基于自适应混合采样和BiLSTM的飞机发动机故障预测
Junying Hu1, Xu Jiang2, Huan Xu3
1School of Economics and Management, Hefei University, 230601, Hefei, People's Republic of China.
Scientific reports
|April 21, 2025
概括
本研究引入了一种新方法,用于航空发动机故障预测,使用自适应混合采样和双向LSTM (BiLSTM) 来克服类失衡. 该方法显著提高了不平衡数据集中故障样本的准确识别.
科学领域:
- 航空航天工程 航空航天工程
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 类失衡是航空发动机故障预测的一个重大挑战,导致少数故障类的识别不佳.
- 现有的方法往往难以有效地处理不平衡的数据集在关键应用程序,如航空发动机健康监测.
研究的目的:
- 为航空发动机故障预测提出一个新的框架,以解决类失衡问题.
- 为了提高在不平衡的航空发动机传感器数据中故障检测的准确性和可靠性.
主要方法:
- 开发了一个基于k-means的自适应混合采样策略,以动态平衡数据集.
- 采用双向长短期内存 (BiLSTM) 网络实现了故障预测模型,以捕获时间依赖.
- 验证了关于真实世界航空发动机传感器数据的框架.
主要成果:
- 适应性混合抽样有效地平衡了不平衡的数据集,通过过量抽样少数群体类边界和过少抽样多数集群.
- BiLSTM模型在捕捉传感器数据中的双向时间依赖性方面表现出强的表现.
- 与不平衡数据集的传统方法相比,拟议的框架显著改善了故障样本的识别.
结论:
- 适应式混合采样和BiLSTM的综合框架为使用不平衡数据进行航空发动机故障预测提供了强大的解决方案.
- 这种方法提高了检测罕见故障事件的能力,这对于确保航空发动机安全和可靠性至关重要.
- 该研究强调了将先进的采样技术与复杂的预测性维护任务的深度学习相结合的有效性.
相关概念视频
01:25Multimachine Stability
101
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
101
01:22Multi-input and Multi-variable systems
86
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
In the absence...
86
01:19Survival Tree
39
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...
39