Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study

By Mikel Canizo, Isaac Triguero, Angel Conde, Enrique Onieva. Neurocomputing, 2019

360 citations recorded by the source corpus.

Abstract Detecting anomalies in time series data is becoming mainstream in a wide variety of industrial applications in which sensors monitor expensive machinery. The complexity of this task increases when multiple heterogeneous sensors provide information of different nature, scales and frequencies from the same machine. Traditionally, machine learning techniques require a separate data pre-processing before training, which tends to be very time-consuming and often requires domain knowledge. Recent deep learning approaches have shown to perform well on raw time series data, eliminating the need for pre-processing. In this work, we propose a deep learning based approach for supervised multi-time series anomaly detection that combines a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) in different ways. Unlike other approaches, we use independent CNNs, so-called convolutional heads, to deal with anomaly detection in multi-sensor systems. We address each sensor individually avoiding the need for data pre-processing and allowing for a more tailored architecture for each type of sensor. We refer to this architecture as Multi-head CNN–RNN. The proposed architecture is assessed against a real industrial case study, provided by an industrial partner, where a service elevator is monitored. Within this case study, three type of anomalies are considered: point, context-specific, and collective.The experimental results show that the proposed architecture is suitable for multi-time series anomaly detection as it obtained promising results on the real industrial scenario.

Subfields: Artificial Intelligence, Biomedical Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d7ddc4830c2ab415949ad09ecfbb674e9e492623

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nada Sahlab
2. Benjamin Maschler
3. Yiyuan Yang
4. Liang Sun
5. Chaoli Zhang
6. Michael Weyrich
7. Qingsong Wen
8. Tian Zhou

In Signal Processing

1. Yiyuan Yang
2. Liang Sun
3. Chaoli Zhang
4. Qingsong Wen
5. Tian Zhou

Citations in the corpus

• DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection (2023, methodology): “Deep learning methods include using autoencoders, variational autoencoders (VAEs) [50, 58], and recurrent neural networks (RNNs) [12, 63] such as long short-term memory (LSTM) networks [81].”
• A Survey on Anomaly Detection for Technical Systems using LSTM Networks (2021, methodology): “In [48], transfer learning is used to improve anomaly detection in the operation of service elevators by allowing for differences in the type and number of sensors at different operating locations.”