发表机构
Georgia Institute of Technology; Ford Motor Company; Amazon Web Services（佐治亚理工学院; 福特汽车公司; 亚马逊云科技）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出SWIF和基于STFT的两种可解释框架，用于电动汽车电驱动桥质量监测，其中SWIF结合孤立森林在缺陷检测与误报控制间取得最佳平衡，并定位异常至特定运行条件以支持根因分析。
AI 中文摘要
本文提出了两种可解释的机器学习框架，用于电动汽车电驱动桥总成的质量筛选：分阶段小波孤立森林（SWIF）框架和基于短时傅里叶变换（STFT）的诊断框架。从前部和后部加速度计采集的高维振动信号在多个运行阶段进行分析，以捕获与阶段相关的振动行为。在SWIF框架中，信号使用五级Daubechies-4离散小波变换进行分解，并从选定的小波细节层提取分块均方系数，以获得紧凑的多尺度特征。在基于STFT的框架中，从时频表示中提取主频率趋势，并通过相对于瞬时电机转速的回归系数进行汇总。异常检测模型使用已验收的生产单元进行训练，假设只有一小部分已验收的组件包含潜在缺陷，并使用经过道路测试且具有已验证质量结果的单元评估其性能。对生产单元和道路测试的电驱动桥单元的实验表明，两种方法均提供可解释的诊断信息，而SWIF在缺陷检测和误报控制之间实现了最有利的平衡。与基于STFT的方法和其他异常检测器相比，SWIF结合孤立森林在验收群体中产生较低的异常率，同时从拒收群体中识别出高风险单元。分阶段结构进一步将异常行为定位到特定的运行条件，支持根本原因分析和有针对性的工艺改进。
英文摘要
This paper presents two interpretable machine-learning frameworks for quality screening of e-transaxle assemblies in electric vehicles: a Stagewise Wavelet Isolation Forest (SWIF) framework and a short-time Fourier transform (STFT)-based diagnostic framework. High-dimensional vibration signals acquired from front and back accelerometers are analyzed across multiple operating stages to capture stage-dependent vibration behavior. In the SWIF framework, signals are decomposed using a five-level Daubechies-4 discrete wavelet transform, and blockwise mean-squared coefficients are extracted from the selected wavelet detail level to obtain compact multiscale features. In the STFT-based framework, dominant-frequency trends are extracted from time-frequency representations and summarized through regression coefficients with respect to instantaneous motor speed. Anomaly detection models are trained using accepted production units under the assumption that only a small fraction of accepted assemblies contain latent defects, and their performance is evaluated using road-tested units with validated quality outcomes. Experiments on production and road-tested e-transaxle units show that both approaches provide interpretable diagnostic information, while SWIF achieves the most favorable balance between defect detection and false-positive control. Compared with the STFT-based method and alternative anomaly detectors, SWIF combined with Isolation Forest yields lower anomaly rates within the Accept population while identifying high-risk units from the Reject population. The stagewise structure further localizes anomalous behavior to specific operating conditions, supporting root-cause analysis and targeted process improvement.
