Title: Distributed fault diagnosis for data reliability in operational status monitoring of large mining electric shovels

Authors: Jinfa Huang; Ye Chen

Addresses: School of Architecture and Design, Jiangxi University of Science and Technology, Jingxi, 341000, China ' School of Architecture and Design, Jiangxi University of Science and Technology, Jingxi, 341000, China

Abstract: Large mining electric shovels are critical to open-pit mining, yet unreliable data hinder their operational monitoring in harsh environments and inefficient centralised fault diagnosis. This paper proposes a distributed fault diagnosis framework to enhance data reliability and diagnostic efficiency. The framework first deploys an edge computing-based architecture to enable local data processing and avoid single-point failures. It then incorporates a two-stage module at the edge to enhance data reliability through adaptive denoising and sensor fault self-checking. Finally, a collaborative diagnosis model is developed by integrating a lightweight convolutional neural network with federated learning, allowing multiple units to train a shared model without exchanging raw data. Field experiments demonstrate that the proposed method significantly outperforms traditional centralised approaches, increasing the signal-to-noise ratio by 28.3%, reducing diagnosis latency by 42.1%, and achieving a fault diagnosis accuracy of 96.7%.

Keywords: large mining electric shovels; operational status monitoring; distributed fault diagnosis; data reliability; federated learning.

DOI: 10.1504/IJSNET.2026.153839

International Journal of Sensor Networks, 2026 Vol.51 No.1, pp.30 - 44

Received: 09 Nov 2025
Accepted: 14 Nov 2025
Published online: 27 May 2026 *

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