A failure of insulation is one of the main reasons for electric machine outage. Online condition monitoring (CM) of an incipient degradation of turn insulation could eliminate the potential fault at the early stage, which is attractive but challenging. To improve the sensitivity and availability of CM, a noninvasive technique is presented in this article for the inverter-fed electric machine utilizing the existing high-frequency (HF) switching oscillations during pulsewidth modulation operation. First, the tail component sensitive to turn insulation is selected among the dominant oscillation modes in switching current, which can penetrate the winding of machine within an adequate frequency band (e.g., several hundreds of kHz). Then, a noncontact and simple HF sensor is developed for switching oscillation current capture. The CM system, procedure, and turn insulation state indicator are devised. After that, experimental work is carried out on a 3-kW permanent magnet synchronous machine test rig. The results show good sensitivity (about 2%) to the local incipient degradation of turn insulation with insusceptibility to the operating conditions. Finally, further discussion is given to demonstrate the performance of the method.

This study proposes a novel resonance demodulation frequency band selection method named the initial center frequency-guided filter (ICFGF) to diagnose the bearing fault. The proposed technology has a better performance on resisting the interference from the random impulses. More explicitly, the ICFGF can be summarized as two steps. In the first step, a variance statistic index is applied to evaluate the energy spectrum distribution, which can adaptively determine the center frequency of the fault impulse and suppress the interference from random impulse effectively. In the second step, a modified mayfly optimization algorithm (MMA) is applied to search the optimal resonance demodulation frequency band based on the center frequency from the first step, which has faster convergence. Finally, the filtered signal is processed by the squared envelope spectrum technology. Results of the proposed method for signals from an outer fault bearing and a ball fault bearing indicate that the ICFGF works well to extract bearing fault feature. Furthermore, compared with some other methods, including fast kurtogram, ensemble empirical mode decomposition, and conditional variance-based selector technology, the ICFGF can extract the fault characteristic more accurately.

Owing to the characteristics of intermittent faults (IFs), such as short duration and randomness in analog circuits, it is difficult to collect a sufficient amount of manually labeled fault data for training detection model. To this end, this article explores the application of generative adversarial network (GAN) in the field of IF detection for analog circuits. The proposed model consists of two networks, an autoencoder and a discriminator, which are trained by competing against each other while working together to grasp the underlying concepts in the target. In addition, a spatial Fourier convolution (SFC) block is proposed and introduced into the discriminator to enhance the detection performance of the model. The training process is divided into two stages, the first stage through the autoencoder and the discriminator of the adversarial learning, so that the discriminator has the initial fault detection ability. The second stage trains the autoencoder and the discriminator with a few fault samples to enhance the fault detection capability of the two networks working cooperatively. The proposed method is applied to two typical analog circuits, namely, a power amplifier circuit and a low-pass filter circuit. The results show that, guided by only a small number of fault samples, the proposed method has a great advantage over other state-of-the-art detection methods.