Abnormal working conditions in coal mine production present critical risks to operational safety and miner welfare. Traditional methods based on time series anomaly detection suffer from insufficient integration of actual production processes, leading to detection outcomes with weak practical relevance. To meet the challenge, this paper proposes a novel AE-RNN-BiLSTM-GHMM framework (ARBG), which systematically integrates an AE-RNN-BiLSTM predictor (ARB) with a Gaussian Hidden Markov Model (GHMM)-based production process analyzer. The ARBG framework operates through a dual-modality architecture. First, the ARB component leverages historical electricity consumption patterns to forecast future energy usage profiles. Concurrently, the GHMM module dynamically models latent operational states within the coal mine production workflow. By synergistically integrating real-time predictions from ARB with GHMM-derived process state diagnostics, the framework achieves context-aware anomaly detection that aligns with physical production constraints. This fusion enables predictive identification of operational deviations through multidimensional correlation analysis between energy consumption forecasts and process state transitions. The experimental results show that the ARBG performs well in discriminating the load conditions and provides reliable technical support for improving the safety and operational efficiency of coal mine production.

Coal mine power load fluctuation serves as a critical precursor to safety accidents, with its clustering analysis holding significant value for safety production monitoring. To address the challenges of high-dimensional load data feature extraction, the separation between feature learning and clustering tasks in existing deep clustering methods, and the embedding space distortion caused by KL divergence loss, this paper proposes a Stacked Autoencoder with Combined Improved Loss for Coal Mine Daily Load Deep Embedded Clustering (SAE-CS-K-means). The methodology comprises three main components: Firstly, a layer-wise pretraining mechanism of stacked autoencoders extracts deep temporal features from load data, achieving effective mapping from high-dimensional data to low-dimensional embedding space. Secondly, a novel CS clustering loss function is designed to optimize intra-cluster compactness and inter-cluster differentiation. Finally, a joint optimization strategy synchronously updates network parameters and cluster centers, maintaining dynamic equilibrium between reconstruction loss and clustering loss, thereby ensuring embedding space stability and enhancing clustering performance. Experimental validation using actual power consumption data from a Yunnan coal mine demonstrates significant improvement in clustering quality through Silhouette Coefficient (SC), Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI) evaluations. The clustering results provide precise quantitative references for abnormal power consumption diagnosis and reliable technical support for coal mine safety monitoring.

As a pivotal energy industry, production safety is of critical importance to coal mining. However, current monitoring approaches mainly rely on manual experience-based analysis of field data, exhibiting limitations such as high operational costs, low efficiency, and inadequate reliability. In contrast, power load data from coal production processes serves as a comprehensive state indicator containing rich operational information. Thus, this paper proposes a time-frequency multi-scale reconstruction anomaly detection approach (TFMSR-AD) leveraging power load data to identify anomalous states during production. The methodology integrates multi-scale Transformer architecture with a compact pyramidal structure for temporal feature extraction, while incorporating Fourier Transform for periodic pattern analysis. This synergistic framework achieves high-fidelity reconstruction to enhance anomaly detection performance. Experimental results demonstrate TFMSR-AD's effectiveness in identifying latent anomaly signatures within power load data, thereby providing a cost-effective, highly robust intelligent monitoring solution for coal mine safety management.