Gear Fault Detection Analysis Method Based on Fractional Wavelet Transform and Back Propagation Neural Network

Sun, Yanqiang; Chen, Hongfang; Tang, Liang; Zhang, Shuang

doi:10.32604/cmes.2019.07950

Cited by 4 publications

(1 citation statement)

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“…In recent decades, fractional-order calculus has been introduced into the gear transmission system. In fault diagnosis of gear, a lot of work has been done based on fractional Fourier transform method [20][21][22][23][24][25][26][27]. In terms of the dynamics of gear, a two-step transmission model with three degrees-of-freedom is established by using Riemann-Liouville (R-L) fractional-order derivative under the assumption that the connection between the teeth of the gears is with the range from ideal classic to viscoelastic by Hedrih [28].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Dynamic Characteristics of a Fractional-Order Spur Gear Pair With Internal and External Excitations

Hou

Yang

et al. 2021

Journal of Computational and Nonlinear Dynamics

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The nonlinear frequency response characteristics of a spur gear pair with fractional-order derivative under combined internal and external excitations are investigated based on the incremental harmonic balance (IHB) method. First, a pure torsional vibration model is proposed that contains various complex factors, such as the time-varying mesh stiffness, transmission error, the fluctuation of input torque, backlash. Then, the IHB method is developed to calculate the higher-order approximate solution of the system, and the correctness of the results is verified by comparing with numerical simulation results obtained by the power series expansion (PSE) method. Furthermore, the types of various impact situations and their judgment conditions are discussed, and the different impact behaviors are analyzed in detail when ω∈[0, 1.5] by using phase diagrams and amplitude–frequency response curves. The influence of important parameters on the dynamic characteristics of gear pair is analyzed at last. The results indicate that the analytical solution derived by IHB method is sufficiently precise. Significantly, the dynamic characteristics of the system could be effectively controlled by adjusting time-varying mesh stiffness coefficient, the order and coefficient of fractional-order term, and the amplitude of internal excitation or external excitation. As a part of the theory of fractional-order mechanical system, the impact performance of fractional-order gear pair is approached for the first time by analytical method.

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Section: Introductionmentioning

confidence: 99%

Analysis of Dynamic Characteristics of a Fractional-Order Spur Gear Pair With Internal and External Excitations

Hou

Yang

et al. 2021

Journal of Computational and Nonlinear Dynamics

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State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Zhang

Zhou

Wang

et al. 2022

IET Collab Intel Manufact

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Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

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Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of Observation through Artificial Neural Network Approach

Akıner

2021

Advances in Meteorology

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Estimating models are becoming increasingly crucial in highlighting the nonlinear connections of the massive level of rough information and chaotic components. The study demonstrates a modern approach utilizing a created artificial neural network (ANN) method that may be an alternative strategy to conventional factual procedures for advancing rainfall estimate execution. A case study was presented for Turkey’s Düzce and Bolu neighboring territories located on the Black Sea’s southern coast. This study’s primary aim is to create an ANN model unique in the field to generate satisfactory results even with limited data. The proposed technique is being used to estimate rainfall and make predictions regarding future precipitation. Bolu daily average rainfall by month data and a limited number of Düzce rainfall data were used. Missing forecasts and potential rainfall projections will be examined in the fundamental research. This research further focuses on ANN computational concepts and develops a neural network for rainfall time series forecasting. The emphasis of this study was a feed-forward backpropagation network. The Levenberg–Marquardt algorithm (LMA) was implemented for training a two-layer feed-forward ANN for the missing rainfall data prediction part of this research. The inaccessible rainfall parameters for Düzce were determined for the years 1995 to 2009. From 2010 to 2020, a two-layer feed-forward ANN was trained using the gradient descent algorithm to forecast daily average rainfall data by month. The findings reported in this study guide researchers interested in implementing the ANN forecast model for an extended period of missing rainfall data.

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Gear Fault Detection Analysis Method Based on Fractional Wavelet Transform and Back Propagation Neural Network

Cited by 4 publications

References 0 publications

Analysis of Dynamic Characteristics of a Fractional-Order Spur Gear Pair With Internal and External Excitations

Analysis of Dynamic Characteristics of a Fractional-Order Spur Gear Pair With Internal and External Excitations

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of Observation through Artificial Neural Network Approach

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