Bevel Gearbox Research Articles

Microstructural Characterization of Failed Mounting Studs of Aero-engine Bevel Gear Box

Abstract This paper reports the microstructural characterization of failed mounting studs of aero-engine bevel gear box. The chemical compositions obtained by EPMA indicate that the studs are manufactured from stainless carbon steel. The optical microstructures of studs show very fine microstructure and display the presence of very fine chromium carbide precipitates. The overall hardness of the stud-2 is marginally higher than that of the stud-1. It has been found that the cadmium coating was applied on the studs. This has been confirmed by EDS spectra and line scan profile of EPMA. The fracture surfaces of the broken mounting studs of bevel gear box of aero-engine portray the beach marks and overload fracture / final rupture. The presence of beach marks indicates that the studs are failed by fatigue. The material is free from inhomogeniety, discontinuity or any other defects. The fracture surface is also free from any corrosion products. There is no apparent cause of the failure like material defect of excess load etc. observed. The studs have failed due to loose-fitting of the studs leading to excessive vibration resulting in fatigue failure

Condition monitoring of bevel gear box using Morlet wavelet coefficients and naïve Bayes classifier

A naïve Bayes classifier is used for classifying the faults of a gear box. The rotating equipment like gear box has enough scope for condition monitoring its conditions as it plays a significant role as a prime mover in many engineering applications. This paper discusses about the extraction of Morlet wavelet coefficients from the vibration signal for different conditions of the gear box under investigation, and application of naïve Bayes classifier for its condition monitoring and diagnostics. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This work investigates the usefulness of statistical features of Morlet wavelet coefficients and naïve Bayes for classification. The results show that the developed method can reliably diagnose different conditions of the gear box and the algorithm can be applied for online applications of supervised machine learning.

FPGA BASED BEVEL GEARBOX FAULT DIAGNOSIS APPROACH

This paper addresses the issue of classifying local gear faults that depends on vibration signal measurements. Most of the early gears fault detection and diagnosis methods result insufficient results when dealing either with time or frequency domain characteristic. In order to overcome these obstacles, a TimeFrequency based approach implemented on Field Programming Gate Array (FPGA) is proposed. The presented approach combines Bagged Trees Classifier (BTC) with Complex Analytic Wavelet Transform (CAWT) analysis. Moreover, the presented approach benefits from the superior realization nature of FPGA. Extensive simulations and experiments have been conducted in order to demonstrate the efficiency of the proposed approach. Experiments are performed on bevel gearbox with both normal and one missing tooth. The obtained results clarify that the presented approach has a superior classification accuracy rate over the other comparative approaches

Bevel Gearbox with Vertical Drive Attachment for Industrial Trucks

Bevel Gearbox with Vertical Drive Attachment for Industrial Trucks

Donut mixer using bevel gears transmissions with 18 kg/hour in capacity

Small scaled donut productions use hand mixer to mix flours, eggs and another dough contents. The usage of this mixer was followed by some obstacles, such as the plastic material astigmatism worm gearbox that resulting the edge of gears has friction loss. Metal material bevel gearbox may overcome the dough stirring problem. This gearbox is able to overcome big load and high spin. As a result, the capacity of stirring container is able to be maximized that increasing working productivity result. There are a few things must be designed in scheming of the design process, such as the scheming of shaft calculation, bearing, bevel gear transmissions and frame. The results show that the production capacity increase 225 % from 7 kg/hour in capacity using hand mixer to 18 kg/hour in capacity.

Diagnosis of gear tooth fault in a bevel gearbox using discrete wavelet transform and autoregressive modeling

Vibration signals from any dynamic system are measured with respect to time. Different options are available to analyze these measured signals due to the advancement in computing and signal processing techniques. In the present work, a methodology comprising of discrete wavelet transform and autoregressive model has been proposed for detection of single tooth fault in single stage reduction bevel gearbox. An autoregressive model is constructed using detailed coefficients of discrete wavelet transform to highlight the presence of the fault in gearbox. The results show that variance of autoregressive coefficients obtained for faulty signal is more than the variance of autoregressive coefficients extracted from healthy signal. Based on the results, it is concluded that the proposed methodology can be used as health condition indicator of the gearbox system.

Vibration signal analysis using histogram features and support vector machine for gear box fault diagnosis

This paper discusses about the extraction of histogram features from the vibration signal of the different conditions of the gear box under investigation, and the application of machine learning method, support vector machine in machine condition monitoring and diagnostics. This paper aims at using classification methods for fault diagnosis of the gear box under investigation. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This work investigates the use histogram features and support vector machine for classification. The results show that the developed method can reliably diagnose different conditions of the gear box.

A solution of worst-case tolerance analysis for partial parallel chains based on the Unified Jacobian-Torsor model

The Unified Jacobian-Torsor model is highly suitable for tolerance analysis of complex assemblies that are constituted by a large number of rigid parts. However, partial parallel chains with leverage effect of geometrical structures widely exist in these assemblies, especially in a transmission device, such as gear-box; but this model only considers the serial kinematic chains in the assemblies. If this type of partial parallel chains was ignored, it would lead to a large loss in accuracy and reliability; few previous studies have focused on it. In this paper, a solution of worst-case tolerance analysis based on Unified Jacobian-Torsor model is proposed to solve this type of partial parallel chains. First, the parallel joints that form parallel chains in the assembly are determined. Second, the contact points originated in the CLIC (localization tolerancing with contact influence) method are used to determine the boundary position of the parallel joints, and the analysis points from the analysis line method are determined to calculate their deviations at each joint. Third, these deviations are used together to calculate the torsor parameter of the parallel joints according to their torsor features and geometric structure. Fourth, the partial parallel chains are replaced by a new serial chains with the obtained torsor parameters to calculate the final deviations of the functional requirements based on the Unified Jacobian-Torsor model. An industrial application of the spiral bevel gear-box is given to demonstrate this solution.

Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis

Compound-fault diagnosis of mechanical equipment is still challenging at present because of its complexity, multiplicity and non-stationarity. In this work, an adaptive redundant multiwavelet packet (ARMP) method is proposed for the compound-fault diagnosis. Multiwavelet transform has two or more base functions and many excellent properties, making it suitable for detecting all the features of compound-fault simultaneously. However, on the other hand, the fixed basis function used in multiwavelet transform may decrease the accuracy of fault extraction; what’s more, the multi-resolution analysis of multiwavelet transform in low frequency band may also leave out the useful features. Thus, the minimum sum of normalized multifractal entropy is adopted as the optimization criteria for the proposed ARMP method, while the relative energy ratio of the characteristic frequency is utilized as an effective way in automatically selecting the sensitive frequency bands. Then, The ARMP technique combined with Hilbert transform demodulation analysis is then applied to detect the compound-fault of bevel gearbox and planetary gearbox. The results verify that the proposed method can effectively identify and detect the compound-fault of mechanical equipment.

Design of a Transmission for Wheelchairs Intended to Humans with Locomotors Disabilities

In this paper is presented the development of a wheelchair intended to be use by the people with locomotors disabilities. They are presented various solutions of kinematics schemes proposed to be implemented as a mechanical transmission for the wheelchair. The developed wheelchair integrates a mechanical transmission which allows the movement in a straight line, and also in cornering. The mechanical transmission implemented contains a bevel gearbox. The purpose of the bevel gearbox is to multiply the moment transmitted to the motor wheels for movement on uneven terrain. They are presented aspects concerning the design of the wheelchair frame an of implemented bevel gearboxes.

Time-Frequency Enhancement Technique for Bevel Gear Fault Diagnosis

Gear failure is one of the most common causes of breakdown in rotating machineries. It is well known that vibration signals from machineries can be effectively used to detect certain gear faults. Yet it is still not an easy task to find a symptom that reflects a particular fault from vibration signals. This paper presents an advanced time-frequency signal processing technique for extracting important gear fault information from the vibration signal that is heavily corrupted by measurement noise. Experiments were performed on a bevel gearbox test rig using vibration measurements. The Time Synchronous Average (TSA) was initially utilized to eliminate all asynchronous component of vibration signal obtained from the gear. The Continuous Wavelet Transform (CWT) method was then used to capture the non-stationary behaviour of the impulse signal generated from the broken bevel gear tooth. It was shown that the diagnosis method using the Continuous Wavelet Transform combined with Time Synchronous Averaging outperformed the conventional spectral analysis, capable of identifying the angular location of broken teeth in the gear.

Bevel Gearbox Fault Diagnosis using Vibration Measurements

The use of vibration measurementanalysis has been proven to be effective for gearbox fault diagnosis. However, the complexity of vibration signals observed from a gearbox makes it difficult to accurately detectfaults in the gearbox. This work is based on a comparative studyof several time-frequency signal processing methods that can be used to extract information from transient vibration signals containing useful diagnostic information. Experiments were performed on a bevel gearbox test rig using vibration measurements obtained from accelerometers. Initially, thediscrete wavelet transform was implementedfor vibration signal analysis to extract the frequency content of signal from the relevant frequency region. Several time-frequency signal processing methods werethen incorporated to extract the fault features of vibration signals and their diagnostic performances were compared. It was shown thatthe Short Time Fourier Transform (STFT) could not offer a good time resolution to detect the periodicity of the faulty gear tooth due the difficulty in choosing an appropriate window length to capture the impulse signal. The Continuous Wavelet Transform (CWT), on the other hand, was suitable to detection of vibration transients generated by localized fault from a gearbox due to its multi-scale property. However, both methods still require a thorough visual inspection. In contrast, it was shown from the experiments that the diagnostic method using the Cepstrumanalysis could provide a direct indication of the faulty tooth without the need of a thorough visual inspection as required by CWT and STFT.

Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theory

The gearbox is an important component in industrial drives, providing safe and reliable operation for industrial production. Wavelet packet transform (WPT) analysis was used to extract fault features in the vibration signals generated by a gearbox. The extracted features from the WPT were used as input in a rough set (RS) for attribute reduction and then combined with a genetic algorithm to obtain global optimal attribute reduction results. The fault features gained after the attribute reductions were used to generate decision rules. The unknown gear status signal attributes were used as input to match the generated decision rules for fault diagnosis purposes. Gearbox vibration signals contain a significant amount of gear status information; a WPT has an acute portion-locked ability to extract attribute information from the vibration signals. However, WPT frequency aliasing would lead to the generation of spurious frequency components, affecting gear fault diagnosis. In this paper, we introduce an improved WPT to eliminate frequency aliasing, thus improving the accuracy of fault diagnosis. This paper studies the use of wavelet packet for feature extraction and the RS for classification; the results demonstrate that this method can accurately and reliably detect failure modes in a gearbox.

Fault Diagnosis on Bevel Gearbox with Neural Networks and Feature Extraction

In recent years, early fault detection and diagnosis of gears have become extremely important due to requirement to decrease the downtime on production machinery caused by the failures. For that reason, researches have been done for the early detection of faults through the analysis of their vibration signals. Modern day machines, due to their complexities, can have many vibration generating sources in addition to noises. Therefore it is important that the vibration signal of faulty gear to be recognized and recovered for the diagnostics. In this paper Back-Propagation neural network has been used for the classification of RPM and oil level related gearbox faults that can occur during operation. With the help of Power spectrum technique, signal was more refined in order to make the feature selection process much more accurate. DOI: http://dx.doi.org/10.5755/j01.eee.21.5.13334

Fault Diagnosis on Bevel Gearbox with Neural Networks and Feature Extraction

In recent years, early fault detection and diagnosis of gears have become extremely important due to requirement to decrease the downtime on production machinery caused by the failures. For that reason, researches have been done for the early detection of faults through the analysis of their vibration signals. Modern day machines, due to their complexities, can have many vibration generating sources in addition to noises. Therefore it is important that the vibration signal of faulty gear to be recognized and recovered for the diagnostics. In this paper Back-Propagation neural network has been used for the classification of RPM and oil level related gearbox faults that can occur during operation. With the help of Power spectrum technique, signal was more refined in order to make the feature selection process much more accurate.DOI: http://dx.doi.org/10.5755/j01.eee.21.5.13334

Gear Box Fault Diagnosis using Hilbert Transform and Study on Classification of Features by Support Vector Machine

The condition of an inaccessible gear in an operating machine can be monitored using the vibration signal of the machine measured at some convenient location and further processed to unravel the significance of these signals. Demodulation is an important issue in gearbox fault detection. Non-stationary modulating signals increase difficulties of demodulation. Though wavelet packet transform has better time–frequency localization, because of the existence of meshing frequencies, their harmonics, and coupling frequencies generated by modulation, fault detection results using wavelet packet transform alone are usually unsatisfactory. This paper proposes a fault detection method that combines Hilbert transform and machine learning method namely support vector machines (SVMs). The statistical feature vectors from Hilbert transform coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing SVM and their efficiency in classifying the faults in the Bevel Gear Box was studied.

Design and Fabrication of Prostrate Ring-Scanning Equipment for Near Infrared Diffuse Optical Imaging1

Diffuse optical imaging (DOI) providing functional information of tissues has drawn great attention for the last two decades [1]. Near infrared (NIR) DOI systems composed of scanning equipment, opt-electrical measuring module, system control, and data processing and image reconstruction schemes are developed for the screening and diagnosis of breast tumors. Mostly, the scanning equipment belonging to fixed source-and-detector configuration limits computed image resolution to an extent [2–4].To cope with the issue, a flexible ring-scanning mechanism design has been investigated and analyzed in order to obtain the most probable effectiveness and efficiency while retaining acceptable scanning time. Before the detail design of ring-scanning equipment was conducted, finite-element-based image reconstruction schemes were performed to obtain tomographical images through changing source-and-detection arrangement of the ring-scanning mechanism. From the simulation [5], the 2Z3S or 3Z3S configuration was found an apparently better design for the ring-scanning mechanism in terms of both the resolution of reconstructed optical-property images and the scanning time of equipment. Following the previous study, this technical brief demonstrates the design, fabrication, and test of the prototype ring-scanning equipment.Prostrate ring-scanning-based architecture in the developed NIR DOI system is considered for the screening and diagnosis of breast tumors, as shown in Fig. 1, the schematic diagram of the system. The ideas and concepts of design are based on the following considerations:The circular scanning module is divided into m zones, and each zone includes n NIR sources and l detection fibers. Due to the space limitation and the physical dimension of source-and-detection-fiber heads, 36 source-and-detection channels are adopted, and 10 deg separation between each two heads is arranged.The radial movement of a single optical channel set functions through a radial guiding slot and an optical channel bracket. A bracket makes an optical channel set fixed, as shown in Fig. 2. A single optical channel set (Fig. 3) is composed of an illumination fiber or a liquid light guide for detection, a light guide unit with two fixers, a linear guideway incorporated with a seat driven by a stepping motor, a link, and a bearing. To drive the whole 36 optical source-and-detector channels, Fig. 4 shows the mechanism that mainly includes a rotating disk, a belt tension pulley, a V-belt pulley, and a V-belt for the purpose of a radial movement.Figure 5 demonstrates the angular-movement mechanism of optical source-and-detector channels, mainly including a stepping motor and a speed-reduction gearbox with bevel gears.In a radial-movement mode to drive each optical channel bracket, it was realized by the link moving in the guiding slot, where a bearing is mounted at the other end of link to allow the link rotating with the inner race. The rotating disk was driven by a stepper motor and a V-belt set for speed reduction, as shown in Fig. 4.In a circular-movement mode to achieve ring scanning, Fig. 5 shows a stepper motor installed beneath the scanning platform connects to a bevel gearbox with a speed reduction ratio 2 for a smooth drive. The ring-scanning angle can be as high as 360 deg, which is dependent on the source-and-detector configuration used; for instance, the scanning angle is 150 deg for the configuration 2Z3S. Here *Z#S denotes all optical channels set into * zones, and # illumination channels in a single zone. Therefore, six illumination channels and 30 detection channels in total are in the design of configuration 2Z3S associated with 36 optical channels.In this prostrate equipment for NIR diffuse optical imaging, our experimental scenario is designated as below.Based on the synthesized design with acceptable scanning time and simulated imaging quality [5], the prototype equipment was machined and assembled, as shown in Fig. 6. The experiments to acquire NIR data and reconstruct images for verification will be performed in the near future. To illustrate probable image reconstruction through the proposed prostrate ring-scanning equipment, a computational case study was synthesized. Figure 7 illustrates two sets of reconstructed μa and μs′ images for the design of configuration 2Z3S associated with 18 and 36 optical channels, respectively. In the simulation case, a 10 mm-diameeter tumor in a 100 mm-diameter background tissue is assumed. The original cross section image was obtained with slicing a 3D breast MRI from Taipei Medical University Hospital.The proposed *Z#S design is rather cost-effective than the fixed optical-channel one. As to the performance of image reconstruction, Figs. 7(c) and 7(e) are still competitive to their counterparts, Figs. 7(b) and 7(d), respectively.The study was partially supported by the grant NCU-LSH-102-A-013.

Dynamic Characteristics Analysis of Spiral Bevel Gearbox Used in Ship Lift Synchronization System

A dynamic finite element model of a coupled gear-rotor-bearing-housing gear system is developed by combining the gearbox transmission model with the housing structure model of spiral bevel gearbox used in ship lift synchronization system with taking stiffness excitation, error exaction and meshing impact exaction as the dynamic excitation. The intrinsic modes and vibration response are calculated, and then the modal frequency, mode shape, vibration displacement, velocity and acceleration of the gearbox are obtained to achieve the goal of prediction on vibration severity. In fact, the proposed approach not only provides a more comprehensive understanding of spiral bevel gearbox, but also can serve as the basis for vibration control of gear system used in ship lift synchronization system.

Spur Bevel Gearbox Fault Diagnosis Based on Wavelet Packet Transform for Feature Extraction and Flow Graph Data Mining

Gearbox vibration signal contains a wealth of the gear status information, used wavelet packet transform (WPT) refinement of the partial lock ability to extract the fault signs attribute information in the vibration signal. Extracted signs attribute information as the input of the flow graph (FG), generated decision rules to achieve the purpose of fault diagnosis. FG was a knowledge representation and data mining method to mine the intrinsic link between the data and improve the clarity of the potential knowledge. The results confirmed that used of WPT feature extraction and FG data mining method can accurate detection the gear fault.

Application of Genetic Algorithm (GA) for Optimum Design of Module, Shaft Diameter and Bearing for Bevel Gearbox

Abstract In this study selection of optimum module, shaft diameter and rolling bearing for conical gear has been done using genetic algorithm (GA). GA, is a novel stochastic method of optimization. GAs are based on the principles of natural selection and evolutionary theory. Objective function was optimized for the design variables between determined boundary values. The GA was constrained by taking into account the power, moment, velocity, wall thickness and bearing distances. Tooth strength and surface crush were considered to be design constraints for module optimization. The other algorithm constraints are maximum bending and torsion moments for shaft optimization, and working life for bearing optimization.