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Modeling Mixed-Type Time Series Data With Neural Networks for Predictive Maintenance

Master's Thesis

Modeling Mixed-Type Time Series Data With Neural Networks for Predictive Maintenance

Advisors

An Ngyuen (M.Sc.), Prof. Dr. B. Eskofier

Duration

09/2020-02/2020

Abstract:

Predictive Maintenance (PdM) allows companies to monitor operating conditions of systems to
schedule maintenance activities based on an as-needed basis. This can prevent unexpected equipment
downtime due to equipment failure as well as too early repairs [1].
Most work on PdM so far focuses on uni- or multivariate and regularly spaced sensor measurements
[2]. These datasets are mostly collected in a controlled experimental environment [3, 4, 5].
Other work focuses on event log data or frequent patterns derived from event log data [6, 7].
However, a complete picture about the condition of modern systems may only be obtained by
integrating different data sources like event logs and sensor measurements [8, 9].
The goal of this thesis is to develop, implement and evaluate neural network-based algorithms to
predict the breakdown of X-Ray tubes installed in CT Scanners. For this purpose, time series data
of a large fleet of CT scanners will be analyzed. The data includes event logs, irregularly spaced
physical sensor measurements, and usage statistics. In summary, the available data imposes the
following challenges:
– Different data sources need to be integrated
– Time series data is irregularly sampled
– Varying utilization of CT scanners between healthcare facilities
– Collected data is noisy since it is collected from real healthcare facilities
There already exist solutions from other domains, like [10, 11, 12], to tackle individual points
of the listed challenges. Therefore, this work aims at integrating and extending different existing
solutions to tackle all of the challenges that come with the specific data at hand.

 

 

References:

[1] Mobley, R. Keith : An Introduction to Predictive Maintenance. Elsevier Science. 2002.
[2] Zhao, Rui et al.: Deep learning and its applications to machine health monitoring Mechanical
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Conference on Machine Learning and Knowledge Discovery in Databases. 2019.
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