Anomaly detection has recently been applied to various areas, and several techniques
based on deep learning have been proposed for the analysis of multivariate time series. In …

Predictive maintenance (PdM) aims the reduction of costs to increase the competitive
strength of the enterprises. It uses sensor data together with analytics techniques to optimize …

Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and
stability of working devices (eg, water treatment system and spacecraft), whose data are …

In recent years, the growing number of devices connected to the internet has increased
significantly. These devices can interact with the external environment and with human …

Fault diagnosis for autonomous vehicles aims to provide available information about the
operation status of the vehicle to avoid potential risks, and sensor data provide the …

Personalised healthcare has seen significant improvements due to the introduction of health
monitoring technologies that allow wearable devices to unintrusively monitor physiological …

By enhancing data accessibility, the implementation of data-driven models has been made
possible to empower strategies in relation to O&M activities. Such models have been …

Prospective customers are becoming more concerned about safety and comfort as the
automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation …

The recent wave of digitalization is characterized by the widespread deployment of sensors
in many different environments, eg, multi-sensor systems represent a critical enabling …

The rise of the Internet of Things allowed higher spatial–temporal resolution soil moisture
data captured through in situ sensing. Such abundance of data enables machine learning …