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Signal-processing techniques have been widely used in structural health monitoring and nondestructive evaluation. Wavelet analysis, a relatively new mathematical and signal-processing tool, for damage detection in various civil and mechanical structures. It is a time-frequency analysis that provides more detailed information about nonstationary signals which traditional Fourier analysis miss. This paper aims to provide the damage identification in an existing 100-year-old deck-type steel truss bridge using-frequency-and time-frequency-based approaches. The dynamic testing of steel bridge was carried out using accelerometers for the damaged state and after partial retrofitting under similar environmental conditions and instrumental set up. The comparison is carried out using power spectral density, short-time Fourier transform, and wavelet packet transform with respect to both the upstream and the downstream trusses of the bridge. Higher and uniform dissipation of energy at resonatingfrequency of the respective node after retrofitting showed intactness of joints. The variations of power spectral density in the first mode of the upstream and the downstream trusses clearly revealed improvements in the bridge signifying the importance of generating a signature of bridge before and after retrofitting. The status upgradations for the upstream and the downstream trusses obtained were different due to differential levels of damage in the bridge. Also, after retrofitting, the structural elemental behavior obtained was not the same as desired.

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An effective form-finding method for form-fixed spatial network structures is presented in this paper. The adaptive formfinding method is introduced along with the example of designing an ellipsoidal network dome with bar length variations being as small as possible. A typical spherical geodesic network is selected as an initial state, having bar lengths in a limit group number. Next, this network is transformed into the ellipsoidal shape as desired by applying compressions on bars according to the bar length variations caused by transformation. Afterwards, the dynamic relaxation method is employed to explicitly integrate the node positions by applying residual forces. During the form-finding process, the boundary condition of constraining nodes on the ellipsoid surface is innovatively considered as reactions on the normal direction of the surface at node positions, which are balanced with the components of the nodal forces in a reverse direction induced by compressions on bars. The node positions are also corrected according to the fixed-form condition in each explicit iteration step. In the serial results of time history, the optimal solution is found from a time history of states by properly choosing convergence criteria, and the presented form-finding procedure is proved to be applicable for form-fixed problems.

Abstract:The rolling element bearings are used broadly in many machinery applications. It is used to support the load and preserve the clearance between stationary and rotating machinery elements. Unfortunately, rolling element bearings are exceedingly prone to premature failures. Vibration signal analysis has been widely used in the faults detection of rotating machinery and can be broadly classified as being a stationary or non-stationary signal. In the case of the faulty rolling element bearing the vibration signal is not strictly phase locked to the rotational speed of the shaft and become "transient" in nature. The purpose of this paper is to briefly discuss the identification of an Inner Raceway Fault (IRF) and an Outer Raceway Fault (ORF) with the different fault severity levels. The conventional statistical analysis was only able to detect the existence of a fault but unable to discriminate between IRF and ORF. In the present work, a detection technique named as bearing damage index (BDI) has been proposed. The proposed BDI technique uses wavelet packet node energy coefficient analysis method. The well-known combination of Hilbert transform (HT) and Fast Fourier Transform (FFT) has been carried out in order to identify the IRF and ORF faults. The results show that wavelet packet node energy coefficients are not only sensitive to detect the faults in bearing but at the same time they are able to detect the severity level of the fault. The proposed bearing damage index method for fault identification may be considered as an 'index' representing the health condition of rotating machines.