Unsupervised Convolutional Transformer Autoencoder for Robust Health Indicator Construction and RUL Prediction in Rotating Machinery

Prognostics for rotating machinery, particularly bearings, encounter significant challenges in constructing reliable health indicators (HIs) that accurately reflect degradation trajectories, thereby enabling precise remaining useful life (RUL) predictions. This article proposes a novel integrated ap...

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences Vol. 15; no. 20; p. 10972
Main Authors: Dahal, Amrit, Huang, Hong-Zhong, Huang, Cheng-Geng, Huang, Tudi, Khanal, Smaran, Niazi, Sajawal Gul
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.10.2025
Subjects:
Algorithms
Artificial intelligence
Bearings
Datasets
Electric transformers
Entropy
Forecasts and trends
Health
health indicator
hybrid convolutional vision transformer
Machinery
Neural networks
Predictive maintenance
Principal components analysis
remaining useful life
Signal processing
Time series
unsupervised learning
Vibration
ISSN:2076-3417, 2076-3417
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Prognostics for rotating machinery, particularly bearings, encounter significant challenges in constructing reliable health indicators (HIs) that accurately reflect degradation trajectories, thereby enabling precise remaining useful life (RUL) predictions. This article proposes a novel integrated approach for predicting the RUL of bearings without manual feature engineering. Specifically, a sequential autoencoder integrating a convolutional neural network (CNN) and vision Transformer (Vi-T) is employed to capture the local spatial patterns and global temporal correlations of time-domain vibration signals. The Wasserstein distance is introduced to quantify the divergence between healthy and degraded signal embeddings, resulting in a robust HI metric. Subsequently, the derived HI is fed into a CNN-bidirectional long short-term memory-regressor with Monte Carlo dropout to provide RUL predictions and Bayesian uncertainty estimates. Experimental results from the Xi’an Jiao-Tong University bearing dataset demonstrate that the proposed method surpasses conventional techniques in HI construction and RUL prediction accuracy, demonstrating its efficacy for complex industrial systems with minimal data preprocessing.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app152010972