用于透明传感器诊断流程搜索的候选命运核算

中文摘要 AI 辅助

工业传感器诊断依赖预处理、表征和分类流程，因此自动化流程搜索可降低人工设计成本。但现有自动化机器学习/深度学习（AutoML/AutoDL）报告通常仅保留拟合试验、得分和优胜者，遗漏了生成的无效、剪枝、跳过、缓存或未拟合的候选，这限制了审核人员检查信号约束、预算使用和未评估合法替代方案的能力。为解决该问题，我们提出候选命运核算，这是一种用于诊断搜索轨迹的候选级审计框架，它将每个观测到的候选记录为可审计证据：哈希值合并重复观测，合法性检查标记无效候选，分配理由解释预算决策，且闭合命运总账为每个候选分配一个终端命运。在三个轴承诊断数据集上的实验表明，该框架可检测无效候选，识别出仅拟合试验报告遗漏的30-41个候选，闭合命运总账验证了完整的候选核算，同时保持了有竞争力的诊断性能。代码可在该https URL获取。

英文摘要

Industrial sensor diagnostics relies on preprocessing, representation, and classification pipelines, making automated pipeline search useful for reducing manual design cost. However, existing automated machine/deep learning (AutoML/AutoDL) reports typically retain only fitted trials, scores, and winners, omitting generated candidates that are invalid, pruned, skipped, cached, or unfitted. This omission limits reviewers' ability to check signal constraints, budget use, and unevaluated legal alternatives. To address this, we propose candidate-fate accounting, a candidate-level audit framework for diagnostic search traces. It records each observed candidate as auditable evidence: hashes merge repeated observations, legality checks flag invalid candidates, allocation rationales explain budget decisions, and a closed fate ledger assigns one terminal fate to each candidate. Experiments on three bearing-diagnostic datasets show that the framework detects invalid candidates and identifies 30--41 candidates omitted by fitted-trial-only reports, with closed fate records verifying complete candidate accounting while maintaining competitive diagnostic performance. The code is available at https://github.com/XXIE999/candidate-fate-accounting.

发表机构

• Hangzhou International Innovation Institute, Beihang University（北京航空航天大学杭州国际创新研究院）
• College of Cyber Security, Jinan University（暨南大学网络空间安全学院）

机构由 AI 辅助整理，请以论文原文为准。