{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.22172",
  "author_total": 8,
  "authors": [
    {
      "name": "Mohammad N. Bisheh"
    },
    {
      "name": "Rajesh Gupta"
    },
    {
      "name": "Qian Wang"
    },
    {
      "name": "Mohammad Babakmehr"
    },
    {
      "name": "Colin Brady"
    },
    {
      "name": "Parinaz Farajiparvar"
    },
    {
      "name": "Saurabh Singh"
    },
    {
      "name": "Kamran Payanabar"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/34a8nf4t5xw92.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.22172",
  "fulltext_url": null,
  "id": "34a8nf4t5xw92",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.22172",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "eess.SP"
    ],
    [
      "arxiv",
      "stat.OT"
    ],
    [
      "openalex",
      "2207|Machine Fault Diagnosis Techniques"
    ]
  ],
  "openalex": "W7214027811",
  "pdf_url": "https://arxiv.org/pdf/2609.22172",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/34a8nf4t5xw92.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:33Z",
      "native_id": "2609.22172",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.22172"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.OT",
      "subfield": "statistics"
    }
  ],
  "title": "Stagewise Anomaly Detection for E-Transaxle Quality Monitoring Using Wavelet and STFT Features",
  "title_text": "Stagewise Anomaly Detection for E-Transaxle Quality Monitoring Using Wavelet and STFT Features",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:33Z",
  "url": "https://research.draconitic.com/paper/34a8nf4t5xw92",
  "venue": "arXiv",
  "versions": []
}