ONGOING: A Human-readable, Model-enriching, Continuous Technician Knowledge Modeling Framework

Abstract

Industry 5.0 reframes manufacturing around human-centric concerns: resilient operations, safe work, and decisions people can understand and contest. For PHM, that means elevating human-based features: competency, recency of practice, mentoring links, explainability, and fair exposure, rather than relying only on sensors or opaque models. Today those signals sit in ticket logs and massive databases, making them hard to audit, transfer, or reuse at scale.
We suggest ONGOING, a representation layer framework that turns unstructured maintenance text into a human-auditable Knowledge Grid and a complex but modular feature vector, independent of any particular embedding model or projector. At its core, the grid tracks technician experiences by incrementing a part of the Knowledge Grid whenever tickets are resolved. Two mechanisms capture more advanced dynamics: knowledge transfer between people (e.g., mentorship) via a convex blend of Knowledge Grids, and neighborhood propagation that diffuses experience increases to semantically adjacent tasks through a Gaussian kernel. From each grid we derive interpretable features, such as hypervolume, sparsity, or maximum knowledge, that summarize knowledge distribution more accurately for better downstream use (e.g., dispatching optimizer models, LLMs, production forecast models).
We implement the framework on a partner company's data, and deploy an instance at-scale (50000 tickets, 100 technicians) in real-time, using a multilingual sentence encoder and a toroidal SOM for ticket embedding.
On our deployed instance, we designed a technician recommendation use-case. A maintenance expert study with human feedback over 55 real tickets found that grid-based recommendation were judged more pertinent than a scalar-based and a vector-based knowledge modeling approaches. Crucially, dispatchers could articulate rationales from visible grid neighborhoods and feature attributions, preserving interpretability.
Beyond dispatch support, the Knowledge Grid enables training planning (identify coverage gaps), fairness monitoring (avoid single-point failure through over-reliance on “heroes”), and promotes workload balancing.