未登录

黎国强 (Guoqiang Li)

jimei university讲师/硕导邮箱:lgq@jmu.edu.cn

H指数:9

被引数:541

个人简介

任职于机械工程领域，本科毕业于大连海事大学，硕博毕业于华中科技大学机械工程专业。研究方向为海工装备可靠性分析与智能故障诊断，致力于海工机电装备的在线监测、异常检测、状态评估、故障诊断等研究，涉及工业大数据分析、人工智能算法、边缘计算、云计算、工业互联网平台设计开发等关键技术。主持、参研多项横纵向科研项目，包括科技部重点研发、国家自然科学基金、福建省自然科学基金、厦门市自然科学基金以及武汉理工大学开发课题等。

教育背景

暂无内容

工作经历

2023.11 – 至今
轮机工程学院
讲师/硕导

荣誉奖项

暂无内容

Physics-guided multimodal time-frequency representation learning for zero-fault-sample anomaly detection in electromechanical coupling machinery

物理指导的多模态时频表示学习在机电耦合机械零故障样本异常检测中的应用

Applied Soft Computing

IF6.6

2026-09-290

PRE

A cost-optimal fault prediction model using machine learning and statistical process control

基于机器学习和统计过程控制的成本最优故障预测模型

Measurement Science and Technology

IF3.4

2026-02-030

PRE

Green and Scalable Production of High-Quality Graphene by Solid Lithiation and Exfoliation

通过固体锂化和剥离绿色和可扩展生产高质量石墨烯

Advanced Functional Materials

IF19

2026-01-190

PRE

Intelligent fault diagnosis of rotating machinery driven by physical information and contrastive learning under extreme sample imbalance conditions

在极端样本不平衡条件下，基于物理信息和对比学习的旋转机械智能故障诊断

Measurement Science and Technology

IF3.4

2025-10-280

PRE

Ultrathin Nonswelling NbTe2 Nanosheet Membranes for Fast and Tight Molecular Sieving

超薄非膨胀NbTe2纳米片膜用于快速紧密的分子筛分

Chemistry of Materials

IF7

2025-10-150

PRE

Physically interpretable Stockwell weight initialization and adaptive fusion average threshold for intelligent fault diagnosis of rolling bearing under noisy environment

物理可解释的Stockwell权重初始化和自适应融合平均阈值，用于噪声环境下滚动轴承的智能故障诊断

Engineering Applications of Artificial Intelligence

IF8

2025-08-040

PRE

Zero-Sample fault diagnosis of rolling bearings via fault spectrum knowledge and autonomous contrastive learning

基于故障谱知识和自主对比学习的零样本滚动轴承故障诊断

EXPERT SYSTEMS WITH APPLICATIONS

IF7.5

2025-02-011

PRE

Deep Reinforcement Learning-Based Online Domain Adaptation Method for Fault Diagnosis of Rotating Machinery

基于深度强化学习的旋转机械故障诊断在线域自适应方法

IEEE-ASME TRANSACTIONS ON MECHATRONICS

IF7.3

2022-10-0132

PRE

Parallel multi-fusion convolutional neural networks based fault diagnosis of rotating machinery under noisy environments

ISA TRANSACTIONS

IF6.5

2022-09-0134

PRE

Self-supervised learning for intelligent fault diagnosis of rotating machinery with limited labeled data

基于自监督学习的有限标记数据旋转机械智能故障诊断

APPLIED ACOUSTICS

IF3.6

2022-03-0137

PRE

Convolutional Neural Network-Based Bayesian Gaussian Mixture for Intelligent Fault Diagnosis of Rotating Machinery

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

IF5.9

2021-01-0132

PREAI

Intelligent Fault Diagnosis via Semisupervised Generative Adversarial Nets and Wavelet Transform

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

IF5.9

2020-07-0178

PREAI

Sensor Data-Driven Bearing Fault Diagnosis Based on Deep Convolutional Neural Networks and S-Transform

IF3.5

2019-06-1982

OAAI