fnctId=thesis,fnctNo=358
[배혜림] Multidomain Supervised Time?Frequency Feature Fusion Autoencoder for Health Indicator Extraction in Rotating Machinery
- 작성자
- scsc연구센터
- 저자
- Hanbyeol Park, Hyerim Bae, Jungho Choo, Gawon Lee
- 발행사항
- 발행일
- 20260618
- 저널명
- IEEE Sensors Journal
- 국문초록
- 영문초록
- Constructing health indicators (HIs) that quantitatively represent the state of a machine is essential for
the condition-based maintenance of rotating machinery. Traditional physics-based HI (PHI) construction methods have
limited effectiveness; for example, virtual HIs (VHIs) integrating multiple features have limited applicability because
of the manual feature extraction process that depends on
expert knowledge and a lack of model adaptability. Existing studies focus on single domains (raw signals) or use
time?frequency domain spectrograms and fail to adequately
extract meaningful frequency band patterns extended along
specific axes. These limitations in representation capacity
lead to a degraded performance in HI extraction. This article
proposes a novel deep-learning-based HI extraction method
that uses a supervised time?frequency feature fusion autoencoder (AE) for fusing multidomain features. The proposed
model includes spatiotemporal feature extraction from raw signals using a multiscale convolutional neural network
(CNN) Transformer block, extraction of temporally resolved frequency features from time?frequency maps using
rectangular kernels and cross-attention mechanisms, fusion of latent representations to extract 1-D HI, and simultaneous
reconstruction of the input signal from the extracted HI unified encoder?decoder architecture. During training, the
model optimizes a composite loss function that comprises a shape constraint function (SCF) and domain-specific
reconstruction errors using a dynamic weight-averaging strategy. The proposed method is validated using publicly
available rotating machinery datasets and demonstrates superior performances across four evaluation metrics compared
with those of existing methods. This study highlights the potential of automated high-performance HI extraction for
rotating machinery and offers practical contributions to industrial maintenance applications.
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- 첨부파일