Damage Detection Of Mechanical Structures Research Articles

Experimental detection by magnetoelastic sensors and computational analysis with finite elements, of the bending modes of a cantilever beam with minor damage

This work introduces for first time the use of the magnetoelastic sensors as vibration probes for damage detection in mechanical structures such as cantilever beams. The purpose is to show some of the advantages of these materials as vibration detectors, as well as the accuracy of them in detecting the natural frequencies of mechanical structures. The sensor used is a ribbon which is composed of an amorphous metallic alloy known as “Metglas 2826MB3”. Various long aluminum alloy beams of the same dimensions but with a single transverse crack at different positions and depths were tested, fixed at one end by using a hydraulic press so as to have consistent boundary conditions. The beams were excited by a single short and intense mechanical contact pulse and then left free to vibrate. The vibrations were forcing the magnetoelastic sensors to change their magnetic state dynamically and thus produce a voltage signal at a close-by external coil. The Fourier analysis reveals seven dominant peaks which lay very close (most of the error values are between 0.5–1.5 %) to the first seven bending mode peaks predicted by the finite-element-method (FEM) commercial software “ANSYS”. Thus the current work is a proof-of-principle that the magnetoelastic sensors can be used for damage detection of mechanical structures.

Auxiliary input design for stochastic subspace-based structural damage detection

This paper considers the problem of auxiliary input design for subspace-based fault detection methods. In several real applications, particularly in the damage detection of mechanical structures and vibrating systems, environment noise is the only input to the system. In some applications, white noise produces low quality output data for the subspace-based fault detection method. In those methods, a residual is calculated to detect the fault based on the output information. However, some modes of the system may not influence the outputs and the residual appropriately if the input is not exciting enough for those modes. In this paper, the method of “rotated inputs” is implemented to excite the system modes. In addition to produce a residual more sensitive to the weak modes, it is possible to detect system order changes due to the fault using the rotated inputs. Simulation results demonstrate the efficiency of injecting the auxiliary input to improve the subspace-based fault detection methodology.

Statistical pattern analysis of ultrasonic signals for fatigue damage detection in mechanical structures

This paper addresses online monitoring of fatigue damage in polycrystalline alloy structures based on statistical pattern analysis of ultrasonic sensor signals. The real-time data-driven method for fatigue damage monitoring is based on the concepts derived from statistical mechanics, symbolic dynamics and statistical pattern identification. The underlying concept is detection and identification of small changes in statistical patterns of ultrasonic data streams due to gradual evolution of anomalies (i.e., deviations from the nominal behavior) in material structures. The statistical patterns in terms of the escort distributions from statistical mechanics are derived from symbol sequences that, in turn, are generated from ultrasonic sensors installed on the structures under stress cycles. The resulting information of evolving fatigue damage would provide early warnings of forthcoming failures, possibly, due to widespread crack propagation. The damage monitoring method has been validated by laboratory experimentation in real time on a computer-controlled fatigue damage testing apparatus which is equipped with a variety of measuring instruments including an optical travelling microscope and an ultrasonic flaw detector.