Acoustic Emission Root Mean Square Research Articles

Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds

Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time–frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.

Friction and Wear Monitoring Methods for Journal Bearings of Geared Turbofans Based on Acoustic Emission Signals and Machine Learning

In this work, effective methods for monitoring friction and wear of journal bearings integrated in future UltraFan® jet engines containing a gearbox are presented. These methods are based on machine learning algorithms applied to Acoustic Emission (AE) signals. The three friction states: dry (boundary), mixed, and fluid friction of journal bearings are classified by pre-processing the AE signals with windowing and high-pass filtering, extracting separation effective features from time, frequency, and time-frequency domain using continuous wavelet transform (CWT) and a Support Vector Machine (SVM) as the classifier. Furthermore, it is shown that journal bearing friction classification is not only possible under variable rotational speed and load, but also under different oil viscosities generated by varying oil inlet temperatures. A method used to identify the location of occurring mixed friction events over the journal bearing circumference is shown in this paper. The time-based AE signal is fused with the phase shift information of an incremental encoder to achieve an AE signal based on the angle domain. The possibility of monitoring the run-in wear of journal bearings is investigated by using the extracted separation effective AE features. Validation was done by tactile roughness measurements of the surface. There is an obvious AE feature change visible with increasing run-in wear. Furthermore, these investigations show also the opportunity to determine the friction intensity. Long-term wear investigations were done by carrying out long-term wear tests under constant rotational speeds, loads, and oil inlet temperatures. Roughness and roundness measurements were done in order to calculate the wear volume for validation. The integrated AE Root Mean Square (RMS) shows a good correlation with the journal bearing wear volume.

Study on the acoustic emission effect of plasma plume in pulsed laser welding

Abstract The acoustic emission (AE) signals were detected in real-time during the process of pulsed YAG laser welding. Some characteristic analysis methods, including AE count statistic, root mean square (RMS) waveform calculation and power spectrum distribution, were used to analyze the AE signals. According to the AE signals, the characteristics of plasma plume and penetration of weld bead were studied. The results showed that the source mechanism for AE generation was the recoil force and thermal vibration generated by the plasma plume acting on molten pool. The AE signals detected in welding contained a lot of valuable information about plasma plume. AE count, RMS waveform and power spectrum of AE signals showed the energy characteristics of plasma plume in time and frequency domain. Therefore, AE signals detected in pulsed laser welding had clear correlations with plasma plume, which were valuable to evaluate the plasma plume and the penetration of weld bead.

Mechanism of bi-Gaussian surface topographies on generating acoustic emissions under a sliding friction

Abstract Researchers revealed the acoustic-emission (AE) mechanism under a sliding friction by establishing an AE root-mean-square (RMS) model where the summit contact was demonstrated as the main AE source. However, only the summit on the upper component of a bi-Gaussian stratified surface can confront a contact and become an AE source. A deterministic AE RMS model is established and applied to real and simulated surfaces to reveal the mechanism of bi-Gaussian stratified topographies on generating AE RMSs. The AE RMS is strongly dependent on component surface parameters rather than entire ones. The surface-height RMS value and the proportion of the upper component are, respectively, positively and negatively related to the AE RMS value by affecting the summit density and curvature radius.

Acoustic emission signal analysis during chip formation process in high speed machining of 7050-T7451 aluminum alloy and Inconel 718 superalloy

Research on acoustic emission (AE) signal during chip formation process in high speed machining can help to reveal the workpiece-chip material separation mechanism. The aim of this paper is to explore the relationship between the AE signal with chip formation and cutting energy consumption. Firstly, the high speed orthogonal cutting experiments of 7050-T7451 aluminum alloy and Inconel 718 are carried out with the cutting speed ranging from 50m/min to 8000m/min, during which the AE signals are detected. Then the micrographs of chip morphology under different cutting speeds are collected and observed. The results show that dimples and ductile fracture are observed on the serrated chip free surface, which are caused both by adiabatic shear and severe plastic deformation, while brittle fracture is the mechanism for fragmented chip formation. All these deformation features are demonstrated to be the sources for AE signals generation. The time and frequency domain characteristics of detected AE signals during high speed machining of the two workpiece materials are analyzed. The chip serrated frequencies for the two workpiece materials during high speed machining are found to be nearly equal to their corresponding AE dominant frequencies. It confirms that the procedure of serrated chip formation makes a significant contribution to the produced AE signal. The dependence of AE root mean square (RMS) values on specific cutting energy during high speed machining has also been revealed. The material deformation and subsequent release of strain energy prove to be dependent on the conditional ductile and brittle mechanical properties of the machined material.

Acoustic emission method for defect detection and identification in carbon steel welded joints

Detecting welding defects in industrial equipment (welded joints and built-up structures) is a key aspect in evaluating the probability of failure in different situations. Acoustic emission (AE) is an effective non-destructive detecting technique, and can be a promising application for welding defect detection. This work presents a systematic experimental investigation on using AE technique for detecting and classifying different weld defects in carbon steel joint material. Four certified carbon steel samples were used in this study. A defect free control sample was used as the reference and three samples with induced defects, namely slag, porosity and crack. A pencil lead break (PLB) test was used to generate simulated AE sources on one side of the joint whereas the AE sensor was mounted on the other side to capture AE signals. A total of four experimental arrangements were used to investigate the effect of propagating distance (sensor to source distance) on the ability of AE to detect and identify defects in welds. For each of these arrangements, AE features such as peak amplitude, rise time, decay time, duration, and count numbers along with statistical features such as AE energy, root mean square (RMS) were extracted and analysed. Also, frequency analysis using FFT and wavelet transform were investigated for each weld test specimen for all arrangements. The results show that AE energy, peak amplitude and RMS value can be used to automatically detect and identify the presence of a defect in carbon steel welds. It is concluded that AE has a considerable potential in use in welding inspection to assess the overall structural health and identify defects that can significantly reduce the strength and reliability of welded material and consequently reduce the risk of component's failure.

알루미늄 합금(Al6061-T6)의 마이크로밀링가공에서 버 발생과 신호 특성의 상관관계 분석

The formation of micro-burrs in micro-milling processes causes several problems related to productivity and surface integrity. It should be minimized and suppressed by effective monitoring of the cutting conditions. This paper presents the correlation between the micro-burr length and cutting signals in the micro-milling process of an Al alloy (Al6061-T6). The acoustic emission (AE) signals and cutting force signals are acquired during the experiments. The characteristics of the cutting signals are obtained by analyzing the AE root mean square value and resultant cutting force. In addition, the micro-burr length is measured according to the cutting conditions by analyzing a scanning electron microscopy image of the machined surface. The results of this study can be used to enhance the surface quality of micro parts.

Study of the tensile behavior of AISI type 316 stainless steel using acoustic emission and infrared thermography techniques

Acoustic emission (AE) and infrared thermography technique (IRT) have been used to study the tensile behavior of AISI type 316 stainless steel. Strain rates of tensile testing were varied from 1.4×10−3s−1 to 1.4×10−2s−1. AE root mean square voltage increases with increase in strain rate due to the increase in source activation. Dominant frequency of the AE signals generated during different regions of tensile deformation has also been used to compare the results for different strain rates. The dominant frequency increases from elastic region to around 590kHz during work hardening and 710kHz around ultimate tensile strength (UTS) for all the strain rates. Temperature changes during different regions of deformation are monitored using infrared thermography. The temperature rise in the work hardening region is found to approximately increase linearly with time and from the slopes of the linear regression analyses the rate of temperature rise in the work-hardening region is obtained which is found to be very sensitive to strain rates. From the experimental results an empirical equation that relates the rate of temperature increase with strain rate and thermal hardening coefficient is obtained. The correlation between the variation of AE dominant frequency and temperature rise during different deformation regions provided better insight into the tensile behavior of AISI type 316 SS for different strain rates.

Signal analysis of CMP process based on AE monitoring system

As chemical mechanical planarization (CMP) enables local and global planarization over a wafer surface by the combined effects of chemical and mechanical interactions, process monitoring is becoming an increasingly important in-situ methodology for process control. According to the materials and process, signal characteristics were distinguishable between material and process. In this study, an acoustic emission (AE) sensor was used to measure the abrasive and molecular-scale phenomena during CMP. An AE signal was acquired using rotational equipment and adapted to two types of equipment. First, a wireless AE system consisting of wireless modules using Bluetooth was used. This system was suitable for acquiring signals in rotational equipment. However, a wireless AE system could be acquired with only Root Mean Square(RMS) signals. Second, mercury slip-ring (wired) AE systems that were suitable for rotational equipment and the acquisition of raw signals were used. The acquired raw signals could be analyzed by a Fast Fourier Transform (FFT) for abrasive and molecular-level phenomena in the CMP process. The AE signal parameters including the AE RMS, frequency, and amplitude were analyzed for abrasive and molecular-level phenomena in the CMP process. The authors analyzed the AE signals for changes in the materials and CMP process.

Face Rub-Impact Monitoring of a Dry Gas Seal Using Acoustic Emission

Face rub-impact of a dry gas seal was investigated using acoustic emission (AE) technology. A force applicator was designed and mounted on the seal test rig to provide controlled misalignment and thus induce rub-impact of the seal faces. Two types of AE sensor were mounted at different positions on the seal. For the PICO AE sensor mounted directly on the seal ring, the root mean square (RMS) of the original AE signal was sensitive to face rub-impact of the seal. When force was applied, the AE RMS of the PICO sensor gave a distinct periodic waveform with a period consistent with the rotational period above an initial noise signal. The magnitude and shape of the waveform changed as the applied force increased. For the R15α AE sensor mounted on the housing of the seal, no obvious changes could be found from the RMS of the original AE signal synchronously obtained during loading. Two kinds of signal processing methods were tried to eliminate noise. After band-pass filtering, the RMS of the AE signals of the R15α sensor indicated face rub-impact when the misalignment was relatively large. The empirical mode decomposition method using masking signals was found to be more effective than band-pass filtering in eliminating the noise but took much more computational time. The results indicate that the AE technology is a potentially effective tool in monitoring and investigating face rub-impact of dry gas seals.

An Acoustic Emission Study on the Starting and Stopping Processes of a Dry Gas Seal for Pumps

The starting process and the stopping process of a double dry gas seal for pumps were investigated by acoustic emission (AE) testing. The characteristic frequency band of contact of the seal faces was distinguished from noises in three different comparative experiments. The root mean square (RMS) of the AE signals after band-pass filtering was determined to effectively reflect the contact condition of the seal faces. Based on the characteristics of the AE RMS, both the starting process and the stopping process could be divided into three periods, and a lift-off rotational speed and a touch-down rotational speed were defined. The lift-off speed varied non-monotonically with the inlet pressure. The lift-off speed was smallest at a certain pressure, which meant that at that speed the seal faces lift-off most easily. The behavior of the touch-down speed was similar to that of the lift-off speed, but was a little smaller than the latter. These results reveal the potential of AE technology for monitoring the contact condition of dry gas seals during the starting and stopping processes and may help in the design and operation of the dry gas seals.

Analysis of acoustic emission in precision and high-efficiency grinding technology

Current demands of machining hard and brittle materials at very small tolerances have predicated the need for precision and high-efficiency grinding. In situ monitoring systems based on acoustic emission (AE) provide a new way to control the surface damage and integrality of the components. However, a high degree of confidence and reliability in characterizing the manufacturing process is required for AE to be utilized as a monitoring tool. The authors established AE based online monitoring system and studied technique parameters versus the waveforms of AE under different working conditions. The results show that there are obvious mapping relations between the technique parameters of grinding and the effective values of the AE signals. Grinding along different directions would result in different strength of AE signal. Comparing with grinding along first longitude, fewer AE signal is released when grinding along latitude and better surface quality is generated. Similar variation tendency is observed no matter between AE root mean square (RMS) and linear speed or between surface roughness and linear speed which justify some kind of correlation may exist between AE RMS and surface roughness. The distance between the AE transducer and the AE source should be less than 80 mm while monitoring the process of grinding composite ceramics.

A study on monitoring the drilling of polymeric nanocomposite laminates using acoustic emission

The aim of this article is to monitor process induced defects such as delamination while drilling carbon fiber reinforced plastic nanocomposite laminates using acoustic emission. The carbon fiber reinforced plastic nanocomposite laminates were prepared and drilling experiments were carried out in the laminates using solid carbide drills of diameter 6 mm. The input parameters considered were cutting speed, feed, and wt% carbon nanofiber present in carbon fiber reinforced plastic nanocomposite laminates. A three level test for each parameter was conducted using L27 orthogonal array. The output response measured were acoustic emission characteristics such as root mean square of the signal, rise time, counts, energy, duration, peak amplitude, counts to peak, hits, average frequency, delamination, thrust force, and torque. The monitored parameters were analyzed and concluded that it is enough to monitor root mean square of the acoustic emission signal alone since all the other acoustic emission parameters are very closely related to acoustic emission root mean square. Monitoring the drilling of carbon fiber reinforced plastic nanocomposite laminates using acoustic emission is presented in this article which has not been reported so far.

Diamond turning using position and AE dual feedback control system

A control scheme to improve machining accuracy by means of an acoustic emission root mean square (AErms) signal measured on-line during diamond turning is proposed. Preliminary experiments have verified a correlation between a machining error and measured AErms. Based on the correlation, a control algorithm to apply a compensation signal to a position feedback control loop, named the position and AE dual feedback control scheme is proposed. Machining tests using a diamond turning machine controlled with a PC-based open architecture controller showed that the proposed control scheme accomplishes 20% reduction of machining error compared to conventional position feedback control. Machining tests using new and worn diamond tools showed that an on-line tool-wear recognition is possible using the AE feedback control system.

Application of acoustic emission in drilling of composite laminates

Acoustic emission (AE) technique was used to characterise drilling of composite laminates. Uni-directional glass fibre reinforced plastic (GFRP) laminates consisting of 12-layers and 16-layers (0/90) s were drilled using a twist drill and the generated AE was monitored. Results of the investigations reveal that the complexion of the acoustic emission root mean square (AE-RMS) signal response changes from the drill entry to the exit thus giving an overall understanding about the different events that take place during drilling. Also, AE-RMS signal level increases with an increase in the applied thrust and further reveals that it is possible to evaluate the drill induced damages in composites through AE signal characterisation.

Acoustic Emission Monitoring of Tool Wear in End-Milling Using Time-Domain Averaging

The characteristics of the acoustic emission signal during the tool wear process in end milling are analyzed, and a signal processing scheme for abstracting the mean time domain averaging deviation of the signal to monitor tool wear is proposed. Experiments indicate that the mean deviation value is sensitive to flank wear and its normalized value is not as dependent on milling parameters as the acoustic emission root mean square signal.