A DCSLBP based intelligent machine malfunction detection model using sound signals for industrial automation systems - Boztas, G.; Tuncer, T.; Aydogmus, O.; Yildirim, M. (Pergamon-Elsevier Science Ltd, 2024)
Machine learning has an important role to create intelligent applications for Industry 4.0, and main objective of this paper is to contribute Industry 4.0 by using a sound signal dataset and a new feature engineering model to detect malfunctioning. This paper has evaluated a new sound classification model by using the comprehensive Malfunctioning Industrial Machine Investigation and Inspection (MIMII) dataset. The model has three stages: (i) feature generation with the newly developed Directed Center Symmetric Local Binary Pattern (DCSLBP) and Unbalanced Tree-Based Multilevel Discrete Wavelet Transform (UTMDWT), (ii) feature selection using the Chi-squared (Chi2) function, and (iii) classification with a Bagged Tree (BT) classifier. The MIMII dataset contains recordings of clean and noisy sounds at-6 dB and 6 dB from four types of machinery (fan, pump, slider, and valve), allowing for the creation of five specific test cases. Each case achieved classification accuracies >90%, demonstrating the effectiveness of the method. The results indicate that the proposed model, while achieving high accuracy in tests and providing a practical, less complex solution, is suitable for adaptations in real-world applications. Continuous improvements are essential due to the diverse industrial noise and operational conditions.
Keywords: Fault detection; Industry 4.0; Machine learning; Directed center symmetric local binary pattern; (DCSLBP); Unbalanced tree-based multilevel discrete; wavelet transform (UTMDWT)