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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.

Facial mask detection technology has become increasingly important even beyond the context of the COVID-19 pandemic. Along with the advancement in facial recognition technology, face mask detection has become a crucial feature for various applications. This paper introduces an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research. The proposed paradigm is built on the basis of economic consideration and is easy to implement. Yet, the used YOLOv4-tiny is one of the fastest object detection models to exist. A mask detection camera (MaskCam) that leverages the computing power of NVIDIA's Jetson Nano edge nanodevices was built side by side with a smart camera application to detect a mask on the face of an individual. MaskCam distinguishes between mask wearers, those who are not wearing masks, and those who are not wearing masks properly according to MQTT protocol. Furthermore, a self-developed web browsing application comes with the MaskCam system to collect and visualize statistics for qualitative and quantitative analysis. The practical results demonstrate the superiority and effectiveness of the proposed smart mask detection system. On the one hand, YOLOv4-full obtained the best results even at smaller resolutions, although the frame rate is too small for real-time use. On the other hand, it is twice as fast as the other detection models, regardless of the quality of detection. Consequently, inferences may be run more frequently over the entire video sequence, resulting in more accurate output. [ABSTRACT FROM AUTHOR]

This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes. • Introducing a cost-effective multi-sensor data fusion for the milling chatter detection. • The WPD is utilized and tuned based on the kurtosis and crest factors. • The irrelevant features are identified and eliminated utilizing the RFE method. • Several machine learning techniques have been adopted to identify chatter vibration. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach of the real-time chatter detection in the milling process is presented based on the scalogram of the continuous wavelet transform (CWT) and the deep convolutional neural network (CNN). The cutting force signals measured from the stable and unstable cutting conditions were converted into two-dimensional images using the CWT. When chatter occurs, the amount of energy at the tooth passing frequency and its harmonics are shifted toward the chatter frequency. Hence, the scalogram images can serve as input to the CNN framework to identify the stable, transitive, and unstable cutting states. The proposed method does not require the subjective feature-generation and feature-selection procedures, and its classification accuracy of 99.67% is higher than the conventional machine learning techniques described in the existing literature. The result demonstrates that the proposed method can effectively detect the occurrence of chatter. [ABSTRACT FROM AUTHOR]

• The uncertainties due to the cyberattacks are tackled with a low computation burden deep learning algorithm. • A new IoT structure for monitoring the induction motor is developed. • A graphical visualization is performed to identify the faults of the induction motors. • Parameterizing the DNN of the fault recognition model is investigated. • The results confirm the robustness and resiliency of the designed model. Maintaining electrical machines in good working order and increasing their life expectancy is one of the main challenges. Precocious and accurate detection of faults is crucial to this process. Induction motors (IMs) are among these machines widely utilized in various fields including industrial and domestic applications that require effective detection of their status. This paper proposes a novel fault recognition and correction (FRC) scheme based on the internet of things (IoT) and deep learning for IMs. In the developed system, vibration signals during motor operation are used to generate bearing fault features, which are inputted to the designed deep learning model to successfully identify bearing faults. The robustness of the proposed approach is tested against a false data injection (FDI) attack. Further, the proposed deep learning approach has been assessed and compared with other state-of-the-art algorithms available in the literature. Experimental testing has been carried out on a real IM to perform the suitability of the developed fault detection and correction scheme. Compared to other fault recognition techniques, the proposed method proves to be more effective. In essence, the results verified the robustness of the novel proposed strategy against the FDI attack making it possible to recognize faults with confidence and improve decision-making to determine the motor's status. [ABSTRACT FROM AUTHOR]

In the "Industry 4.0" era, autonomous and self-adaptive industrial machining attracts significant attention in professional manufacturing. This trend originates from the rising demands for machining quantity and part quality, as well as the need for optimization of the overall operational cost. Under such circumstances, tool condition monitoring (TCM) systems contribute to the mentioned goals by monitoring the cutting tool operation states, which detect signs of tool failures such as cracks, chippings, fractures, and estimate the remaining tool life for the ultimate goal of optimizing the performance of machining processes. This article presents the latest advancements in each stage of TCM systems, namely fusion sensors methods, modern data acquisition systems (DAQ), virtual machining, and lightweight TCM models, wherein artificial intelligence (AI) and Internet of Things (IoT) technologies demonstrate promising operational efficiency and accuracy while showing their potential in practical applications. Moreover, emphasis is paid to the current blank spots and limitations of implementing machine learning, deep learning, and IoT technologies in tool-embedded TCM, namely big data handling, generalization of Machine Learning models, as well as cloud computing latency, which suggests corresponding solutions to enable the practicality of mass implementation of Industrial Internet of Things (IIoT) for TCM systems, such as cloud migration, custom TCM network, and shared knowledge databases between multiple factories. In addition to a comprehensive review of state-of-the-art innovations for TCM systems, the future prospects of intelligent TCM systems are also presented for smart manufacturing. [ABSTRACT FROM AUTHOR]

• A new feature selection using fuzzy entropy method for chatter diagnosis is proposed. • The measured signals are filtered using EEMD method and analyzed using HHT approach. • The proposed method is compared with different metaheuristics algorithms. • The proposed method has a low computational burden and high classification accuracy. Feature selection represents the main challenge against the classification strategies for several applications of signal processing. Besides, the high computational speed and accuracy represent a critical requirement of chatter diagnosis. In this paper, a new fuzzy entropy measure with a similarity classifier is developed for feature selection vibration diagnosis in CNC machines. The measured signals are filtered using ensemble empirical mode decomposition and analyzed using Hilbert-Huang transform approach. The proposed feature selection technique can decrease noise and this strategy improves the classification accuracy. The proposed fuzzy entropy approach is compared with different feature selection and machine learning methods in the literature. The experimental results and comparative analyses confirm the superiority of the proposed fuzzy entropy method for chatter vibration identification with a low computational burden and a high accuracy of 98% that is significantly greater than other existing intelligent diagnosis techniques. [ABSTRACT FROM AUTHOR]

A novel concept of wind farm fault detection by monitoring the wind speed in the wake region is proposed in this study. A wind energy dissipation model was coupled with a computational fluid dynamics solver to simulate the fluid field of a wind turbine array, and the wind velocity and direction in the simulation were exported for identifying wind turbine faults. The 3D steady Navier–Stokes equations were solved by using the cell center finite volume method with a second order upwind scheme and a k − ε turbulence model. In addition, the wind energy dissipation model, derived from energy balance and Betz's law, was added to the Navier–Stokes equations' source term. The simulation results indicate that the wind speed distribution in the wake region contains significant information regarding multiple wind turbine faults. A feature selection algorithm specifically designed for the analysis of wind flow was proposed to reduce the number of features. This algorithm proved to have better performance than fuzzy entropy measures and recursive feature elimination methods under a limited number of features. As a result, faults in the wind turbine array could be detected and identified by machine learning algorithms. [ABSTRACT FROM AUTHOR]