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In order to effectively identify and extract the composite fault characteristics of the gear transmission system, a composite fault diagnosis method combining nonlinear mode decomposition (NMD) and Teager energy operator demodulation is proposed. Because the envelope demodulation of Hilbert transform has the disadvantages of large amount of calculation and end effect, it uses Teager energy operator to solve the problem of large amount of calculation, and NMD solves the problem that the fault signal features is not easy to extract under the mode aliasing. First, the NMD method is used to decompose the fault simulation signal, and the nonlinear modal component with practical physical significance is obtained. Secondly, the Teager energy operator demodulation is carried out for the nonlinear modal components, and the demodulation results are analyzed to verify the feasibility of the method. Then, the method is applied to the composite fault diagnosis of gear pitting wear in gear transmission system, and the characteristic frequency obtained from the test data is compared with the calculated characteristic frequency. The comparative analysis shows that the method can separate the fault characteristic frequencies of large and small gears. The comparative analysis with EMD and EEMD methods in simulation signal analysis and experimental research shows that this method is superior. [ABSTRACT FROM AUTHOR]

In order to solve the hysteretic character of the piezoelectric material for application, the initial weight factors of the hysteretic units are calculated by the Preisach theory and the first-order reversal curves test data, a hysteretic Preisach model based on the improved fuzzy least square support vector machine (improved FLS-SVM) is established. In the established model, the fuzzy least square support vector machine is introduced to calculate more weight factors of the hysteretic units and the adaptive variable chaos immune algorithm is introduced to optimize the penalty factor and the kernel parameter of the FLS-SVM (the penalty factor c = 35 and the kernel parameter σ = 1.35 are obtained). Moreover, the quadratic polynomial interpolation method is used to eliminate the sawtooth phenomenon. The validity of established model reveals that fuzzy least square support vector machine method based on adaptive variable chaos immune algorithm (FLS-SVMAVCIA) is more accurate than FLS-SVM method according to application results of the real actuators (the absolute mean error of the FLS-SVMAVCIA model is less than 1 mm and its maximum error is less than 2 mm). As a result, the hysteretic phenomenon can be effectively eliminated by the hysteretic Preisach model based on the FLS-SVMAVCIA method. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses various articles within the issue on topics including noise control, vibration, and mechanical and electrical systems.