Showing papers in "Mechanical Systems and Signal Processing in 2004"

TL;DR: The application of the wavelet transform for machine fault diagnostics has been developed for last 10 years at a very rapid rate as mentioned in this paper, and a review on all of the literature is certainly not possible.

TL;DR: In this article, a new scheme for the diagnosis of localised defects in ball bearings based on the wavelet transform and neuro-fuzzy classification was proposed. But this scheme was only applied to a single motor-driven experimental system, and the results demonstrate that the method can reliably separate different fault conditions under the presence of load variations.

TL;DR: A study is presented to compare the performance of gear fault detection using artificial neural networks (ANNs) and support vector machines (SMVs) and for most of the cases considered, the classification accuracy of SVM is better than ANN, without GA.

TL;DR: In this article, it is shown that vibration signals exhibit cyclostationarity if and only if the random speed fluctuation of the machine is periodic, stationary or cyclostatary.

TL;DR: In this article, the use of macro-fiber composites (MFC) for vibration suppression and structural health monitoring has been presented, where an MFC could be used as a sensor and actuator to find modal parameters of an inflatable structure.

TL;DR: In this paper, the performance of recurrent neural networks (RNNs) and neuro-fuzzy (NF) predictors is evaluated using two benchmark data sets and it is found that if an NF system is properly trained, it performs better than RNNs in both forecasting accuracy and training efficiency.

TL;DR: A new neural network for fault diagnosis of rotating machinery which synthesises the theory of adaptive resonance theory (ART) and the learning strategy of Kohonen neural network (KNN), is proposed, finding it more suitable than original ART for faultdiagnosis of machinery.

TL;DR: The presentation is carried out from the viewpoint of prediction theory and gives a more solid theoretical basis to a number of recommendations for setting the algorithm parameters, as well as speeding up the computation by fast convolution using FFT processing.

TL;DR: A new method for fault diagnosis using a newly developed method, support vector machine (SVM), which is very effective and requires only few training samples, which is an attractive feature for shop floor applications.

TL;DR: In this paper, two separate algorithms for estimating the running speed and the bearing key frequencies of an induction motor using vibration data were presented, and the test results proved the algorithms to be very reliable.

TL;DR: In this article, a technique based on effective independence was proposed to place triaxial accelerometers as single units in an optimal fashion in order to conserve the test resources of the X-33 vehicle.

TL;DR: In this paper, a solution in the frequency domain, which is faster and simpler to use than adapative algorithms, is presented, and the performance of this algorithm is thoroughly investigated and compared to those of the self-adaptive noise cancellation algorithm.

TL;DR: In this article, the authors studied the damping performance of k-wave absorbers made of distributed piezoelectric devices for the control of beam vibrations in modular piezo-electromechanical systems.

TL;DR: In this paper, the fault growth parameter (FGP) from the residual error signal was calculated using the proportional-hazards modelling technique and several statistical and replacement decision models were built based upon the observed condition data and ensuing failure events.

TL;DR: In this article, a multiobjective optimisation technique is introduced to extremise several objective terms simultaneously, and an improved method to guide the parameter selection is suggested to avoid an ill-conditioned numerical problem, the number of updating parameters should be kept as small as possible.

TL;DR: This paper aims to propose an approach for gear fault classification by using cumulants and the radial basis function (BRF) network, and shows that the method of classification by combining cumulant and RBF network is promising and achieved better accuracy.

TL;DR: In this article, a case study is presented where failure and diagnostic data obtained from roller bearings operating in the dryer section of a paper machine are used to predict future failure times of bearings.

TL;DR: In this article, a simple method is proposed to normalise the complex modes so that they are closest to their corresponding classical normal modes, based on these optimal complex modes, an index of damping non-proportionality is proposed.

TL;DR: In this article, the authors present work on smart sensor arrays for distributed structural health monitoring (SHM) and damage diagnosis using local vibration-based diagnostic algorithms inside a smart black box.

TL;DR: In this article, a cost-effective and reliable tool condition monitoring system (TCMS) was developed, utilising the advantages of neural networks for a typical industrial machining operation, where the operation considered is interrupted turning (facing and boring) of Aluminium alloy components for the automotive industry.

TL;DR: In this paper, a simple PC-software program for signal processing and extraction of diagnostic features was developed and tested for fault detection, localisation, and assessment at helical spur gears.

TL;DR: In this paper, a model of signals generated by an accelerometer sensor is established on which the theoretical expression for the power cepstrum is partially calculated, which makes it possible to develop an indicator which is little affected by the signal amplitude, the signal-to-noise ratio or the position of the sensor.

TL;DR: In this paper, the authors focus on slow swept-sine excitation, which is a good trade-off between magnitude of excitation level needed for large aircraft and testing time.

TL;DR: In this article, a new combined method based on wavelet transformation, fuzzy logic and neuro-networks is proposed for fault diagnosis of a triplex, where failure characteristics of the fluid- and dynamic-end can be divided into wavelet transform in different scales at the same time.

TL;DR: In this paper, a wavelet-based envelope function derived from Morlet wavelet is proposed to apply in the envelope extraction for vibration signal, which is a complex function constructed by an orthogonal function pair.

TL;DR: This study is devoted to identification of abrupt, anomalous and potentially onerous events in the time histories of static, hourly sampled strains recorded by a multi-sensor SHM system installed in a major bridge structure and operating continuously for a long time.

TL;DR: In this article, three new methods that are incorporated into the definition of the continuous wavelet transform are presented: reflected-window method, the equal-window-area method and the adaptive-wavelet-function method.

TL;DR: In this paper, the authors focus on the boring bar vibrations in alloyed steel, and focus on how these vibrations affect internal turning operations of a turning operation in the manufacturing process.

TL;DR: In this paper, the authors used nonlinear mathematical models for simulation of the dynamical behavior of transmission lines cables and compared the simulated results with experimental data obtained in an automated testing system for overhead line cables.