Author name cluster

Xiaoming Yuan

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

EAAI Journal 2026 Journal Article

Multi-modal semantic interaction fusion with dual-consistency contrastive learning for rotating machinery fault diagnosis

Ying Li
Xiaoping Liu
Xutong Zhang
Pengfei Liang
Xuetao Xu
Xiaoming Yuan
Lijie Zhang

Intelligent fault diagnosis of rotating machinery relies on the ability to extract discriminative and robust representations from multi-modal sensor data. However, in realistic industrial environments, multi-modal signals are often weakly labeled, and many existing data-driven methods suffer from insufficient semantic interaction across modalities and scales, leading to unstable diagnostic decisions. To address these issues, this paper develops a novel artificial intelligence framework for fault diagnosis based on multiscale semantic interaction and dual-consistency contrastive representation learning. Heterogeneous sensor signals are first transformed into unified multi-channel time-frequency representations through continuous wavelet analysis and tensor fusion. A hierarchical representation learning architecture is then constructed to progressively capture global dependencies, intermediate semantic patterns, and fine-grained local fault features. A feedback-driven interaction mechanism is further introduced to propagate discriminative local information to higher-level representations, thereby enhancing global-local semantic consistency. To overcome the scarcity of labeled data and improve generalization, a dual-consistency contrastive learning strategy is designed, which enforces both intra-channel stability and inter-channel semantic alignment across different sensor modalities. This consistency-driven formulation constrains the representation space such that fault-related features remain separable, and robust under limited supervision and heterogeneous sensing conditions. Comprehensive evaluations using multiple performance metrics on two rotating machinery benchmark datasets demonstrate that the proposed method outperforms existing state-of-the-art approaches. Further ablation, sensitivity, and efficiency analyses confirm a favorable balance between diagnostic performance and model complexity. These results indicate that the proposed artificial intelligence-based framework provides an effective solution for intelligent condition monitoring and predictive maintenance in complex industrial systems.

Details DOI

EAAI Journal 2022 Journal Article

Intelligent fault diagnosis of rolling bearing based on wavelet transform and improved ResNet under noisy labels and environment

Pengfei Liang
Wenhui Wang
Xiaoming Yuan
Siyuan Liu
Lijie Zhang
Yiwei Cheng

The fault diagnosis (FD) of rolling bearing (RB) has a great significance in safe operation of engineering equipment. Many intelligent diagnosis methods have been successfully developed. However, the performances of traditional fault diagnosis methods are affected by noisy labels and environment which widely exist in realistic industrial applications. This article proposed a novel FD method of RB based on wavelet transform (WT) and an improved residual neural network (IResNet), named WT-IResNet. The proposed WT-IResNet approach uses a new pooling layer for dimension reduction and a global singular value decomposition (SVD) adaptive strategy for feature extraction. Furthermore, the original softmax layer and the logistic loss for training are replaced by a new loss function containing two adjustable parameters to address fault diagnosis with label noises. Two typical bearing failure datasets are used to evaluate the feasibility and effectiveness of WT-IResNet under noisy labels and noisy environment respectively. The experimental results indicate that WT-IResNet has better robustness against noise in comparison with other methods. Whatever under noisy labels or noisy environment, the performance of WT-IResNet outperforms other methods.

Details DOI

JMLR Journal 2020 Journal Article

Discerning the Linear Convergence of ADMM for Structured Convex Optimization through the Lens of Variational Analysis

Xiaoming Yuan
Shangzhi Zeng
Jin Zhang

Despite the rich literature, the linear convergence of alternating direction method of multipliers (ADMM) has not been fully understood even for the convex case. For example, the linear convergence of ADMM can be empirically observed in a wide range of applications arising in statistics, machine learning, and related areas, while existing theoretical results seem to be too stringent to be satisfied or too ambiguous to be checked and thus why the ADMM performs linear convergence for these applications still seems to be unclear. In this paper, we systematically study the local linear convergence of ADMM in the context of convex optimization through the lens of variational analysis. We show that the local linear convergence of ADMM can be guaranteed without the strong convexity of objective functions together with the full rank assumption of the coefficient matrices, or the full polyhedricity assumption of their subdifferential; and it is possible to discern the local linear convergence for various concrete applications, especially for some representative models arising in statistical learning. We use some variational analysis techniques sophisticatedly; and our analysis is conducted in the most general proximal version of ADMM with Fortin and Glowinski's larger step size so that all major variants of the ADMM known in the literature are covered. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

PDF Details

ICML Conference 2017 Conference Paper

Adaptive Consensus ADMM for Distributed Optimization

Zheng Xu 0002
Gavin Taylor
Hao Li 0022
Mário A. T. Figueiredo
Xiaoming Yuan
Tom Goldstein

The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) convergence rate for adaptive ADMM methods with node-specific parameters, and propose adaptive consensus ADMM (ACADMM), which automatically tunes parameters without user oversight.

Details

NeurIPS Conference 2015 Conference Paper

Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing

Tom Goldstein
Min Li
Xiaoming Yuan

The alternating direction method of multipliers (ADMM) is an important tool for solving complex optimization problems, but it involves minimization sub-steps that are often difficult to solve efficiently. The Primal-Dual Hybrid Gradient (PDHG) method is a powerful alternative that often has simpler substeps than ADMM, thus producing lower complexity solvers. Despite the flexibility of this method, PDHG is often impractical because it requires the careful choice of multiple stepsize parameters. There is often no intuitive way to choose these parameters to maximize efficiency, or even achieve convergence. We propose self-adaptive stepsize rules that automatically tune PDHG parameters for optimal convergence. We rigorously analyze our methods, and identify convergence rates. Numerical experiments show that adaptive PDHG has strong advantages over non-adaptive methods in terms of both efficiency and simplicity for the user.

PDF Details

JMLR Journal 2015 Journal Article

The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R

Xingguo Li
Tuo Zhao
Xiaoming Yuan
Han Liu

This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, $\ell_q$ Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling flexibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM). The package flare is coded in double precision C, and called from R by a user-friendly interface. The memory usage is optimized by using the sparse matrix output. The experiments show that flare is efficient and can scale up to large problems. [abs] [ pdf ][ bib ] [ code ] [ webpage ] &copy JMLR 2015. ( edit, beta )

PDF Details