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Highly conductive polypyrrole/graphene/manganese dioxide (PPy/graphene/MnO 2 ) composites are fabricated via ultrasonic irradiation using p-toluenesulfonic acid as a dopant. The microstructures of PPy/graphene/MnO 2 are evidenced by the SEM and TEM examinations. The effects of the graphene and MnO 2 loading on the electrical conductivity are investigated. The maximum conductivity of PPy/graphene/MnO 2 composites about 17.15 S/cm found with 3 wt.%graphene and 48.7 wt.% MnO 2 at room temperature. Such uniform structure together with the observed high conductivities afforded high specific capacitance. A specific capacitance of as high as 258 F/g at a current density of 1 A/g was achieved over the PPy/graphene/MnO 2 composite.

The early fault impulses of rolling bearing are often submerged by harmonic interferences and background noise. In this paper, a fault diagnosis scheme called probabilistic principal component analysis assisted optimal scale average of erosion and dilation hat filter (OSAEDH-PPCA) is presented for the fault detection of rolling bearing. Based on morphological erosion operator and morphological dilation operator, a new morphological top-hat operator, namely average of erosion and dilation hat (AEDH) operator is firstly proposed to extract the fault impulses in the vibration signal. Simulation analysis shows the filter characteristics of proposed AEDH operator. Comparative analyses demonstrate that the feature extraction property of the AEDH operator is superior to existing top-hat operators. Then, the probabilistic principal component analysis is introduced to enhance the filter property of AEDH for highlighting the fault feature information of rolling bearing further. Experimental signals collected from the test rig and the engineering are employed to validate the availability of proposed method. Experimental results show that the OSAEDH-PPCA can effectively extract the early fault impulses from vibration signal of rolling bearing. Comparison results verify that the OSAEDH-PPCA has advantage in early fault detection of rolling bearing than other morphological filters in existence. INDEX TERMSRolling bearing; Morphological filter; Morphological operator; Probabilistic principal component analysis; Fault diagnosis. NOMENCLATURE Acronyms MM mathematical morphology MF morphological filter AVG average of opening and closing CMF average of opening-closing and closing-opening MG gradient of dilation and erosion DIF gradient of opening and closing AVGH average of opening and closing hat CMFH average of opening-closing and closing-opening hat SE structure element WTH white top-hat AED average of erosion and dilation AEDH average of erosion and dilation hat OSAEDH optimal scale average of erosion and dilation hat filter PPCA probabilistic principal component analysis CC correlation coefficient FEF feature energy factor SNR signal-to-noise ratio AMCMFH adaptive multiscale CMFH transform AMAVGH adaptive multiscale AVGH transform AMMGDE averaged multiscale MG filter AMMGCO averaged multiscale DIF filter ACDIF average combination difference morphological filter

A theoretical research on eliminating the instability vibration and improving the stability of the rotor/seal system using the inerter-based dynamic vibration absorber (IDVA) is presented in this paper. The modified Jeffcott rotor and Muszynska nonlinear seal force models are employed. The proposed IDVA is the damping element of the traditional dynamic vibration absorber (DVA) replaced by one of the six configurations of the inerter. The instability threshold speed of the system is obtained by applying the Routh–Hurwitz stability criterion. The quantum particle swarm optimization (QPSO) method is utilized to optimize the parameters of the IDVA. The numerical method is applied to investigate the nonlinear dynamic responses and stability. The results show that the IDVA can effectively improve the stability and reduce the instability vibration of the rotor/seal system. Furthermore, the performance of the IDVA is more effective than that of the traditional DVA.

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