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%.

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.

The improvement of the energy utilization rate of a hydraulic vibration‐excitation system is critical to the research and development of hydraulic vibration equipment. In this paper, a hydraulic vibration‐excitation system controlled by a new type of shock rotary vibrator is proposed. A system model considering the pipeline effect was established for the hydraulic shock phenomenon. In addition, the model was compared with the one that does not consider the pipeline effect. The effectiveness of the proposed model was verified experimentally. Finally, the shock phenomenon during the process of switching the working state of the vibrator and the influence of certain important parameters of the system on the vibration output were investigated based on the proposed model. The results showed that (1) the hydraulic shock phenomenon occurred when the working state of the hydraulic vibrator was switched and (2) the hydraulic shock wave could effectively improve the excitation force of the system. The excitation force increased with an increase in the oil supply pressure, spindle speed, and load. However, it was negatively correlated with the spring stiffness. The amplitude of the vibration waveform output was positively correlated with the oil supply pressure and negatively correlated with the spindle speed and load. The amplitude first increased and then decreased as the stiffness of the vibration spring increased. The only influence of the precompressed length of the spring on the system output was its alteration of the vibration center of the system output vibration.

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.

1. The presence of defect can adversely affect the functional performance of structural components and has the potential to precipitate catastrophic failures [1,2]. Consequently, timely and precise...