# Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance

In this paper we propose the use of a combination of LSTM and EDM models to address the issue of anomaly classification and prediction in time series data. Working with sensor data for automated storage and retrieval systems for a German hypermarket chain, we show that predictors based on variance and median methods show sufficient promise in the handling of anomalies.

16
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#### 2 thoughts on “Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance”

1. 4
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Positive Aspects
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– very good introduction
– excellent descriptions & explanations
– good flow & project plan
– decent data description
– comparisons on machine and sensor level
– reasonable modeling approaches
– evaluation of different approaches
– suggestion of combination of methods

Negative Aspects
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– no word on hyperparameter tuning

2. 0
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Very interesting article, thx for sharing this!

Have you tested it on more than 1 minute interval? I mean in real world you need to predict within factory lets say in range of 10-30 minutes, if supporting remote sites even hours. Did you try to predict lets say 1 hour into the future? What could be bottleneck and possible tuning such approach. On the other hand in my case I have dataset for past 4 years with very detailed markings on different actions within devices (maintainances, runtime, warning, error, etc.)