A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

This study addresses the early detection of half-broken bars in induction motors. Most research on fault detection focuses on current signature analysis because it can provide online and remote monitoring at low cost. However, errors caused by spectral leakage around the fundamental frequency supply reduce its reliability for fault diagnosis at the incipient stages. The proposed detection technique is based on a spectral analysis of the motor speed. To detect a half-broken bar, measurements of stator voltages and currents are processed by an adaptive extended Kalman filter for system state estimation. Then, spectral analysis is applied to the motor speed to obtain its power spectral distribution. Finally, the fault harmonic amplitude at 2sfs is used as a fault indicator for fault diagnosis. The proposed approach has three main advantages: 1) speed measurements are unaltered by spectral leakage around the main frequency supply, 2) the cost of speed sensors is avoided, and 3) the absence of connecting shaft couplings prevents corruption of speed data by mechanical interference. Numerical simulations and experimental results were carried out for the squirrel cage induction motor to provide encouraging validation of the proposed early fault detection technique

Section: Introductionmentioning

confidence: 99%

Rotor speed estimation for half-broken bar detection in induction motors using Kalman filtering

Garcia-Calva,

Morinigo-Sotelo,

Mwata-Velu

et al. 2024

Enhancing motor impedance measurements: broadening the spectrum from low to high frequencies

Jie,

See,

Chang

et al. 2024

Three-phase induction motors serve as critical parts in various industrial applications, lauded for their high energy efficiency and notable power density. Obtaining their broadband impedance information is paramount for analyzing conducted emissions, evaluating overvoltage ringing, and assessing motor health status. Nonetheless, conventional methods for motor impedance measurements typically rely on Kelvin clip leads or extension cables, which are effective only in a relatively low-frequency region (i.e., below 1 MHz). This paper presents an improved approach to extend the measurable spectrum from low to high frequencies, up to 120 MHz. The proposed method develops a series of fixture adapters to enable seamless interconnection between the terminals of an induction motor and the coaxial ports of an impedance analyzer. The parasitics introduced by these adapters are identified using boundary-element analysis, and their impacts are minimized based on the de-embedding concept. Experimental results affirm the accuracy and effectiveness of the proposed method for four types of motor impedances (i.e., single-phase, phase-to-ground, common-mode, and differential-mode) across a broad frequency range from 100 Hz to 120 MHz. Moreover, the inaccuracy of motor impedance measurements at high frequencies (i.e., above 1 MHz) using conventional methods, including Kelvin clip leads and extension cables, is also demonstrated.

Damage-related imbalance identification for UAV composite propeller blades based on bidirectional temporal convolutional network and a flexible sensing system

Gu,

Zhang,

Zhu

et al. 2024

Damage to the composite propeller blades could lead to rotational imbalance, which seriously affects the operational safety of unmanned aerial vehicles (UAVs), therefore, a novel method combining the Teager energy operator and bidirectional temporal convolutional network is proposed for detecting, localizing, and quantifying the damage-related imbalance in the blades. A flexible sensing system that contains MEMS accelerometers, signal conditioning, and wireless transmission is integrated with the composite propeller for in-situ signal acquisition of the propeller blades. Teager energy operator (TEO) is applied to demodulate and enhance the pulse compositions in vibration signals and singular value decomposition (SVD) is employed to suppress random noise, resulting in denoised Teager energy spectrums for model input. Temporal convolutional network (TCN) has been widely used in sequence signal modeling because the causal dilated convolution could learn the context information of sequence signals while maintaining the advantages of parallel computing. To fully extract the signal features, bidirectional temporal convolutional network (BiTCN) models are established to learn both the forward and backward signal features. Experimental verification results show that the proposed method detects the existence of imbalance with 100% accuracy, and the accuracies of localization and quantization are 99.65% and 98.61%, respectively, which are much higher than those of the models with the original signal as input. In addition, compared with the other four different algorithms, BiTCN is superior in terms of convergence speed and prediction accuracy.