Dynamics deflection analysis of nanoelectromechanical switches based on carbon nanotubes

This paper aims to develop an effective numerical simulation technique for the dynamic deflection analysis of nanotubes-based nanoswitches. The nanoswitch is simplified to a continuum structure, and some key material parameters are extracted from typical molecular dynamics (MD). An advanced local meshless formulation is applied to obtain the discretized dynamic equations for the numerical solution. The developed numerical technique is firstly validated by the static deflection analyses of nanoswitches, and then, the fundamental dynamic properties of nanoswitches are analyzed. A parametric comparison with the results in the literature and from experiments shows that the developed modelling approach is accurate, efficient and effective.

Condition-based prognosis of machine health

Modern machines are complex and often required to operate long hours to achieve production targets. The ability to detect symptoms of failure, hence, forecasting the remaining useful life of the machine is vital to prevent catastrophic failures. This is essential to reducing maintenance cost, operation downtime and safety hazard. Recent advances in condition monitoring technologies have given rise to a number of prognosis models that attempt to forecast machinery health based on either condition data or reliability data. In practice, failure condition trending data are seldom kept by industries and data that ended with a suspension are sometimes treated as failure data. This paper presents a novel approach of incorporating historical failure data and suspended condition trending data in the prognostic model. The proposed model consists of a FFNN whose training targets are asset survival probabilities estimated using a variation of Kaplan-Meier estimator and degradation-based failure PDF estimator. The output survival probabilities collectively form an estimated survival curve. The viability of the model was tested using a set of industry vibration data.

The structure of selected magnesium carbonate minerals : a near infrared and mid-infrared spectroscopic study

The approach to remove green house gases by pumping liquid CO2 several kilometres below the ground implies that many carbonate containing minerals will be formed. Among these minerals the formation of dypingite and artinite are possible; thus necessitating a study of such minerals. Two carbonate bearing minerals dypingite and artinite with a hydrotalcite related formulae have been characterised by a combination of infrared and near-infrared spectroscopy. The infrared spectra of both minerals are characterised by OH and water stretching vibrations. Both the first and second fundamental overtones of these bands are observed in the NIR spectra in the 7030 to 7235 cm-1 and 10490 to 10570 cm-1. Intense (CO3)2- symmetric and antisymmetric stretching vibrations confirm the distortion of the carbonate anion. The position of the water bending vibration indicates water is strongly hydrogen bonded to the carbonate anion in the mineral structure. Split NIR bands at around 8675 and 11100 cm-1 indicates that some replacement of magnesium ions by ferrous ions in the mineral structure has occurred.

Raman spectroscopic study of the antimonate mineral bahianite Al5Sb35+O14(OH)2

Raman spectroscopy has been used to characterise the antimonate mineral bahianite Al5Sb35+O14(OH)2 , a semi-precious gem stone. The mineral is characterised by an intense Raman band at 818 cm-1 assigned to Sb3O1413- stretching vibrations. Other lower intensity bands at 843 and 856 cm-1 are also assigned to this vibration and this concept suggests the non-equivalence of SbO units in the structure. Low intensity Raman bands at 669 and 682 cm-1 are probably assignable to the OSbO antisymmetric stretching vibrations. Raman bands at 1756, 1808 and 1929 cm-1 may be assigned to δ SbOH deformation modes, whilst Raman bands at 3462 and 3495 cm-1 are assigned to AlOH stretching vibrations. Complexity in the low wave number region is attributed to the composition of the mineral.

Comparison of high-speed videokeratoscopy and ultrasound distance sensing for measuring the longitudinal corneal apex movements

Two different methods to measure binocular longitudinal corneal apex movements were synchronously applied. High-speed videokeratoscopy at a sampling frequency of 15 Hz and a customdesigned ultrasound distance sensor at 100 Hz were used for the left and the right eye, respectively. Four healthy subjects participated in the study. Simultaneously, cardiac electric cycle (ECG) was registered for each subject at 100 Hz. Each measurement took 20 s. Subjects were asked to suppress blinking during the measurements. A rigid headrest and a bite-bar were used to minimize undesirable head movements. Time, frequency and time-frequency representations of the acquired signals were obtained to establish their temporal and spectral contents. Coherence analysis was used to estimate the correlation between the measured signals. The results showed close correlation between both corneal apex movements and the cardiopulmonary system. Unraveling these relationships could lead to better understanding of interactions between ocular biomechanics and vision. The advantages and disadvantages of the two methods in the context of measuring longitudinal movements of the corneal apex are outlined.

Raman spectroscopic study of the antimonate mineral lewisite (Ca,Fe,Na)2(Sb,Ti)2O6(O,OH)7

The mineral lewisite, (Ca,Fe,Na)2(Sb,Ti)2O6(O,OH)7 an antimony bearing mineral has been studied by Raman spectroscopy. A comparison is made with the Raman spectra of other minerals including bindheimite, stibiconite and roméite. The mineral lewisite is characterised by an intense sharp band at 517 cm-1 with a shoulder at 507 cm-1 assigned to SbO stretching modes. Raman bands of medium intensity for lewisite are observed at 300, 356 and 400 cm-1. These bands are attributed to OSbO bending vibrations. Raman bands in the OH stretching region are observed at 3200, 3328, 3471 cm-1 with a distinct shoulder at 3542 cm-1. The latter is assigned to the stretching vibration of OH units. The first three bands are attributed to water stretching vibrations. The observation of bands in the 3200 to 3500 cm-1 region suggests that water is involved in the lewisite structure. If this is the case then the formula may be better written as Ca, Fe2+, Na)2(Sb, Ti)2(O,OH)7 •xH2O.

Raman spectroscopic study of the antimonate mineral brizziite NaSbO3

Raman spectra of antimonate mineral brizziite NaSbO3 were studied and related to the structure of the mineral. Two sharp bands at 617 and 660 cm-1 are attributed to the SbO3- symmetric stretching mode. The reason for two symmetric stretching vibrations depends upon the bonding of the SbO3- units. The band at 617 cm-1 is assigned to bonding through the Sb and the 660 cm-1 to bonding through the oxygen. The low intensity band at 508 cm-1 is ascribed to the SbO antisymmetric stretching vibration. Low intensity bands were found at 503, 526 and 578 cm-1. Sharp Raman bands observed at 204, 230, 307 and 315 cm-1are assigned to OSbO bending modes. Raman spectroscopy enables a better understanding of the molecular structure of the mineral brizziite.

Best basis-based intelligent machine fault diagnosis

The wavelet packet transform decomposes a signal into a set of bases for time–frequency analysis. This decomposition creates an opportunity for implementing distributed data mining where features are extracted from different wavelet packet bases and served as feature vectors for applications. This paper presents a novel approach for integrated machine fault diagnosis based on localised wavelet packet bases of vibration signals. The best basis is firstly determined according to its classification capability. Data mining is then applied to extract features and local decisions are drawn using Bayesian inference. A final conclusion is reached using a weighted average method in data fusion. A case study on rolling element bearing diagnosis shows that this approach can greatly improve the accuracy ofdiagno sis.