Mechanical intelligent fault diagnosis is an important method to accurately identify the health
status of mechanical equipment. Traditional fault diagnosis methods perform poorly in the …

针对实际中某种工况滚动轴承带标签振动数据获取困难, 健康指标难以构建及寿命预测误差大的
问题, 提出一种基于无监督深度模型迁移的滚动轴承剩余使用寿命(Remaining useful life, RUL) …

Rolling bearing is a key component of rotating machines, its working state directly affects the
performance and safety of the whole equipment. Deep learning based on big data is a …

The inverter is one of the most important parts of the motor driver system which also the
weak link prone to failure. Its reliability and stability are critical to the smooth operation of the …

提出一种新的深度残差对冲网络模型. 该模型借助Inception 堆叠思想, 提出了堆叠卷积对冲结构
块, 以加快网络收敛速度, 并设计了新的恒等映射块, 实现输入层与中间各层的残差连接, 最后 …

In the past few years, wireless communication and unmanned control have been widely
used for intelligent robots and a variety of intelligent robot systems are designed for logistics …

针对高速列车车轮失圆识别难以兼顾效率与精度问题, 提出一种基于轴箱振动与动力学模型的
高速列车车轮失圆状态智能识别方法. 首先, 利用静态检测设备采集车轮非圆原始数据 …

设备点检记录是支撑故障原因分析与处理的重要信息来源, 目前亟需对设备点检故障中的根因
信息进行有效挖掘, 以提升设备预防性维护的可靠性. 鉴于此, 首次将因果科学论引入制造领域 …

针对轨道不平顺检测成本高与时效性低等不足, 从车辆动态响应与轨道不平顺之间的相关性为
切入点, 提出一种多源车载数据驱动的轨道不平顺智能识别方法. 首先, 建立地铁车辆系统动力学 …

A fault diagnosis method for the rotating machinery based on improved Convolutional
Neural Network (CNN) with Gray-Level Transformation (GLT) is proposed to increase the …