Abstract
Appropriate predictive maintenance is critical to shearers under poor working conditions to prevent major safety accidents and casualties. A predictive maintenance method for shearer key parts based on qualitative and quantitative analysis of monitoring data is proposed. Firstly, a high-fidelity model and hyper-realistic behavior simulations of the shearer key parts are implemented based on offline monitored data and online monitoring data, the obtained position, posture, trajectory and other information can be used for qualitative analysis. Subsequently, an autoencoder (AE) combined with a deep bidirectional gated recurrent unit (bi-GRU) prediction model is constructed to predict the RUL based on online monitoring data for quantitative analysis. The adjusted_R 2 value obtained for the deep bi-GRU model was 0.9916, determined in a model-accuracy verification test. Finally, taking the rocker arm of shearer as an example, its working status is visualized using digital mirror from physical space to virtual space, and then its RUL is predicted based on the AE bi-GRU prediction model. The decision-making that guides predictive maintenance can be obtained based on the synthesis of qualitative and quantitative analysis. The application test results demonstrate that the proposed method can be effective for improving the intelligence of predictive process and the accuracy of predictive results.
Highlights
The shearer is one of the main working parts of mine equipment, which is used to cut and load coal from coal seam
The parameters of the virtual-space hyper-realistic simulation environment are updated by online monitoring data, the behavior and state of the physicalspace shearer rocker arm are obtained from virtual-space
A predictive maintenance method for shearer key parts is proposed based on digital model and deep learning theory
Summary
The shearer is one of the main working parts of mine equipment, which is used to cut and load coal from coal seam. The complex and variable working environment of the shearer, overload work, frequent start-up, narrow operable space, and other factors affect the timely maintenance of equipment, resulting in difficult monitoring of the equipment status and frequent failures. This poses a serious threat to the safety of coal mine production and to the lives of workers [2], [3]. Hoseinie et al [4]
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