Robust Fault Diagnosis of Drilling Machinery Under Complex Working Conditions Based on Carbon-Intelligent Industrial Internet of Things

• Published in: IEEE internet of things journal Vol. 12; no. 17; pp. 34663 - 34678
• Main Authors: Fang, Kai, Tong, Lianghuai, Xu, Xiaojie, Cai, Jijing, Peng, Xueyuan, Omar, Marwan, Kashif Bashir, Ali, Wang, Wei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Ablation; Adaptation models; Artificial intelligence; Bidirectional long short-term memory (BILSTM); Carbon; carbon-intelligent computing; Clean energy; Computational modeling; Data processing; Deep learning; Drilling; Drilling machines; Drilling machines (tools); Energy consumption; Experiments; Fault diagnosis; Feature extraction; Filtering algorithms; Industrial applications; Industrial Internet of Things; Internet of Things (IoT); Kolmogorov-Arnold network (KAN); Machine learning; Machinery; Noise; noise filtering; Noise generation; Robustness; Sustainable development; Task scheduling; Time-frequency analysis; Wavelet transforms; Working conditions
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2025.3542404

As sustainable development gains attention, integrating carbon-intelligent computing into fault diagnosis systems has emerged as a critical strategy to reduce energy consumption and carbon footprints. This approach uses artificial intelligence (AI) and the Internet of Things (IoT) to optimize task scheduling, aligning it with low-carbon energy sources based on time and location. In fault diagnosis, energy-intensive tasks, such as data processing and model inference, can be scheduled during periods of abundant renewable energy, thereby minimizing emissions. However, drilling machines operate under complex conditions that generate nonstationary noise, which distorts signals and complicates fault diagnosis. Therefore, this article combines bidirectional long short-term memory (BiLSTM) with the Kolmogorov-Arnold network (KAN) and integrates Wavelet Transform and Convolutional Autoencoder, proposing a highly robust fault diagnosis model for drilling machines, named WCBK. The Wavelet Transform converts pressure time-series data, which contains fault information, into time-frequency images, facilitating the detection of fault frequency components. The Convolutional Autoencoder preserves essential features while removing noise by learning low-dimensional representations of the signal, effectively capturing local features in time-frequency images through local connections to enhance denoising performance. Finally, the composite deep learning network, which combines BiLSTM and KAN, achieves highly robust fault diagnosis under complex working conditions. The effectiveness of the proposed WCBK model was validated through ablation experiments, experiments on different individuals, experiments on different parts, and model adaptability evaluations. In experiments involving different individuals and parts, the WCBK model improved fault diagnosis accuracy by 10.9% and 8.8%, respectively, compared to existing models.