Homomorphic encryption-based fault diagnosis in IoT-enabled industrial systems

In IoT-enabled industrial environments, ensuring the privacy and security of operational data is paramount for fault diagnosis systems. This study presents a novel framework that seamlessly integrates homomorphic encryption (HE) with deep learning to achieve secure and efficient fault diagnosis for industrial bearings. By performing computations directly on encrypted sensor data, the framework guarantees full data confidentiality throughout the diagnostic process without requiring decryption. Key technical contributions of this work include the development of a minimax polynomial approximation for ReLU activations, which enhances diagnostic accuracy while preserving efficiency, and the design of an efficient 1D convolution method that combines two existing HE convolution techniques for optimal performance. Additionally, the framework incorporates frequency-domain optimizations using the Discrete Fourier Transform (DFT), which significantly enhance processing efficiency. The proposed model was trained on the CWRU bearing dataset and validated on a private dataset, achieving a diagnostic accuracy of 95.92%, comparable to state-of-the-art models operating on plaintext data. Furthermore, the DFT-based optimizations reduced inference time by nearly threefold while maintaining superior accuracy, underscoring the framework's potential to provide secure and efficient fault diagnosis for industrial applications.