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Changelog (v1.0.0)

All notable changes to CANSYD are documented here. The format is based on Keep a Changelog, and this project adheres to semantic versioning once it reaches 1.0.

[Unreleased]

Added

  • Pluggable PhysicsProvider interface — the diagnosis engine is now domain-agnostic; machine classes plug in as providers.
  • BearingProvider (ball-pass-frequency physics) and SpectralProvider (the universal zero-knowledge fallback).
  • GearProvider and gear-mesh physics (GMF + sidebands) for gearbox diagnosis.
  • Configuration layer: YAML-driven physics and taxonomy, resolved into providers by cansyd/builder.py.
  • PR #19/20: Integrated full 5-layer pipeline (CNN -> Symbolic -> Causal Sensitivity -> Causal Counterfactuals -> Consensus Router).
  • PR #19/20: Upgraded Rung-3 counterfactuals to use continuous vibration RMS outcomes instead of binary labels, enabling direction-sensible physical deltas.
  • Universal Dataset contract (Dataset.from_arrays) so any vibration dataset plugs in without a bespoke loader.
  • Validation scripts for CWRU (validate_cwru.py) and SEU gears (validate_seu.py), and a cross-condition robustness script.
  • Test suite covering the physics, symbolic, causal, consensus, and provider layers.

Changed

  • Symbolic layer rewritten to verify predictions against physics via a provider and a configurable taxonomy (replacing a hardcoded, CWRU-specific lookup).
  • Causal layer scoped honestly to Pearl Rung 2 (effect of operating condition) on a corrected DAG; the counterfactual layer carries Rung 3.
  • Changed rung-3 continuous degradation outcome (RMS) for unit-level counterfactuals (#20)

Notes

  • Interfaces may change. Not intended for safety-critical deployment without independent validation.

  • First release-grade implementation, consolidating the earlier CNSD research prototype and exploratory notebooks (to be released separately) into a tested, installable framework.

History

CANSYD began as a five-layer causal-neuro-symbolic diagnosis prototype and was rebuilt into a domain-agnostic framework: the physics-verification layer was made real (independent characteristic-frequency checks that can override the neural prediction), the causal claims were scoped honestly, and the system was reorganized into a deployable package.

Project history