{ "schema_version": 1, "builder_plan": { "scenario": "Your workload might fit a smaller or differently structured model.", "change": "Check for available weights and a compatible runtime before planning a product trial.", "baseline": "Your current model on identical held-out tasks and the same resource budget.", "steps": [ "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.", "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.", "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.", "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test." ], "measure": "Task success, latency, memory, context limits, and migration cost.", "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation." }, "builder_review": null, "arxiv_id": "2610.13040", "title": "Lightweight Time-Frequency Vibration Sensing for Consumer Device Condition Monitoring", "summary": "This replaces heavier end-to-end 1-D deep models (or manual feature engineering) for vibration fault diagnosis with a two-stage pipeline: CWT or Hilbert-Huang conversion of raw vibration signals into 2-D time-frequency images, followed by lightweight image classifiers (MobileNetV3-Small, ConvNeXt-Tiny, YOLOv11-cls) sized for embedded CPUs. The cost is the added CWT/HHT preprocessing step on the edge device, image-based inference that is heavier than 1-D alternatives, and a documented accuracy drop when moving from random splits to structured isolation, i.e., leakage-free evaluation. It can fail through data leakage inflating benchmark results, poor generalization across machines, loads, or sensor placements not represented in the bearing datasets, and preprocessing latency exceeding the budget of low-end consumer microcontrollers.", "rationale": "The method runs on constrained hardware and addresses a real edge-inference problem, but the abstract gives no factor-level gains and the structured-isolation results indicate generalization risk, so the reader should validate it on their own machinery data before adoption.", "technique": "CWT/HHT time-frequency vibration classification with lightweight CNNs", "family": "arquitetura_eficiente", "recommendation": "testar", "published": "2026-10-09", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.13040", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "This replaces heavier end-to-end 1-D deep models (or manual feature engineering) for vibration fault diagnosis with a two-stage pipeline: CWT or Hilbert-Huang conversion of raw vibration signals into 2-D time-frequency images, followed by lightweight image classifiers (MobileNetV3-Small, ConvNeXt-Tiny, YOLOv11-cls) sized for embedded CPUs. The cost is the added CWT/HHT preprocessing step on the edge device, image-based inference that is heavier than 1-D alternatives, and a documented accuracy drop when moving from random splits to structured isolation, i.e., leakage-free evaluation. It can fail through data leakage inflating benchmark results, poor generalization across machines, loads, or sensor placements not represented in the bearing datasets, and preprocessing latency exceeding the budget of low-end consumer microcontrollers.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.13040", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "CWT with YOLOv11-cls achieves high diagnostic accuracy on CWRU, PU, and IMS bearing benchmarks, while MobileNetV3-Small offers lower-latency CPU inference; no multiplicative factor is reported in the abstract.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.13040", "source_page": null, "source_excerpt": "" } ], "exposure_map": [ { "dimension": "quality", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compute", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "latency", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "operations", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "compatibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "security", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "data_and_training", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" }, { "dimension": "reproducibility", "basis": "not_evaluated", "finding": "", "interpretation": "", "limitation": "", "next_check": "", "source_url": "", "source_page": null, "source_excerpt": "" } ], "risks": [], "minimum_test": [], "open_questions": [], "independent_tests": [], "lab_reproductions": [], "report_available": false, "validation_tier": "unknown", "evidence_tier": "unknown", "infrastructure_basis": "unknown", "equations": [], "equations_status": "not_fetched", "core_kind": "", "equations_fetched_at": "", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }