Intelligent diagnosis and fault prediction of rock breaking process in milling machines based on big data analysis
1
State Grid Hebei Construction Company, Shijiazhuang 050021, China
2
State Grid Hebei Company, Shijiazhuang 050081, China
3
CGE(Chongqing) Exploration Machinery Co., Ltd., Chongqing 400030, China
Submission date: 2025-08-08
Final revision date: 2026-01-05
Acceptance date: 2026-01-27
Online publication date: 2026-01-28
Publication date: 2026-01-28
KEYWORDS
TOPICS
ABSTRACT
This study develops an intelligent method for diagnosing and predicting disc cutter deflection wear faults during rock excavation processes. The proposed method integrates wavelet time-frequency graph encoding with an Inception-Bidirectional Gated Recurrent Unit (Bi-GRU) model through big data analysis techniques. The approach utilizes large-scale vibration signal data to effectively extract time-frequency characteristics of non-stationary signals through wavelet time-frequency graphs, enhancing the model's fault identification capability under complex working conditions. The one-dimensional vibration signal is mapped into a two-dimensional time-frequency graph and input into the Inception-BiGRU model. The spatial features are extracted by using the multi-scale convolution of the Inception module. Meanwhile, the dynamic evolution law is captured by combining the Bi-GRU bidirectional time series modeling to achieve the deep integration of spatial-temporal features. The Inception-BiGRU model demonstrates excellent robustness in multi-condition tests, maintaining a diagnostic accuracy consistently exceeding 98% ... and reduces variance by 50% compared to baseline models. The research results verify the effectiveness and engineering application potential of the proposed method in diagnosing and predicting disc cutter faults. This study provides technical support for advancing the intelligent operation and maintenance of milling machines while offering a feasible path for future multimodal sensor fusion and early fault warning.
FUNDING
This study was supported by the State Grid Corporation of China Science and Technology Project Funding: Research on Key Technologies for Mechanized Construction of Large Aperture Foundations in Mountainous Areas Based on Cableway Transportation Conditions (SGHEJSOOJGJS2400186).
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