Fault Diagnosis of Gearboxes Using Nonlinearity and Determinism by Generalized Hurst Exponents of Shuffle and Surrogate Data

Vibrations of defective gearboxes show great complexities. Therefore, dynamics and noise levels of vibrations of gearboxes vary with operation of gearboxes. As a result, nonlinearity and determinism of data can serve to describe running conditions of gearboxes. However, measuring of nonlinearity and determinism of data is challenging. This paper defines a two-dimensional measure for simultaneously quantifying nonlinearity and determinism of data by comparing generalized Hurst exponents of original, shuffle and surrogate data. Afterwards, this paper proposes a novel method for fault diagnosis of gearboxes using the two-dimensional measure. Robustness of the proposed method was validated numerically by analyzing simulative signals with different noise levels. Moreover, the performance of the proposed method was benchmarked against Approximate Entropy, Sample Entropy, Permutation Entropy and Delay Vector Variance by conducting two independent gearbox experiments. The results show that the proposed method achieves superiority over the others in fault diagnosis of gearboxes

A Novel Feature for Fault Classification of Rotating Machinery: Ternary Approximate Entropy for Original, Shuffle and Surrogate Data

Existing works have paid scant attention to the multivariate entropy of complex data. Thus, existing methods perform poorly in fully exposing the nature of complex data. To mine a rich vein of data features, this paper applies a shuffle and surrogate approach to complex data to decouple probability density information from correlation information and then obtain shuffle data and surrogate data. Furthermore, this paper applies approximate entropy (ApEn) to individually estimate complexities and irregularities of the original, the shuffle, and the surrogate data. As a result, this paper develops a ternary ApEn approach by integrating the ApEn of the original, shuffle, and surrogate data into a three-dimensional vector for describing the dynamics of complex data. Next, the proposed ternary ApEn approach is compared with conventional temporal statistics, conventional ApEn, two-dimensional energy entropy based on empirical mode decomposition or wavelet decomposition, and binary ApEn using both gear vibration data and roller-bearing vibration data containing different types and severity of faults. The results suggest that the ternary ApEn approach is superior to the other methods in identifying the conditions of rotating machinery

A Novel Feature for Fault Classification of Rotating Machinery: Ternary Approximate Entropy for Original, Shuffle and Surrogate Data

Existing works have paid scant attention to the multivariate entropy of complex data. Thus, existing methods perform poorly in fully exposing the nature of complex data. To mine a rich vein of data features, this paper applies a shuffle and surrogate approach to complex data to decouple probability density information from correlation information and then obtain shuffle data and surrogate data. Furthermore, this paper applies approximate entropy (ApEn) to individually estimate complexities and irregularities of the original, the shuffle, and the surrogate data. As a result, this paper develops a ternary ApEn approach by integrating the ApEn of the original, shuffle, and surrogate data into a three-dimensional vector for describing the dynamics of complex data. Next, the proposed ternary ApEn approach is compared with conventional temporal statistics, conventional ApEn, two-dimensional energy entropy based on empirical mode decomposition or wavelet decomposition, and binary ApEn using both gear vibration data and roller-bearing vibration data containing different types and severity of faults. The results suggest that the ternary ApEn approach is superior to the other methods in identifying the conditions of rotating machinery

Condition Monitoring of Gearbox based on Statistical Linguistic Analysis of Time Series

The vibration data from defective gearboxes generally exhibit non- stationary and nonlinear property. As a result,it seems hard to effectively monitor the running status of gearbox by using the conventional time domain statistical parameters method which is based on stationary and linear theory. In order to solve this problem,the method of time series statistical linguistic analysis is adopted to examine gearbox vibration data and a novel method for gearbox condition monitoring based on time series statistical linguistic analysis is proposed. Firstly,an original series is mapped into a word- occurrence- frequency series,afterwards,a correlation coefficient of two word- occurrence- frequency series for the initial condition and another condition is used as a characteristic parameter for detecting a change of gearbox conditions. Finally,the proposed method is applied to condition monitoring of a realistic gearbox. The results indicated that the proposed method can both effectively detect changes of gearbox conditions and unravel a natural evolutionary process of gearbox conditions.In addition,the proposed method has a clear advantage over the conventional time domain statistical parameters method in condition monitoring of gearbox

Plasma-induced FeSiAl@Al2O3@SiO2 core–shell structure for exceptional microwave absorption and anti-oxidation at high temperature

© 2019 Elsevier B.V. Structural and chemical stability is the key factors of microwave absorbers for their applications in case of high-temperature oxidation. In this study, a plasma-induced method is developed to get a multistrata core-shell structure of FeSiAl@Al2O3@SiO2 with bifunctional performances of microwave absorption and anti-oxidation. Within a dense microstructure, the Al2O3 and SiO2 ceramic shell layers mitigate oxygen transport to prevent corrosion at high temperature. Consequently, the magnetic FeSiAl core is well-protected against oxidation up to 1279 °C in air and exhibits excellent microwave absorption property. In particular, dense ceramic layers effectively reduce the permittivity of FeSiAl without losing permeability. Furthermore, the novel FSA@GCLs microstructures are enriched with multiple interfaces to favor the interfacial polarization and vast internal scattering probabilities. Because of the strong synergistic magnetic-dielectric effects, the multistrata core-shell structure of FeSiAl@Al2O3@SiO2 owns a minimum reflection loss of −46.29 dB at 16.93 GHz and its wide bandwidth (with an RL value of −10 dB) particularly acquire 7.33 GHz in the frequency range of 10.14–17.45 GHz. The highly stable multistrata core-shell opens up the opportunities of extending the microwave absorption as well as anti-oxidation applications