Industrial processes rely on sensory data for decision-making processes, risk assessment, and performance evaluation. Extracting actionable insights from the collected data calls for an infrastructure that can ensure the dissemination of trustworthy data. For the physical data to be trustworthy, it needs to be cross-validated through multiple sensor sources with overlapping fields of view. Cross-validated data can then be stored on the blockchain, to maintain its integrity and trustworthiness. Once trustworthy data is recorded on the blockchain, product lifecycle events can be fed into data-driven systems for process monitoring, diagnostics, and optimized control. In this regard, Digital Twins (DTs) can be leveraged to draw intelligent conclusions from data by identifying the faults and recommending precautionary measures ahead of critical events. Empowering DTs with blockchain in industrial use-cases targets key challenges of disparate data repositories, untrustworthy data dissemination, and the need for predictive maintenance. In this survey, while highlighting the key benefits of using blockchain-based DTs, we present a comprehensive review of the state-of-the-art research results for blockchain-based DTs. Based on the current research trends, we discuss a trustworthy blockchain-based DTs framework. We highlight the role of Artificial Intelligence (AI) in blockchain-based DTs. Furthermore, we discuss current and future research and deployment challenges of blockchain-supported DTs that require further investigation.
翻译:从所收集的数据中提取的可操作的见解需要能够确保传播可靠数据的基础设施。为使物理数据值得信赖,需要通过多个具有相互重叠视野的传感器源进行交叉验证。交叉验证的数据可以储存在链条上,以保持其完整性和可信度。一旦在链条上记录了可信赖的数据,产品生命周期事件就可以被输入数据驱动的系统,用于流程监测、诊断和优化控制。在这方面,数字双胞胎(DTs)可以被利用,以便通过查明缺陷和提出重大事件前的预防措施,从数据中得出明智的结论。工业使用案例中具有阻隔链的DTs能够应对数据储存不全、不可信数据传播的关键挑战,以及需要预测性维护。在本次调查中,在强调使用基于链的DTs系统的关键好处的同时,我们对基于链的当前挑战的状态研究成果进行全面审查。在基于链条的当前DTTs中,我们进一步讨论了基于基于链条的当前供应链的研究趋势。