Candidate-Fate Accounting for Transparent Sensor Diagnostic Pipeline Search

2026-08-19

AI Summary

为解决工业传感器诊断中自动化管道搜索的透明度问题，提出了一种候选命运记账方法，通过记录每个候选者的状态来提高搜索过程的可审查性。

Abstract

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.

Problem

• sensor diagnostics
• automated machine learning
• pipeline search
• candidate-fate accounting
• audit framework

Innovation

• candidate-fate accounting
• audit framework
• diagnostic pipeline search
• industrial sensor diagnostics
• AutoML/AutoDL