Gear Conditions Research Articles

Condition Indicators for Gears


 
 
 Diagnostics of faults in gears requires development of reliable condition indicators. A large number of condition indicators, which are based on statistical moments of the synchronous average and its derivatives (difference and residual signals) were previously suggested. This study evaluates the efficiency of different gear condition indicators that are based on statistical moments and compares them with two new types of condition indicators that are suggested. The two new types of condition indicators are based on the order spectrum and the spectral kurtosis of the synchronous average.
 The study was conducted on the labeled data of PHM'09 challenge. This data included recordings of vibrations in helical and spur gearboxes with seeded faults.
 
 

Keep Your Eye on the Target: Safety Behavior Reduces Targeted Threat Beliefs Following a Behavioral Experiment

This study examined the effect of safety behavior on belief change during a behavioral experiment. Highly spider fearful participants (N = 126) evaluated a targeted negative belief about spiders during a brief behavioral experiment with a live tarantula. Participants were randomly assigned either to use or not use safety gear during the session. Results demonstrated that after the behavioral experiment, targeted negative beliefs were significantly lower in the safety gear condition than in the control condition. Both conditions benefited from comparable improvements across a broader constellation of negative spider-related beliefs. Safety gear facilitated closer approach to the spider during the session; however, participants who did not use safety gear experienced greater improvement in perceived control. These findings suggest that safety behavior need not impair corrective learning during cognitive-behavioral interventions and that it might indeed enhance it. Results are discussed in terms of cognitive-behavioral treatments for anxiety disorders and of the role of safety behavior therein.

A Mathematical Model of Tooth Geometry and Undercutting Condition and Contact Ratio Analysis of the Cosine Gear Drive Generated by a Cosine Rack Cutter

This paper proposes a cosine gear drive provided with a pair of cosine gears which are generated by a cosine rack cutter. Based on the coordinate transformation theory and the theory of gearing, a mathematical model of tooth geometry of the cosine gear drive is developed. The condition of undercutting for the cosine gear generated by a cosine rack cutter is established to determine the minimum number of teeth of non-undercutting. An analytic mathematical model for evaluating contact ratio of the cosine gear drive is built. A cause-and-effect analysis for contact ratio is executed under considering gear module, gear ratio, addendum coefficient, and dedendum coefficient as causes. Results of the analysis show that gear module does not affect contact ratio at all but the other causes do affect contact ratio. The proposed cosine gear drive is able to reduce the size of gear box since the minimum number of teeth of non-undercutting is only 6. Furthermore, the production cost and production lead time of the proposed cosine gear drive are low because it can be manufactured efficiently and economically by regular hobbing machines. The use of CNC machines is not necessary.

Gearbox Fault Features Extraction Using Vibration Measurements and Novel Adaptive Filtering Scheme

Vibration signals measured from a gearbox are complex multicomponent signals, generated by tooth meshing, gear shaft rotation, gearbox resonance vibration signatures, and a substantial amount of noise. This paper presents a novel scheme for extracting gearbox fault features using adaptive filtering techniques for enhancing condition features, meshing frequency sidebands. A modified least mean square (LMS) algorithm is examined and validated using only one accelerometer, instead of using two accelerometers in traditional arrangement, as the main signal and a desired signal is artificially generated from the measured shaft speed and gear meshing frequencies. The proposed scheme is applied to a signal simulated from gearbox frequencies with a numerous values of step size. Findings confirm that 10−5 step size invariably produces more accurate results and there has been a substantial improvement in signal clarity (better signal-to-noise ratio), which makes meshing frequency sidebands more discernible. The developed scheme is validated via a number of experiments carried out using two-stage helical gearbox for a healthy pair of gears and a pair suffering from a tooth breakage with severity fault 1 (25% tooth removal) and fault 2 (50% tooth removal) under loads (0%, and 80% of the total load). The experimental results show remarkable improvements and enhance gear condition features. This paper illustrates that the new approach offers a more effective way to detect early faults.

Gear Faults Diagnosis Based on Nonlinear Blind Source Separation

The nonlinear blind source separation is a practical and effective method in processing mechanical vibration signal, but it has the limitation which learning rate is fixed. It will take a long time for iterative parameters to get convergence. In this paper, a variable rate nonlinear BSS is proposed. The learning rate of the algorithm is adjusted based on iterative error in the different stopping iterating time and inverse proportion. The proposed algorithm increasing the efficiency of the nonlinear BSS and de-noising the vibration signals. Experiment on gears shows that the signal gained by the method more impersonality represents the gear condition

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line.

A Fatigue Model for Spur Gear Contacts Operating Under Mixed Elastohydrodynamic Lubrication Conditions

This paper presents a model to predict the crack formation fatigue lives of spur gear contacts operating under mixed lubrication conditions where surface roughnesses introduce intermittent metal-to-metal contacts and severe stress concentrations. The proposed model consists of several submodels, including (i) a gear load distribution model to determine the normal tooth force distribution along the tooth surface, incorporating any profile modifications and manufacturing deviations, (ii) a mixed elastohydrodynamic lubrication model customized to handle transient contact conditions of gears, (iii) a stress formulation that assumes the plane strain condition to compute the transient elastic stress fields on and below the tooth surface induced by the mixed lubrication surface pressure and shear stress distributions, and (iv) a multi-axial fatigue model to predict the crack nucleation life distribution. The proposed spur gear fatigue model is used to simulate the contacts of gear pairs having different surface roughness amplitudes. The predictions are compared to the measured gear fatigue stress-life data for each surface condition to assess the model accuracy in the prediction of the crack nucleation fatigue lives as well as the location of the critical failure sites.

Characterization of gear faults in variable rotating speed using Hilbert-Huang Transform and instantaneous dimensionless frequency normalization

The objective of this research is to investigate the feasibility of utilizing the instantaneous dimensionless frequency (DLF) normalization and Hilbert-Huang Transform (HHT) to characterize the different gear faults in case of variable rotating speed. The normalized DLF of the vibration signals are calculated based on the rotating speed of shaft and the instantaneous frequencies of Intrinsic Mode Functions (IMFs) which are decomposed by Empirical Mode Decomposition (EMD) process. The faulty gear features on DLF-energy distribution of vibration signal can be extracted without the presence of shaft rotating speed, so that the proposed approach can be applied for characterizing the malfunctions of gearbox system under variable shaft rotating speed. A test rig of gear transmission system is performed to illustrate the gear faults, including worn tooth, broken tooth and gear unbalance. Different methods to determine the instantaneous frequency are employed to verify the consistence of characterization results. The DLF-energy distributions of vibration signals are investigated in different faulty gear conditions. The analysis results demonstrate the capability and effectiveness of the proposed approach for characterizing the gear malfunctions at the DLFs corresponding to the meshing frequency as well as the shaft rotating frequency. The support vector machine (SVM) is then employed to classify the vibration patterns of gear transmission system at different malfunctions. Using the energy distribution at the characteristic DLFs as the features, the different fault types of gear can be identified by SVM with high accuracy.

東日本大震災で岩手県沿岸域に放置された底刺網の状態とゴーストフィッシングの実態

東日本大震災で岩手県沿岸域に放置された底刺網の状態とゴーストフィッシングの実態

Gear Fault Identification Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System

This study compares the performance of gear fault detection using Artificial Neural Networks (ANNs) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The feature vector is extracted from vibration signals by standard deviation of wavelet packet coefficients at the fourth level. An improved distance evaluation technique is proposed for feature selection, and with it, the four salient features are selected from the original feature set. These features are normalized in the range zero to one and then fed into ANN and ANFIS. The gear conditions were considered to be healthy gearbox, slightly worn, and medium worn and broken-teeth gears faults. The output layer of ANN and ANFIS consists of one node indicating the status of the gearbox by four labels. A 3-layer Multi-Layer Perceptron (MLP) neural-network and an ANFIS were designed to carry out the task. The results show that the classification accuracy of ANFIS is better than MLP. Also, the effectiveness of the proposed feature selection method is demonstrated by the test results.

Study and Experimental Analysis of Oval Bevel Gear Transmission

Oval bevel gear is introduced as a new gear transmission type of intersecting axes, in which high-order, denaturing and eccentric one are included. Based on the theory of spatial engagement, spatial spherical coordinate system of the noncircular bevel gearing was developed and a generalized tooth profile equation of noncircular bevel gears was also proposed. The gear profile can be enveloped for the generating movement between conical cutter and oval bevel gear according to the envelope principle of conjugate profiles. Then a new and general parametric design method for oval bevel gear was founded. The classification and gear variability condition of oval bevel gear were discussed. Meanwhile, its pitch curve, addendum and dedendum curve were studied, and the corresponding equations were deduced. A roll check experiment is performed on the prototype workpiece. Then the influences of motor speed and load on transmission ratio were analyzed. Finally, the design method, profile generation method and manufacture method of oval bevel gear were certified as correct by comparing experimental curve of transmission ratio with theoretical one.

Morphological covering based generalized dimension for gear fault diagnosis

This investigation presents a new generalized dimension estimation method based on morphological covering (MC) technique for characterizing the nonlinearity and complexity of vibration signals measured from gearbox. A synthetic fractal signal is employed to evaluate and compare the proposed MC technique with the traditional box-counting (BC) method and a similar approach developed in literature. Results revealed that the presented MC method is the one providing the most reliable generalized dimension estimation results. Furthermore, we applied this scheme to analyze the vibration signals from a gearbox with three operation states. The estimated general dimensions are used as the input feature vector for classifiers to the gear working states. Experimental results showed that our presented scheme achieves the best performance on discriminating the gear conditions. We also explore the calculational efficiency of the MC method. Results demonstrated that the MC method requires much less computational cost than BC method and seems to be more suitable for on-line condition monitoring of gearboxes.

Dynamic simulation of spur gear with tooth root crack propagating along tooth width and crack depth

Gear tooth crack will cause changes in vibration characteristics of gear system, based on which, operating condition of the gear system is always monitored to prevent a presence of serious damage. However, it is also a unsolved puzzle to establish the relationship between tooth crack propagation and vibration features during gear operating process. In this study, an analytical model is proposed to investigate the effect of gear tooth crack on the gear mesh stiffness. Both the tooth crack propagations along tooth width and crack depth are incorporated in this model to simulate gear tooth root crack, especially when it is at very early stage. With this analytical formulation, the mesh stiffness of a spur gear pair with different crack length and depth can be obtained. Afterwards, the effects of gear tooth root crack size on the gear dynamics are simulated and the corresponding changes in statistical indicators – RMS and kurtosis are investigated. The results show that both RMS and kurtosis increase with the growth of tooth crack size for propagation whatever along tooth width and crack length. Frequency spectrum analysis is also carried out to examine the effects of tooth crack. The results show that sidebands caused by the tooth crack are more sensitive than the mesh frequency and its harmonics. The developed analytical model can predict the change of gear mesh stiffness with presence of a gear tooth crack and the corresponding dynamic responses could supply some guidance to the gear condition monitoring and fault diagnosis, especially for the gear tooth crack at early stage.

The WW Gear case, conditions precedent and construction contracts

PurposeThis paper aims to look at the recent case of WW Gear Construction Ltd v. McGee Group Ltd (henceforth “WW Gear”) and the complex legal concept of a condition precedent in construction contracts.Design/methodology/approachThe paper looks at the origin of a condition precedent and explores recent cases in which a condition precedent was involved as an issue. The wording of the relevant contract documents used in WW Gear is examined and the attitude adopted by the judiciary when dealing with an alleged condition precedent is explored in depth.FindingsFrom the judgment handed down by the court it is clear that provided such a clause in a contract is correctly framed and the words used indicate a clear intention of the parties which is reasonable, then the English Courts will find that a condition precedent is enforceable.Research limitations/implicationsWhen dealing with a condition precedent, the judges will interpret any condition precedent strictly but not so strictly that a minor drafting error makes the condition precedent unenforceable. The exact boundaries of strictness cannot be settled by WW Gear but will have to await further development of case law through future litigation. The implications of WW Gear for building surveyors and the wider construction industry are that considerable care must be taken in the administration of construction contracts where conditions precedent are present. The adjudicator was clearly incorrect in his finding that the relevant sub‐clauses were meaningless and could be safely ignored by the parties.Originality/valueWhilst the legal concept of a condition precedent has featured in several modern construction law cases there is a paucity of literature dealing with the implications for building surveyors and construction professionals. This paper explores the importance of this often ignored legal concept not as abstract jurisprudence but in a succinct and practical way allowing practitioners to understand more fully the implications of a condition precedent on projects in which they are involved.

Dynamic Mechanical Analysis of Automotive Gearbox Casing

After the three-dimensional solid modeling of the SG135 automobile gearbox, finite element analysis models of static, transient and harmonic response load are built based on correlation theory. On the basis of ADAMS dynamic simulation of different gearbox gears, transient load of the gearbox casing at different working gears is found out. With frequency response characteristic of gearbox casing analyzed at typical working conditions of first gear and reverse gear, the evaluation of some factors such as strength and stiffness are given out, which provides theory basis for further improvement of gearbox casing structure and strengthening weak links. Based on harmonic response analysis, the impact on gearbox casing structural vibration by harmonic excitation of the engine is imitated, and the match relation of frequency response between the gearbox and the engine is analyzed. Improvements about engine operational limits matching and gearbox structural design are proposed.

Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks

A marine propulsion system is a very complicated system composed of many mechanical components. As a result, the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft. It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis. For this reason, a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems. To monitor the gear conditions, the bispectrum analysis was first employed to detect gear faults. The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique, which could be regarded as an index actualizing forepart gear faults diagnosis. Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox. The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum, and the ANN classification method has achieved high detection accuracy. Hence, the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases, and thus have application importance.

Condition Monitoring Opportunities Using Vehicle-Based Sensors

Recent increases in railway patronage worldwide have created pressure on rolling stock and railway infrastructure through the demand to improve the capacity and punctuality of the whole system, and this demand must also be balanced with reducing life-cycle costs. Condition monitoring is seen as a significant contributor in achieving this. The emphasis of this article is on the use of sensors mounted on rolling stock to monitor the condition of infrastructure and the rolling stock itself. This is set in the context of modern rolling stock being fitted with high-capacity communication buses and multiple sensors, resulting in the potential for advanced processing of collected data. This article brings together linked research that uses a similar set of rolling stock sensors, and discusses: general usage and benefits, a track defect detection method, running gear condition monitoring, and absolute train speed detection.

Component-level fault diagnostics of a bevel gear using a wavelet packet transform

This article presents a novel diagnostic algorithm based on wavelet packet transform. Although existing fault diagnostic techniques may detect the occurrence of failure or excessive malfunction in a system, they rarely reveal such detailed information as fault position, not to mention their reduced effectiveness in a system whose fault feature is not so pronounced. The proposed algorithm takes advantage of time—frequency localized features of wavelet packets in order to classify the system condition and pinpoint the fault position. Fisher's linear discriminant is used to classify system conditions, all the wavelet packets are exhaustively searched in order to unravel the optimal base (or packet) that discriminates different system conditions based on the Fisher value. It is the time—frequency localized feature of the wavelet packet that further makes it possible to detect fault position and even faulty component in the bevel gear. The proposed algorithm is validated using a machine fault simulator that consists of a motor, a bevel gear, and a bearing, where the condition of the bevel gear may be varied to simulate normal, cracked, and broken conditions using vibration signal. It turns out that the proposed algorithm precisely locates fault position as well as identifies system conditions.

A Fault Diagnosis Approach for Gears Using Multidimensional Features and Intelligent Classifier

Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient fault detection and accurate fault diagnosis are therefore critical to machinery normal operation. A new hybrid intelligent diagnosis method is proposed in this work to identify multiple categories of gear defection. In this method, wavelet packet transform (WPT), empirical mode decomposition (EMD) and Wigner-Ville distributions (WVD), combined with autoregressive (AR) model algorithm, were performed on gear vibration signals to extract useful fault characteristic information. Then, multidimensional feature sets including energy distribution, statistical features and AR parameters were obtained to represent gear operation conditions from different perspectives. The nonlinear dimensionality reduction algorithm, i.e. isometric mapping (Isomap), was employed in statistics to mine the intrinsic structure of the feature space in a low-dimensional space, and thus to speed up the training of the probabilistic neural network (PNN) classifier and enhance its diagnosis accuracy. Experiments with different gear faults were conducted, and the vibration signals were measured under different drive speeds and loads. The analysis results indicate that the proposed method is feasible and effective in the gear multi-fault diagnosis, and the isolation of different gear conditions, including normal, single crack, compound fault of wear and spalling, etc., has been accomplished. Since the recognition results are available directly from the output of PNN, the proposed diagnosis technique provides the possibility to fulfill the automatic recognition on gear multiple faults

Test rig for micro gear and experimental analysis on the meshing condition and failure characteristics of steel micro involute gear and metallic glass one

A test rig to evaluate the performance of a micro involute gear with a diameter below 1 mm has been developed. Shaft position and shaft center distance of gears have great influence on the gear performance and therefore they must be adjusted precisely. In this research, the measurement and adjustment method of gear assembly condition and an in-situ observation system of gear condition are proposed, that are appropriately designed for micro gears. The meshing condition of micro involute gears is investigated experimentally. Based on the observation of sliding marks on the tooth flank, it is clarified that the meshing condition of a micro involute gear follows the meshing theory of an involute gear. Failure modes of SK4 micro gears and metallic glass micro gears are investigated. The experimental results indicated that SK4 micro gears suffer severe wear under unlubricated conditions that generate powderlike wear particles. On the other hand, flakelike wear particles appear in the metallic glass micro gear.