scholarly journals A Novel MPE-LPP-ELM Recognition Method for the Fault Diagnosis of Spiral Bevel Gears

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jiang Lingli ◽  
Tan Hongchuang ◽  
Li Xuejun ◽  
Yang Dalian
Keyword(s):  
Fault Diagnosis ◽  
Permutation Entropy ◽  
Mechanical Equipment ◽  
Analysis Tool ◽  
Learning Abilities ◽  
Spiral Bevel Gears ◽  
Feature Vectors ◽  
Bevel Gears ◽  
Diagnostic Time ◽  
Spiral Bevel

Spiral bevel gears are basic transmission components which are widely used in mechanical equipment. These components are important elements used in the monitoring and diagnosis of running states for ensuring the safe operations of entire equipment setups. The vibration signals of spiral bevel gears are typically quite complicated, as they present both nonlinear and nonstationary characteristics. In previous studies, multiscale permutation entropy (MPE) has been proven to be an effective nonlinear analysis tool for complexity and irregularity evaluations of complex mechanical systems. Therefore, it is considered that MPE values can be used as the sensitive features for spiral bevel gears fault identifications. However, if the MPEs are used to directly construct the feature vectors, some problems will be encountered, such as large numbers of characteristic quantities, high dimensions, and issues related to diagnosis accuracy and efficiency, which have been proven difficult to obtain at the same time. In order to improve the accuracy and efficiency of fault recognition in spiral bevel gear evaluations, locality preserving projection (LPP) methods can be applied to reduce the high dimensionality feature vectors constructed by MPEs. They have the ability to extract low-dimensional sensitive information from high-dimensional feature data. In order to directly obtain the diagnostic results, classifications are necessary. When compared with traditional neural networks, it has been found that extreme learning machines (ELMs) have the advantages of faster training speeds and stronger learning abilities. In summary, this study proposed the use of MPE values which could be optimized and dimensionality reduced by LPP as the feature vectors, along with ELMs as the classifiers of the fault mode identifications, in order to carry out valuable research of fault diagnosis methods for spiral bevel gears. The proposed method was applied to the diagnoses of four types of fault state spiral bevel gears. Then, the MPE-LPP-ELM results were compared with those obtained using MPE-PCA-ELM and MPE-ELM methods. Their respective diagnostic accuracy is 100%, 98.75%, and 98.75%, and diagnostic time is 0.0023 s, 0.0033 s, and 0.0078 s. It was determined in this study that the results confirmed the accuracy and superiority of the proposed method.

scholarly journals CEEMDAN-Based Permutation Entropy: A Suitable Feature for the Fault Identification of Spiral-Bevel Gears

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Lingli Jiang ◽  
Hongchuang Tan ◽  
Xuejun Li ◽  
Liman Chen ◽  
Dalian Yang
Keyword(s):  
Bevel Gear ◽  
Permutation Entropy ◽  
Fault Identification ◽  
Support Vector ◽  
Parameter Method ◽  
Spiral Bevel Gear ◽  
Mechanical Equipment ◽  
Spiral Bevel Gears ◽  
Bevel Gears ◽  
Spiral Bevel

A spiral-bevel gear is a basic transmission component and is widely used in mechanical equipment; thus, it is important to monitor and diagnose its running state to ensure safe operation of the entire equipment setup. The vibration signals of spiral-bevel gears are typically quite complicated, as they present both nonlinear and nonstationary characteristics and are interfered with by strong noise. The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method has been proven to be an effective method for analyzing this kind of signal. However, the fault feature information after CEEMDAN is not obvious and needs to be quantified. Permutation entropy can be used to quantify the randomness, complexity, and mutation of vibration time-series signals. This paper proposes to take the CEEMDAN-based permutation entropy as the sensitive feature for spiral-bevel gear fault identification. First, the raw vibration signal is decomposed by the CEEMDAN method to obtain a series of intrinsic modal functions (IMFs). The IMFs which included greater amounts fault information are selected as the optimal IMFs based on the correlation coefficient. Next, the permutation entropy values of the optimal IMFs are calculated. In order to obtain accurate permutation entropy values, the two key parameters, namely, embedding dimension and delay time, are optimized by using the overlapping parameter method. In order to assess the sensibility of the permutation entropy features, the support vector machine (SVM) is used as the classifier for fault mode identification, and the diagnostic accuracy can verify its sensibility. The permutation entropy of CEEMDAN-based/EEMD-based/EMD-based features, combined with SVM, is applied to identify three different fault modes of spiral-bevel gears. Their respective diagnostic accuracies are 100%, 88.33%, and 83.33%, which indicate that the CEEMDAN-based permutation entropy is the most sensitive feature for the fault identification of spiral-bevel gears.

scholarly journals Fault Detection and Severity Level Identification of Spiral Bevel Gears under Different Operating Conditions Using Artificial Intelligence Techniques

Machines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 173
Author(s):  
Syed Muhammad Tayyab ◽  
Steven Chatterton ◽  
Paolo Pennacchi
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Research Study ◽  
High Load ◽  
Operating Conditions ◽  
Spiral Bevel Gears ◽  
Bevel Gears ◽  
Spiral Bevel ◽  
Level Identification

Spiral bevel gears are known for their smooth operation and high load carrying capability; therefore, they are an important part of many transmission systems that are designed for high speed and high load applications. Due to high contact ratio and complex vibration signal, their fault detection is really challenging even in the case of serious defects. Therefore, spiral bevel gears have rarely been used as benchmarking for gears’ fault diagnosis. In this research study, Artificial Intelligence (AI) techniques have been used for fault detection and fault severity level identification of spiral bevel gears under different operating conditions. Although AI techniques have gained much success in this field, it is mostly assumed that the operating conditions under which the trained AI model is deployed for fault diagnosis are same compared to those under which the AI model was trained. If they differ, the performance of AI model may degrade significantly. In order to overcome this limitation, in this research study, an effort has been made to find few robust features that show minimal change due to changing operating conditions; however, they are fault discriminating. Artificial neural network (ANN) and K-nearest neighbors (KNN) are used as classifiers and both models are trained and tested by using the selected robust features for fault detection and severity assessment of spiral bevel gears under different operating conditions. A performance comparison between both classifiers is also carried out.

scholarly journals Contact stiffness and damping of spiral bevel gears under transient mixed lubrication conditions

Friction ◽  
2021 ◽  
Author(s):  
Zongzheng Wang ◽  
Wei Pu ◽  
Xin Pei ◽  
Wei Cao
Keyword(s):  
Contact Stiffness ◽  
Mixed Lubrication ◽  
Asperity Contact ◽  
Spiral Bevel Gears ◽  
Bevel Gears ◽  
Dry Contact ◽  
Spiral Bevel ◽  
Stiffness And Damping ◽  
Lubrication Conditions ◽  
Film Lubrication

AbstractExisting studies primarily focus on stiffness and damping under full-film lubrication or dry contact conditions. However, most lubricated transmission components operate in the mixed lubrication region, indicating that both the asperity contact and film lubrication exist on the rubbing surfaces. Herein, a novel method is proposed to evaluate the time-varying contact stiffness and damping of spiral bevel gears under transient mixed lubrication conditions. This method is sufficiently robust for addressing any mixed lubrication state regardless of the severity of the asperity contact. Based on this method, the transient mixed contact stiffness and damping of spiral bevel gears are investigated systematically. The results show a significant difference between the transient mixed contact stiffness and damping and the results from Hertz (dry) contact. In addition, the roughness significantly changes the contact stiffness and damping, indicating the importance of film lubrication and asperity contact. The transient mixed contact stiffness and damping change significantly along the meshing path from an engaging-in to an engaging-out point, and both of them are affected by the applied torque and rotational speed. In addition, the middle contact path is recommended because of its comprehensive high stiffness and damping, which maintained the stability of spiral bevel gear transmission.

Deformations and Stresses in Face-Hobbed Spiral Bevel Gears

2011 ◽  
Author(s):  
Vilmos V. Simon
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Nodal Point ◽  
Design Parameters ◽  
Element Discretization ◽  
Spiral Bevel Gears ◽  
Bevel Gears ◽  
Tooth Surfaces ◽  
Spiral Bevel ◽  
Element Method

In this study an attempt is made to predict displacements and stresses in face-hobbed spiral bevel gears by using the finite element method. A displacement type finite element method is applied with curved, 20-node isoparametric elements. A method is developed for the automatic finite element discretization of the pinion and the gear. The full theory of the generation of tooth surfaces of face-hobbed spiral bevel gears is applied to determine the nodal point coordinates on tooth surfaces. The boundary conditions for the pinion and the gear are set automatically as well. A computer program was developed to implement the formulation provided above. By using this program the influence of design parameters and load position on tooth deflections and fillet stresses is investigated. On the basis of the results, obtained by performing a big number of computer runs, by using regression analysis and interpolation functions, equations for the calculation of tooth deflections and fillet stresses are derived.

scholarly journals Lubrication characteristics of gear after shot peening base on 25-pellet model

2018 ◽  
Vol 10 (7) ◽  
pp. 168781401879065 ◽  
Author(s):  
Shuai Mo ◽  
Shengping Zhu ◽  
Guoguang Jin ◽  
Jiabei Gong ◽  
Zhanyong Feng ◽  
...  
Keyword(s):  
Shot Peening ◽  
High Speed ◽  
Target Material ◽  
Bevel Gear ◽  
Tooth Surface ◽  
Spiral Bevel Gear ◽  
Spiral Bevel Gears ◽  
Bevel Gears ◽  
Spiral Bevel ◽  
Lubrication Characteristics

High-speed heavy-load spiral bevel gears put forward high requirement for flexural strength; shot peening is a technique that greatly improves the bending fatigue strength of gears. During shot peening, a large number of fine pellets bombard the surface of the metal target material at very high speeds and let the target material undergo plastic deformation, at the same time strengthening layer is produced. Spiral bevel gear as the object of being bombarded inevitably brought the tooth surface micro-morphology changes. In this article, we aim to reveal the effect of microtopography of tooth shot peening on gear lubrication in spiral bevel gear, try to establish a reasonable description of the microscopic morphology for tooth surface by shot peening, to reveal the lubrication characteristics of spiral bevel gears after shot peening treatment based on the lubrication theory, and do comparative research on the surface lubrication characteristics of a variety of microstructures.

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