Automobile Gearbox Research Articles

Methods of detection and localization of the sources of noise and vibration on car gearboxes: a review

One of the primary sources of noise and vibration in automobiles is gearboxes. Shafts, gears, and bearings are the main causes of noise and vibration in vehicle gearboxes. Various studies have reported that vibrations’ root cause is bearing excitation. Besides bearing fatal defects or extreme structure resonance amplification, gear mesh is the primary source of high-frequency vibration and noise, even in newly built units. Gear damage detection is frequently crucial in automotive gearboxes and vehicle safety. Furthermore, vibrations caused by shaft imbalances, shaft misalignments, and other factors can cause noise and vibrations in the drivetrain's transfer path. In addition, the vibration of an automobile gearbox is closely related to poor design, construction quality, and production accuracy. This paper reviewed previous research and methods on car gearboxes for conventional vehicles. It was obvious that frequency analysis and order analysis were commonly used in noise and vibration analysis on car gearboxes. Envelope analysis is usually used to analyze bearing faults. Finally, rolling-element bearing diagnostic techniques were also reviewed.

Research on pollution characteristics of volatile organic compounds based on the remanufacturing process of automobile gearbox

The automobile gearbox remanufacturing industry adheres to the concept of resource recycling, but its environmental pollution problems are easily ignored. In this study, a total of 54 VOCs were analyzed in the gearbox remanufacturing plant, the distribution of VOCs at different heights was simulated, and emission factors were calculated. Combined with working conditions and sources of pollutants, the correlation between VOCs pollution and the remanufacturing process was analyzed, and relevant suggestions were put forward from the perspective of cleaner production and environmental protection. The results showed that the concentration of VOCs at 3.0 m was higher than that at 1.5 m, and more VOCs were released during cleaning (19.6 mg/m3 at 3.0 m) and sandblasting (15.7 mg/m3 at 3.0 m) than the others. In addition, the monitoring results also reflected that the VOCs in the workshop showed a tendency to accumulate from near ground to higher places. At the same time, the more concentrated VOCs in the closed area were more likely to diffuse to the surrounding semi-open/open areas, or migrate to other areas with the gearbox remanufacturing process. Aromatic organic compounds (MACHs) and volatile halogenated hydrocarbons (VHHs) were the most widely distributed and accounted for the highest overall. Processing agents and lubricating oil were the main source of toluene and dichloromethane. Additionally, remanufacturing a gearbox emitted approximately 25.59 g of VOCs into the environment.

Vibration analysis of planetary gearbox using empirical mode decomposition and automatic fault prediction using artificial neural network

The planetary gearbox works on an epicyclic gear train consisting of sun gear meshed with planets gears and ring gear. It got advantages due to its large torque to weight ratio and reduced vibrations. It is mostly employed in analog clocks, automobile automatic gearbox, Lathe machines, and other heavy industries. Therefore, it was imperative to analyze the various faults occurring in a gearbox. Furthermore, come up with a method so that failures can be avoided at the early stage. It was also a reason why it became the field of intensive research. Moreover, the technology of neural networks emerged recently, where machine learning models are trained to detect uneven vibrations on their own. This attracted many researchers to perform the study to devise their own methods of prediction. The central concept of fault prediction by the neural network without human beings’ interference inspired this study. Most industries always wanted to know if their operation line is working fine or not. In this study, an attempt was made to apply the method of deep learning on one of the most critical gearboxes because of its components and functionality. A significant part of the study also involved filtering the vibration data obtained while testing. Comparative analysis of the variation of the peak of acceleration was performed for healthy and faulty conditions.

Quality Control Study on High Pressure Casting Based on Magma and Ultra-red Simulation

The temperature control of high-pressure die casting mold is one difficult problem in die industry. The inner quality of product is greatly affected by mold temperature. Taking the automobile gearbox parts as an example, by the combination of waterproof thermocouple and TP700-16/TP700-40 multi-channel temperature tester, one set of device for monitoring mold real-time temperature is developed. The high pressure casting die and mold temperature field simulation are carried out by using Magma software. By simulating analysis and reducing process parameter interval of Magma, the ideal mold temperature is obtained and the quality problem of die casting is solved after the simulation parameters are copied to the die casting machine. The practical results show that the instability of die casting is decreased, the time cost of die casting is decreased by increasing die casting Overall Equipment Effectiveness (OEE), the risk of personal accident is reduced and the product scrap rate can be effectively reduced. Finally, the future research directions are pointed out.

The Novel Successive Variational Mode Decomposition and Weighted Regularized Extreme Learning Machine for Fault Diagnosis of Automobile Gearbox

In order to improve the diagnosis accuracies of the current diagnosis methods, a novel fault diagnosis method of automobile gearbox based on novel successive variational mode decomposition and weighted regularized extreme learning machine is presented for fault diagnosis of gearbox in this paper. The novel successive variational mode decomposition (SVMD) is presented to improve the traditional variational mode decomposition, which finds modes one after the other, and this succession helps increase convergence rate and also not extract the unwanted modes; weighted regularized extreme learning machine (WRELM) is presented to improve the traditional extreme learning machine, which uses the weight of each sample with the nonparametric kernel density estimation and can find the optimal weight for each sample. The test results indicate that the diagnosis accuracy of SVMD-WRELM for gearbox is better than that of VMD-WRELM, VMD-ELM.

Testing and analysis of automotive gearshift performance based on gear shift analysis

AbstractWith the increasing demand for vehicle handling comfort in the market, the development of gear shifting performance test systems for automobile transmission has become one of the keys to improving the competitiveness of automobile products. In order to continuously improve the shifting quality of the automobile gearshift control, this research focuses on the study of shifting quality, uses GSA (gear shift analysis) test technology to test and verify the shifting performance of a domestic automobile gearbox, establishes the subjective and objective evaluation criteria for the shifting performance of automobile gearbox, provides a reliable theoretical basis for product optimization design and shifting performance evaluation, and formulates shifting control pertinently. Strategies and optimization measures to ensure that the power, ride, and transmission of the whole vehicle system meet the technical requirements and can shift quickly and smoothly. Not only does this allows to reduce the driver's fatigue strength, but also increases the comfort of the whole shifting process, so as to provide direction to improve the quality of the vehicle's shifting performance and effectively reduce the impact on the driver's health.

Research on fault identification of gearbox bearing based on quantum theory

In view of the problem that the fault signal of bearing in automobile gearbox is easy to be covered by background noise, a fault signal recognition method based on quantum theory is proposed. This method will quantize the individual sampling signal, and take the vibration signal as the state superposition of fault signal and background noise, deeply study the randomness and local characteristics of sampling signal, and analyze the core information of sampling point, so that the amplitude characteristics of sampling point signal can be converted into the probability distribution of fault signal and noise signal in quantum domain, and change integrated attenuation of noise reduction for the fault signal, and realize the fine quantization noise as well as the highlight of the fault information. Experimental results show that the algorithm is feasible and efficient.

Topology Optimization of Automotive Gear using Fea

Gears are the key elements used to transmit power or motion from on shaft to another and it has wide range of applications. It's one of the major application is in the automobile gear box. Generally, gears fail when the working stress exceeds than the maximum permissible value. Here the generated stresses are directly in relation to amount power produced inside an engine, as well as also on torque. This study mainly focuses to identify the magnitude of the bending stresses generated in the selected gear set of CVT of a two-wheeler during power transmission as well as also tried to find different ways for reducing weight of the gear. Hence integrating the feature of topology optimization as a part of weight reduction over an existing part is considered as the key parameter of assessment for this work. In this study, gears of CVT gearbox of two-wheeler is analyzed for static loading under the application of tangential load resulting from maximum torque in the given application. After the study of the stress distribution on existing gears, the material removal area for weight reduction is identified on selected gears then the same study for stress distribution is carried out for proposed designs of topological optimization. Hence the both existing and proposed optimized designs are analyzed under static structural analysis for same loading and results for stress distribution are compared.

Fine-tuned variational mode decomposition for fault diagnosis of rotary machinery

Variational mode decomposition is a powerful signal processing technique that can adaptively decompose a multi-component signal into a number of modes, via solving an optimization problem. The optimal performance of this method in signal decomposition and avoiding of the mode mixing problem strictly relies on the true selection of decomposition parameters, that is, the number of extracted modes (K) and the mode frequency bandwidth control parameter (α). In the literature, the optimal values of these parameters are achieved by evaluating fault-related indices like kurtosis, but such an index is inefficient in judging the decomposition of healthy (without fault-related components), low-defective, and high-noise signals. In this research, a novel method called fine-tuned variational mode decomposition is proposed to determine the optimal values of decomposition parameters K and α, by judging the adaptive indices. In this proposed method, the optimal values of these parameters are obtained by minimizing the mean bandwidth of the extracted modes. In order to achieve these optimal values, the mean correlation coefficients between the adjacent modes and the energy loss coefficient between the original signal and the reconstructed signal, should not exceed of defined thresholds for optimal values. The proposed method is applied to the simulation signal and experimental ones collected from the automobile gearbox system. Comparing this method with those in the literature exhibits its higher effectiveness in the true decomposition of signals with different natures. It is also shown that using the proposed method for signal decomposition is able to correctly classify the healthy and defective states of the gearbox system alongside the principal component analysis method and support vector machine classifier.

An Automatic Marker–Object Offset Calibration Method for Precise 3D Augmented Reality Registration in Industrial Applications

Industrial augmented reality (AR) applications demand high on the visual consistency of virtual-real registration. To present, the marker-based registration method is most popular because it is fast, robust, and convenient to obtain the registration matrix. In practice, the registration matrix should multiply an offset matrix that describes the transformation between the attaching position and the initial position of the marker relative to the object. However, the offset matrix is usually measured, calculated, and set manually, which is not accurate and convenient. This paper proposes an accurate and automatic marker–object offset matrix calibration method. First, the normal direction of the target object is obtained by searching and matching the top surface of the CAD model. Then, the spatial translation is estimated by aligning the projected and the imaged top surface. Finally, all six parameters of the offset matrix are iteratively optimized using a 3D image alignment framework. Experiments were performed on the publicity monocular rigid 3D tracking dataset and an automobile gearbox. The average translation and rotation errors of the optimized offset matrix are 2.10 mm and 1.56 degree respectively. The results validate that the proposed method is accurate and automatic, which contributes to a universal offset matrix calibration tool for marker-based industrial AR applications.

Material Selection for Optimum Design of Planetary Gear Train used in Automobile Gear Box

Planetary Gear Trains are used in different applications like power transmitting systems of machine tools and so on. Planetary gear boxes are used frequently to match the inertias, lower the motor speed, boost the Torque, and at the same time provide a study mechanical interface for pulleys, cams, drums and other mechanical components. Gears in planetary gear boxes sometimes get short life due to wear and breakage by repetitive load during operation time. Even if failure of a single tooth of a gear will make the machine to stop. Hence our aim is to strengthen the gear which is a key element of gear box. In this project I needed to suggest appropriate gear material by considering its Young's modulus, Yield Strength, Hardness, coefficient of friction. The materials utilized for gears are nodular graphite cast iron. The material properties and costing of pinion and gear material were studied, and standard gear materials were identified from PSG Design Data Book. The selection of the gear materials is based on some multi criteria decision making methods (MCDM) like TOPSIS, VIKOR and Weighted properties. The material or gear has selected by using above mentioned criteria methods. After the material selection than a planetary gear set has designed by convectional method and MAT Lab coding is generated within allowable bending and compressive strength. Then comparison is done to predict the Weight and Volume of the both gear trains. Then a motion analysis on currently used material and the selected material of gear train. The modelling and motion analysis is done by using the solid works.

Matching Quality Detection System of Synchronizer Ring and Cone

A synchronizer is a key component in automotive transmission. It is necessary to detect the matching quality between a synchronizer ring and cone. For this purpose, a friction torque based detection system of matching quality between a synchronizer ring and cone is designed in this paper. In the system, the acceptance criteria are established by the residual sum of squares (RSS), and the quality of the synchronizer is determined by measuring the friction torque and backup gap. This synchronizer ring and cone matching quality detection system has been implemented. The system is mainly used for quality detection of the synchronizer ring and cone in the automobile gearbox before packing. It improves the consistency of the synchronizer ring and synchronizer cone, which makes the synchronizer lighter and more reliable during shifting of the gearbox. According to market research, the system designed and implemented in this paper is advanced and original.

The improved elite multi-parent hybrid optimization algorithm based on bat algorithm to optimum design of automobile gearbox

Bat algorithm is a new intelligent optimization algorithm that is simple and easy to implement. But bat algorithm is easy to fall into local optimum and will appear premature convergence to lead to poor convergence precision. The elite multi-parent hybrid optimization algorithm is better than other optimization algorithms when solving complex function optimization problems. However, the algorithm consists of hybrid operation without mutation so as not to keep the diversity of population in the search process. Combining bat algorithm with elite multi-parent evolutionary optimization algorithm, the improved elite multi-parent hybrid optimization algorithm optimizing hybrid discrete variables was proposed. In this algorithm, firstly the rough optimization is carried out by bat algorithm, and then the accurate optimization is implemented by the elite multi-parent hybrid optimization algorithm. This kind of algorithm takes advantage of two algorithms and overcomes their shortcomings. The procedure as DIEMPCOA1.0 is to optimum design for three-shaft four-speed automobile gearbox with 20 design variables, 50 inequality constraints and eight equations. Optimization example shows that this algorithm has characteristics of no special requirements for the optimization design problems, better universality, reliable operation, higher calculation efficiency and stronger global convergence ability, so as to shorten the design cycle, reduce quality, reduce cost and improve quality.

A New Design of the Test Rig to Measure the Transmission Error of Automobile Gearbox

Noise and vibration affect the performance of automobile gearbox. And transmission error has been regarded as an important excitation source in gear system. Most of current research is focused on the measurement and analysis of single gear drive, and few investigations on the transmission error measurement in complete gearbox were conducted. In order to measure transmission error in a complete automobile gearbox, a kind of electrically closed test rig is developed. Based on the principle of modular design, the test rig can be used to test different types of gearbox by adding necessary modules. The test rig for front engine, rear-wheel-drive gearbox is constructed. And static and modal analysis methods are taken to verify the performance of a key component.

Validation of Temperature Effect on Lubricating oil for Automobile Gear box using ANSYS

Validation of Temperature Effect on Lubricating oil for Automobile Gear box using ANSYS

Pattern recognition based on-line vibration monitoring system for fault diagnosis of automobile gearbox

Gearbox is an important equipment in an automobile to transfer power from the engine to the wheels with various speed ratios. The maintenance of the gearbox is a top criterion as it is prone to a number of failures like tooth breakage and bearing cracks. Techniques like vibration monitoring have been implemented for the fault diagnosis of the gearbox over the years. But, the experiments are usually conducted in lab environment where the actual conditions are simulated using setup consisting of an electric motor, dynamometer, etc. This work reports the feasibility of performing vibrational monitoring in real world conditions, i.e. by running the vehicle on road and performing the analysis. The data was acquired for the various conditions of the gearbox and features were extracted from the time-domain data and a decision tree was trained for the time-domain analysis. Fast Fourier Transform was performed to obtain the frequency domain which was divided into segments of equal size and the area covered by the data in each segment was calculated for every segment to train decision trees. The classification efficiencies of the decision trees were obtained and in an attempt to improve the classification efficiencies, the time-domain and frequency-domain analysis was also performed on the normalised time-domain data. From, the results obtained, it was found that performing time-domain analysis on normalised data had a higher efficiency when compared with the other methods. Instantaneous processing of the acquired data from the accelerometer enables faster diagnosis. Hence, online condition monitoring has gained importance with the advent of powerful microprocessors. A windows application that has been developed to automate the process was found to be essential and accurate.

Diagnosis of combined faults in Rotary Machinery by Non-Naive Bayesian approach

When combined faults happen in different parts of the rotating machines, their features are profoundly dependent. Experts are completely familiar with individuals faults characteristics and enough data are available from single faults but the problem arises, when the faults combined and the separation of characteristics becomes complex. Therefore, the experts cannot declare exact information about the symptoms of combined fault and its quality. In this paper to overcome this drawback, a novel method is proposed. The core idea of the method is about declaring combined fault without using combined fault features as training data set and just individual fault features are applied in training step. For this purpose, after data acquisition and resampling the obtained vibration signals, Empirical Mode Decomposition (EMD) is utilized to decompose multi component signals to Intrinsic Mode Functions (IMFs). With the use of correlation coefficient, proper IMFs for feature extraction are selected. In feature extraction step, Shannon energy entropy of IMFs was extracted as well as statistical features. It is obvious that most of extracted features are strongly dependent. To consider this matter, Non-Naive Bayesian Classifier (NNBC) is appointed, which release the fundamental assumption of Naive Bayesian, i.e., the independence among features. To demonstrate the superiority of NNBC, other counterpart methods, include Normal Naive Bayesian classifier, Kernel Naive Bayesian classifier and Back Propagation Neural Networks were applied and the classification results are compared. An experimental vibration signals, collected from automobile gearbox, were used to verify the effectiveness of the proposed method. During the classification process, only the features, related individually to healthy state, bearing failure and gear failures, were assigned for training the classifier. But, combined fault features (combined gear and bearing failures) were examined as test data. The achieved probabilities for the test data show that the combined fault can be identified with high success rate.

Development of methodology for the disassemblability index of automobile systems using a structural approach

The time and effort required for maintenance of an automobile system are highly dependent on its disassemblability, which is one of the most important attributes of its maintainability. To evaluate the disassemblability index, i.e. to measure the ease of disassembly, the disassemblability factors (both the design factors and the contextual factors) of an automobile system are identified. These and their interrelations are modelled by considering their structure using the graph theory. The directed graph (digraph) of the disassemblability of the automobile system is defined; the nodes of this represent its disassemblability factors, while the edges represent their degrees of influence. An equivalent matrix of the digraph establishes the system’s disassemblability function which characterizes the disassemblability of the system, leading to development of the disassemblability index. A high value of the disassemblability index indicates that it is very easy to remove or replace parts. The disassemblability index ratio is used to compare the actual conditions of disassembly with the ideal conditions of disassembly. A case study of an automobile gearbox is illustrated using the step-by-step procedure of the proposed methodology of disassemblability. The proposed methodology is helpful to evaluate and compare various alternative designs of the automobile system and, therefore, can aid the design and development of automobile systems from the disassembly viewpoint.

Automobile Gearbox Fault Diagnosis Using Naive Bayes and Decision Tree Algorithm

Gearbox plays a vital role in various fields in the industries. Failure of any component in the gearbox will lead to machine downtime. Vibration monitoring is the technique used for condition based maintenance of gearbox. This paper discusses the use of machine learning techniques for automating the fault diagnosis of automobile gearbox. Our experimental study monitors the vibration signals of actual automobile gearbox with simulated fault conditions in the gear and bearing. Statistical features are extracted and classified for identifying the faults using decision tree and Naïve bayes technique. Comparison of the techniques for determining the classification accuracy is discussed.

Optimisation analysis of gear transmission system of the automobile gearbox based on the MASTA software

The dynamic meshing characteristics of gear drives have been a major concern in the design of gear transmission system. The paper takes a five-speed gearbox as an example to accomplish the gear mesh analysis based on the MASTA software. According to the transmission characteristics of gear transmission system of the automobile gearbox, the simulation modelling of the gearbox is built to determine a reasonable configuration of the overall gearbox, and then simulate the actual load conditions and complete the process analysis for the gear. The computational results show that using the specialized software MASTA for transmission modelling and simulation analysis can heavily improve the design level and mechanical properties of the gearbox. It is a useful work for the industry to employ the powerful MASTA to design gearbox from the whole system approach. In conclusion the present work can provide effective guidance for the fault diagnosis of gear meshing.