Technical deep dives, predictive maintenance strategy, and product updates from the DiagAI team.
Setting a torque limit of 60Nm and waiting for an alarm isn't predictive maintenance — it's reactive maintenance with extra steps. Here's why ML-based detection changes everything.
We ran 500 historical fault scenarios through a single-prompt LLM and through DiagAI's 4-agent LangGraph pipeline. The results were decisive — and the reasons are instructive.
A deep dive into the architecture behind DiagAI's anomaly detection: how LSTM reconstruction error captures temporal dependencies that z-score methods miss.
ML models learn correlations; knowledge graphs encode causation. For RCA you need both — and SWRL rules are the bridge between the two.
The AI4I 2020 predictive maintenance dataset is the standard benchmark for industrial ML. Here's what the 14 features mean, what the 5 failure modes look like, and how to use it properly.
Sub-second LLM inference changes what's possible in real-time industrial diagnostics. We benchmark Groq's Llama 3.3 70B against GPT-4o on structured fault-tree reasoning tasks.
A step-by-step framework for quantifying the financial return of a predictive maintenance programme — including downtime cost modelling, PM reduction, and break-even analysis.
We shipped the full 4-agent LangGraph RCA pipeline to production. Here's what changed, what improved, and what's coming next.
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