# Stagewise Anomaly Detection for E-Transaxle Quality Monitoring Using Wavelet and STFT Features

arXiv: https://arxiv.org/abs/2609.22172 · HTML: https://mlsanity.com/paper/2609.22172/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 27 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation](https://mlsanity.com/paper/2508.02903/) (2025)
- [An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture](https://mlsanity.com/paper/2205.00511/) (2022)
- [Bayesian NVH metamodels to assess interior cabin noise using measurement databases](https://mlsanity.com/paper/2207.02120/) (2022)
- [Blind Separation of Vibration Sources using Deep Learning and Deconvolution](https://mlsanity.com/paper/2405.12774/) (2024)
- [Correcting Domain Shifts in Electric Motor Vibration Data for Unseen Operating Conditions](https://mlsanity.com/paper/2504.10661/) (2025)
- [Single-Step Reconstruction-Free Anomaly Detection and Segmentation via Diffusion Models](https://mlsanity.com/paper/2508.04818/) (2025)

## Built on by (most cited first, 1 of 1)

- [A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company](https://mlsanity.com/paper/2610.06980/) (2026)

## Abstract (arXiv)

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.