Abstract
The detection results need to be analyzed and distinguished by professional technicians in the fault detection methods for induction motors based on signal processing and it is difficult to realize the automatic identification of stator and rotor faults. A method for identifying stator and rotor faults of induction motors based on machine vision is proposed to solve this problem. Firstly, Park’s vector approach (PVA) is used to analyze the three-phase currents of the motor to obtain Park’s vector ring (PVR). Then, the local binary patterns (LBP) and gray level cooccurrence matrix (GLCM) are combined to extract the image features of PVR. Finally, the vectors of image features are used as input and the types of induction motor faults are identified with the help of a random forest (RF) classifier. The proposed method has achieved high identification accuracy in both the Maxwell simulation experiment and the actual motor experiment, which are 100% and 95.83%, respectively.
Highlights
Induction motors (IMs) are currently one of the motors with the largest usage and widest application range [1, 2]
The fault diagnosis methods of IM can be divided into three categories roughly, which are process modeling, signal processing, and artificial intelligence. e method based on process modeling usually establishes a mathematical model of faulty motor and converts the fault detection problem into the identification problem of corresponding parameters
A model with interturn short-circuit fault was established based on reference frame transformation theory and the fault detection was realized by analyzing the changes of currents, speed, and torque in [13, 14]. e influence of the harmonics of the supply voltage was considered to establish a dynamic model with interturn short-circuit fault and the instantaneous power of the motor was used to detect the Mathematical Problems in Engineering stator fault in [15]. e location parameters of interturn short-circuit fault were considered to establish a faulty model with any short-circuit coil and the fault was detected by analyzing the negative sequence components of the currents in [16]
Summary
Induction motors (IMs) are currently one of the motors with the largest usage and widest application range [1, 2]. A model with interturn short-circuit fault was established based on reference frame transformation theory and the fault detection was realized by analyzing the changes of currents, speed, and torque in [13, 14]. Park’s vector modulus was combined with the Hilbert transform to extract the spectrum information of the currents and the detection of interturn short-circuit fault was realized in [25]. Based on the fault detection method by signal processing, this paper introduces machine vision to analyze and recognize the detection results to identify the interturn short-circuit and BRB faults of IM automatically.
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