bias1 is the first DC bias value, T1 is the first preset period, ω is the electrical frequency of the motor, and t is time.

[0103] Another reconstruction manner of phase current is to reconstruct the phase current based on the maximum value and the minimum value of the first sampling data.

[0104] For example, the calculation manner of the reconstructed data is as follows:

[0105]

[0106] wherein, is the reconstructed data, i max is the maximum value of the first sampling data, i min is the minimum value of the first sampling data, ω is the electrical frequency of the motor, and t is time.

[0107] In step S3, mainly according to the difference between the first sampling data and the reconstructed data, it is detected whether the motor is in a fault state. That is, if most of the sampling current values in the first sampling data are greatly different from the reconstructed current values corresponding to the reconstructed data, it is indicated that the motor is in a fault state.

[0108] In one embodiment, step S3 specifically comprises:

[0109] obtaining error data of the phase current according to the first sampling data and the reconstructed data;

[0110] calculating a first fault evaluation value according to the error data; wherein the first fault evaluation value is used to measure the dispersion degree of the error data;

[0111] if the first fault evaluation value is greater than a first preset threshold value, it is detected that the motor is in a fault state.

[0112] Specifically, the error data is the difference between the first sampling data and the reconstructed data at the same time.

[0113] In one embodiment, the first fault evaluation value is the variance of the error data.

[0114] For example, the first fault assessment value is calculated as follows:

[0115]

[0116] wherein var1 is the first fault assessment value, T1 is the first preset period, t is time, Δi is the error data at time t, and Δi0 is the average value of the error data.

[0117] In another embodiment, the first fault assessment value is the standard deviation of the error data.

[0118] In addition, the motor fault detection method can further include a fault detection step before starting the motor:

[0119] Before starting the motor, the phase current of the motor is sampled within a second preset period to obtain second sampling data.

[0120] The motor is detected as being in a fault state according to the second sampling data.

[0121] The step of detecting the motor as being in a fault state according to the second sampling data includes:

[0122] The average value of the second sampling data is determined as a second DC bias value.

[0123] If the second DC bias value does not satisfy a second preset range, the motor is detected as being in a fault state.

[0124] If the second DC bias value satisfies the second preset range, a second fault assessment value is obtained according to the second sampling data, wherein the second fault assessment value is used to measure the dispersion degree of the second sampling data.

[0125] If the second fault assessment value is greater than a second preset threshold, the motor is detected as being in a fault state.

[0126] The second preset range is the same as the first preset range.

[0127] Specifically, the second DC bias value is calculated in the same way as the first DC bias value.

[0128] For example, the second DC bias value is calculated as follows:

[0129]

[0130] wherein i bias2 is the second DC bias value, i is the second sampling data at time t, T2 is the second preset period, and t is time.

[0131] In an embodiment, the second fault assessment value is the variance of the second sampling data.

[0132] For example, the second fault evaluation value is calculated as follows:

[0133]

[0134] wherein var2 is the second fault evaluation value, T2 is the second preset period, t is time, i is the second sampling data at time t, i bias2 is the second DC bias value.

[0135] In another embodiment, the second fault evaluation value is the standard deviation of the second sampling data.

[0136] It should be noted that the fault state in the above embodiments is specifically a DC bias fault.

[0137] In one practical application scenario, the three-phase currents of the permanent magnet synchronous motor are sampled using a circuit as shown in Figure 3 , and the sampled phase currents are amplified using a differential amplification circuit as shown in Figure 4 . That is, the currents flowing through resistors V ain , V bin , and V cin are amplified and sampled to obtain the three-phase currents of the permanent magnet synchronous motor.

[0138] In one specific example, the motor fault detection is as shown in the flowchart of Figure 5 , specifically as follows:

[0139] Before the motor is started, the six MOS tubes that control the on-off of the three-phase bridge arms are all closed, the phase currents of the motor are sampled, the second DC bias value before the motor is started is calculated according to the second sampling data, and it is determined whether the second DC bias value before the motor is started meets the second preset range. If the second DC bias value before the motor is started does not meet the second preset range, it indicates that the motor has a DC bias fault; if the second DC bias value before the motor is started meets the second preset range, the variance of the second sampling data is calculated, and if the variance is greater than a second preset threshold, it indicates that the motor has a DC bias fault; if the variance does not exceed the second preset threshold, the motor can be started normally.

[0140] The six MOS tubes controlling the on-off of the three-phase bridge arm are all turned on, and after the motor is started, it is first judged whether the motor operation is stable, i.e. whether the Q-axis current and the D-axis current of the motor are stable. If not, the motor needs to be restarted, and if stable, the phase current of the running motor is sampled, the first DC bias value of the motor during operation is calculated according to the first sampling data of the motor during operation, and it is judged whether the first DC bias value of the motor during operation meets the first preset range. If the first DC bias value of the motor during operation does not meet the first preset range, it indicates that the motor has a DC bias fault, and if the first DC bias value of the motor during operation meets the first preset range, the phase current is reconstructed according to the first sampling data of the motor during operation.

[0141] After obtaining the reconstructed data of the phase current, the difference between the first sampling data and the reconstructed data at the same time is calculated to obtain error data. The variance of the error data is calculated, and if the variance is greater than the first preset threshold, it indicates that the motor has a DC bias fault.

[0142] Among them, the first preset threshold and the second preset threshold can be set according to the actual situation.

[0143] For example, the value range of the first preset threshold and the second preset threshold can be 0.005 to 0.015. In a specific embodiment, the first preset threshold and the second threshold are the same, both are 0.01.

[0144] Embodiment 2

[0145] The embodiment provides a motor fault detection system.

[0146] As shown in Figure 6 The motor fault detection system comprises:

[0147] The first sampling module 41 is configured to sample the phase current of the motor in a first preset period during the operation of the motor to obtain first sampling data.

[0148] The phase current reconstruction module 42 is configured to reconstruct the phase current based on the first sampling data to obtain reconstructed data of the phase current.

[0149] The first fault detection module 43 is configured to detect whether the motor is in a fault state according to the first sampling data and the reconstructed data.

[0150] In the first sampling module 41, the motor in actual application is usually a three-phase motor, such as a permanent magnet synchronous motor, and the currents of the three-phase motor are independent of each other, so the phase current in the embodiment is any one of the three-phase currents.

[0151] In an embodiment, the first sampling module 41 specifically comprises:

[0152] The motor stability detection unit is configured to determine whether the D-axis current and the Q-axis current of the motor are stable after the motor is started.

[0153] The first sampling unit is configured to sample the phase current of the motor in a first preset period when the D-axis current and the Q-axis current of the motor are stable.

[0154] The motor restart unit is configured to restart the motor until the D-axis current and the Q-axis current of the motor are stable when the D-axis current and the Q-axis current of the motor are unstable.

[0155] In actual application, the current may be unstable due to unstable power supply voltage, unstable load, poor contact in the line and other factors. In order to ensure stable operation of the motor, it is necessary to determine whether the D-axis current and the Q-axis current are stable.

[0156] For example, the D-axis current is sampled in a period of time, and whether the current is stable can be determined according to the variance or average value of the D-axis current. The same is true for the Q-axis current.

[0157] In the phase current reconstruction module 42, the first sampling data obtained by actually sampling the phase current during the operation of the motor is usually not a standard sine wave, and the reconstruction data obtained by reconstructing the phase current is a standard sine wave. By comparing the first sampling data with the phase current of the standard sine wave, the direct current bias fault can be determined according to the comparison result.

[0158] For example, the first sampling data and the reconstruction data are as shown in Figure 2 There is a certain deviation between the part of the sampling current value in the first sampling data and the reconstruction current value in the reconstruction data.

[0159] In an embodiment, the phase current reconstruction module 42 specifically comprises:

[0160] The first direct current bias calculation unit is configured to determine a first direct current bias value according to the first sampling data.

[0161] The reconstruction unit is configured to reconstruct the phase current based on the first sampling data when the first direct current bias value meets a first preset range.

[0162] The first fault detection unit is configured to detect that the motor is in a fault state when the first direct current bias value does not meet the first preset range.

[0163] That is, the phase current is reconstructed only when the first direct current bias value meets the first preset range. If the first direct current bias value does not meet the first preset range, it means that the motor has failed, and the phase current no longer needs to be reconstructed.

[0164] The first preset range is determined according to a preset direct current bias value of the motor.

[0165] For example, the phase current sampling range is -5A to 5A, and the preset DC bias value of the motor is 5A, and then the first preset range can be 4.85A to 5.15A. The first preset range can be further reduced to 4.9A to 5.1A.

[0166] Specifically, the first DC bias value is the average value of the first sampling data.

[0167] For example, the first DC bias value is calculated as follows:

[0168]

[0169] wherein, i bias1 is the first DC bias value, i is the first sampling data at time t, T1 is the first preset period, and t is time.

[0170] Specifically, the embodiment provides two reconstruction methods of the phase current.

[0171] One reconstruction method of the phase current is to reconstruct the phase current based on the variance of the first sampling data and the first DC bias value.

[0172] For example, the reconstruction data is calculated as follows:

[0173]

[0174] wherein, is the reconstruction data, i is the first sampling data at time t, and i bias1 is the first DC bias value, T1 is the first preset period, and ω is the electrical frequency of the motor.

[0175] Another reconstruction method of the phase current is to reconstruct the phase current based on the maximum value and the minimum value in the first sampling data.

[0176] For example, the reconstruction data is calculated as follows:

[0177]

[0178] wherein, is the reconstruction data, i max is the maximum value of the first sampling data, i min is the minimum value of the first sampling data, and ω is the electrical frequency of the motor.

[0179] In the first fault detection module 43, whether the motor is in a fault state is detected mainly according to the difference between the first sampling data and the reconstructed data. That is, if most of the sampling current values in the first sampling data are greatly different from the reconstructed current values corresponding to the reconstructed data, it is indicated that the motor is in a fault state.

[0180] In an embodiment, the first fault detection module 43 specifically comprises:

[0181] an error statistical unit, configured to obtain error data of the phase current according to the first sampling data and the reconstructed data;

[0182] a first evaluation unit, configured to calculate a first fault evaluation value according to the error data; wherein the first fault evaluation value is used to measure the dispersion degree of the error data;

[0183] a second fault detection unit, configured to detect that the motor is in a fault state when the first fault evaluation value is greater than a first preset threshold.

[0184] Specifically, the error data is the current difference value of the first sampling data and the reconstructed data at the same time.

[0185] In an embodiment, the first fault evaluation value is the variance of the error data.

[0186] For example, the calculation method of the first fault evaluation value is as follows:

[0187]

[0188] wherein var1 is the first fault evaluation value, T1 is the first preset period, t is time, Δi is the error data at time t, and Δi0 is the average value of the error data.

[0189] In another embodiment, the first fault evaluation value is the standard deviation of the error data.

[0190] In addition, the motor fault detection system further comprises the following modules for detecting the motor before starting:

[0191] a second sampling module, configured to sample the phase current of the motor in a second preset period before starting the motor to obtain second sampling data;

[0192] a second fault detection module, configured to detect whether the motor is in a fault state according to the second sampling data.

[0193] The second fault detection module comprises:

[0194] a second DC bias calculation unit, configured to determine the average value of the second sampling data as a second DC bias value;

[0195] a third fault detection unit configured to detect that the motor is in a fault state when the second DC bias value does not satisfy the second preset range;

[0196] a second evaluation unit configured to obtain a second fault evaluation value according to the second sampling data when the second DC bias value satisfies the second preset range; wherein the second fault evaluation value is used to measure the dispersion degree of the second sampling data;

[0197] a fourth fault detection unit configured to detect that the motor is in a fault state when the second fault evaluation value is greater than a second preset threshold.

[0198] In an embodiment, the second preset range is the same as the first preset range.

[0199] Specifically, the calculation manner of the second DC bias value is the same as the calculation manner of the first DC bias value.

[0200] For example, the calculation manner of the second DC bias value is as follows:

[0201]

[0202] wherein i bias2 is the second DC bias value, i is the second sampling data at time t, T2 is the second preset period, and t is time.

[0203] In an embodiment, the second fault evaluation value is the variance of the second sampling data.

[0204] For example, the calculation manner of the second fault evaluation value is as follows:

[0205]

[0206] wherein var2 is the second fault evaluation value, T2 is the second preset period, t is time, i is the second sampling data at time t, and i bias2 is the second DC bias value.

[0207] In another embodiment, the second fault evaluation value is the standard deviation of the second sampling data.

[0208] It should be noted that the fault state in the above embodiments is specifically a DC bias fault.

[0209] In an actual application scenario, the three-phase currents of a permanent magnet synchronous motor are sampled by using a circuit as shown in Figure 3 , and the sampled phase currents are amplified by using a differential amplification circuit as shown in Figure 4 . That is, the currents flowing through resistors V ain , V bin , and V cin are amplified.The three-phase currents of the permanent magnet synchronous motor are amplified and sampled to obtain three-phase currents of the permanent magnet synchronous motor.

[0210] In one specific example, the motor fault detection is as shown in the flowchart, and specifically: Figure 5

[0211] Before the motor starts, the six MOS tubes for controlling the on-off of the three-phase bridge arms are all closed, the phase current of the motor is sampled, the second DC bias value before the motor starts is calculated according to the second sampling data, and it is judged whether the second DC bias value before the motor starts meets the second preset range. If the second DC bias value before the motor starts does not meet the second preset range, it indicates that the motor has a DC bias fault; if the second DC bias value before the motor starts meets the second preset range, the variance of the second sampling data is calculated, and if the variance is greater than the second preset threshold, it indicates that the motor has a DC bias fault; if the variance does not exceed the second preset threshold, the motor can be started normally.

[0212] After the six MOS tubes for controlling the on-off of the three-phase bridge arms are all opened, the motor starts, and it is first judged whether the motor is stable, i.e., whether the Q-axis current and the D-axis current of the motor are stable. If not, the motor needs to be restarted, and if stable, the phase current of the running motor is sampled, the first DC bias value when the motor runs is calculated according to the first sampling data when the motor runs, and it is judged whether the first DC bias value when the motor runs meets the first preset range. If the first DC bias value when the motor runs does not meet the first preset range, it indicates that the motor has a DC bias fault, and if the first DC bias value when the motor runs meets the first preset range, the phase current is reconstructed according to the first sampling data when the motor runs.

[0213] After the reconstructed data of the phase current is obtained, the current difference value between the sampling current value in the first sampling data at the same time and the reconstructed current value in the reconstructed data is calculated to obtain error data. The variance of the error data is calculated, and if the variance is greater than the first preset threshold, it indicates that the motor has a DC bias fault.

[0214] The first preset threshold and the second preset threshold can be set according to actual conditions.

[0215] For example, the value range of the first preset threshold and the second preset threshold can be 0.005 to 0.015. In one specific embodiment, the first preset threshold and the second threshold are the same, both being 0.01.

[0216] Embodiment 3

[0217] The embodiment provides a range hood.

[0218] The range hood includes as Figure 7 ​The shown memory 52, processor 51 and computer program stored on the memory 52 and executable on the processor 51, when the processor 51 executes the computer program, implement the motor fault detection method described in Embodiment 1.

[0219] Figure 7 The shown range hood is only an example and should not bring any limitation to the function and use range of the embodiments of the present disclosure.

[0220] The components of the range hood 50 can include, but are not limited to, the at least one processor 51, the at least one memory 52, and a bus 53 that connects the different system components, including the memory 52 and the processor 51.

[0221] The bus 53 includes a data bus, an address bus, and a control bus.

[0222] The memory 52 can include volatile memory, such as random access memory (RAM) 521 and / or cache memory 522, and can further include non-volatile memory, such as read-only memory (ROM) 523.

[0223] The memory 52 can further include a program / utility 525 having a set (at least one) of program modules 524, including but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or a combination can include an implementation of a network environment.

[0224] The processor 51 performs various function applications and data processing by running the computer program stored in the memory 52, such as the similar user mining method described in Embodiment 1 of the present disclosure.

[0225] The range hood 50 can also communicate with one or more external devices 54 (such as a keyboard, a pointing device, etc.) via an input / output (I / O) interface 55. Also, the range hood 50 can communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 56. As Figure 7 As shown, the network adapter 56 communicates with other modules of the range hood 50 through the bus 53.

[0226] It should be understood that although not shown in the figure, other hardware and / or software modules can be used in conjunction with the range hood 50, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID (Redundant Array of Independent Disks) systems, tape drives, and data backup storage systems, etc.

[0227] It should be noted that, although several units / modules or sub-units / modules of the range hood are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, according to embodiments of the present disclosure, features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, features and functions of one unit / module described above can be further divided into embodied by multiple units / modules.

[0228] Embodiment 4

[0229] The embodiment provides a computer readable storage medium, and a computer program is stored on the computer readable storage medium. The program is executed by a processor to implement the motor fault detection method in the embodiment 1.

[0230] More specifically, the readable storage medium can include, but is not limited to, a portable disc, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0231] In possible embodiments, the present disclosure can also be implemented in the form of a program product, which includes program code for causing a terminal device to execute the motor fault detection method in the embodiment 1 when the program product is run on the terminal device.

[0232] The program code for executing the present disclosure can be written in any combination of one or more programming languages, and can be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on a remote device.

[0233] Although the specific embodiments of the present disclosure are described above, those skilled in the art should understand that this is only an illustration, and the protection scope of the present disclosure is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, and such changes and modifications all fall within the protection scope of the present disclosure.

Claims

1. A method of detecting a fault in an electric machine, characterized by, The motor fault detection method comprises: In the operation process of the motor, the phase current of the motor is sampled in a first preset period to obtain first sampling data; The phase current is reconstructed based on the first sampling data to obtain reconstructed data of the phase current; Whether the motor is in a fault state is detected according to the first sampling data and the reconstructed data; The step of reconstructing the phase current based on the first sampling data comprises: The phase current is reconstructed based on the variance and average value of the first sampling data; and / or, the phase current is reconstructed based on the maximum value and minimum value in the first sampling data; The step of detecting whether the motor is in a fault state according to the first sampling data and the reconstructed data comprises: Error data of the phase current is obtained according to the first sampling data and the reconstructed data; A first fault evaluation value is calculated according to the error data; wherein the first fault evaluation value is used to measure the dispersion degree of the error data; If the first fault evaluation value is greater than a first preset threshold, it is detected that the motor is in the fault state.

2. The motor fault detection method of claim 1, wherein The step of reconstructing the phase current based on the first sampling data comprises: A first DC bias value is determined according to the first sampling data; If the first DC bias value meets a first preset range, the phase current is reconstructed based on the first sampling data.

3. The motor fault detection method of claim 2, wherein, The first DC bias value is the average value of the first sampling data.

4. The method of claim 1, wherein, The motor fault detection method further comprises: Before starting the motor, the phase current of the motor is sampled in a second preset period to obtain second sampling data; Whether the motor is in a fault state is detected according to the second sampling data.

5. The method of claim 4, wherein, The step of detecting whether the motor is in a fault state according to the second sampling data comprises: The average value of the second sampling data is determined as a second DC bias value; If the second DC bias value meets a second preset range, a second fault evaluation value is obtained according to the second sampling data; wherein the second fault evaluation value is used to measure the dispersion degree of the second sampling data; If the second fault evaluation value is greater than a second preset threshold, it is detected that the motor is in the fault state.

6. The method of claim 1, wherein, The step of sampling the phase current of the motor in a first preset period comprises: Whether the D-axis current and the Q-axis current of the motor are stable is judged; If stable, the phase current of the motor is sampled in a first preset period.

7. A motor fault detection system characterized by, The motor fault detection system comprises: A first sampling module for sampling the phase current of the motor in a first preset period in the operation process of the motor to obtain first sampling data; A phase current reconstruction module for reconstructing the phase current based on the first sampling data to obtain reconstructed data of the phase current; A first fault detection module for detecting whether the motor is in a fault state according to the first sampling data and the reconstructed data; The phase current reconstruction module comprises: A first reconstruction unit for reconstructing the phase current based on the variance and average value of the first sampling data; And / or, a second reconstruction unit configured to reconstruct the phase current based on a maximum value and a minimum value in the first sampling data; the first fault detection module comprises: an error statistics unit configured to obtain error data of the phase current according to the first sampling data and the reconstructed data; a first evaluation unit configured to calculate a first fault evaluation value according to the error data; wherein the first fault evaluation value is used to measure a dispersion degree of the error data; a second fault detection unit configured to detect that the motor is in the fault state when the first fault evaluation value is greater than a first preset threshold.

8. An extractor hood comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that The processor executes the computer program to implement the motor fault detection method in any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the motor fault detection method in any one of claims 1-6.

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