Showing papers in "Mechanical Systems and Signal Processing in 2011"

TL;DR: This tutorial is intended to guide the reader in the diagnostic analysis of acceleration signals from rolling element bearings, in particular in the presence of strong masking signals from other machine components such as gears.

TL;DR: Business issues that need to be considered when selecting an appropriate modelling approach for trial are discussed and classification tables and process flow diagrams are presented to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment.

TL;DR: A basic introduction to the most important procedures of computational model updating is provided, including tutorial examples to reinforce the reader’s understanding and a large scale model updating example of a helicopter airframe.

TL;DR: In this article, a tutorial on Hilbert transform applications to mechanical vibration is presented, with a large number of examples devoted to illustrating key concepts on actual mechanical signals and demonstrating how the Hilbert transform can be taken advantage of in machine diagnostics, identification of mechanical systems and decomposition of signal components.

TL;DR: In this paper, the authors proposed a method based on the kurtosis of the envelope spectrum amplitudes of the demodulated signal, rather than on the filter time signal, to detect transients with smaller signal-to-noise ratio comparing to the spectral kurtogram.

TL;DR: In this article, the authors investigated failure in Carbon Fibre Reinforced Plastics CFRP using Acoustic Emission (AE) signals collected and post-processed for various test configurations: tension, Compact Tension (CT), Compact Compression (CC), Double Cantilever Beam (DCB), and four-point bend End Notched Flexure (4-ENF).

TL;DR: Wang et al. as mentioned in this paper proposed an improved kurtogram method adopting wavelet packet transform (WPT) as the filter of kurtograms to overcome the shortcomings of the original Kurtogram, which can filter out noise and precisely match the fault characteristics of noisy signals.

TL;DR: In this paper, the authors present some preliminary results for the test/analysis/correlation of data measured using the 3D digital image correlation (DIC) approach along with traditional accelerometers and a scanning laser vibrometer for comparison to a finite element model.

TL;DR: In this article, a comparison of three different model-based approaches for wind turbine fault detection in online SCADA data, by applying developed models to five real measured faults and anomalies, is presented.

TL;DR: Reading this tutorial is expected to get a flavour of what moving-load problems are about, what general methods are available and what research has been done from studying this tutorial.

TL;DR: A comprehensive overview of various radio channel access protocols and resource management approaches are provided, and their suitability for infotainment and safety service support in VANETs is discussed.

TL;DR: In this article, wavelet transform tools are considered as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored.

TL;DR: The current paper is intended as a tutorial overview of the basic theory of some of the most common methods of natural computing as they are applied in the context of mechanical systems research.

TL;DR: In this paper, two approaches are proposed to enhance the entry event while keeping the impulse response in order to enable a clear separation of the two events, and produce an averaged estimate of the size of the fault.

TL;DR: The experimental results indicate potential applications of LPP-based FE and Gaussian mixture model (GMM)-based negative log likelihood probability (NLLP) as effective tools for bearing performance degradation assessment.

TL;DR: In this paper, a continuous monitoring system installed on the Dowling Hall Footbridge on the campus of Tufts University was used for structural health monitoring, and several nonlinear models were proposed to represent the relationship between the identified natural frequencies and measured temperatures.

TL;DR: In this article, the authors present a literature review pertaining to joint characteristics, types of joint models, and briefly examines models used in the simulation of assembled structures, including node-to-node contact using a Jenkins friction model, thin layer elements, and zero thickness elements.

TL;DR: In this paper, the authors introduce the topic of operational modal analysis to non-specialists on the subject and present three of the most powerful algorithms for output-only modal identification.

TL;DR: A broad outlook on rotor fault monitoring techniques for the researchers and engineers can be found in this paper, where the authors review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection.

TL;DR: In this article, a merit index is introduced that allows the automatic selection of the intrinsic mode functions that should be used for the calculation of the Hilbert-Huang spectrum of a spiral bevel gearbox.

TL;DR: In this article, a signal analysis geared to periodic signals was introduced, with the potential of extracting more complex phenomena typical of some rotating machinery, such as periodic oscillating transients, with various additive interferences.

TL;DR: In this article, an adaptive spectral kurtosis (SK) technique was proposed for the fault detection of rolling element bearings, which is implemented with successive attempts to rightexpand a given window along the frequency axis by merging it with its subsequent neighboring windows.

TL;DR: In this article, three on-line monitoring techniques are implemented in the tests and data fusion is accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix.

TL;DR: Based on wavelet and correlation filtering, a technique incorporating transient modeling and parameter identification is proposed for rotating machine fault feature detection in this paper, and the proposed method is also utilized in gearbox fault diagnosis and the effectiveness is verified through identifying the parameters of the transient model and the period.

TL;DR: In this paper, the Kriging predictor is chosen as the meta-model and is found to be capable of predicting the regions of input and output parameter variations with very good accuracy, which enables the use of updating parameters that are difficult to use by conventional correction of the finite element model.

TL;DR: The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it.

TL;DR: In this article, the applicability of Acoustic Emission (AE) and vibration technologies in monitoring a naturally degraded roller bearing has been investigated and the first known attempt investigating the comparative effectiveness of applying the Kurtogram to both vibration and AE data from a defective bearing.

TL;DR: In this paper, a non-linear multivariate and multiscale statistical process monitoring and signal denoising method which combines the strengths of the Kernel Principal Component Analysis (KPCA) nonlinear multi-linear monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD) is presented.

TL;DR: In this paper, the authors proposed the concept of difference spectrum of singular value, which consists of the forward differences of the singular value sequence and can describe the sudden change status of singular values of a complicated signal.

TL;DR: In this paper, the discrete hidden Markov model (HMM) is applied to detect and diagnose mechanical faults in machining processes and rotating machinery, which is tested and validated successfully using two scenarios: tool wear/fracture and bearing faults.