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Spectral proper orthogonal decomposition and machine learning algorithms for bearing fault diagnosis

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Vibration analysis has been extensively exploited for bearing fault diagnosis. However, signal acquisition is quite expensive since external hardware is required. Moreover, for inaccessible systems, vibration analysis is considered to be impracticable. Motor current signal analysis (MCSA) provides fault diagnosis feasibly without the use of sensors since motor current signals can be easily collected and gathered using inverters. However, besides the defect frequency signature, the motor current also carries the power supply frequency and other mechanical frequencies which may significantly increase the fault extraction complexity. In addition, the MCSA's overall performance remains behind to the vibration analysis, especially for external bearings installed outside the electric motors. In this paper, an intelligent algorithm for bearing fault diagnosis is suggested to enhance the external ball bearings condition monitoring. The presented approach is based on several methods, mainly maximal overlap discrete wavelet packet transform (MODWPT), spectral proper orthogonal decomposition (SPOD), time-domain features for feature extraction and random forest (RF), ensemble tree (ET) K-nearest neighbors (KNN), in addition to multi-class support vector machine (MSVM) as classifiers. For the first contribution, motor current and vibration data under several working conditions with artificial and real inner and outer ring defects are decomposed by MODWPT into several nodes. Thereafter, SPOD is used to calculate the 1st, 2nd, and 3rd energy spectra for each node and plots them in a three-dimensional sample distribution. SPOD extracts the dominating feature at each frequency from the raw signals. The second contribution consists mainly of two steps: feature extraction and classification. For feature extraction, feature matrices for each bearing state are obtained by applying three time-based features to each node obtained by MODWPT. Thereafter, for feature classification, KNN, ET, MSVM, and RF are used to calculate the classification accuracy and display confusion matrices. Compared to the modern signal processing technique “Accugram”, experimental results prove that the proposed approach is more efficient for detecting, identifying, and classifying bearing state under different operation modes.

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Authors and Affiliations

  1. Department of Mechanical and Process Engineering, Faculty of Mechanical Engineering and Process Engineering, Houari Boumediene University of Science and Technology, Algiers, Algeria

    Adel Afia

  2. Department of Mechanical Engineering, Solid Mechanics and Systems Laboratory (LMSS), University M’Hamed Bougara Boumerdes, Boumerdes, Algeria

    Adel Afia, Fawzi Gougam, Walid Touzout, Chemseddine Rahmoune, Hand Ouelmokhtar & Djamel Benazzouz

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  1. Adel Afia
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  2. Fawzi Gougam
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  3. Walid Touzout
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  4. Chemseddine Rahmoune
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  5. Hand Ouelmokhtar
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  6. Djamel Benazzouz
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Correspondence to Adel Afia.

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Afia, A., Gougam, F., Touzout, W. et al. Spectral proper orthogonal decomposition and machine learning algorithms for bearing fault diagnosis. J Braz. Soc. Mech. Sci. Eng. 45, 550 (2023). https://doi.org/10.1007/s40430-023-04451-z

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  • DOI: https://doi.org/10.1007/s40430-023-04451-z

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