Wrench: compressor maintenance with cited answers

Problem. A train's air compressor fails slowly: a leak grows for a day or two before it trips. A dashboard shows the signs; it does not say whether to act now, what the manual says to do, or give a work order to sign. What Wrench does. 1. Alert. A 1D convolutional autoencoder trained only on healthy data scores every 10 minutes. Two thresholds give ok / watch / act, with the driving sensors. No language model decides the alarm. 2. Answer with checked quotes. From the service manual and past records (BM25). Each claim's quote is checked word for word against a document retrieved for that question; claims that fail are dropped. Out-of-scope questions are refused before any model call. 3. Work order. From an act alert, Wrench finds the fault section for the driving sensors and the procedure it points to, and drafts the steps with a priority. A person approves it. Results (MetroPT-3, a real metro air unit, 2020, split by time): - Two of the three evaluation failures were flagged early: 47.0 h and 35.2 h before the recorded start. The third and the validation failure were flagged within about an hour of starting. - False act alerts per normal week: 0.36 on evaluation, 0.71 on validation. - Retrieval: right section in the top 3 for 20/20 test questions, first for 12/20. AMD. Answers and work-order steps come from Gemma-4-31B-IT on AMD Token Factory (AMD's hosted API on AMD GPUs). If the key is rate-limited, recorded answers from the same model are replayed, marked RECORDED. The autoencoder was trained on CPU (69.1 s, 6 epochs); no ROCm run yet. Who pays. Rail maintenance teams. Rail asset management is estimated at USD 12.79 billion in 2025 (MarketsandMarkets). Proposed, untested pricing: a subscription per compressor after a free one-depot pilot. Limitations. Four failures, all air leaks, on one compressor. The evaluation period was seen once before tuning, so it is not a clean hold-out. The manual and records are fictional, by the author of the test questions.