Predictive maintenance for industrial equipment
Zylo Labs · Manufacturing / Industrial Automation
Measured across the printer fleet over the first 90 days versus the prior baseline.
- Client
- Zylo Labs
- Industry
- Manufacturing / Industrial Automation
- Scope
- Predictive-maintenance system with a generative repair-guidance assistant for industrial equipment.
Inside the project
Challenge
Significant equipment downtime from unpredictable failures was driving up production and maintenance costs. The goal was AI-driven predictive maintenance to improve reliability and reduce cost.
Solution
We deployed a predictive-maintenance system using AI and generative models to forecast issues, interpret technician feedback, and deliver step-by-step maintenance instructions in real time. Machinery manuals were digitized to JSON for metadata indexing; a RAG layer (BM25 + GPT-4) produced real-time repair instructions; BERT/RoBERTa interpreted worker input.
Our approach
We digitized equipment manuals into structured JSON for metadata indexing, built a retrieval-augmented layer (BM25 + GPT-4) to generate step-by-step repair instructions in real time, and used BERT/RoBERTa to interpret technician feedback. It runs as a closed loop — detect a weak signal, estimate build/downtime risk, retrieve the exact manual procedure, prioritize and assign the case, record the intervention, and feed the outcome back into maintenance history — with qualified technicians keeping final authority and every recommendation citing its source procedure.