Ben H. Jansen

Slot depth resolution in vibration signature monitoring of beams using frequency shift

A major issue in defect detection by vibration monitoring is the resolution of defect size. To investigate this issue, a simple structure, namely, a beam, was used. A fixture was designed for experimental testing on beams with several classic end conditions. By allowing for the variation of moment of inertia of a slotted beam, a theoretical model was developed that fits the experimental data closely. It is shown how the relationship between slot size and frequency shift can be used to resolve the question of the minimum detectable crack size. It is concluded that slots of the size of critical cracks should be detectable with commercial frequency counters. Finally, the practicalities that might prevent cracks of such size from being detected are discussed. In the present experiment, the minimum slot depth detected was found to be 10% of the beam depth under the particular conditions of our experiment.

Application of an automated package of pattern recognition techniques to acoustic signature inspection of railroad wheels

An operational acoustic signature inspection system for railroad wheels is described. The purpose is to inspect the wheels on a train as it goes past an inspection site and analyze the data in real time. The wheels on the train are sensed by magnetic wheel sensors that alert an electromechanical hammer installed on the inside of each rail. The hammers strike the wheels on each axle, and the sound is picked up by a microphone installed in front of each hammer and transmitted to a minicomputer at the remote location via dedicated telephone lines. Studies were performed on data obtained from a test consist, specially assembled for this work, and from revenue service trains. Various features were computed in both time and frequency domains, including the spectral cross correlation, the percentage of common peaks, and a measure based on resonance frequencies, and were analyzed using a pattern recognition package. It was concluded that the use of the package resulted in significant improvement in detection of d...

Neural net‐based monitoring of steel beams

An artificial neural network (ANN) approach was used to characterize vibration data for nondestructive evaluation purposes. Acoustic signatures were obtained from clamped–clamped metal beams of rectangular cross section. The beams were either intact, or had one (small) slot in them. The digitized data were used to train the ANN to predict future samples of the measured time series given past and present samples. The trained ANNs were used in two ways. In the first method, an ANN was trained with vibration data from intact beams. Once the ANN could adequately predict the training signal, vibration signals obtained from beams with slots were presented. Significant differences between prediction errors for the intact beam and beams with slots as shallow as 0.1 in. were found. Furthermore, the resulting prediction errors gradually increased as the slots in the beams grow deeper, suggesting that this method is useful to estimate defect size. In the second method, the connection weights of the ANNs trained on v...

Vibration monitoring of steel beams by evaluation of resonance frequency decay rates

Defects in steel structures cause relatively small changes in the resonance frequencies, necessitating long observation intervals or the use of powerful shaking mechanisms. Much simpler monitoring procedures could be devised if the rate of decay of the resonance frequencies contains information about the structural integrity. The study presented here was aimed at determining which decay rates are most sensitive to small structural defects in steel beams. A technique, based on time‐dependent spectral analysis of acoustic vibrations, has been developed to measure the decay rates for modes 2 through 6. An experimental study was conducted with 36‐in. (0.9 m)‐long steel beams with a rectangular cross section of 0.75 (19 mm) by 2.0 in. (51 mm). A slot, approximating a real crack, was cut to 0.7 in. (18 mm) in increments, in the center of one beam, so as to evaluate the sensitivity of the decay rates to the crack size. It was found that the logarithm of the amplitude of the resonance frequencies decays linearly ...

Vibration monitoring of slotted beams using an analytical model

This paper reports part of a research program aimed at developing the scientific basis for vibration monitoring in rapid and automatic global inspection of structures for faults. Slotted steel beams in various conditions were used as experimental samples. A mathematical model of the slotted beam’s vibration was solved using a perturbation method. Relationships between the defect parameters and the vibration signature showed a strong dependence of the modal frequency on the slot characteristics, including location and size. The method is computationally efficient, robust and could be used for on-line signal processing. The experimental modal parameters were determined from the mobility function calculated from the excitation and response signals. The modal frequencies and their shifts were then used to quantify the slot. A test of 11 beams with various conditions demonstrated that the slot can be characterized inversely. Frequency shift contours which were numerically calculated from the analytical model w...

Response of steel beams with elastically restrained end conditions with and without cracks

The vibration response of uniform and cracked beams with elastically restrained end conditions under impact excitation is being experimentally investigated. Preliminary work has revealed that the response obtained does not match that predicted theoretically for pinned–pinned end conditions. In practice, most beams have nonclassical end conditions as a certain amount of flexibility is unavoidable. Therefore a theory of vibration of beams with elastically restrained end conditions was applied. The results of this theory were in close agreement with the response obtained from beams mounted on elastic supports. An antiresonance for the pinned–pinned beam has been found for the fifth mode of vibration whose frequency matches the natural frequency of the supports. Cracks simulated by slots are currently being made at various locations along the length of the beam. The impact response of these slotted beams will also be reported and comparisons will be made between the theoretical and the experimental results. [...

Acoustic signatures: From natural to systems science

The interest in acoustic signatures began with the problem of detecting cracks in railroad wheels. When a wheel is struck with a hammer it produces a sound like that of a bell. If the wheel is cracked it sounds dissonant and muffled. By comparing sounds from the two members of a wheelset a measure of the difference in their mechanical properties is obtained. A fully automatic system was developed and installed on a Southern Pacific track in the 1980’s. The story of this undertaking is an object lesson in systems science. Recently beams have been used as test objects in an attempt to resolve certain basic questions in the science of the acoustic monitoring method. These questions will be illustrated with results from a test fixture with various beams. The limitation of the vibration monitoring method is that other conditions, such as uncertainties in the geometry of the test object, its surface conditions, and loading can also affect the vibration response, and it is necessary to distinguish the effects du...

Decay of resonances of reinforced concrete beams with cracks

Reinforced concrete beams in practical use always contain cracks. This paper reports a study of the changes in decay rates of resonance frequencies when cracks occur in a vibrating concrete beam. The experiments were performed using model reinforced concrete beams with general end conditions as well as ideal pinned–pinned end conditions. The power spectrum and its time variation were studied. The short time Fourier transform (STFT) technique was used. Theoretical studies were made to establish the effects of damping on the vibration of the beam. A complication exists in that the reinforced concrete beam is in effect a composite material. The results will be compared with the data obtained from a steel beam of homogeneous material [J. Robin, Acoust. Soc. Am. 92, 2441 (A) (1992)]. The changed behavior of decay rates of resonance frequencies could be a feature in the vibration monitoring of reinforced concrete structures such as highway bridges. [Work supported by NSF Grant No. MSS‐9024224.]

Signal analysis techniques for acoustic monitoring

Impacting structures with a hammer and measuring the resonant frequency of the induced vibrations is a commonly used technique in acoustic monitoring. However, the transient and nonstationary characteristics of the vibrations limit the performance of classical spectral analysis. In this paper, a number of alternative techniques specifically designed to deal with the transient nature of impact‐induced vibration signals are presented. Among the techniques discussed are the short‐time Fourier transform (STFT), the discrete wavelet transform (DWT), and artificial neural networks (ANN). The results obtained with applying these techniques to the acoustic vibrations of intact and slotted beams are presented as an example. It was found with the STFT method, that a relationship exists between slot depth and decay rate. The DWT provided evidence that the decay undulates as a function of time, with a frequency that is related to the integrity of the beam. Early results with ANNs suggest that a net can be trained to ...

Artificial neural nets for acoustic nondestructive evaluation

Artificial neural networks (ANNs) are being used for the detection of cracks in metal and concrete beams and structures. Vibration signals are obtained from these structure by impacting with a small hammer and recording the activity of an accelerometer attached to the structure. These vibration signals are digitized, and the samples are input to an ANN. Detection of cracks proceeds in two different ways. For both methods, the networks are trained on data obtained from intact structures, but in the case of the first method, training is halted once the ANN can accurately predict future samples of the vibration signal from present and past observations. These ANNs are then fed with data obtained from structures with varying degrees of defects, and the prediction error is noted. In the second method, training continues after a small defect has been made to the structure, and the weights of the nets are compared. With both methods, we can detect cracks as small as 0.1 in. Examples of the results obtained for m...