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Digital Twins (DTs) are virtual representations of physical products in many dimensions, such as geometry and behaviour. As a backbone of Industry 4.0, DTs help interpret and even predict the behaviour of physical processes, provide a virtual testbed for maintenance and upgrade, and enable automatic decision-making supported by artificial intelligence. Despite the promising future, challenges exist, such as the absence of a framework that facilitates the development and application of DTs in industrial contexts. We propose a service-oriented architecture (SOA) DT framework for dynamic and robust distributed systems. The framework contains two types of services. One includes the services provided to the users and is supported by an orchestration mechanism to ensure a quality of service (QoS). The other one refers to the common functions of all DTs. Further, we describe the DT-based decision-making enabled by our QoS-oriented learning of the framework and a Hoare-logic-based verification of QoS. 

Anomaly detection in industrial systems is crucial for preventing equipment failures, ensuring risk identification, and maintaining overall system efficiency. Traditional monitoring methods often rely on fixed thresholds and empirical rules, which may not be sensitive enough to detect subtle changes in system health and predict impending failures. To address this limitation, this paper proposes, a novel self-supervised learning technique using autoencoder based hybrid models called Attention-based convolutional autoencoder (ABCD) for risk detection and mapping risk value derive to the maintenance planning. ABCD learns the normal behavior of conductivity from historical data of a real-world industrial cooling system and reconstructs the input data, identifying anomalies that deviate from the expected patterns. The framework also employs calibration techniques to ensure the reliability of its predictions. Evaluation of results demonstrate that with the attention mechanism in ABCD a 57.4% increase in performance and a reduction of false alarms by 9.37% is seen compared to without attentions. This approach can effectively detect risks, the risk priority rank mapped to maintenance, providing valuable insights for cooling system designers and service personnel. Calibration error of 0.03% indicates that the model is well-calibrated and enhances model's trustworthiness, enabling informed decisions about maintenance strategies.

Digital Twins (DTs) serve as the backbone of Industry 4.0, offering virtual representations of actual systems, enabling accurate simulations, analysis, and control. These representations help in predicting system behaviour, facilitating multiple real-time tests, and reducing risks and costs while identifying optimization areas. DTs meld cyber and physical realms, accelerating the design and modelling of sustainable innovations. Despite their potential, the complexity of DTs presents challenges in their industrial application. We sketch here an approach to build an adaptable and trustable framework for building and operating DT systems, which is the basis for the academia-industry project A Digital Twin Framework for Dynamic and Robust Distributed Systems (D-RODS). D-RODS aims to address the challenges above, aiming to advance industrial digitalization and targeting areas like system efficiency, incorporating AI and verification techniques with formal support. 

Digital Twins (DTs) serve as the backbone of Industry 4.0, offering virtual representations of actual systems, enabling accurate simulations, analysis, and control. These representations help predict system behaviour, facilitate multiple real-time tests, and reduce risks and costs while identifying optimization areas. DTs meld cyber and physical realms, accelerating the design and modelling of sustainable innovations. Despite their potential, the complexity of DTs presents challenges in their industrial application. We continue here the development of our approach to build an adaptable and trustable framework for building and operating DT systems - A Digital Twin Framework for Dynamic and Robust Distributed Systems (D-RODS). D-RODS aims to address the challenges above, aiming to advance industrial digitalization and targeting areas like system efficiency, incorporating AI and verification techniques with formal support. We employ existing large-usage tools to illustrate the approach in development based on a synthetic adaptable use case.

With the exponential growth of remote sensing images in recent years, there has been a significant increase in demand for micro-target detection. Recently, effective detection methods for small targets have emerged; however, for micro-targets (even fewer pixels than small targets), most existing methods are not fully competent in feature extraction, target positioning, and rapid classification. This study proposes an enhanced detection method, especially for micro-targets, in which a combined loss function (consisting of NWD and CIOU) is used instead of a singular CIOU loss function. In addition, the lightweight Content-Aware Reassembly of Features (CARAFE) replaces the original bilinear interpolation upsampling algorithm, and a spatial pyramid structure is added into the network model’s small target layer. The proposed algorithm undergoes training and validation utilizing the benchmark dataset known as AI-TOD. Compared to speed-oriented YOLOv7-tiny, the mAP0.5 and mAP0.5:0.95 of our improved algorithm increased from 42.0% and 16.8% to 48.7% and 18.9%, representing improvements of 6.7% and 2.1%, respectively, while the detection speed was almost equal to that of YOLOv7-tiny. Furthermore, our method was also tested on a dataset of multi-scale targets, which contains small targets, medium targets, and large targets. The results demonstrated that mAP0.5:0.95 increased from “9.8%, 54.8%, and 68.2%” to “12.6%, 55.6%, and 70.1%” for detection across different scales, indicating improvements of 2.8%, 0.8%, and 1.9%, respectively. In summary, the presented method improves detection metrics for micro-targets in various scenarios while satisfying the requirements of detection speed in a real-time system.

Anomaly detection plays a crucial role in industrial settings, particularly in maintaining the reliability and optimal performance. Traditional anomaly detection methods typically rely on the use of single models with labelled data and often face challenges in handling diverse data characteristics and variations in noise levels, resulting in limited effectiveness. Self-supervised learning (SSL) allows models to learn from unlabeled data by creating their own supervisory signals through tasks like reconstruction (as in autoencoders), making it a powerful technique for tasks of anomaly detection. This work proposes a novel approach named as Self-Supervised Learning Framework with Dual Ensemble Voting Fusion for Maximizing Anomaly Prediction in Timeseries (S2DEVFMAP). Wherein the use of five heterogeneous independent models combined with a dual ensemble fusion of voting techniques is demonstrated. Diverse models capture various system behaviors, while the fusion strategy maximizes detection effectiveness and minimizes false alarms. Each base autoencoder model learns a unique representation of the data, leveraging their complementary strengths to improve anomaly detection. The use of dual ensemble technique is proven to maximize the identification of anomalies. Experimentation is done on a real-world dataset of an industrial cooling system in a power station to demonstrate the effectiveness of the proposed approach.

Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack of data in the minority class due to the use of MapReduce paradigm have posed new challenges for tackling the imbalance between classes in Big Data scenarios. Ensembles have been shown to be able to successfully address imbalanced data problems. Smart Data refers to data of enough quality to achieve high-performance models. The combination of ensembles and Smart Data, achieved through Big Data preprocessing, should be a great synergy. In this paper, we propose a novel Smart Data driven Decision Trees Ensemble methodology for addressing the imbalanced classification problem in Big Data domains, namely SD_DeTE methodology. This methodology is based on the learning of different decision trees using distributed quality data for the ensemble process. This quality data is achieved by fusing random discretization, principal components analysis, and clustering-based random oversampling for obtaining different Smart Data versions of the original data. Experiments carried out in 21 binary adapted datasets have shown that our methodology outperforms random forest.

Understanding the reasons behind a model's predictions is as important as achieving accurate results. The limited adoption of AI methods in fields like Energy and Industry is mainly attributed to a lack of trust, which is a crucial factor in user acceptance. Explainable AI is a recent approach to address this issue and enable the rapid deployment of AI in complex domains. This paper presents a framework for explainable anomaly detection and risk prognostics that utilizes an Ensembled Stacked Self-Supervised Model and Shapley additive approach to generate local and global explanations. The quality of the explanations is evaluated with the assistance of human experts to enhance model performance and streamline an automated online approach. The explanation produced is evaluated utilizing local accuracy metric. The proposed framework is tested on real-world industrial cooling system data, on which out of the ten anomalies identified, eight were successfully linked to maintenance actions, while two were attributed to random sensor measurement disturbances. This prognostic outcome establishes a new benchmark in the field.

In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model’s detection head (IDetect) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model’s effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that mAP@0.5:0.95 and mAP@0.5 increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection. 

Classification is an important task in the field of machine learning. Most classifiers based on offline learning are invalid for open data streams. In contrast, incremental learning is feasible for continuous data. This paper presents the Incremental Bayesian Classifier “Incremental_BC”, which continuously updates the probabilistic information according to each new training sample via recursive calculation. Further, the Incremental_BC is improved to deal with the flowing data whose distribution and property evolve with time, i.e., the concept drift. The effectiveness of the proposed methods has been verified by the results of simulation tests on benchmark data sets.