An LSTM-Based Method for Detection and Classification of Sensor Anomalies
An LSTM-Based Method for Detection and Classification of Sensor Anomalies
复制标题
基于 LSTM 的传感器异常检测和分类方法
DOI：
10.1145/3409073.3409089
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Sumitra Mukherjee
中科院分区：
文献类型：
--
作者：
A. Verner;Sumitra Mukherjee
大多数现有的基于机器学习（ML）的传感器数据异常检测解决方案都依赖于精心制作的特征。这种方法有一个基本的限制，因为它通常是特定于应用程序的，需要领域专家的大量人力。深度学习模型已被证明具有从原始数据中抽象出相关高级特征的能力。长短期记忆（LSTM）递归神经网络在复杂的时间序列预测问题中已被证明是有效的。本文提出了一种基于lstm的传感器数据异常检测方法。我们系统地研究了它在真实医疗传感器测量的原始时间序列上的有效性，并表明它达到了与在精心设计的特征向量上操作的传统ML模型相同的性能水平。该方法的微观、宏观和加权精度、召回率和f1分数均在0.99以上。
Most existing machine learning (ML) based solutions for anomaly detection in sensory data rely on carefully hand-crafted features. This approach has a fundamental limitation since it is often application-specific and requires considerable human effort from domain experts. Deep learning models have been demonstrated to have the ability to abstract relevant high-level features from raw data. Long short-term memory (LSTM) recurrent neural networks have proven effective in complex time-series prediction problems. In this paper, we propose an LSTM-based method for anomaly detection in sensory data. We systematically investigate its effectiveness on raw time-series of real medical sensors measurements and show that it achieves the same level of performance as traditional ML models operating on carefully designed feature vectors. The proposed method achieved micro, macro, and weighted precision, recall, and F1-score of over 0.99.