Quantized Probabilistic AI for Gear Fault Diagnosis in Motor Drives
Enables deployment of lightweight AI models on low-cost edge processors for power electronics applications.
The paper proposes a quantization-aware training method to reduce pre-trained Bayesian Neural Networks from FP32 to INT8, achieving 30-45% computational efficiency gains without accuracy loss for gear fault diagnosis in motor drives.
Deploying large artificial intelligence (AI) models in power electronics often demands high computational resources. Driven by the quantization paradigm, this digest proposes a quantization-aware training (QAT) principle to substantially minimize the number of bits required and simultaneously maximize the accuracy of computations in pre-trained AI models. Considering a pre-trained probabilistic Bayesian Neural Network (BNN) for gear fault diagnosis in motor drives as an example, we quantize its weights and activation functions from floating-point FP32 to low-precision INT8 values, which enhances the computational efficiency by a significant margin of 30-45% (for different model versions) without any compromise in the accuracy and uncertainty estimates. This substantiates a sustainable mechanism of deploying most quantized light-weight AI models into low-cost edge processors for power electronic applications.