A review on data-driven fault severity assessment in rolling bearings

A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders

Covid-19 Classification by FGCNet with Deep Feature Fusion from Graph Convolutional Network and Convolutional Neural Network.

Sustainability is a pressing need, as well as an engineering challenge, in the modern world. Developing smart technologies is a critical way to ensure that future manufacturing systems are sustainable. Blockchain is a next-generation development of information technology for realizing sustainability in businesses and industries. Much research on blockchain-empowered sustainable manufacturing in Industry 4.0 has been conducted from technical, commercial, organizational, and operational perspectives. This paper surveys how blockchain can overcome potential barriers to achieving sustainability from two perspectives, namely, the manufacturing system perspective and the product lifecycle management perspective. The survey first examines literature on these two perspectives, following which the state of research in blockchain-empowered sustainable manufacturing is presented, which sheds new light on urgent issues as part of the UN's Sustainable Development Goals. We found that blockchain-empowered transformation of a sustainable manufacturing paradigm is still in an early stage of the hype phase, proceeding toward full adoption. The survey ends with a discussion of challenges regarding techniques, social barriers, standards, and regulations with respect to blockchain-empowered manufacturing applications. The paper concludes with a discussion of challenges and social barriers that blockchain technology must overcome to demonstrate its sustainability in industrial and business spheres.

Blockchain-empowered sustainable manufacturing and product lifecycle management in industry 4.0: A survey

Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities

The recent White House report on Artificial Intelligence (AI) (Lee, 2016) highlights the significance of AI and the necessity of a clear roadmap and strategic investment in this area. As AI emerges from science fiction to become the frontier of world-changing technologies, there is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Within the 5C architecture previously proposed in Lee et al. (2015), this paper provides an insight into the current state of AI technologies and the eco-system required to harness the power of AI in industrial applications.

Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

Digital twin (DT) is gaining popularity due to its significant impacts on bridging the gap between the physical and cyber worlds. As reported by Grand View Research, Inc., the global market of DT is expected to reach $26.07 billion by 2025 with a Compound Annual Growth Rate of 38.2%. The growing adoption of cyber-physical system (CPS), Internet of Things, big data analytics, and cloud computing in manufacturing sector has paved the way for low cost and systematic implementation of DT, with promising impacts on (a) product design and development, (b) machine and equipment health monitoring, and (c) product support and services. Successful implementation of DT would increase transparency, cooperation, flexibility, resilience, production speed, scalability, and manufacturing efficiency. Realisation of smart manufacturing requires collaborative and autonomous interactions between sensing, networking, and computational resources across manufacturing assets where data is gathered from physical systems is utilised for the extraction of actionable insights and provision of predictive services. In this study, a reference architecture based on deep learning, DT, and 5C-CPS is proposed to facilitate the transformation towards smart manufacturing and Industry 4.0.

Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing

Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis

Cyber-Physical Production Systems (CPPSs) are complex manufacturing systems which aim to integrate and synchronize machine world and manufacturing facility to the cyber computational space. However, having intensive interconnectivity and a computational platform is crucial for real-world implementation of CPPSs. In this paper, the potential impacts of blockchain technology in development and realization of real-world CPPSs are discussed. A unified three-level blockchain architecture is proposed as a guideline for researchers and industries to clearly identify the potentials of blockchain and adapt, develop, and incorporate this technology with their manufacturing developments towards Industry 4.0.

Jaskaran Singh

Papers

Industrial Artificial Intelligence for industry 4.0-based manufacturing systems

Integration of digital twin and deep learning in cyber-physical systems: towards smart manufacturing

Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis

A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems

Bearing damage assessment using Jensen-Rényi Divergence based on EEMD