Datathons Solutions

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
votes

3 thoughts on “Datathon Kaufland Solution – LSTM and EDM Models for Predictive Maintenance

  1. 4
    votes

    Positive Aspects
    —————————-
    – 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
    —————————-
    – no word on hyperparameter tuning

  2. 0
    votes

    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.)

  3. 0
    votes

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