利用时间序列分析和深度学习对物联网设备进行预测性维护

Q4 Engineering 弹道学报 Pub Date : 2023-12-20 DOI:10.52783/dxjb.v35.113
Et al. Mohan Raparthy
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引用次数: 0

摘要

物联网(IoT)设备在各行各业的广泛集成,开创了一个以数据驱动运营效率的新时代。然而,这些互联设备的可靠性和不间断功能需要创新的维护方法。本研究的重点是利用时间序列分析(TSA)和深度学习(DL)技术之间的协同作用,为物联网设备开发和实施预测性维护框架。本研究的主要目标是提高预测性维护流程的准确性和效率,最终最大限度地减少停机时间并优化资源利用率。研究方法包括从物联网设备中收集各种类型的数据,包括传感器读数、错误日志和历史维护记录。随后是细致的数据预处理阶段,包括清理、规范化和特征提取,为分析数据集做好准备。拟议框架的核心分析组件包括用于揭示物联网数据中时间模式的时间序列分析。应用统计方法和时间序列分解来识别趋势和季节性,从而为了解设备随时间变化的性能提供有价值的见解。同时,采用深度学习模型,特别是递归神经网络(RNN)和长短期记忆网络(LSTM),根据历史模式预测维护需求。将预测性维护框架应用于真实世界物联网数据集所获得的结果表明,在预测维护需求方面,该框架具有良好的准确性和效率。论文指出了物联网设备预测性维护方面的现有挑战,并提出了未来的研究方向。这些挑战包括探索边缘计算、联合学习以及整合可解释人工智能以提高模型的可解释性。总之,本研究强调了预测性维护在确保物联网设备可靠性方面的重要意义,为各行业寻求利用数据分析和人工智能的全部潜力实现卓越运营提供了路线图。
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Predictive Maintenance in IoT Devices using Time Series Analysis and Deep Learning
The pervasive integration of Internet of Things (IoT) devices across industries has ushered in a new era of data-driven operational efficiency. However, the reliability and uninterrupted functionality of these interconnected devices necessitate innovative approaches to maintenance. This research focuses on the development and implementation of a predictive maintenance framework for IoT devices, leveraging the synergies between Time Series Analysis (TSA) and Deep Learning (DL) techniques. The primary objective of this study is to enhance the accuracy and efficiency of predictive maintenance processes, ultimately minimizing downtime and optimizing resource utilization. The research methodology involves the collection of diverse data types from IoT devices, encompassing sensor readings, error logs, and historical maintenance records. A meticulous data preprocessing stage follows, involving cleaning, normalization, and feature extraction to prepare the dataset for analysis. The core analytical components of the proposed framework include Time Series Analysis for uncovering temporal patterns in the IoT data. Statistical methods and time series decomposition are applied to identify trends and seasonality, providing valuable insights into the device's performance over time. Concurrently, Deep Learning models, specifically recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), are employed to predict maintenance needs based on historical patterns. Results obtained from the application of the predictive maintenance framework to real-world IoT datasets demonstrate promising accuracy and efficiency in anticipating maintenance requirements. The paper identifies existing challenges in predictive maintenance for IoT devices and suggests future research directions. These include the exploration of edge computing, federated learning, and the integration of explainable AI to enhance model interpretability. In conclusion, the study underscores the significance of predictive maintenance in ensuring the reliability of IoT devices, offering a roadmap for industries seeking to harness the full potential of data analytics and artificial intelligence for operational excellence.
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来源期刊
弹道学报
弹道学报 Engineering-Mechanical Engineering
CiteScore
0.90
自引率
0.00%
发文量
2632
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