Multi-Filter Clustering Fusion for Feature Selection in Rotating Machinery Fault Classification

Solichin Mochammad, Yoojeong Noh*, Young Jin Kang, Sunhwa Park, Jangwoo Lee, Simon Chin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In the fault classification process, filter methods that sequentially remove unnecessary features have long been studied. However, the existing filter methods do not have guidelines on which, and how many, features are needed. This study developed a multi-filter clustering fusion (MFCF) technique, to effectively and efficiently select features. In the MFCF process, a multi-filter method combining existing filter methods is first applied for feature clustering; then, key features are automatically selected. The union of key features is utilized to find all potentially important features, and an exhaustive search is used to obtain the best combination of selected features to maximize the accuracy of the classification model. In the rotating machinery examples, fault classification models using MFCF were generated to classify normal and abnormal conditions of rotational machinery. The obtained results demonstrated that classification models using MFCF provide good accuracy, efficiency, and robustness in the fault classification of rotational machinery.

Original languageEnglish
Article number2192
Issue number6
Publication statusPublished - 1 Mar 2022


  • Clustering
  • Fault classification
  • Feature selection
  • Fusion
  • Multi-filter
  • Rotating machinery


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