An algorithm to remove noise from locomotive bearing vibration signal based on self-adaptive EEMD filter

An improved ensemble empirical mode decomposition (EEMD) algorithm is described in this work, in which the sifting and ensemble number are self-adaptive. In particular, the new algorithm can effectively avoid the mode mixing problem. The algorithm has been validated with a simulation signal and locomotive bearing vibration signal. The results show that the proposed self-adaptive EEMD algorithm has a better filtering performance compared with the conventional EEMD. The filter results further show that the feature of the signal can be distinguished clearly with the proposed algorithm, which implies that the fault characteristics of the locomotive bearing can be detected successfully

Fault identification and severity assessment of rolling element bearings based on EMD and fast kurtogram

Faults in rolling element bearings often cause the breakdown of rotating machinery. Not only the fault type identification but also the fault severity assessment is important. So this paper emphasizes the fault severity assessment. The method proposed in this paper contains two steps: first, identify the fault type based on the combination of empirical mode decomposition (EMD) and fast kurtogram; Second, assess the fault severity. In the first step, the original signal is firstly decomposed into some intrinsic mode functions (IMFs) and the representative IMFs are selected based on correlation analysis, and then the reconstruction signal (RS) is generated; Secondly, the fast kurtogram method is applied to the RS, and the optimum band width and center frequency is obtained. The fault type can be identified based on the fault characteristic frequency marked in the envelope demodulation spectrum. In the second step, the energy percentage of the most fault-related IMF is chosen as an indicator of the fault severity assessment. Experimental data of rolling element bearings inner raceway fault (IRF) with three severities at four running speeds were analyzed. The results show that the IRF identification and fault severity assessment is realized. The breakthrough attempt provides the great potential in the application of condition monitoring of bearings

12th International Conference on Vibrations in Rotating Machinery

Since 1976, the Vibrations in Rotating Machinery conferences have successfully brought industry and academia together to advance state-of-the-art research in dynamics of rotating machinery. 12th International Conference on Vibrations in Rotating Machinery contains contributions presented at the 12th edition of the conference, from industrial and academic experts from different countries. The book discusses the challenges in rotor-dynamics, rub, whirl, instability and more. The topics addressed include: - Active, smart vibration control - Rotor balancing, dynamics, and smart rotors - Bearings and seals - Noise vibration and harshness - Active and passive damping - Applications: wind turbines, steam turbines, gas turbines, compressors - Joints and couplings - Challenging performance boundaries of rotating machines - High power density machines - Electrical machines for aerospace - Management of extreme events - Active machines - Electric supercharging - Blades and bladed assemblies (forced response, flutter, mistuning) - Fault detection and condition monitoring - Rub, whirl and instability - Torsional vibration Providing the latest research and useful guidance, 12th International Conference on Vibrations in Rotating Machinery aims at those from industry or academia that are involved in transport, power, process, medical engineering, manufacturing or construction

12th International Conference on Vibrations in Rotating Machinery

Since 1976, the Vibrations in Rotating Machinery conferences have successfully brought industry and academia together to advance state-of-the-art research in dynamics of rotating machinery. 12th International Conference on Vibrations in Rotating Machinery contains contributions presented at the 12th edition of the conference, from industrial and academic experts from different countries. The book discusses the challenges in rotor-dynamics, rub, whirl, instability and more. The topics addressed include: - Active, smart vibration control - Rotor balancing, dynamics, and smart rotors - Bearings and seals - Noise vibration and harshness - Active and passive damping - Applications: wind turbines, steam turbines, gas turbines, compressors - Joints and couplings - Challenging performance boundaries of rotating machines - High power density machines - Electrical machines for aerospace - Management of extreme events - Active machines - Electric supercharging - Blades and bladed assemblies (forced response, flutter, mistuning) - Fault detection and condition monitoring - Rub, whirl and instability - Torsional vibration Providing the latest research and useful guidance, 12th International Conference on Vibrations in Rotating Machinery aims at those from industry or academia that are involved in transport, power, process, medical engineering, manufacturing or construction

Improving the Fatigue Life Prediction of Automotive Components Using Simulated Strain Signal Methods

This study aims to determine a suitable approach for generating strain signal leading to fatigue damage estimation using a significant acceleration model. It was hypothesised that the simulated model could reproduce a characteristic strain signal in similar to the actual strain signal. Three strain signals, all at 120 seconds, measured at the McPherson frontal coil spring of a Proton sedan had been used as a case study. The strain signals were acquired from a data acquisition involving car movements on various types of road surfaces at different speeds. The strains were caused by accelerations of the tyre while the car was being driven on rough road surfaces. Using a mathematical expression that was developed for car movements, the measured strain signals yielded acceleration signals usually used to describe the bumpiness of road surfaces. Furthermore, the fatigue-based acceleration signals were considered as disturbances acting on the automotive suspension system. These disturbances on the car body had an effect on generating strain signals via computer-based simulation, as responses of the coil spring, in the form of strains. Based on the simulations, all the simulated strain signals showed similar patterns to the actual strain signals. The simulated results also gave low fatigue damage deviations, which were less than 7.5 % for all the strain signals, with a root-mean square error of 0.011 % and a coefficient of determination of 0.9995. Furthermore, the extractions of higher amplitude cycle based on the energy of the wavelet transform were performed. From the extraction results, it was found that the wavelet transform was able to shorten the strain signal time up to 95.3 % and that 96.1 % of lower amplitude cycles were reduced, which these cycles theoretically contribute to a minimum fatigue damage. Thus, maintenance of fatigue damage by more than 92.7 % was produced. The segments that resulted from the extraction processes had been clustered using the Fuzzy C-means. The clustering results showed that the simulated strain signals had a significant coefficient of determination to the actual strain signals, reaching 0.8904 with a root-mean square error of only 0.5 %. Based on the cyclic testing results, the fatigue lives were distributed in a range of 1:2 or 2:1 correlation with a significant coefficient of determination of 0.9056. The testing time was successfully reduced by more than 85.1 % using the edited actual strain signals. In addition, using the edited simulated strain signals reduced the testing time up to 95.1 %. Indirectly, the use of modified strain signals could reduce device operating costs. The current study results are believed to provide a new knowledge towards generating simulated strain signals. Thus, the results bring greater meaning to the field of fatigue research. This work helps engineers in automotive industries involved in collecting road surface profiles, which are the main input for vehicle structures

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017

Enhancement and optimization of a multi-command-based brain-computer interface

Brain-computer interfaces (BCI) assist disabled person to control many appliances without any physically interaction (e.g., pressing a button). SSVEP is brain activities elicited by evoked signals that are observed by visual stimuli paradigm. In this dissertation were addressed the problems which are oblige more usability of BCI-system by optimizing and enhancing the performance using particular design. Main contribution of this work is improving brain reaction response depending on focal approaches