Towards federated multivariate statistical process control (FedMSPC)

TL;DR

This paper introduces a privacy-preserving federated multivariate statistical process control framework using federated PCA and secure multiparty computation, enhancing fault detection and diagnosis across value chains without sharing sensitive data.

Contribution

It presents a novel federated PCA-based process control framework that enables secure, collaborative fault detection and diagnosis across companies in a value chain.

Findings

Superior fault detection compared to standard PCA.

Effective privacy-preserving fault diagnosis.

Validated on industrial benchmark datasets.

Abstract

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.