Intelligent diagnosis and fault prediction of rock breaking process in milling machines based on big data analysis

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 reaching a maximum of 99.35%.In multi-class fault identification, the accuracy for normal and severely deflected cutters reaches 99%.Meanwhile, minor misjudgments occur in cases of mild and moderate deflection, with overall identification precision exceeding 97%.A comparative evaluation against various encoding methods and traditional algorithms demonstrates that the Inception-BiGRU model outperforms baseline models in both accuracy and stability, achieving an average accuracy of 98.63% with a variance of only 0.03.The proposed model improves average accuracy by 1.84% 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.