Real-time monitoring of wire rope tension is of great significance to the safe operation of mine hoist. Due to the longitudinal and lateral coupling vibration of wire ropes during the operation of hoist, there are high frequency components in measured tension signals of wire ropes, which cannot effectively characterize the actual lifting load. To overcome this problem, a particle damping sensor with a vibration dissipation function is designed in this paper. Multilayered steel balls are placed into the cylindrical cavity of the sensor. Damping vibration and energy dissipation will occur when the sensor is subjected to external excitation. Then, to obtain the optimal sensor characteristics, relevant parameters of the particles and the spoke structure are simulated. Finally, the sensor based on the optimized parameters is manufactured and tested in a coal mine. Compared with the general pressure sensor, the particle damping sensor can effectively eliminate the influence of wire ropes vibration on tension measurement and achieve accurate measurement results.

The vibration signals are usually characterized by nonstationary, nonlinearity, and high frequency shocks, and the redundant features degrade the performance of fault diagnosis methods. To deal with the problem, a novel fault diagnosis approach for rotating machinery is presented by combining improved local mean decomposition (LMD) with support vector machine–recursive feature elimination with minimum redundancy maximum relevance (SVM-RFE-MRMR). Firstly, an improved LMD method is developed to decompose vibration signals into a subset of amplitude modulation/frequency modulation (AM-FM) product functions (PFs). Then, time and frequency domain features are extracted from the selected PFs, and the complicated faults can be thus identified efficiently. Due to degradation of fault diagnosis methods resulting from redundant features, a novel feature selection method combining SVM-RFE with MRMR is proposed to select salient features, improving the performance of fault diagnosis approach. Experimental results on reducer platform demonstrate that the proposed method is capable of revealing the relations between the features and faults and providing insights into fault mechanism.

The real-time tension monitoring of wire ropes is a universal way to judge whether the hoist is overloaded in the special working environment of the coal mine. However, due to the strong drafts, unevenness of guide and flexible vibration of wire ropes, it is a challenge to monitor the tension with high accuracy. For this purpose, a new type of acoustic filtering sensor is designed in this study. To adapt to the violent vibration during the monitoring process, a structure with a cylindrical cavity and a narrow gap is designed in the sensor. The coupling between the internal fluid and sensor structure can greatly absorb the vibration energy. With the view of optimizing the filtering performance of the sensor, the influences on the filtering characteristics are presented and analyzed through employing different structural and acoustic parameters in simulations. Finally, acoustic filtering sensor prototypes based on optimized parameters are calibrated and tested in a real coal mine. The results have revealed that our acoustic filtering sensor can not only address the deficiencies of current pressure sensors in coal mining and achieve tension monitoring in real-time, but is also able to diagnose and forecast the occurrence of tension imbalance accidents.