Privacy-preserving and sensor-fused framework for prognostic & health management in leased manufacturing system

Abstract

Nowadays, increased investments and fierce competition have made the leased manufacturing paradigm being widely adopted. Advances in multi-sensor technologies and data-driven methodologies have also supported the original equipment manufacturer (OEM) to focus on outsourcing prognostic and health management (PHM) services for the leased manufacturing system. However, under this product-service paradigm, the degraded-related signal analysis for massive sensors of leased machines is always limited by privacy concerns, fusion lack, and varying correlation. To promote the security performance and analysis capability of PHM from the OEM to the lessee, this paper proposes a privacy-preserving and sensor-fused framework to achieve time-to-failure (TTF) updating and predictive maintenance (PdM) arrangement for the leased manufacturing system. We first aggregate degraded-related historical datasets through encrypted functional features to preserve the privacy signals of two parties. Next, informative sensors of each leased machine are identified by the penalized functional (log)-location-scale (LLS) regression, while keeping the signals private. Then, the real-time observed signals from the identified sensors on the lessee side are locally fused and cryptographically uploaded to the OEM for the adaptive functional LLS regression. The TTF distributions can thus be securely updated in real time and integrated to output PdM intervals for each leased machine. We further validate our privacy-preserving and sensor-fused framework on a real leased crankshaft manufacturing system, which shows our secure mechanism and fused prognostic can significantly ensure privacy protection, computational performance, fusion effectiveness, and PdM cost reduction.