The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of the fault identification of rolling bearings, a fault-detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In the real operational life of bearings, fault information often gets submerged within the noise. Furthermore, employing Long Short-Term Memory (LSTM) neural networks for time series prediction necessitates the configuration of appropriate parameters. Manual parameter selection is often a time-consuming process and demands substantial prior knowledge. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. The improved sparrow search algorithm (ISSA) is employed for configuring parameters in the Long Short-Term Memory (LSTM) network. Each technique’s principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

The mine ventilator plays a role in protecting the life safety of underground workers, which is very significant to the production and development of coal mines. In total, 70% of ventilator failures are mechanical failures, and bearing failures are the most likely to occur in mechanical failures, which are also difficult to find. In order to identify fan bearing faults accurately, this paper proposes a fault diagnosis method based on improved variational mode decomposition and density peak clustering. First, the variational mode decomposition’s modal number K and secondary penalty factor α are chosen employing the improved sparrow optimization process. The bearing vibration signal is decomposed by the variational mode decomposition algorithm with optimized parameters. To create the characteristic vector, the multi-scale permutation entropy of the fourth order intrinsic mode function is determined. Then, the characteristic matrix is dimensionally reduced by kernel principal component analysis, and the two-dimensional matrix after dimensionality reduction is divided by density peak clustering method to find the clustering center of the training sample features. Lastly, the membership degree is assessed using the normalized clustering distance between the characteristic matrix of the test sample and the cluster center of the training sample. The accuracy of bearing fault identification on the self-constructed experimental platform can reach 100%, which verifies the effectiveness and potential of the proposed method.

The chain drive system represents a critical subsystem within the scraper conveyor. This paper proposes a joint simulation model for the drive system of the scraper conveyor, driven by the permanent magnet synchronous motor, in order to conduct a comprehensive analysis of the dynamic characteristics of the chain drive system during the operational process. Firstly, the dynamic simulation model for the mining scraper conveyor’s chain drive system was established in ADAMS, taking into account its structural characteristics. Then, the mathematical model of the permanent magnet synchronous motor was established using the coordinate transformation theory, and the speed controller based on vector control was designed by using the theory related to sliding mode control. The coupling relationship between the chain drive system of the scraper conveyor and the permanent magnet synchronous motor drive system was investigated. Finally, a joint simulation model of the mechanical system and motor control system was created using ADAMS (View 2019) and MATLAB/Simulink (2020a). The dynamic characteristics of the chain drive system were analyzed, and the three typical working conditions of no load, half load, and rated load were considered. The results show that the contact force between the flat ring and the sprocket undergoes an initial increase, followed by a decrease, and finally another increase. As the load increases from no load to full load, there is a marked increase in the contact force between the loaded side chainrings. Due to the polygon effect, both the speed curve of the permanent magnet drive motor and the contact force curve between the ring chains exhibit periodic fluctuations. The research in this paper provides an idea for the coupling analysis of the scraper conveyor electromechanical system.

To address the mismatch between materials and operational speed in mine scraper conveyors under time-varying load conditions, this paper proposes a methodology for the regulation of speed based on the quantity of coal transported by the scraper conveyor. Furthermore, a vector control strategy for permanent magnet synchronous motors (PMSMs) is presented, underpinned by a global fast terminal sliding mode controller. Firstly, a calculation model for the real-time coal volume of the scraper conveyor was developed based on the double-end oblique cutting coal mining technology in fully mechanized mining operations. This model takes into account the operational condition of the shearer and the scraper conveyor. In addition, a graded speed regulation control method was introduced. Secondly, a global fast terminal controller was developed by integrating the features of linear and terminal sliding mode surfaces. An enhanced sliding mode vector control strategy for the permanent magnet drive motor of the scraper conveyor was subsequently proposed. Finally, a simulation and ground test were subsequently performed on the PMSM experimental bench and SGZ2×1200 scraper conveyor to validate the proposed control strategy. The results indicated that the proposed control strategy not only diminished the overshoot of the rotational speed and decreased the dynamic response time but also improved the anti-interference capabilities of the PMSM relative to the original PI control. Moreover, the ground test validated the feasibility of the suggested speed regulation method.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.