Research on positioning control strategy of hydraulic support pushing system based on multistage speed control valve.

The precise position control of the hydraulic support pushing system in the coal mining face is a key technical support for intelligent coal mining. At present, the hydraulic support pushing system uses the electro-hydraulic directional valve as the control element. However, the electro-hydraulic directional valve has problems such as discrete input values, low switching frequency, delay, inability to adjust flow, and large flow fluctuations during the switching process, which results in relatively low positioning control accuracy of the hydraulic support pushing system. Therefore, this study introduces a multi-stage speed control valve that is suitable for underground coal mine conditions and can achieve flow regulation. At the same time, a segmented control strategy combining Bang-Bang control and online predictive control is proposed. Bang-bang control is used for fast propulsion with large flow rate, large range, and short time. Online predictive control method is used to achieve precise positioning control with small flow rate and small range, thereby solving the problem of low positioning control accuracy caused by the imperfect characteristics of electro-hydraulic directional valves. Finally, this study verified the effectiveness of the proposed scheme through simulation and experiments. The results showed that compared with existing logic positioning control methods based on electro-hydraulic directional valves, the proposed scheme can improve the accuracy of single cylinder positioning control from 62 mm to within 10 mm.

A Faster and Lighter Detection Method for Foreign Objects in Coal Mine Belt Conveyors.

Coal flow in belt conveyors is often mixed with foreign objects, such as anchor rods, angle irons, wooden bars, gangue, and large coal chunks, leading to belt tearing, blockages at transfer points, or even belt breakage. Fast and effective detection of these foreign objects is vital to ensure belt conveyors' safe and smooth operation. This paper proposes an improved YOLOv5-based method for rapid and low-parameter detection and recognition of non-coal foreign objects. Firstly, a new dataset containing foreign objects on conveyor belts is established for training and testing. Considering the high-speed operation of belt conveyors and the increased demands for inspection robot data collection frequency and real-time algorithm processing, this study employs a dark channel dehazing method to preprocess the raw data collected by the inspection robot in harsh mining environments, thus enhancing image clarity. Subsequently, improvements are made to the backbone and neck of YOLOv5 to achieve a deep lightweight object detection network that ensures detection speed and accuracy. The experimental results demonstrate that the improved model achieves a detection accuracy of 94.9% on the proposed foreign object dataset. Compared to YOLOv5s, the model parameters, inference time, and computational load are reduced by 43.1%, 54.1%, and 43.6%, respectively, while the detection accuracy is improved by 2.5%. These findings are significant for enhancing the detection speed of foreign object recognition and facilitating its application in edge computing devices, thus ensuring belt conveyors' safe and efficient operation.

Application of Adaptive MOMEDA with Iterative Autocorrelation to Enhance Weak Features of Hoist Bearings.

Low-speed hoist bearings are characterized by fault features that are weak and difficult to extract. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is an effective method for extracting periodic pulses in a signal. However, the decomposition effect of MOMEDA largely depends on the selected pulse period and filter length. To address these drawbacks of MOMEDA and accurately extract features from the vibration signal of a hoist bearing, an adaptive feature extraction method is proposed based on iterative autocorrelation (IAC) and MOMEDA. To automatically identify the pulse period, a new evaluation index named autocorrelation kurtosis entropy (AKE) was constructed to select the optimal IAC. To eliminate the influence of the filter length on the decomposition effect, an iterative MOMEDA strategy was designed to gradually enhance signal impulse features. The Case Western Reserve University bearing dataset and bearing data from a self-made hoisting test setup were used to verify the effectiveness of IAC-MOMEDA in extracting weak features. Moreover, the capability of IAC-MOMEDA for features extraction of normal bearing vibration signal was further confirmed by field test data.

Application of ICEEMDAN Energy Entropy and AFSA-SVM for Fault Diagnosis of Hoist Sheave Bearing.

The mine hoist sheave bearing is a large heavy-duty bearing, located in a derrick of tens of meters. Aiming at the difficulty of sheave bearing fault diagnosis, a combined fault-diagnosis method based on the improved complete ensemble EMD (ICEEMDAN) energy entropy and support vector machine (SVM) optimized by artificial fish swarm algorithm (AFSA) was proposed. Different location of the bearing defect will result in different frequency components and different amplitude energy of the frequency. According to this feature, the position of the bearing defect can be determined by calculating the ICEEMDAN energy entropy of different vibration signals. In view of the difficulty in selecting the penalty factor and radial basis kernel parameter in the SVM model, the AFSA was used to optimize them. The experimental results show that the accuracy rate of the optimized fault-diagnosis model is improved by 10% and the diagnostic accuracy rate is 97.5%.

Effect of Ni, W and Mo on the microstructure, phases and high-temperature sliding wear performance of CoCr matrix alloys.

CoCrNi, CoCrW and CoCrMo alloys were fabricated by powder metallurgy technology. The effect of nickel, tungsten and molybdenum, as alloying elements, on the microstructure, phase, mechanical and high-temperature tribological properties of CoCr matrix alloys were systematically studied. The wear and friction behaviors were investigated from room temperature (23 °C) to 1000 °C. The alloys were found to contain different ratios of γ(fcc) and ε(hcp) phases; Ni stabilized γ(fcc), while W and Mo stabilized ε(hcp). The hardness measurements showed that the strengthening effect increased with the addition of Ni, W, and Mo, respectively. Addition of Mo and W resulted in the lowest and highest friction coefficients with the addition of Ni resulting in a friction coefficient between the two. The wear and friction behaviors of the three alloys depended on the phase, alloying elements and oxidation from room temperature to 1000 °C. Coefficients of friction of the alloys were not directly correlated with the wear rates. CoCr matrix alloys reinforced with Mo showed the highest hardness and the best high-temperature tribological performance. It was attributed to the high hardness, stable oxide film, and in situ formed high-temperature solid lubricants. With an increase in temperature, the wear mechanism was found to change from abrasive wear to oxidative wear.

Nano-TiO2 reinforced CoCr matrix wear resistant composites and high-temperature tribological behaviors under unlubricated condition.

The CoCrMo matrix composites with nano-TiO2 particle (2 wt.%, 4 wt.% and 6 wt.%) were fabricated by using a powder metallurgy technique (P/M), and the nano-TiO2 content was optimized in matrix. The microstructures, mechanical and high-temperature tribological properties of the synthesized composites were systematically studied. Friction and wear behaviors were studied by using a disk-on-ball tribo-tester sliding against Si3N4 ceramic ball from room temperature (23 oC) to 1000 oC in air. TiO2 obviously strengthened the hardness and high-temperature wear resistance of composites. It was attributed to the high load-carrying capacity of matrix, in-situ formed high-temperature solid lubricants and stable oxides film on the wear tracks. 4 wt.% TiO2 was the critical threshold at which there was a transition of tribological properties over a broad temperature range. The composite containing 4 wt.% nano-TiO2 exhibited the most reasonable high-temperature friction coefficient and wear rate at all testing temperatures. At different testing temperatures, the composites showed different wear mechanisms.

Microstructure and High-Temperature Wear Performance of FeCr Matrix Self-Lubricating Composites from Room Temperature to 800 °C.

FeCr matrix high-temperature self-lubricating composites reinforced by Mo, Ag, and CuO were fabricated by the powder metallurgy technique. The tribological behaviors of composites were studied at temperatures up to 800 °C. The CuO content was optimized according to the tribological results. Mo showed an obvious lubricating effect when it converted into MoO3. The bimetallic oxide system formed high-temperature solid lubricants with low shear strength. CuO reacted with MoO3 and formed CuMoO4 and Cu3Mo2O9. The composites showed an increase in the friction coefficient with the increase of CuO. However, the wear rates decreased with the increase of CuO. The critical threshold at which there was a transition of friction coefficients and wear rates from room temperature (RT) to 800 °C was 10 wt.% CuO. The Fe(Cr)-14% Mo-10.5% Ag-10% CuO composite showed the most reasonable high-temperature tribological behaviors. This was ascribed to the synergistic effects of silver, Mo, in situ formed solid lubricants (metal oxides and salt compounds), and the stable oxide film on the worn surfaces. At elevated temperatures, the dominant wear mechanism was oxidation wear.

Preparation, Mechanical Properties, and High-Temperature Wear Resistance of Ti-Al-B alloy.

Ti-Al matrix alloy reinforced with a high content of boron was fabricated by using a high-temperature alloying method and powder metallurgy technique (P/M). The preparation method of Ti-Al-B alloying powder was put forward. Phases, microstructure, and mechanical properties of the alloys were investigated. Wear and friction performance were studied by using a ball-on-disc tribotester sliding against a Si3N4 ceramic ball from 23 °C (room temperature) to 900 °C. The Ti-Al-B alloy had a higher specific strength than that of the Ti-Al alloy. The boron element obviously enhanced the wear resistance and mechanical properties of the alloys because of the formation of borides (TiB2 and AlB2) in matrices and the stable oxide film on the wear tracks. Friction coefficients of alloys were independent of the boron element. The wear mechanisms of the alloys transferred from fatigue wear to oxidative wear with the increase in temperature.

Application of Mutual Information-Sample Entropy Based MED-ICEEMDAN De-Noising Scheme for Weak Fault Diagnosis of Hoist Bearing.

In this paper, a novel weak fault features extraction scheme is proposed to extract weak fault features in head sheave bearings of floor-type multi-rope friction mine hoists in strong noise environments. A mutual information-based sample entropy (MI-SE) is proposed to select the effective intrinsic mode function (IMF). The numerical simulation presented in this paper has demonstrated that the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) has a poor performance on weak signals processing under a strong noise background, and fault features cannot be identified clearly. The de-noised signal is decomposed into several IMFs by the ICEEMDAN method, with the help of the minimum entropy deconvolution (MED), which works as a pre-filter to increase the kurtosis value by about 3.2 times. The envelope spectrum of the effective IMF selected by the MI-SE method shows almost all fault features clearly. An analogous experiment system was built to verify the feasibility of the proposed scheme, whose results have also shown that the proposed hybrid scheme has better performance compared with ICEEMDAN or MED on the weak fault features extraction under a strong noise background. This paper provides a novel method to diagnose the weak faults of the slow speed and heavy load rolling bearings in a strong noise environment.

Overall performance evaluation of tubular scraper conveyors using a TOPSIS-based multiattribute decision-making method.

Properly evaluating the overall performance of tubular scraper conveyors (TSCs) can increase their overall efficiency and reduce economic investments, but such methods have rarely been studied. This study evaluated the overall performance of TSCs based on the technique for order of preference by similarity to ideal solution (TOPSIS). Three conveyors of the same type produced in the same factory were investigated. Their scraper space, material filling coefficient, and vibration coefficient of the traction components were evaluated. A mathematical model of the multiattribute decision matrix was constructed; a weighted judgment matrix was obtained using the DELPHI method. The linguistic positive-ideal solution (LPIS), the linguistic negative-ideal solution (LNIS), and the distance from each solution to the LPIS and the LNIS, that is, the approximation degrees, were calculated. The optimal solution was determined by ordering the approximation degrees for each solution. The TOPSIS-based results were compared with the measurement results provided by the manufacturer. The ordering result based on the three evaluated parameters was highly consistent with the result provided by the manufacturer. The TOPSIS-based method serves as a suitable evaluation tool for the overall performance of TSCs. It facilitates the optimal deployment of TSCs for industrial purposes.