Pretrain Once, Deploy Many Times: Frequency-Aware Memory for Training-Free Anomaly Detection on Unseen Machines

Fault diagnosis foundation models promise reusable vibration representations, yet deployment still commonly requires target-specific training for each new machine. We introduce a pretrain-once, deploy-many-times framework: a vibration encoder is pretrained once on a heterogeneous cross-source corpus, frozen globally, and reused on unseen targets without target-domain gradient updates, while each target defines normality locally from a small set of normal references. FAMB (Frequency-Aware Memory Bank) instantiates this local detector through frequency-domain structure: it anchors matching to local Mel-frequency neighbourhoods and uses second-order latent frequency-shape residuals for bounded score enhancement. Same-sensor matching and unrestricted time positions preserve meaningful frequency comparisons. On four datasets excluded from pretraining, FAMB with one normal reference sample reaches 96.25%/96.92% macro-average AUROC/AUPR. On 16 liquid rocket engines, 3-fold device-level cross-validation keeps reference and test devices disjoint. With 100 samples from one normal engine, FAMB reaches 96.49%/96.88% AUROC/AUPR (across-fold standard deviation 1.98%/2.14%), exceeding the strongest baseline by 3.80 and 4.28 percentage points. These results support pretrain-once, deploy-many-times fault diagnosis through globally reusable vibration representations and locally defined normality.