A Multiplier-Free Convolution Neural Network Hardware Accelerator for Real-Time Bearing Condition Detection of CNC Machinery.
Sensors (Basel)
; 23(23)2023 Nov 27.
Article
en En
| MEDLINE
| ID: mdl-38067811
ABSTRACT
In various industrial domains, machinery plays a pivotal role, with bearing failure standing out as the most prevalent cause of malfunction, contributing to approximately 41% to 44% of all operational breakdowns. To address this issue, this research employs a lightweight neural network, boasting a mere 8.69 K parameters, tailored for implementation on an FPGA (field-programmable gate array). By integrating an incremental network quantization approach and fixed-point operation techniques, substantial memory savings amounting to 63.49% are realized compared to conventional 32-bit floating-point operations. Moreover, when executed on an FPGA, this work facilitates real-time bearing condition detection at an impressive rate of 48,000 samples per second while operating on a minimal power budget of just 342 mW. Remarkably, this system achieves an accuracy level of 95.12%, showcasing its effectiveness in predictive maintenance and the prevention of costly machinery failures.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Idioma:
En
Revista:
Sensors (Basel)
Año:
2023
Tipo del documento:
Article
País de afiliación:
Taiwán