在实验室规模批量反应堆上使用机器学习算法进行在线传感器故障检测:LSTM方法
Natasha Chrissane Lobo1, Himani H Poojary1, Lubna Katapady1
1Department of Computer Science Engineering, Shri Madhwa Vadiraja Institute of Technology and Management, Bantakal 574115, India.
ACS omega
|March 2, 2026
概括
本研究介绍了一种新的批量反应器故障检测系统,使用基于卷积神经网络 (CNN) - 挤压和刺激的改进多层长短期记忆 (CS-IMLSTM) 模型. 该CS-IMLSTM系统有效地实时识别传感器故障,提高化学过程的安全性和可靠性.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 人工智能的人工智能
- 过程控制 过程控制
背景情况:
- 批量反应堆在化学过程中至关重要,但容易受到传感器故障的影响.
- 实时故障检测对于运营安全和效率至关重要.
- 现有的方法可能会与复杂,叠加或稀疏的传感器故障作斗争.
研究的目的:
- 开发和评估实验室规模批量反应堆的在线故障检测系统.
- 在动态的工业环境中提高故障识别的准确性和速度.
- 通过智能预测性维护,提高化学工艺操作的可靠性和安全性.
主要方法:
- 实现一个卷积神经网络 (CNN) - - 基于挤压和刺激的改进多层长期短期记忆 (CS-IMLSTM) 模型.
- 连续监测批量反应堆参数,包括温度,冷却液流量和加热电流.
- 整合一个通道空间注意力机制,以减少噪音和增强特征意义.
主要成果:
- 与传统的LSTM和CNN-LSTM模型相比,CS-IMLSTM模型在故障检测方面表现出卓越的准确性.
- 拟议的系统显示了在线学习的更快的适应能力.
- 在实时实现了对叠加和稀疏传感器故障的有效识别.
结论:
- CS-IMLSTM模型为批量反应堆的在线故障检测提供了一个强大的解决方案.
- 开发的系统可以应用于动态工业环境中的智能预测性维护.
- 这种方法显著提高了化学工艺操作的安全性和可靠性.
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