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    Enhancing robotic manipulator fault detection with advanced machine learning techniques
    (Iop Publishing Ltd, 2024) Khan, Faiq Ahmad; Jamil, Akhtar; Khan, Shaiq Ahmad; Hameed, Alaa Ali
    The optimization of rotating machinery processes is crucial for enhanced industrial productivity. Automatic machine health monitoring systems play a vital role in ensuring smooth operations. This study introduces a novel approach for fault diagnosis in robotic manipulators through motor sound analysis to enhance industrial efficiency and prevent machinery downtime. A unique dataset is generated using a custom robotic manipulator to examine the effectiveness of both deep learning and traditional machine learning in identifying motor anomalies. The investigation includes a two-stage analysis, initially leveraging 2D spectrogram features with neural network architectures, followed by an evaluation of 1D MFCC features using various conventional machine learning algorithms. The results reveal that the proposed custom CNN and 1D-CNN models significantly surpass traditional methods, achieving an F1-score exceeding 92%, highlighting the potential of sound analysis for automated fault detection in robotic systems. Additional experiments were carried out to investigate 1D MFCC features with various machine learning algorithms, including KNN, DT, LR, RF, SVM, MLP, and 1D-CNN. Augmented with additional data collected from the locally designed manipulator, our experimental setup significantly enhances model performance. Particularly, the 1D-CNN stands out as the top-performing model on the augmented dataset.
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    Interpretable Motor Sound Classification for Enhanced Fault Detection Leveraging Explainable AI
    (Institute of Electrical and Electronics Engineers Inc., 2024) Khan, Shaiq Ahmad; Ahmad Khan, Faiq; Jamil, Akhtar; Hameed, Alaa Ali
    In industries, machines communicate through sounds, decoded by predictive maintenance to prevent issues. Understanding motor sounds is crucial for seamless industrial operations. This research undertakes a comprehensive explo-ration of machine learning models, specifically Artificial Neural Network (ANN), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest, applied to motor sound data for classifying instances as either healthy or faulty. The ANN, boasting an 81.22 % accuracy, reveals commendable precision and recall values for both classes, indicating its robust predictive capabilities. However, there is room for improvement, particu-larly in accurately classifying healthy motors. SVM marginally outperforms the ANN with an accuracy of 81.32%, showcasing balanced precision and recall for both classes. Notably, KNN, while exhibiting a slightly lower accuracy of 80.22 %, excels in recall for the healthy class, emphasizing its efficacy in correctly identifying healthy motor sounds. Random Forest attains an accuracy of 81.32 %, featuring notably high recall for the healthy class (0.91), underscoring its proficiency in capturing instances of healthy motor sounds. In-depth metrics provide nuanced insights into the strengths and specificities of each model, offering a foundation for informed decisions based on application priorities and requirements. The study contributes not only quantitative metrics but also interpretability tools, including LIME and SHAP, to enhance transparency and elucidate the intricate patterns within motor sound data. © 2024 IEEE.