Properties Of Acoustic Emission Signals Research Articles

Properties of Amplitude Distributions of Acoustic Emission Signals Generated in Pressure Vessel During Testing

In the paper, the results of investigations on the properties of acoustic emission signals generated in a tested pressure vessel are presented. The investigations were performed by repeating several times the following procedure: an increase in pressure, maintaining a given pressure level, a further increase in pressure, and then maintaining the pressure at new determined level. During the tests the acoustic emission signals were recorded by the measuring system 8AE-PD with piezoelectric sensors D9241A. The used eight-channel measuring system 8AE-PD enables the monitoring, recording and then basic and advanced analysis of signals. The results of basic analysis carried out in domain of time and the results of advanced analysis carried out in the discrimination threshold domain of the recorded acoustic emission signals are presented in the paper. In the framework of the advanced analysis, results are described by the defined by the author descriptors with acronyms ADC, ADP and ADNC. Such description is based on identifying the properties of amplitude distributions of acoustic emission signals by assigning them the level of advancement. It is shown that for signals including continoues AE or single burst AE signals descriptions of such registered signals by means of ADC, ADP and ADNC descriptors and by Upp and Urms descriptors provide identical ordering of registered acoustic emission signals. For complex signals, the description using ADC, ADP and ADNC descriptors based on the analysis of amplitude distributions of recorded signals gives the order of signals with more accurate connection with deformational processes being sources of acoustic emission signals.

A Novel Pipeline Leak Detection Technique Based on Acoustic Emission Features and Two-Sample Kolmogorov-Smirnov Test.

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis.

Acoustic emission characteristics of a single cylinder diesel generator at various loads and with a failing injector

Acoustic emission sensing techniques have been applied in recent years to dynamic machinery with varying degrees of success in diagnosing various component faults and distinguishing between operating conditions. This work explores basic properties of acoustic emission signals measured on a small single cylinder diesel engine in a laboratory setting. As reported in other works in the open literature, the measured acoustic emission on the engine is mostly continuous mode and individual burst events are generally not readily identifiable. Therefore, the AE are processed into the local (instantaneous) root mean square (rms) value of the signal which is averaged over many cycles to obtain a mean rms AE in the crank angle domain. Crank-resolved spectral representation of the AE is also given but rigorous investigation of the AE spectral qualities is left to future study. Cycle-to-cycle statistical dispersion of the AE signal is considered to highlight highly variable engine processes. Engine speed was held constant but load conditions are varied to investigate AE signal sensitivity to operating condition. Furthermore, during the course of testing the fuel injector developed a fault and acoustic emission signals were captured and several signal attributes were successful in distinguishing this altered condition. The sampling and use of instantaneous rms acoustic emission signal demonstrated promise for non-intrusive and economical change detection of engine injection, combustion and valve events.

Properties of Acoustic Emission Signals Coming from Partial Discharges Caused by Modeled Sources Immersed in Transformer Oil

Investigation results concerning acoustic emission signals coming from partial discharges generated by modeled sources are presented in the paper. These sources have been made using different bushing ends (without any extra elements and with a thread or a pike) situated directly in the oil, but without screens typical for partial discharge investigations in a bushing. Measurements have been carried out using own measuring acoustic emission system DEMA-COMP and — in a parallel way — computer-aided partial discharges measuring system TE 571 (produced by the firm Haefely Trench). Fundamental and advanced analysis of acoustic emission signals has been made. These signals were recorded in 20 measuring situations which need to multiple installation of acoustic emission sensors. Conclusions resultant from own originate advanced analysis of signals content description of properties revealed by defined acoustic emission descriptors.

Application of the calibrated acoustic emission to investigate properties of acoustic emission signals coming from partial discharge sources modelled in laminar systems

The measuring system used to investigate of partial discharge by means of the calibrated acoustic emission as well as the analysis of investigation results of partial discharges in modelled laminar systems are presented. Modelled laminar systems enable us to observe inner and surface discharge together with discharge within oil. The results of analysis determine different properties of acoustic emission signals coming from different modelled partial discharge sources in the time, discrimination threshold and time-frequency domain.

Quality assessment of electric low-power motor insulation on the ground of partial discharge investigations using the calibrated acoustic emission method

Investigation of partial discharge (PD) within the models representing winding insulation of low-power induction motors have been carried out. Apparent electric charge introduced by PD sources has been measured basing on the electric method, whereas the acoustic one has been used to determine properties of acoustic emission signals in the time, discrimination threshold and time-frequency domain. Acoustic emission descriptors (signified by acronyms ADC and ADP, constructed in discrimination threshold domain) distinguish and classify the models connected with different impregnation technologies of winding insulation.

Statistical properties of acoustic emission signals from metal cutting processes

Acoustic emission (AE) data from single point turning machining are analyzed in this paper in order to gain a greater insight of the signal statistical properties for tool condition monitoring applications. A statistical analysis of the time series data amplitude and root mean square (rms) value at various tool wear levels are performed, finding that aging features can be revealed in all cases from the observed experimental histograms. In particular, AE data amplitudes are shown to be distributed with a power-law behavior above a crossover value. An analytic model for the rms values probability density function is obtained resorting to the Jaynes’ maximum entropy principle; novel technique of constraining the modeling function under few fractional moments, instead of a greater amount of ordinary moments, leads to well-tailored functions for experimental histograms.

A statistical analysis of acoustic emission signals for tool condition monitoring (TCM)

The statistical properties of acoustic emission signals for tool condition monitoring (TCM) applications in mechanical lathe machining are analyzed in this paper. Time series data and root mean square (RMS) values at various tool wear levels are shown to exhibit features that can be put into relation with aging in both cases. In particular, the histograms of raw data show power-law distributions above a cross-over value, in which newer cutting tools exhibit more numerous larger events compared with more worn-out ones. For practical purposes, statistics based on RMS values are more feasible, and the analysis of these also reveals discriminating age-related features. The assumption that experimental RMS histograms follow a Beta (β) distribution has also been tested. The residuals of the modeling β functions indicate that the search for a more appropriate fitting function for the experimental distribution is desirable.

Some properties of acoustic emission signals measured in the vicinity of a crack during the cyclic pressure loading of a vessel

Some properties of acoustic emission signals measured in the vicinity of a crack during the cyclic pressure loading of a vessel