Papers by Keyword: Gearbox

Abstract: The radiated noise reduction of vehicular power transmission systems is one of the most actively researched areas. Noise not only impacts the comfort and safety of the driver and passengers but also regulated by the legislators. The simulation-based prediction of radiated noise of gear-drives is a rapidly evolving area and combines gear meshing models, finite element analysis, multibody dynamics and airborne noise simulation tools. The interfacing of these tools makes virtual noise prediction challenging. In this research, we conducted a literature review on vibroacoustic simulations, with a particular focus on reducing noise in power transmission systems. Based on the reviewed articles, it became evident that, although numerous measurement data are available, the usability of the data is limited. Most research focuses on individual stages of the structure and on smaller-sized powertrains. The measurement methods contain abundant valuable information; however, the literature lack of comprehensive articles that track the simulation process from the inception of excitation to body and air noises. Moreover, the majority of articles investigate the relationship between transmission error and NVH, considering it as a primary source of noise. New methodological approaches, such as the application of FEM meshes on gears, open new horizons in this domain. Throughout the literature review, we compiled potential noise-reduction solutions and highlighted directions for future methodology development research.

Abstract: The article presents the design and discusses the operating principle of a planetary roller gear with a satellite performing spherical motion. A comparative analysis of gears of eccentric and spherical types with the same kinematic and comparable geometric parameters was carried out. The comparison criterion is kinetic energy. Dependencies were obtained to determine the kinetic energy for both types of gears on the gear ratio, angular velocity of the driving link and geometric parameters. It has been established theoretically and confirmed by the results of computer modeling that the use of a spherical gear instead of a flat one makes it possible to reduce the radial dimensions of the drive, reduce energy consumption (starting torques) by 3...5 times and dynamic pressures in the satellite supports by 1,8 times.

Abstract: FIA regulations for the 2015 Formula One World Championship introduced an upper limit to the number of transmission assemblies employed during the season; a new approach to reliability has been forced on the designers, along with a reconsideration of the calculation procedures. Whereas mechanical transmission reliability calculations are well coded within the commercial transportations field, the peculiar aspects of the motorsport branch - namely a) the quest for an extreme lightweight design, b) the harsh dynamic transitions in speed and torque at gear shifts with a seamless shift transmission and wheel-road chattering, c) the circumscribed consequences of a breakage due to the controlled nature of the racing track environment, and d) the frenzied design procedures pace - urged for the development of specific validation procedures, that have to be rapidly redefined with the 2015 regulation adjustment. The present contribution rethinks those reliability assessment procedures - mostly based on nonlinear, dynamic Finite Element (FE) calculations - for a Formula One gearbox. In particular, the required model complexity is discussed with respect to the inclusion of shafts, bearings and carter compliance, chassis load induced deformation, significant load case selection, misuse robustness. The finalized validation procedure is shown to be predictive with respect to the augmented reliability requirements, while remaining feasible within the motorsport timescale environment.

Abstract: Gearbox is the only medium which balances the power and torque relations for the appropriate operating conditions, at very high speeds it controls the power output of the drive unit. Its application is wide in the field of automotive and industries. Condition monitoring of gearbox access the operating condition of the gearbox components such as gears and, bearings to take necessary condition based maintenance to avoid the machine downtime and operation losses. This paper identifies the suitable accelerometer position to acquire vibration signals for identification of gear faults using machine learning techniques. The study includes 2 fault class, 2 gear speeds (1^st and 4^th gear), 3 loading conditions and, 3 operating speeds each for 2 sensor locations. Features were collected for each class in both sensor location points from accelerometer. Statistical features were extracted and the classification efficiencies were calculated from both SVM and J48 Decision tree algorithm.

Abstract: 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.

Abstract: Bearings are one of the most important components in rotating machineries. Their failures will lead to great production loss and increase the maintenance cost. So, condition monitoring work of bearings can save and avoid the potential loss caused by bearing fault. Lucy-Richardson deconvolution (LRD) algorithm, as an image processing technique, started to be used in bearing fault diagnosis. However, only data of bearings working in electric motor are used to validate the method. In engineering cases, most bearings are working in gearbox. Therefore, the bearing fault signals are very weak compared to the gear vibration signal. It is usually difficult to detect the bearing fault in this case. LRD algorithm is used to enhance the bearing fault diagnosis and some characteristics in this case are discussed. Experiment data analysis demonstrates that LRD can enhance the periodic impulse signal effectively. Otherwise, if the desired fault signal is weak compared to non-desired signal, then, the desired fault signal will be continued weaken by LRD which is not benefit to bearing’s incipient fault detection.

Abstract: Envelope analysis is a popular incipient fault identification tool for rolling element bearings (REBs) and gears. However, in some harsh conditions where more than one fault of REBs and gears exists simultaneously in a gearbox. In general, only the characteristic frequencies of the stronger vibration can be exposed clearly, and the others may be missed by conventional envelope analysis. To address this issue, an incipient faults detection scheme combining the kurtogram and independent component analysis (ICA) for gearbox faults diagnosis is proposed in this paper. In the proposed scheme, multi-channel vibrations are acquired from the gearbox synchronously at first. Subsequently, the vibration envelopes from each channel are extracted by the novel fast kurtogram algorithm. Then, the independent component analysis algorithm is utilized to separate the envelopes. As a result, the independent envelope components corresponding to different sources are obtained. Finally, the characteristic frequencies of the incipient faults of rolling element bearings and/or gears in a gearbox can be clearly exposed in envelope spectral plots. An experiment on a gearbox test rig which has both a REB fault and a gear fault is conducted to compare the conventional envelope analysis scheme and the proposed scheme. Test results show that the proposed scheme is more effective to identify the incipient faults of REBs and gears simultaneously existing in a gearbox.

Abstract: This research mainly according to automotive modular production characteristic, proposed for tracing method module production in batches; parts relation and order information system construction of internal and external traceability system model; using the mass matrix and the batch list theory, research on the above two kinds of design theory of quality traceability scheme based on distributed management system, and establish the integrated application model and parts material network technology suppliers; tracing back combined back and realize the goal of batches of the product quality control in advance and the defects of the products. Through the research on the theory of reference to the use of the Internet of things technology, technology focus of the study lies in the application of IDEF modeling method, the integrated use of Excel's powerful data processing tools and Pareto Diagram, combined with the RFID tag technology, and achieve the goal of defective product traceability and batch tracking combined. The key technology for the RFID radio frequency technology, the IDEF method and the two dimensional code identification etc.

Abstract: Through the mechanism of the gearbox’s vibration signal and establish the corresponding mathematical model, then establish a fault diagnosis method based on the wavelet theory and Hilbert demodulation spectrum. First, the wavelet threshold de-noising can be used to reducing noise of the gearbox’s vibration signal. Then, use the wavelet packet decomposition to decomposing the de-noising signal into different frequency band. After that, use the Hilbert transform to demodulate the frequency band that focused power. Finally, extract the fault characteristic value for the fault diagnosis. Through a fault simulation vibration signal test the method, the results show that the method can effectively extract the fault information of the wind turbine gearbox.

Abstract: Local damage detection in bearings/gearboxes is one of the most intensively explored problems in condition monitoring literature. Also for mechanical systems used in mining industry this issue might be critical due to short time local overloading of surfaces in contact in gear-pair or bearings that often happens during operation. In general, the problem of local damage detection is well defined in literature, however, specific factors related to the mining industry, require adaptation of existing methods or even developing new approaches. In the paper, some of the most promising techniques with mining machinery context are briefly re-called. The key problems identified for mining machines are: operation under time-varying load/speed conditions, presence of time varying signal to noise ratio and non-Gaussian noise (impulses that appear incidentally, randomly, not with expected cycle or cyclically, however with different cycle related to another damage). All these situations motivated us to find novel solution. The paper might be considered as brief review of recent achievements in the field rather that comprehensive, holistic description of the problem.