Induction Motor Torque Prediction Using Dual Function Radar Received Polarised Signals and Machine Learning Algorithm

Induction motor is used for different applications in industries such as grinding, milling, mining and automation. Induction motor performance degrades over time due to continuous operation, over load, transient unbalanced supply voltage and current. Hence, induction motor performance and efficiency are evaluated through measurement of motor torque. The torque measurement using various sensors such as acoustic, mems-vibration, strain gauge, in line sensors, never provides the inaccurate results due to contact type sensors and improper mounting of sensor on the motor. Moreover, existing methods never provides inaccurate torque measurement for different types of motor duty cycle such as continuous duty, intermittent periodic duties, short-time duty and continuous operation with intermittent load. In this paper, novel non-contact method measures torque with Dual-function ultra-wide band (UWB) (DFR) Radar sensor. DFR acquires polarized signal reflected from air gap of motor magnetic flux. Moreover, air gap magnetic flux radiation through motor ventilator is directly proportional to motor torque. In this paper, motor torque is measured through air gap magnetic flux radiation and DFR sensor. The electromagnetic waves from DFR is reflected by the air gap magnetic flux emitted from motor and the reflected waves are polarized. The reflected DFR sensor signal from air gap magnetic flux has high polarity charge and resonance. From received polarized signal, Instantaneous Frequency (IF) is obtained through Multi-Synchro Squeezing Transform (MSST) algorithm. The motor torque is measured with IF and Gaussian regression algorithm. Torque spikes in Induction motor is analyzed during frequent change in heavy, low load, and induced transient Torque prediction through MSST IF frequency and Gaussian Regression is compared with Torque predicted through different transform such as Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT), Dyadic Wavelet Transform (DyWT). The MSST based torque measurement provides 96% of accuracy, when compared with traditional strain gauge torque measuring instrument. Initial review.