The recent generalized unscented transform (GenUT) is formulated into a recursive Kalman filter framework. The GenUT constrains 2n + 1 sigma points and their weights to match the first four statistical moments of a probability distribution. The GenUT integrates well into the unscented Kalman filter framework, creating what we call the generalized unscented Kalman filter (GUKF). The measurement update equations for the skewness and kurtosis are derived within. Performance of the GUKF is compared to the UKF under two studies: noise described by a Gaussian distribution and noise described by a uniform distribution. The GUKF achieves lower errors in state estimation when the UKF uses the heuristic tuning parameter κ = 3 − n. It is also stated that when the parameter κ is tuned to an optimal value, the UKF performs identically to the GUKF. The advantage here is that GUKF requires no such tuning.
Manufacturing has entered the fourth industrial revolution. Modern manufacturing is reliant on assets such as robotics and computer numerical control (CNC) machine tools. To optimize the performance and value of these assets it would be wise to implement digital twin (DT) technology. DT technology has the ability to provide valuable services to owners of machine tools and other manufacturing assets. The current issue facing DTs is that they currently exist at a lower level of sophistication, meaning they are incapable of implementing more complex services. Cognitive dynamic systems (CDS) are a type of smart system based on human cognition which can augment the performance of many engineering systems. This paper proposes a framework of implementing aspects of CDSs to enable DTs to exist at a higher level of sophistication called the cognitive dynamic digital twin (CDDT). Examples exist in the literature of implementing cognitive based methods to improve DT services, they primarily implement artificial intelligence and estimation based methods. Most of these methods implement only one aspect of cognition at a time. In this work the CDDT framework was implemented to build a DT machine tool wear prediction service. The service was shown to be accurate at predicting the levels of wear in cutting tools. This service utilizing the CDDT framework used each of the aspects of human cognition to augment its performance. This framework can be used by many different sorts of DTs to improve their level of sophistication.
As Industry 4.0 evolves with the abundance of data, networking capabilities and new computing technologies, manufacturers are looking for ways to exploit this revolution. The demands of machine tools and their feed drive systems require manufacturers to optimally plan and schedule maintenance actions to minimize costs. These actions can be supplemented by capitalizing on machine data and the idea of cyber-physical systems, with the use of edge and cloud computing, by monitoring important machine characteristics. A substantial benefit to manufacturers would be the ability to monitor the health characteristics of machine tools to aid them in their maintenance planning. Some of the challenges manufacturers face with this are the computing time and effort needed to analyze and evaluate the vast amount of machine data available. A step towards real-time condition monitoring of machine characteristics includes rapid parameter estimation of CNC machine tool systems. The estimation of mass and friction allow for the monitoring of CNC feed drive health. This work proposes the estimation of such parameters from real-world industrial machine tool data. A Feed drive testing procedure is developed for smart data acquisition. Data analysis and recursive least squares methods are used to extract key parameters representative of machine health that are realizable on edge computing devices.
KEYWORDS: Clouds, Data storage, Data processing, Computer security, Network architectures, Distributed computing, Network security, Control systems, Computing systems
As data collected through IoT systems worldwide increases and the deployment of IoT architectures is expanded across multiple domains, novel frameworks that focus on application-based criteria and constraints are needed. In recent years, big data processing has been addressed using cloud-based technology, although such implementations are not suitable for latency-sensitive applications. Edge and Fog computing paradigms have been proposed as a viable solution to this problem, expanding the computation and storage to data centers located at the network's edge and providing multiple advantages over sole cloud-based solutions. However, security and data integrity concerns arise in developing IoT architectures in such a framework, and blockchain-based access control and resource allocation are viable solutions in decentralized architectures. This paper proposes an architecture composed of a multilayered data system capable of redundant distributed storage and processing using encrypted data transmission and logging on distributed internal peer-to-peer networks.
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