Divide and conquer anomaly detection: A case study predicting defective engines

Research output: Contribution to journalConference articlepeer-review

6 Citations (Scopus)


The goal of this paper is to test if data partitioning based on domain knowledge improves the performance of unsupervised anomaly detection algorithms for the detection of faulty internal combustion engines. We test three common anomaly detection algorithms to predict defective engines at the end of an assembly line, both with and without data partitioning. The algorithms are trained on high-dimensional vibration data. To evaluate and compare the detection performance of partitioned and unpartitioned approaches, we use a labeled collection of known anomalies. Using domain knowledge to partition the data improves the anomaly detection performance of all tested algorithms. A divide and conquer strategy based on data partitioning, thus, appears to be a viable anomaly detection approach in cases where abundant unlabeled data is available.

Original languageEnglish
Pages (from-to)57-61
Number of pages5
JournalProcedia Manufacturing
Publication statusPublished - 2020
Event1st International Conference on Industry 4.0 and Smart Manufacturing, ISM 2019 - Rende (CS), Italy
Duration: 20 Nov 201922 Nov 2019


  • Anomaly detection
  • Ensemble learning
  • Industry 4.0
  • Machine learning
  • Manufacturing
  • Quality assurance
  • Quality control
  • Vibration data


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