Unsupervised Feature Frequency Map Extraction for Simple C-EPS Defect Detection Hyundai Mobis

Format:

Book

Conference/Event

Author/Creator:

Park, Jun-Seo, author.

Jo, Hyeon-Choel, author.

Cho, In-Je, author.

Seo, Jae-Yong, author.

Yoo, Seong-Sik, author.

Conference Name:

14th International Styrian Noise, Vibration & Harshness Congress: The European Automotive Noise Conference (2026-06-17 : Graz, Austria)

Language:

English

Subjects (All):

Quality assurance.

Power steering.

Machine learning.

Local Subjects:

Quality assurance.

Power steering.

Machine learning.

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2026

Summary:

In recent years, the automotive industry has actively explored the application of various AI-based models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformers to improve defect detection rates at the End-of-Line (EOL) stage. However, implementing these approaches in the Noise, Vibration, and Harshness (NVH) area face several practical challenges: extended evaluation times compared to other data types, which limit the quantity of training data and lead to overfitting; label imbalance caused by the relatively small amount of defect data; reduced labeling accuracy due to human error; decreased robustness under domain shifts such as changes in jig fixtures, test environments, and signal-to-noise ratio (SNR); diminished model reliability when new defect arise during development; and constraints imposed by compatibility requirements with existing test equipment.This study proposes a Convolutional Autoencoder (CAE) based framework trained on NVH datasets collected from normal and defective Column-type Electric Power Steering (C-EPS) systems. Latent variables at the bottleneck layer are used for dimension reduction, enabling visualization and unsupervised classification using a clustering algorithm. A classification model derived from the encoder is fine-tuned with clustered data, and Gradient-weighted Class Activation Mapping (Grad-CAM), an eXplainable AI (XAI) technique, is applied to extract Feature Frequency Maps (FFM) highlighting defect-related noise and vibration characteristics. The proposed approach does not rely on the deep learning model to directly classify defect. Instead, it utilizes extracted FFM as weights(mask) to detect defect. This method enables quantitative data representation and ensures high applicability with existing EOL equipment. Post-processing within the FFM enables root cause analysis, reducing issue resolution time and supporting integration with conventional signal analysis techniques

Notes:

Vendor supplied data

Access Restriction:

Restricted for use by site license