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

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Abstract

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.

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Authors and Affiliations

  1. School of Information Technology, Monash University, Clayton, VIC, 3800, Australia

    Md. Mamunur Rashid & Muhammad Amar

  2. School of Engineering and Information Technology, Federation University, Gippsland, Australia

    Iqbal Gondal & Joarder Kamruzzaman

Authors
  1. Md. Mamunur Rashid
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  2. Muhammad Amar
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  3. Iqbal Gondal
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  4. Joarder Kamruzzaman
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Corresponding author

Correspondence to Md. Mamunur Rashid.

Appendix: Abbreviations

Appendix: Abbreviations

VDS Vibration Data Stream

FFT Fast Fourier Transform

SAFP-tree Sliding Window Associated Frequency Pattern Tree

SAFP-AD Sliding Window Associated Frequency Pattern - Anomaly Detection

NBM Normal Behavior Measure

MCM Machine Condition Monitoring

ANN Artificial Neural Network

SVM Support Vector Machine

CPBs Conditional Pattern-Bases

CTs Conditional Trees

AFP Associated Frequency Pattern

SNR Signal-to-Noise Ratio

FPOF Frequent Pattern Outlier Factor

ROC Receiver Operating Characteristics

AUC Area Under the ROC Curve

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Rashid, M.M., Amar, M., Gondal, I. et al. A data mining approach for machine fault diagnosis based on associated frequency patterns. Appl Intell 45, 638–651 (2016). https://doi.org/10.1007/s10489-016-0781-3

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  • DOI: https://doi.org/10.1007/s10489-016-0781-3

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