Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model

2016/12/20 by Pavel Nikolaevich Filonov, Filonov, Pavel, Andrey Lavrentyev +4 · 7 citations
Computer Science · Engineering · #Advanced Computational Techniques and Applications #Advanced Data Processing Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Time Series Analysis and Forecasting

paper · pdf · doi:10.48550/arxiv.1612.06676

openalex publication_date 2016/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and causes of fault behavior in the plant. Having a self-consistent data set with labeled faults, we used an LSTM architecture with a forecasting error threshold to obtain precision and recall quality metrics. The dependency of the quality metric on the threshold level is considered. An appropriate mechanism such as "one handle" was introduced for filtering faults that are outside of the plant operator field of interest.

Cited by

• MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection
• Reasoning-based Anomaly Detection Framework: A Real-time, Scalable, and Automated Approach to Anomaly Detection Across Domains
• MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysis
• Time-RA: Towards Time Series Reasoning for Anomaly with LLM Feedback
• xLSTMAD: A Powerful xLSTM-based Method for Anomaly Detection
• mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale
• TAB: Unified Benchmarking of Time Series Anomaly Detection Methods

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