Hybrid Quantum Convolutional Neural Network for CNC Machine Bearing Fault Detection Using Vibration and Acoustic Signals DOI
Naveen Kumar,

G. Swetha,

A. Lakshmanarao

и другие.

Journal of Machine and Computing, Год журнала: 2025, Номер unknown, С. 857 - 866

Опубликована: Апрель 5, 2025

Flexible manufacturing systems (FMS) rely heavily on CNC machine tools, and the machines' failure can be attributed to bearing failure. Bearing fault detection is critical in avoiding downtime expediting expensive repair work. To enhance precision of via vibration sound signals, present research suggests a Hybrid Quantum Convolutional Neural Network with Skill Optimization Algorithm (QCNN- SOA). For enhanced defect classification, method integrates skill optimization technique quantum convolutional networks. Preprocessing signals performed using SWVO-RKF eliminate noise outliers without distorting fault-related patterns. The Inception Vision Transformer (ICVT) model used for feature extraction capture local temporal dependencies. QCNN employed classify features that are extracted. A classical fully connected layer classification after employing gates convolution encoding signal. With an error rate 0.8%, proposed achieves 99.2% accuracy, 99.6% recall, 98.7% precision, 99.1% F1-score.

Язык: Английский

Review of Machine Learning applications in Additive Manufacturing DOI Creative Commons

Sirajudeen Inayathullah,

Raviteja Buddala

Results in Engineering, Год журнала: 2024, Номер 25, С. 103676 - 103676

Опубликована: Дек. 8, 2024

Язык: Английский

Процитировано

13
Health Index Degradation Prediction of Induction Motor Using Deep Neural Network Algorithm DOI Creative Commons
Arslan Ahmed Amin, Turki Alsuwian,

Abdulla Shahid

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104357 - 104357

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Hybrid Quantum Convolutional Neural Network for CNC Machine Bearing Fault Detection Using Vibration and Acoustic Signals DOI
Naveen Kumar,

G. Swetha,

A. Lakshmanarao

и другие.

Journal of Machine and Computing, Год журнала: 2025, Номер unknown, С. 857 - 866

Опубликована: Апрель 5, 2025

Flexible manufacturing systems (FMS) rely heavily on CNC machine tools, and the machines' failure can be attributed to bearing failure. Bearing fault detection is critical in avoiding downtime expediting expensive repair work. To enhance precision of via vibration sound signals, present research suggests a Hybrid Quantum Convolutional Neural Network with Skill Optimization Algorithm (QCNN- SOA). For enhanced defect classification, method integrates skill optimization technique quantum convolutional networks. Preprocessing signals performed using SWVO-RKF eliminate noise outliers without distorting fault-related patterns. The Inception Vision Transformer (ICVT) model used for feature extraction capture local temporal dependencies. QCNN employed classify features that are extracted. A classical fully connected layer classification after employing gates convolution encoding signal. With an error rate 0.8%, proposed achieves 99.2% accuracy, 99.6% recall, 98.7% precision, 99.1% F1-score.

Язык: Английский

Процитировано

0