The ongoing transition from a linear (produce-use-dispose) to a circular economy poses significant challenges to current state-of-the-art information and communication technologies. In particular, the derivation of integrated, high-level views on material, process, and product streams from (real-time) data produced along value chains is challenging for several reasons. Most importantly, sufficiently rich data is often available yet not shared across company borders because of privacy concerns which make it impossible to build integrated process models that capture the interrelations between input materials, process parameters, and key performance indicators along value chains. In the current contribution, we propose a privacy-preserving, federated multivariate statistical process control (FedMSPC) framework based on Federated Principal Component Analysis (PCA) and Secure Multiparty Computation to foster the incentive for closer collaboration of stakeholders along value chains. We tested our approach on two industrial benchmark data sets - SECOM and ST-AWFD. Our empirical results demonstrate the superior fault detection capability of the proposed approach compared to standard, single-party (multiway) PCA. Furthermore, we showcase the possibility of our framework to provide privacy-preserving fault diagnosis to each data holder in the value chain to underpin the benefits of secure data sharing and federated process modeling.
翻译:目前从线性(生产使用量)向循环经济的过渡,对目前最先进的信息和通信技术提出了重大挑战,特别是,从价值链中产生的(实时)数据对材料、流程和产品流的综合、高级别观点,由于若干原因具有挑战性。最重要的是,由于隐私问题,往往有足够的充足数据,但公司边界之间没有共享,因此无法建立综合过程模型,以记录投入材料、流程参数和价值链中主要业绩指标之间的相互关系。在目前的贡献中,我们提议基于联邦主要构成部分分析(PCA)和确保多党对数据进行整合和多变统计进程控制(FedMSPC)框架,以促进价值链中利益攸关方更密切合作的动力。我们测试了两个工业基准数据集 -- -- SECOM和ST-AWFD。我们的经验结果表明,与标准、单一方(多路)CPA相比,拟议方法的检测能力高得体。此外,我们展示了我们的框架,即为每个数据持有者提供保密、保密和保密数据链分析的模型。