MOTOR FAULT DETECTING METHOD AND MOTOR FAULT DETECTING SYSTEM

• US 20160011268A1
• Filed: 01/30/2015
• Published: 01/14/2016
• Est. Priority Date: 07/10/2014
• Status: Abandoned Application

• Current Assignee: National Cheng KUNG University (Government of The Republic of China)
• Original Assignee: National Cheng KUNG University (Government of The Republic of China)
• Inventors: TSAI, Mi-Ching, LIN, Shao-Kai, WANG, Sheng-He
• Application Number:
  US14/609,444
• Publication Number:
  US 20160011268A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01R 31/343 in operation

Abstract

A motor fault detecting method and a motor fault detecting system are provided. An electrical frequency and sensing current data of a brushless motor is obtained. An empirical mode decomposition (EMD) is performed on the sensing current data to obtain intrinsic mode functions (IMF). A feature IMF is obtained from the IMFs, in which the feature IMF is feature current data, and a frequency of the feature current data complies with the electrical frequency of the brushless motor. An electrical impedance is calculated according to an input voltage of the brushless motor and the feature current data. The electrical impedance is compared with a reference electrical impedance to determine if the brushless motor is abnormal. The reference electrical impedance is calculated according to training sensing current data of a training brushless motor which is in a healthy state. Accordingly, whether the brushless motor is abnormal is effectively determined.

Claims

1. A motor fault detecting method for inspecting a health state of a brushless motor, the motor fault detecting method comprising:

obtaining an electrical frequency of the brushless motor;

obtaining sensing current data of the brushless motor when the brushless motor is operating;

performing an empirical mode decomposition (EMD) of a Hilbert-Huang transform (HHT) on the sensing current data to obtain a plurality of intrinsic mode functions (IMFs);

obtaining a feature IMF from the IMFs, wherein the feature IMF is feature current data, and a frequency of the feature current data complies with the electrical frequency of the brushless motor;

calculating at least one electrical impedance according to an input voltage of the brushless motor and the feature current data; and

comparing the at least one electrical impedance with a reference electrical impedance to determine whether the brushless motor is abnormal, wherein the reference electrical impedance is calculated according to training sensing current data of at least one training brushless motor which is in a healthy state.

2. The motor fault detecting method of claim 1, wherein the number of the at least one electrical impedance is greater than 1, and the electrical impedances are respectively corresponding to a plurality of sampling time points, and the operation of comparing the at least one electrical impedance with the reference electrical impedance comprises:

calculating a root mean square impedance of the electrical impedances; and

comparing the root mean square impedance with the reference electrical impedance to determine whether the brushless motor is abnormal,

wherein the operation of obtaining the electrical frequency of the brushless motor comprises;

calculating the electrical frequency of the brushless motor according to a rotational speed of the brushless motor and the number of poles of the brushless motor.

3. The motor fault detecting method of claim 2, further comprising:

calculating an electrical frequency of the at least one training brushless motor according to a rotational speed of the at least one training brushless motor and the number of poles of the at least one training brushless motor;

obtaining the training sensing current data of the at least one training brushless motor;

performing the EMD of the HHT on the training sensing current data to obtain a plurality of training IMFs;

obtaining at least one training feature IMF from the training IMFs, wherein the at least one training feature IMF is training feature current data, and a frequency of the training feature current data complies with the electrical frequency of the at least one training brushless motor;

calculating a plurality of training electrical impedances according to an input voltage of the at least one training brushless motor and the training feature current data; and

generating the reference electrical impedance according to at least one root mean square impedance of the training electrical impedances.

4. The motor fault detecting method of claim 3, wherein the number of the at least one training brushless motor is greater than 1, and the number of the at least one root mean square impedance is greater than 1, and each of the training brushless motors has the corresponding root mean square impedance, and the operation of generating the reference electrical impedance according to the at least one root mean square impedance of the training electrical impedances comprises:

calculating an average value of the root mean square impedances to generate the reference electrical impedance.

5. The motor fault detecting method of claim 3, wherein the operation of obtaining the at least one training feature IMF from the training IMFs comprises:

performing a Hilbert transform on each of the training IMFs to obtain a plurality of transforming functions;

obtaining a plurality of instant frequencies of each of the transforming functions, and obtaining an average frequency of the instant frequencies; and

comparing the electrical frequency of the at least one training brushless motor with the average frequency of each of the transforming functions, thereby obtaining the at least one training feature IMF.

6. The motor fault detecting method of claim 5, further comprising:

obtaining a training number of the at least one training feature IMF among the training IMFs,

wherein the operation of the obtaining the feature IMF from the IMFs comprises;

obtaining the feature IMF according to the training number.

7. A motor fault detecting system, comprising:

a brushless motor comprising an inverter and a motor;

a current sensing unit disposed between the inverter and the motor; and

a processor configured to obtain an electrical frequency of the brushless motor, and obtain sensing current data of the brushless motor through the current sensing unit when the brushless motor is operating, and perform an empirical mode decomposition (EMD) of a Hilbert-Huang transform (HHT) on the sensing current data to obtain a plurality of intrinsic mode functions (IMFs), and obtain a feature IMF from the IMFs, wherein the feature IMF is feature current data, and a frequency of the feature current data complies with the electrical frequency of the brushless motor,

wherein the processor is configured to calculate at least one electrical impedance according to an input voltage of the brushless motor and the feature current data, and compare the at least one electrical impedance with a reference electrical impedance to determine whether the brushless motor is abnormal, wherein the reference electrical impedance is calculated according to training sensing current data of at least one training brushless motor which is in a healthy state.

8. The motor fault detecting system of claim 7, where the number of the at least one electrical impedance is greater than 1, and the electrical impedances are respectively corresponding to a plurality of sampling time points;

and

the processor is further configured to calculate the electrical frequency of the brushless motor according to a rotational speed of the brushless motor and the number of poles of the brushless motor, and calculate a root mean square impedance of the electrical impedances, and compare the root mean square impedance with the reference electrical impedance to determine whether the brushless motor is abnormal,

9. The motor fault detecting system of claim 8, wherein the processor is further configured to calculate an electrical frequency of the at least one training brushless motor according to a rotational speed of the at least one training brushless motor and the number of poles of the at least one training brushless motor;

the processor is further configured to obtain the training sensing current data of the at least one training brushless motor, and perform the EMD of the HHT on the training sensing current data to obtain a plurality of training IMFs, and obtain at least one training feature IMF from the training IMFs, wherein the at least one training feature IMF is training feature current data, and a frequency of the training feature current data complies with the electrical frequency of the at least one training brushless motor; and

the processor is further configured to calculate a plurality of training electrical impedances according to an input voltage of the at least one training brushless motor and the training feature current data, and generate the reference electrical impedance according to at least one root mean square impedance of the training electrical impedances.

10. The motor fault detecting system of claim 9, wherein the number of the at least one training brushless motor is greater than 1, and the number of the at least one root mean square impedance is greater than 1, and each of the training brushless motors has the corresponding root mean square impedance;

and

the processor is further configured to calculate an average value of the root mean square impedances to generate the reference electrical impedance.

11. The motor fault detecting system of claim 9, wherein the processor is further configured to perform a Hilbert transform on each of the training IMFs to obtain a plurality of transforming functions, and obtain a plurality of instant frequencies of each of the transforming functions, and obtain an average frequency of the instant frequencies, and compare the electrical frequency of the at least one training brushless motor with the average frequency of each of the transforming functions, thereby obtaining the at least one training feature IMF.

12. The motor fault detecting system of claim 11, wherein the processor is further configured to obtain a training number of the at least one training feature IMF among the training IMFs, and to obtain the feature IMF from the IMFs according to the training number.