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1. IIoT Deployment of a Physics-Informed Deep Learning Model for Online Bearing Fault Diagnostics

   Lu, Hao; Allen, Cade; Nemani, Venkat; Hu, Chao; Zimmerman, Andrew (January 2023, Proceedings of the 2022 International Symposium on Flexible Automation)

   Early fault detection in rolling element bearings is pivotal for the effective predictive maintenance of rotating machinery. Deep Learning (DL) methods have been widely studied for vibration-based bearing fault diagnostics largely because of their capability to automatically extract fault-related features from raw or processed vibration data. Although most DL models in the current literature can provide fairly accurate classification outputs, the typical diagnostic procedure is performed in an offline environment utilizing powerful computers. This centralized approach can lead to unacceptable delays in safety-critical applications and can prohibit cost-sensitive wireless data collection. Meanwhile, very few studies have reported on deploying DL models on microprocessor-based Industrial Internet of Things (IIoT) devices, where edge computing can give users a real-time evaluation of bearing health without requiring expensive computational infrastructure. This paper demonstrates an IIoT deployment of a physics-informed DL model inside a commercially available wireless vibration sensor for online health classification. The diagnostic model here is developed and trained offline, and the trained model is then deployed inside the embedded system for online prediction. We demonstrate the model’s online diagnostic performance by imitating bearing vibration signals on a vibration shaker and by performing edge computing on the embedded system mounted on the shaker. 

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2. Design patterns for the industrial Internet of Things

   https://doi.org/10.1109/WFCS.2018.8402353  

   Bloom, Gedare; Alsulami, Bassma; Nwafor, Ebelechukwu; Bertolotti, Ivan Cibrario (June 2018, 2018 14th IEEE International Workshop on Factory Communication Systems (WFCS))

   The Internet of Things (IoT) is a vast collection of interconnected sensors, devices, and services that share data and information over the Internet with the objective of leveraging multiple information sources to optimize related systems. The technologies associated with the IoT have significantly improved the quality of many existing applications by reducing costs, improving functionality, increasing access to resources, and enhancing automation. The adoption of IoT by industries has led to the next industrial revolution: Industry 4.0. The rise of the Industrial IoT (IIoT) promises to enhance factory management, process optimization, worker safety, and more. However, the rollout of the IIoT is not without significant issues, and many of these act as major barriers that prevent fully achieving the vision of Industry 4.0. One major area of concern is the security and privacy of the massive datasets that are captured and stored, which may leak information about intellectual property, trade secrets, and other competitive knowledge. As a way forward toward solving security and privacy concerns, we aim in this paper to identify common input-output (I/O) design patterns that exist in applications of the IIoT. These design patterns enable constructing an abstract model representation of data flow semantics used by such applications, and therefore better understand how to secure the information related to IIoT operations. In this paper, we describe communication protocols and identify common I/O design patterns for IIoT applications with an emphasis on data flow in edge devices, which, in the industrial control system (ICS) setting, are most often involved in process control or monitoring. 

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3. TRUST XAI: Model-Agnostic Explanations for AI With a Case Study on IIoT Security

   https://doi.org/10.1109/JIOT.2021.3122019  

   Zolanvari, Maede; Yang, Zebo; Khan, Khaled; Jain, Raj; Meskin, Nader (October 2021, IEEE Internet of Things Journal)

   Despite AI’s significant growth, its “black box” nature creates challenges in generating adequate trust. Thus, it is seldom utilized as a standalone unit in high-risk applications. Explainable AI (XAI) has emerged to help with this problem. Designing effectively fast and accurate XAI is still challenging, especially in numerical applications. We propose a novel XAI model named Transparency Relying Upon Statistical Theory (TRUST) for XAI. TRUST XAI models the statistical behavior of the underlying AI’s outputs. Factor analysis is used to transform the input features into a new set of latent variables. We use mutual information to rank these parameters and pick only the most influential ones on the AI’s outputs and call them “representatives” of the classes. Then we use multi-model Gaussian distributions to determine the likelihood of any new sample belonging to each class. The proposed technique is a surrogate model that is not dependent on the type of the underlying AI. TRUST is suitable for any numerical application. Here, we use cybersecurity of the industrial internet of things (IIoT) as an example application. We analyze the performance of the model using three different cybersecurity datasets, including “WUSTLIIoT”, “NSL-KDD”, and “UNSW”. We also show how TRUST is explained to the user. The TRUST XAI provides explanations for new random samples with an average success rate of 98%. Also, the advantages of our model over another popular XAI model, LIME, including performance, speed, and the method of explainability are evaluated. 

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4. Privacy-Preserving Database Assisted Spectrum Access for Industrial Internet of Things: A Distributed Learning Approach

   https://doi.org/10.1109/TIE.2019.2938491  

   Zhang, Mengyuan; Chen, Jiming; He, Shibo; Yang, Lei; Gong, Xiaowen; Zhang, Junshan (September 2019, IEEE Transactions on Industrial Electronics)

   Industrial Internet of Things (IIoT) has been shown to be of great value to the deployment of smart industrial environment. With the immense growth of IoT devices, dynamic spectrum sharing is introduced, envisaged as a promising solution to the spectrum shortage in IIoT. Meanwhile, cyber-physical safety issue remains to be a great concern for the reliable operation of IIoT system. In this paper, we consider the dynamic spectrum access in IIoT under a Received Signal Strength (RSS) based adversarial localization attack. We employ a practical and effective power perturbation approach to mitigate the localization threat on the IoT devices and cast the privacy-preserving spectrum sharing problem as a stochastic channel selection game. To address the randomness induced by the power perturbation approach, we develop a two-timescale distributed learning algorithm that converges almost surely to the set of correlated equilibria of the game. The numerical results show the convergence of the algorithm and corroborate that the design of two-timescale learning process effectively alleviates the network throughput degradation brought by the power perturbation procedure. 

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5. Moisture Estimation in Woodchips Using IIoT Wi-Fi and Machine Learning Techniques

   https://doi.org/10.1016/B978-0-323-85159-6.50276-1  

   Suthar, K.; He, Q.P. (January 2022, Computer aided chemical engineering)

   For the pulping process in a pulp & paper plant that uses woodchips as raw material, the moisture content (MC) of the woodchips is a major process disturbance that affects product quality and consumption of energy, water, and chemicals. Existing woodchip MC sensing technologies have not been widely adopted by the industry due to unreliable performance and/or high maintenance requirements that can hardly be met in a manufacturing environment. To address these limitations, we propose a non-destructive, economic, and robust woodchip MC sensing approach utilizing channel state information (CSI) from industrial Internet-of-Things (IIoT) based Wi-Fi. While these IIoT devices are small, low-cost, and rugged to stand for harsh environment, they do have their limitations such as the raw CSI data are often very noisy and sensitive to woodchip packing. Thus, direct application of machine learning (ML) algorithms leads to poor performance. To address this, statistics pattern analysis (SPA) is utilized to extract physically and statistically meaningful features from the raw CSI data, which are sensitive to woodchip MC but not to packing. The SPA features are then used for developing multiclass classification models as well as regression models using various linear and nonlinear ML techniques to provide potential solutions to woodchip MC estimation for the pulp and paper industry. 

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