This study presents a transfer learning-based approach for fault diagnosis of AC motors. Specifically, it addresses the diagnosis of motor faults under conditions where motor capacity changes, random speed variations (5%–15%), and load fluctuations exist, using only current signals. Various motor faults were experimentally analyzed using a laboratory testbed. Transfer learning was applied to extract characteristic features that are robust to capacity, speed, and load variations, ensuring that these features are unaffected by fluctuations in the loss function. The results demonstrate that transfer learning can effectively diagnose faults even in changing environments, suggesting its potential application in motor condition monitoring in real industrial settings.

This paper presents a non-contact fault diagnostic method for ball bearing using adaptive wavelet denoising, statistical-spectral acoustic features, and one-dimensional (1D) convolutional neural networks (CNN). The health conditions of the ball bearing are monitored by microphone under noisy condition. To eliminate noise, adaptive wavelet denoising method based on kurtosis-entropy (KE) index is proposed. Multiple acoustic features are extracted base on expert knowledge. The 1D ResNet is used to classify the health conditions of the bearings. Case study is presented to examine the proposed method’s capability to monitor the condition of ball bearings. The fault diagnosis results were compared with and without the adaptive wavelet denoising. The results show its effectiveness of the proposed fault diagnostic method using acoustic signals.

The rise of smart factories has heightened the demand for automated maintenance, and normal-data-based anomaly detection has proved particularly effective in environments where anomaly data are scarce. This method, which does not require anomaly data during training, has prompted researchers to focus not only on detecting anomalies but also on classifying severity levels by using anomaly scores. However, the existing performance metrics, such as the area under the receiver operating characteristic curve (AUROC), do not effectively reflect the performance of models in classifying severity levels based on anomaly scores. To address this limitation, we propose the weighted sum of the area under the receiver operating characteristic curve (WS-AUROC), which combines AUROC with a penalty for severity level differences. We conducted various experiments using different penalty assignment methods: uniform penalty regardless of severity level differences, penalty based on severity level index differences, and penalty based on actual physical quantities that cause anomalies. The latter method was the most sensitive. Additionally, we propose an anomaly detector that achieves clear separation of distributions and outperforms the ablation models on the WS-AUROC and AUROC metrics.

This article proposes a deep learning-based thermal-infrared (TIR) remote target detection system for maritime rescue with a self-collected real TIR dataset and corresponding data augmentation method based on generative adversarial network (GAN). We have collected and established a real field TIR dataset consisting of multiple scenes imitating actual human rescue scenarios using a TIR camera (FLIR M364C). In addition, synthetic TIR data from a game (ARMA3) to augment the real TIR data are further collected to address dataset scarcity and improve the model performance. However, a significant domain gap exists between the real and synthetic TIR datasets. Hence, a proper domain adaptation (DA) algorithm is essential to overcome the gap. We suggest a target-background separation (TBS) scheme during the DA to mitigate this gap while preserving the shapes and locations of the small-size targets even after the domain transfer. Furthermore, a fixed-pattern kernel module inserted at the network front is proposed to improve the signal-to-noise ratio (SNR) as TIR remote targets inherently suffer from unclear boundaries and heavy clutters. The experimental results reveal that the segmentation network trained on both real and domain-translated synthetic TIR data shows improved performance compared to that trained on only real TIR data. Moreover, the segmentation network with the fixed-weight (FW) kernel module shows better performance than state-of-the-art methods in terms of every evaluation metric.

This paper proposes machine-independent feature engineering for winding inter-turn short circuit fault that uses electrical current signals. Electrical current signal collected from permanent magnet synchronous motor (PMSM) is subjected to different environmental and operational conditions. To solve these problems, robust current signal imaging method and deep learning-based feature extraction method are developed. The overall procedure includes the following three key steps: (1) transformation of a one-dimensional time-series current signal to a two-dimensional image, (2) extracting features using convolutional neural networks, and (3) calculating a health indicator using Mahalanobis distance. Transformation of the time-series signal is based on recurrence plots (RP). The proposed RP method develops from feature engineering that provides the dominant fault feature representations in a robust way. The proposed RP is designed that maximizes the features of inter-turn short fault and minimizes the effect of noise from systems with various capacities. To demonstrate the validity of the proposed method, two case studies are conducted using an artificial fault seeded testbed with two different capacities of motor. By calculating the feature using only the electrical current signal of the motor without the parameters related to the capacity of the motor, the proposed feature can be applied to motors with different capacities while maintaining the same performance.

The driving speed of bearing in rotating machines is usually variable rather than constant, so methods for accurate fault diagnosis under varying speed condition is required. In this paper, we propose the fault diagnosis model using dynamic convolutional neural network (DY-CNN) that considers variation of fault frequency characteristics by utilizing content-adaptive kernels for fault diagnosis of bearing under varying speed condition. As the input of model, 1-second intervals of vibration data with varying speed condition were used. These kernels adapt to short interval vibration data with varying speed condition by applying weighted sum of trained basis kernels. DY-CNN-based fault diagnosis model improved diagnosis accuracy by 7.07% compared to CNN-based fault diagnosis model. In addition, we showed that the adaptive kernels changed depending on fault types. DY-CNN-based fault diagnosis model adapted itself to fault types, and it performed accurate and robust fault diagnosis of ball bearing under varying speed condition.