A data mining approach for machine fault diagnosis based on associated frequency patterns

Md. Mamunur Rashid, Muhammad Amar, Iqbal Gondal, Joarder Kamruzzaman

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    21 Citations (Scopus)


    Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.

    Original languageEnglish
    Pages (from-to)638-651
    Number of pages14
    JournalApplied Intelligence
    Issue number3
    Publication statusPublished - 1 Oct 2016


    • Anomaly detection
    • Associated frequency pattern tree
    • Bearing fault
    • Machine condition monitoring (MCM)
    • Vibration data

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