Davide Crivelli

A Preliminary Analysis about the Application of Acoustic Emission and low Frequency Vibration Methods to the Structural Health Monitoring of Railway Axles

Railway axles are safety-critical components whose failure could result in large additional costs for the infrastructure manager and the railway operator and, in most serious cases, even lead to unacceptable human losses. For this reason, they are periodically inspected by means of non-destructive techniques usually requiring expensive service interruptions. Considering the special case of solid axles for freight cars, this paper investigates the feasibility to apply two structural health monitoring (SHM) techniques to increase vehicle safety and reliability and, at the same time, to decrease the costs of damage inspection. In particular, the considered SHM techniques are Acoustic Emission and the measurement of low frequency vibrations. In the present paper, some preliminary results about the application of both these SHM techniques to freight railway axles are introduced and discussed.

Chebyshev descriptors for SHM with acoustic emission and acousto ultrasonics

Purpose – The purpose of this paper is to study the feasibility on the use of alternative parameters for representing acoustic emission (AE) and acousto-ultrasonic (AU) signals, using a wavelet-based approach and the computation of Chebyshev moments. Design/methodology/approach – Two tests were performed, one on AE artificial signals generated on a CFRP plate and one on an AU setup used for actively detecting impact damage. The waveforms were represented using a data reduction technique based on the Daubechies wavelet and an image processing technique using Chebyshev moments approximation, to get 32 descriptors for each waveform. Findings – The use of such descriptors allowed in the AE case to verify that the moments are similar when the waveforms are similar; in the AU setup the correlation coefficient of the descriptors with respect to a reference data set was found to be linked to the delimitation size. Practical implications – Such a data reduction while retaining all the useful information will be po...

Gear tooth root fatigue test monitoring with continuous acoustic emission: advanced signal processing techniques for detection of incipient failure

The phenomenon of fatigue in gears at the tooth root can be a cause of catastrophic failure if not detected in time. Where traditional low-frequency vibration may help in detecting a well-developed crack or a completely failed tooth, a system for early detection of the nucleation and initial propagation of a fatigue crack can be of great use in condition monitoring. Acoustic emission is a potentially suitable technique, as it is sensitive to the higher frequencies generated by crack propagation and is not affected by low-frequency noise. In this article, a static gear pair is tested where a crack was initiated at a tooth root. Continuous acoustic emission was periodically recorded throughout the test. Data were processed in multiple ways to support the early detection of crack initiation. Initially, traditional feature–based acoustic emission was employed. This showed qualitative results indicating fracture initiation around 8000 cycles. A rolling cross-correlation was then employed to compare two given system states, showing a sensitivity to large changes towards the final phases of crack propagation. A banded fast Fourier transform approach showed that the 110- to 120-kHz band was sensitive to the observed crack initiation at 8000 cycles, and to the later larger propagation events at 22,000 cycles. Two advanced data processing techniques were then used to further support these observations. First, a technique based on Chebyshev polynomial decomposition was used to reduce each wavestream data to a vector of 25 descriptors; these were used to track the system deviation from a baseline state and confirmed the previously observed deviations with a higher sensitivity. Further confirmation came from the analysis of wavestream entropy content, providing support from multiple data analysis techniques on the feasibility of system state tracking using continuous acoustic emission.

Rewinding the waves: tracking underwater signals to their source

Analysis of data, recorded on March 8th 2014 at the Comprehensive Nuclear-Test-Ban Treaty Organisation’s hydroacoustic stations off Cape Leeuwin Western Australia, and at Diego Garcia, reveal unique pressure signatures that could be associated with objects impacting at the sea surface, such as falling meteorites, or the missing Malaysian Aeroplane MH370. To examine the recorded signatures, we carried out experiments with spheres impacting at the surface of a water tank, where we observed almost identical pressure signature structures. While the pressure structure is unique to impacting objects, the evolution of the radiated acoustic waves carries information on the source. Employing acoustic–gravity wave theory we present an analytical inverse method to retrieve the impact time and location. The solution was validated using field observations of recent earthquakes, where we were able to calculate the eruption time and location to a satisfactory degree of accuracy. Moreover, numerical validations confirm an error below 0.02% for events at relatively large distances of over 1000 km. The method can be developed to calculate other essential properties such as impact duration and geometry. Besides impacting objects and earthquakes, the method could help in identifying the location of underwater explosions and landslides.

Feasibility of detecting orthopaedic screw overtightening using acoustic emission.

A preliminary study of acoustic emission during orthopaedic screw fixation was performed using polyurethane foam as the bone-simulating material. Three sets of screws, a dynamic hip screw, a small fragment screw and a large fragment screw, were investigated, monitoring acoustic-emission activity during the screw tightening. In some specimens, screws were deliberately overtightened in order to investigate the feasibility of detecting the stripping torque in advance. One set of data was supported by load cell measurements to directly measure the axial load through the screw. Data showed that acoustic emission can give good indications of impending screw stripping; such indications are not available to the surgeon at the current state of the art using traditional torque measuring devices, and current practice relies on the surgeon’s experience alone. The results suggest that acoustic emission may have the potential to prevent screw overtightening and bone tissue damage, eliminating one of the commonest sources of human error in such scenarios.