Engine Failure Detection of Raw Mill Machine via Discrete Variational Auto-encoder

Izhar Brur Abruzi, Mohammad Iqbal, Imam Mukhlash, Alvida Mustika Rukmi, Nani Kurniati, Masaomi Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

We present a deep learning model to detect failure engine state by observing the discrete latent sensor behaviors. Further, we investigate the behaviors from the reconstruction loss of the model until we find its value starting to jump out (anomaly stage). As a result, this work aims to forecast the failure time of the engine as early as possible. To notice the anomaly, we formulate a piecewise function based on alpha -quantile of the loss value inside the proposed model. Unlike the existing studies focusing on the continuous latent, this work draws the discrete latent from discrete variational auto-encoder (DVAE) to predict the failure state better. For evaluation purposes, we evaluated the proposed model on a real dataset from the raw mill machine of a cement factory in Indonesia. From the experiments, we are satisfied to see the proposed model performances detecting the failure state of the raw mill machine as early as possible compared to the state-of-the-art model.

Original languageEnglish
Title of host publicationProceedings of 2022 International Conference on Data and Software Engineering, ICoDSE 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages59-64
Number of pages6
ISBN (Electronic)9798350397055
DOIs
Publication statusPublished - 2022
Event2022 International Conference on Data and Software Engineering, ICoDSE 2022 - Bali, Indonesia
Duration: 2022 Nov 22022 Nov 3

Publication series

NameProceedings of 2022 International Conference on Data and Software Engineering, ICoDSE 2022

Conference

Conference2022 International Conference on Data and Software Engineering, ICoDSE 2022
Country/TerritoryIndonesia
CityBali
Period22/11/222/11/3

Keywords

  • Discrete latent
  • Failure engine detection
  • Raw mill
  • Variational auto-encoder

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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