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Induction motors are important equipment in modern industry. However, the occurrence of fatigue failure following an extended period of operation invariably results in a catastrophic failure. As a result, monitoring and diagnosing induction motors is critical to avoiding unplanned shutdowns caused by premature failures. This article aims to develop an effective method for motor fault detection using time–frequency contents of vibration signals and an attention-based convolutional neural network model. First, the vibration signals are collected and labeled into five different categories: normal condition, outer ring fault, inner ring fault, misalignment condition, and broken rotor bar. Then, using the Morlet function, continuous wavelet transform (CWT) converts the vibratory time-series signals to the scalogram feature images. The time–frequency feature images are created after downsampling and converting the measured vibration signals to the frequency domain. These images are then resized and fed into the proposed convolutional attention neural network (CANN) to identify various induction motor failures. The experimental results demonstrate that the suggested model can provide an excellent diagnosis accuracy of 99.43%, significantly better than the state-of-the-art deep learning approaches for fault diagnosis. Moreover, the developed model’s robustness is validated against adversarial attacks based on the fast gradient sign method (FGSM) by including white Gaussian noise. [ABSTRACT FROM AUTHOR]

• Developing a reliable and secure IoT against transformer cyberattacks. • Introducing a deep 1D-CNN for fault classification against uncertainties. • Deep 1D-CNN is combined with the IoT for online transformer monitoring. • The introduced architecture increases the transformer life time. The distribution of the power transformers at a far distance from the electrical plants represents the main challenge against the diagnosis of the transformer status. This paper introduces a new integration of an Internet of Things (IoT) architecture with deep learning against cyberattacks for online monitoring of the power transformer status. A developed one dimension convolutional neural network (1D-CNN), which is characterized by robustness against uncertainties, is introduced for fault diagnosis of power transformers and cyberattacks. Further, experimental scenarios are performed to confirm the effectiveness of the proposed IoT architecture. While compared to previous approaches in the literature, the accuracy of the new deep 1D-CNN is greater with 94.36 percent in the usual scenario, 92.58 percent when considering cyberattacks, and ±5% uncertainty. The proposed integration between the IoT platform and the 1D-CNN can detect the cyberattacks properly and provide secure online monitoring for the transformer status via the internet network. [ABSTRACT FROM AUTHOR]

This paper introduces an integrated IoT architecture to handle the problem of cyber attacks based on a developed deep neural network (DNN) with a rectified linear unit in order to provide reliable and secure online monitoring for automated guided vehicles (AGVs). The developed IoT architecture based on a DNN introduces a new approach for the online monitoring of AGVs against cyber attacks with a cheap and easy implementation instead of the traditional cyber attack detection schemes in the literature. The proposed DNN is trained based on experimental AGV data that represent the real state of the AGV and different types of cyber attacks including a random attack, ramp attack, pulse attack, and sinusoidal attack that is injected by the attacker into the internet network. The proposed DNN is compared with different deep learning and machine learning algorithms such as a one dimension convolutional neural network (1D-CNN), a supported vector machine model (SVM), random forest, extreme gradient boosting (XGBoost), and a decision tree for greater validation. Furthermore, the proposed IoT architecture based on a DNN can provide an effective detection for the AGV status with an excellent accuracy of 96.77% that is significantly greater than the accuracy based on the traditional schemes. The AGV status based on the proposed IoT architecture with a DNN is visualized by an advanced IoT platform named CONTACT Elements for IoT. Different test scenarios with a practical setup of an AGV with IoT are carried out to emphasize the performance of the suggested IoT architecture based on a DNN. The results approve the usefulness of the proposed IoT to provide effective cybersecurity for data visualization and tracking of the AGV status that enhances decision-making and improves industrial productivity. [ABSTRACT FROM AUTHOR]

Induction motors are widely used in various industrial applications due to their simplicity, robustness, and high efficiency. In recent years, deep learning approaches have shown great potential for detecting and diagnosing faults in induction motors. However, it was observed that the convolutional neural network (CNN) might miss the crucial long-range temporal dependencies of the significant features. At the same time, the recursive neural network (RNN) may face issues such as vanishing gradient and less computational efficiency if the data is too long with complex patterns. This paper proposes a novel deep-learning model that combines a one-dimensional CNN and an RNN into two independent pipelines. The spatial, temporal, long-term, and short-term features can be extracted from the raw input signal. A multi-head mechanism, a state-of-the-art deep learning technique in natural language processing (NLP), enables the model to focus more on relevant features and prevent overfitting. Specifically, our method simplifies the conventional fault diagnosis workflow while maintaining competitive accuracy compared to several state-of-the-art methods. By leveraging the ability to detect faults directly from raw, unprocessed electrical signals, our approach offers a practical, cost-effective solution for real-time fault detection in industrial applications. This innovation has the potential to significantly reduce downtime and the associated operational costs, thereby providing immediate practical value in various industrial settings. [ABSTRACT FROM AUTHOR]