without the proposed improvements. The comparison is performed on a dataset collected and released by [@airbus18], containing highly complex vibration measurements from real helicopters flight tests. The different encodings provide competitive results for anomaly detection.
author:
- |
    Gabriel Rodriguez Garcia\
    ETH Zürich,\
    Zürich, Switzerland\
    Gabriel Michau\
    ETH Zürich,\
    Zürich, Switzerland\
    Mélanie Ducoffe\
    Airbus AI Research,\
    Toulouse France\
    Jayant Sen Gupta\
    Airbus AI Research,\
    Toulouse France\
    Olga Fink\
    ETH Zürich,\
    Zürich, Switzerland
bibliography:
- 'references.bib'
date: 13rd of May 2020
subtitle: Preprint
title: 'Time Series to Images: Monitoring the Condition of Industrial Assets with Deep Learning Image Processing Algorithms'
---

Introduction {#sec:intro}
============

With the globalisation of many industrial markets, it becomes increasingly important for the operators to optimise the use and the operating costs of their assets. This optimisation requires the ability to maintain a high availability at regulatory safety levels. Since the maintenance of industrial assets accounts for a large part of the operating costs, the ability to monitor the system to perform maintenance based on its condition would benefit the operators greatly. The ability to detect anomalies could help in the short term to reduce the amount of required unscheduled maintenance. Once it has been demonstrated that 