时间和异质图神经网络用于剩余有用的生命预测
IEEE transactions on neural networks and learning systems
|August 1, 2025
概括
本研究介绍了时间和异质图形神经网络 (THGNN),以改善工业系统的剩余使用寿命 (RUL) 预测. THGNN捕获细粒度的时间和空间传感器数据依赖性,显著提高了RUL预测的准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 预测剩余的使用寿命 (RUL) 对工业预测和健康管理至关重要.
- 深度学习模型擅长识别传感器数据中的时间依赖.
- 现有的方法经常错过细粒度的时间信息和传感器异质性.
研究的目的:
- 开发一种用于RUL预测的新型模型,可以捕捉传感器数据中的时间和空间依赖性.
- 利用各种传感器类型的异质性来实现更准确的RUL预测.
- 为了解决捕获细粒度时间动态和传感器相关性的现有方法的局限性.
主要方法:
- 引入时间和异质图形神经网络 (THGNNs).
- THGNNs汇总来自邻近节点的历史数据,进行精细的时间和空间分析.
- 使用特征智能线性调制 (FiLM) 来处理传感器异质性.
主要成果:
- THGNNs有效地捕捉到微细的时间动态和传感器数据中的空间相关性.
- 该模型显示了RUL预测准确度的显著改进.
- 在N-CMAPSS数据集上,与最先进的方法相比,实现了高达19.2%和31.6%的改进.
结论:
- 通过建模复杂的传感器关系,THGNN为RUL预测提供了强大的方法.
- 通过FiLM利用传感器异质性可以提高模型性能.
- 拟议的方法代表了预后和健康管理的重大进步.
相关概念视频
Time-Series Graph
4.5K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.5K
End Point Prediction: Gran Plot
590
A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
For potentiometric titration, the Gran plot is created by plotting...
590
Survival Tree
160
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
160
Kaplan-Meier Approach
270
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
270
Neural Circuits
1.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.6K
Sequence Networks of Rotating Machines
142
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
142

