Improved Process Diagnosis Using Fault Contribution Plots from Sparse Autoencoders

Asgeir Daniel Hallgrimsson, Hans Henrik Niemann, Morten Lind

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    Development of model-based fault diagnosis methods is a challenge when industrial systems are large and exhibit complex process behavior. Latent projection (LP), a statistical method that extract features of data via dimensionality reduction, is an alternative approach to diagnosis as it can be formulated to not rely on process knowledge. However, LP methods may perform poorly at identifying abnormal process variables due a “fault smearing” effect - variables unaffected by a fault are unintentionally characterized as being abnormal. The effect occurs because data compression permits faulty and non-faulty variables to interact. This paper presents an autoencoder (AE), a nonlinear LP method based on neural networks, as a monitoring
    method of a simulated nonlinear triple tank process (TTP). Simulated process data was used to train the AE to generate a monitoring statistic representing the condition of the TTP. Sparsity was introduced in the AE to reduce variable interactivity. The AE’s ability to detect a fault was demonstrated. The individual contributions of process variables to the AE’s monitoring statistic were analyzed to reveal the process variables that were no longer consistent with normal operating conditions. The key result in this study was that sparsity reduced fault smearing onto unaffected variables and increased the contributions of actual faulty variables.
    Original languageEnglish
    Title of host publicationProceedings of 21st IFAC World Congress
    Number of pages8
    PublisherInternational Federation of Automatic Control
    Publication date2021
    Publication statusPublished - 2021
    Event21st IFAC World Congress - Virtual Event, Berlin, Germany
    Duration: 12 Jul 202017 Jul 2020
    Conference number: 21


    Conference21st IFAC World Congress
    LocationVirtual Event
    Internet address


    • Fault detection and isolation
    • Machine learning
    • Grey box modelling
    • Learning for control
    • Subspace methods


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