Remote structural health monitoring with serially multiplexed fiber optic acoustic emission sensors

Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points along the length of a single fiber.Multiplexing provides for single channel detection of cracks and their locations in large structural systems. An algorithm was developed for signal recognition and tagging of the AE waveforms for detection of' crack locations,Labora- tory experiments on plain concrete beams and post-tensioned FRP tendons were pcrlormed to evaluate the crack detection capability of the sensor system.The acoustic emission sensor was able to detect initiation,growth and location of the cracks in concrete as well as in the FRP tendons.The AE system is potentially suitable lot applications involving health monitoring of structures following an earthquake.

Acoustic emission monitoring of rockbursts during TBM-excavated headrace tunneling at Jinping Ⅱ hydropower station

To better understand the mechanical properties of marble at Jinping II hydropower station, this paper examines the changes of brittle rocks in excavation damaged zones(EDZs) before and after excavation of tunnel with the tunnel boring machine(TBM). The paper attempts to employ the acoustic emission(AE) to study the AE characteristics and distribution of rockburst before and after TBM-excavated tunnel. It is known that the headrace tunnel #2, excavated by the drill-and-blast(D&B) method, is ahead of the headrace tunnel #3 that is excavated by TBM method. The experimental sub-tunnel #2–1, about 2000 m in depth and 13 m in diameter, between the two tunnels is scheduled. In the experimental sub-tunnel #2–1, a large number of experimental boreholes are arranged, and AE sensors are installed within 10 m apart from the wall of the headrace tunnel #3. By tracking the microseismic signals in rocks, the location, frequency, quantity, scope and intensity of the microseismic signals are basically identifed. It is observed that the AE signals mainly occur within 5 m around the rock wall, basically lasting for one day before tunnel excavation and a week after excavation. Monitoring results indicate that the rockburst signals are closely related to rock stress adjustment. The rock structure has a rapid self-adjustment capacity before and after a certain period of time during tunneling. The variations of rock stresses would last for a long time before reaching a fnal steady state. Based on this, the site-specifc support parameters for the deep tunnels can be accordingly optimized.

Acoustic Emission Monitoring and Microscopic Investigation of Cracks in ERCuNi Cladding

A corrosion resistant CuNi cladding was deposited on SM45C (equivalent to AISI1045) substrate by DC inverse arc welding. During the welding process, a three channel acoustic emission (AE) monitoring system was applied to detect the crack signals generating from both the cladding process and after cladding. Characteristics of the welding crack signal and noise signal had been analyzed systematically. Based on the record time of the signal, the solidification crack and delayed crack were distinguished. By two-dimensional AE source location, the crack position was located, and then investigated by scanning electron microscopy (SEM). Results showed that the AE system could detect the welding crack with high sensitivity and the two-dimensional source location could accurately determine the crack position. Microstructures of the cladding and heat affected zone (HAZ) were examined. Dendrites in the cladding and coarse grains in the HAZ were found.

Damage evaluation of notched aluminum alloy and weld based on acoustic emission and digital image monitoring

Acoustic emission （ AE ） features during the fracture process of notched wrought aluminum alloy 7N01 and weld were investigated under the three-point bending load. Wavelet transform is used to investigate the time-frequency features of AE signals during the test. The experimental results showed that AE energy was effective indicators to detect the crack initiation for 7N01 aluminum. The digital images from monitoring the notch tip region of 7 NO1 aluminum sample verify the prediction of AE signals. The weld emits low energy, weak signal strength, and low peak amplitude, while stronger AE energy, amplitude, and more AE event counts for the base metal. In short, the AE technique was more sensitive to the changes in the fracture mode and could be used to monitor the damage development in welded structures.

Rolling Bearing Condition Monitoring Technique Based on Cage Rotation Analysis and Acoustic Emission

In this paper,we present an alternative technique for detecting changes in the operating conditions of rolling element bearings(REBs)that can lead to premature failure.The developed technique is based on measuring the kinematics of the bearing cage.The rotational motion of the cage is driven by traction forces generated in the contacts of the rolling elements with the races.It is known that the cage angular frequency relative to shaft angular frequency depends on the bearing load,the bearing speed,and the lubrication condition since these factors determine the lubricant film thickness and the associated traction forces.Since a large percentage of REB failures are due to misalignment or lubrication problems,any evidence of these conditions should be interpreted as an incipient fault.In this paper,a novel method for the measurement of the instantaneous angular speed(IAS)of the cage is developed.The method is evaluated in a deep groove ball bearing test rig equipped with a cage IAS sensor,as well as a custom acoustic emission(AE)transducer and a piezoelectric accelerometer.The IAS of the cage is analyzed under different bearing loads and shaft speeds,showing the dependence of the cage angular speed with the calculated lubricant film thickness.Typical bearing faulty operating conditions(mixed lubrication regime,lubricant depletion,and misalignment)are recreated.It is shown that the cage IAS is dependent on the lubrication regime and is sensitive to misalignment.The AE signal is also used to evaluate the lubrication regime.Experimental results suggest that the proposed technique can be used as a condition monitoring tool in industrial environments to detect abnormal REB conditions that may lead to premature failure.

Investigation of active vibration drilling using acoustic emission and cutting size analysis

This paper describes an investigation of active bit vibration on the penetration mechanisms and bit-rock interaction for drilling with a diamond impregnated coring bit. A series of drill-off tests(DOTs) were conducted where the drilling rate-of-penetration(ROP) was measured at a series of step-wise increasing static bit thrusts or weight-on-bits(WOBs). Two active DOTs were conducted by applying 60 Hz axial vibration at the bit-rock interface using an electromagnetic vibrating table mounted underneath the drilling samples, and a passive DOT was conducted where the bit was allowed to vibrate naturally with lower amplitude due to the compliance of the drilling sample mountings. During drilling, an acoustic emission(AE) system was used to record the AE signals generated by the diamond cutter penetration and the cuttings were collected for grain size analysis. The instrumented drilling system recorded the dynamic motions of the bit-rock interface using a laser displacement sensor, a load cell, and an LVDT(linear variable differential transformer) recorded the dynamic WOB and the ROP, respectively. Calibration with the drilling system showed that rotary speed was approximately the same at any given WOB, facilitating comparison of the results at the same WOB. Analysis of the experimental results shows that the ROP of the bit at any given WOB increased with higher amplitude of axial bit-rock vibration, and the drill cuttings increased in size with a higher ROP. Spectral analysis of the AEs indicated that the higher ROP and larger cutting size were correlated with a higher AE energy and a lower AE frequency. This indicated that larger fractures were being created to generate larger cutting size. Overall, these results indicate that a greater magnitude of axial bit-rock vibration produces larger fractures and generates larger cuttings which, at the same rotary speed, results in a higher ROP.

On-Line Monitoring of Cutting Tool Fracture & Wear

A technique of detecting cutting tool fracture and ultimate wear by si- multaneously monitoring both the spindle motor current and cutting process related acoustic emission(AE)in the cutting process is reported.The technique can detect breakage of drills having diameter over 0.8mm,turning cutter crack of area over 0.2mm,and the ultimate wear.The principle,system construction,experimental method and result of the technique are discussed.The ratio of success in detection approaches 96% or higher.

Research on the Monitoring System of CNC Grinding Process Based on Acoustic Emission

Using on-line monitoring during the CNC grinding process,the hazard case such as the crushing of grinding wheel and various safety accidents could be avoided,and the optimum time for dressing and replacing grinding wheel could also be determined,and hence,the service life of the grinding wheel could be prolonged and grinding quality could be improved. To overcome the limitation of some traditional techniques in which some parameters including the grinding power and force,torque and so on were monitored,the acoustic emission (AE) technique,which provides high sensitivity and responding speed,were developed in the present paper. The mechanism of AE during grinding was reviewed. Moreover,a virtual AE monitoring system,which could monitor the grinding state under different working conditions during the grinding,has been developed based on the Virtual Instruments technique. Some experiments were also performed on the internal grinder. The results showed that the AE signals became stronger with increasing the main shaft speed and grinding depth or decreasing the distance between the AE sensor and grinding area.

New Improvement for Monitoring Small Drill Breakage with Acoustic Emission

The risk of fracture is higher in the case of small diameter drills than that with any other cutting tools Analysis of cutting forces or motor current is fundamentally unsuitable for practical applications to the detection of small drlls, owing to the low sensitivity and less parameter variation involved[1] Suitably located AE (Acoustic Emission )sensor can, however, be used to obtain signals which allow tool fracture to be monitored. The monitoring system developed in this study employs a new conducting medium and a signal processing method in a suitable frequence band,which not only solves the installation problem of AE sensor, but also largely improves the reliability of the tool fracture monitoring system.

Study on Acoustic Emission Characteristics of Deformation Damage Process of Zirconia Ceramics

Zirconia ceramics have become increasingly widely used in recent years and are favored by relevant enterprises. From the traditional dental field to aerospace, parts manufacturing has been used, but there is limited research on the deformation and damage process of zirconia ceramics. This article analyzes the acoustic emission characteristics of each stage of ceramic damage from the perspective of acoustic emission, and explores its deformation process characteristics from multiple perspectives such as time domain, frequency, and EWT modal analysis. It is concluded that zirconia ceramics exhibit higher brittleness and acoustic emission strength than alumina ceramics, and when approaching the fracture, it tends to generate lower frequency acoustic emission signals.

APPLICATION OF ACOUSTIC EMISSION SENSOR IN DETECTING TOOL BREAKAGE IN MICRO-MILLING

Acoustic emission （AE） sensors are used to monitor tool conditions in micro-milling operations. Together with the microphone, the AE sensor can detect the tool breakage more accurately and more effectively by applying the wavelet analysis. The processed tool breakage technique by AE sensor is used to perform the wavelet analysis on the experimental data. Results indicate the feasibility of using the AE signals for monitoring the tool condition in micro-milling.

EFFECT OF TOOL WEAR ON MICROSTRUCTURE, MECHANICAL PROPERTIES AND ACOUSTIC EMISSION OF FRICTION STIR WELDED 6061 Al ALLOY

Tool condition is one of the main concerns in friction stir welding （FSW）, because the geometrical condition of the tool pin including size and shape is strongly connected to the microstrueture and mechanical performance of the weld. Tool wear occurs during FSW, especially for welding metal matrix composites with large amounts of abrasive particles, and high melting point materials, which significantly expedite tool wear and deteriorate the mechanical performance of welds. Tools with different pin-wear levels are used to weld 6061 Al alloy, while acoustic emission （AE） sensing, metallographic sectioning, and tensile testing are employed to evaluate the weld quality in various tool wear conditions. Structural characterization shows that the tool wear interferes with the weld quality and accounts for the formation of voids in the nugget zone. Tensile test analysis of samples verifies that both the ultimate tensile strength and the yield strength are adversely affected by the formation of voids in the nugget due to the tool wear. The failure location during tensile test clearly depends on the state of the tool wear, which led to the analysis of the relationships between the structure of the nugget and tool wear. AE signatures recorded during welding reveal that the AE hits concentrate on the higher amplitudes with increasing tool wear. The results show that the AE sensing provides a potentially effective method for the on-line manitoring of tool wear.

Research on acoustic emission monitoring of pitting corrosion on 304 stainless steel

In order to study acoustic emission （AE） characteristics of pitting corrosion on 304 stainless steel under higher than normal temperature, pitting corrosion process of 304 stainless steel in 6° ferric chloride solution at 70° was monitored by AE technology. Conventional parameter-based approach and signal-based analysis were combined to deal with recorded AE signals, and micrographic observation was performed for further verification. The results showed that AE hit and energy increased gradually with time and reached the peak at certain time, and then declined and maintained a stable condition. Signal waveform was mainly composed of low-frequency （〈 100 kHz） flexural wave with larger amplitude and energy and high-frequency （〉 100 kHz） expansion wave with lower amplitude and energy. The research results have some certain significance for AE monitoring of pitting corrosion on 304 stainless steel under higher than normal temperature.

Multi-Sensor Intelligent System for On-Line and Real-Time Moneitoring Tool Cutting State in FMS

The principle and the constitution of an intelligent system for on-line and real-time montitoring tool cutting state were discussed and a synthetic sensors schedule combined a new type fluid acoustic emission sensor (AE) with motor current sensor was presented. The parallel communication between control system of machine tools, the monitoring intelligent system,and several decision-making systems for identifying tool cutting state was established It can auto - matically select the sensor way ,monitoring mode and identifying method in machining process- ing so as to build a successful and effective intelligent system for on -line and real-time moni- toring cutting tool states in FMS.

AN INTELLIGENT TOOL CONDITION MONITORING SYSTEM USING FUZZY NEURAL NETWORKS

Reliable on line cutting tool conditioning monitoring is an essential feature of automatic machine tool and flexible manufacturing system (FMS) and computer integrated manufacturing system (CIMS). Recently artificial neural networks (ANNs) are used for this purpose in conjunction with suitable sensory systems. The present work in Norwegian University of Science and Technology (NTNU) uses back propagation neural networks (BP) and fuzzy neural networks (FNN) to process the cutting tool state data measured with force and acoustic emission (AE) sensors, and implements a valuable on line tool condition monitoring system using the ANNs. Different ANN structures are designed and investigated to estimate the tool wear state based on the fusion of acoustic emission and force signals. Finally, four case studies are introduced for the sensing and ANN processing of the tool wear states and the failures of the tool with practical experiment examples. The results indicate that a tool wear identification system can be achieved using the sensors integration with ANNs, and that ANNs provide a very effective method of implementing sensor integration for on line monitoring of tool wear states and abnormalities.

Experimental Investigation of Transmitting Properties of Acoustic Emission Signal and Its Application

The transmitting properties of acoustic emission(AE) signal is investigated in order to provide experimental basis for application of AE technique. In the paper, the influences of the geometric shape and material properties of the mediums and the bearing type on AE transmission are analyzed for providing the theoretic foundation for selecting the transmission route of AE signal and the fixing location of the AE sensor reasonably. According to the transmitting properties of AE, an apparatus of detecting AE signal for the tool breakage monitoring system has been set up. Its application results show that it is of benefit to improving the sensitivity of the tool breakage monitoring system.

Frequency Dependence of the <i>b</i>-Value Used for Acoustic Emission Analysis of Glass Fiber Reinforced Plastics

Acoustic Emission Testing (AT) is one of the major non-destructive testing methods used for severity evaluation of structures. Amplitude distributions of AE signals are characterized by b-value and the value is mainly used for the severity evaluation of concrete structures until now. The value is assumed to be independent with propagation distance between acoustic emission sources to AE sensors. We evaluate the influence of the wide frequency band encountered in the fracture behavior of glass fiber reinforced plastic (GFRP) on the b-value analysis. In tensile tests, the b-value was determined from an acoustic emission (AE) source generated near a centered hole in a specimen of GFRP. At 15 mm from the hole, the b-value analysis indicated a decreasing trend with increasing tensile stress. At a propagation length of 45 mm, farthest from the hole, a?small number of AE signals were received. The attenuation is more rapid for high-frequency AE signals. Thus, the amplitude distribution bandwidth is wide and the b-value changes. This change in b-value for GFRPs is investigated by analyzing the spectral components of the AE signals. For a single-frequency AE source, the b-value is unchanged with propagation length. In contrast, multiple-frequency AE sources produce changes in b-value proportional to the fraction of each spectral component in the received signal. This is due to the frequency dependence of the attenuation with propagation length.?From these results, the b-value analysis cannot be applied to considering frequency dependence of AE attenuation.

Application Research on Hydraulic Coke Cutting Monitoring System Based on Optical Fiber Sensing Technology

With the development of the optical fiber sensing technology, the acoustic emission sensor has become one of the focal research topics. On the basis of studying the traditional hydraulic coke cutting monitoring system, the optical fiber acoustic emission sensor has been applied in the hydraulic coke cutting monitoring system for the first time, researching the monitoring signal of the optical fiber acoustic emission sensor in the system. The actual test results show that using the acoustic emission sensor in the hydraulic coke cutting monitoring system can get the real-time and accurate hydraulic coke cutting state and the effective realization of hydraulic coke cutting automatic monitoring in the Wuhan Branch of Sinopec.

Health condition monitoring with multiple physical signals in tensile test for double-material friction welding

The manifold physical signals including micro resistance,infrared thermal signal and acoustic emission signal in the tensile test for double-material friction welding normative samples were monitored and collected dynamically by TH2512 micro resistance measuring apparatus,flir infrared thermal camera and acoustic emission equipment which possesses 18 bit PCI-2 data acquisition board.Applied acoustic emission and thermal infrared NDT(non-destructive testing) means were used to verify the feasibility of using resistance method and to monitor dynamic damage of the samples.The research of the dynamic monitoring system was carried out with multi-information fusion including resistance,infrared and acoustic emission.The results show that the resistance signal,infrared signal and acoustic emission signal collected synchronously in the injury process of samples have a good mapping.Electrical,thermal and acoustic signals can more accurately capture initiation and development of micro-defects in the sample.Using dynamic micro-resistance method to monitor damage is possible.The method of multi-information fusion monitoring damage possesses higher reliability,which makes the establishing of health condition diagnosing and early warning platform with multiple physical information monitoring possible.

Predicting the coefficient of friction in a sliding contact by applying machine learning to acoustic emission data

It is increasingly important to monitor sliding interfaces within machines,since this is where both energy is lost,and failures occur.Acoustic emission(AE)techniques offer a way to monitor contacts remotely without requiring transparent or electrically conductive materials.However,acoustic data from sliding contacts is notoriously complex and difficult to interpret.Herein,we simultaneously measure coefficient of friction(with a conventional force transducer)and acoustic emission(with a piezoelectric sensor and high acquisition rate digitizer)produced by a steel‒steel rubbing contact.Acquired data is then used to train machine learning(ML)algorithms(e.g.,Gaussian process regression(GPR)and support vector machine(SVM))to correlated acoustic emission with friction.ML training requires the dense AE data to first be reduced in size and a range of processing techniques are assessed for this(e.g.,down-sampling,averaging,fast Fourier transforms(FFTs),histograms).Next,fresh,unseen AE data is given to the trained model and the resulting friction predictions are compared with the directly measured friction.There is excellent agreement between the measured and predicted friction when the GPR model is used on AE histogram data,with root mean square(RMS)errors as low as 0.03 and Pearson correlation coefficients reaching 0.8.Moreover,predictions remain accurate despite changes in test conditions such as normal load,reciprocating frequency,and stroke length.This paves the way for remote,acoustic measurements of friction in inaccessible locations within machinery to increase mechanical efficiency and avoid costly failure/needless maintenance.