Classification of Induction Motor Fault and Imbalance Based on Vibration Signal Using Single Antenna's Reactive Near Field

Early fault diagnosis is imperative for the proper functioning of rotating machines. It can reduce economic losses in the industry due to unexpected failures. Existing fault analysis methods are either expensive or demand expertise for the installation of the sensors. This article proposes a novel method for the detection of bearing faults and imbalance in induction motors using an antenna as the sensor, which is noninvasive and cost-efficient. Time-varying S11 is measured using an omnidirectional antenna, and it is seen that the spectrogram of S11 shows unique characteristics for different fault conditions. The experimental setup has analytically evaluated the vibration frequencies due to fault and validated the characteristic fault frequency by applying FFT analysis on the captured S11 data. This article has evaluated the average power content of the detected signals at normal and different fault conditions. A deep learning model is used to classify the faults based on the reflection coefficient ( S11). It is found that classification accuracy of 98.2% is achieved using both magnitude and phase of S11, 96% using the magnitude of S11 and 92.1% using the phase of S11. The classification accuracy for different operating frequencies, antenna location, and time windows are also investigated.

Classification of Lower Limb Activities Based on Discrete Wavelet Transform Using On-Body Creeping Wave Propagation

This article investigates how the creeping wave propagation around the human thigh could be used to monitor the leg movements. The propagation path around the human thigh gives information regarding leg motions that can be used for the classification of activities. The variation of the transmission coefficient is measured between two on-body polyethylene terephthalate (PET) flexible antennas for six different leg-based activities that exhibit unique time-varying signatures. A discrete wavelet transform (DWT) along with different classifiers, such as support vector machine (SVM), decision trees, naive Bayes, and K-nearest neighbors (KNN), is applied for feature extraction and classification to evaluate the efficiency for classifying different activity signals. Additional algorithms, such as dynamic time warping (DTW) and deep convolutional neural network (DCNN), have also been implemented, and in each case, SVM with DWT outperforms the others. Simulation to evaluate a specific absorption rate (SAR) is carried out as the antenna is positioned on the human thigh leaving no gap. The results show that the SAR is within the threshold as per the Federal Communications Commission (FCC) standard.