Work

Rec-PdM: Decision-calibrated recursive multi-agent systems for uncertainty-aware prescriptive maintenance in smart manufacturing

Year: 2026

Type: article

Abstract: Predictive maintenance creates industrial value only when uncertain health evidence leads to an appropriate intervention under asymmetric failure costs and operational constraints. We present Rec-PdM, a decision-calibrated recursive multi-agent system for uncertainty-aware prescriptive maintenance. Its six decision agents maintain specialized beliefs about asset condition, fault state, remaining useful life, economic risk, action selection, and verification; production-impact and human-prescription interfaces connect these beliefs to factory context and operator-facing decisions. RecursiveLink reconciles local beliefs with downstream decision consequences while preserving the provenance of each update. The method treats maintenance as a partially observable degradation process and calibrates prediction and uncertainty against decision regret, tail risk, feasibility, and communication overhead. Evaluation on six public datasets spans degradation prognosis, process failure, vibration-based diagnosis, and acoustic anomaly detection, with controlled decision studies of cost asymmetry, coordination, resource constraints, dependent faults, and operating drift. RecursiveLink achieves the best mean-cost rank among the evaluated coordination mechanisms, while calibrated Rec-PdM ranks in the top two in 98.3% of the evaluated cost regimes without an unexpected failure. Under shared fleet constraints, the coordinator remains in the top two by mean cost in 88.8% of 240 scenarios; prequential recursive guarding reduces unserved failures from 70 to 5 under drift. These results show that decision cost, tail risk, and traceable coordination complement predictive accuracy when maintenance evidence must be converted into action.

Source: Journal of Manufacturing Systems

DOI: 10.1016/j.jmsy.2026.08.012

Authors: Xin Tang, Cheng Wang, Yongbin Gao, Chenmou Wu, Zhijun Fang

Institutions: Shanghai University of Engineering Science, Fudan University

Language: English

Topic: Reliability and Maintenance Optimization

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $3,850