Emission Sensors Research Articles

Debonding detection of scarf-repaired composite laminates with laser ultrasonic Lamb waves

Abstract In this article, the Lamb wave at low frequency range is chosen to detect the debonding of scarf surface at different depths. The structure of interest is made up of two carbon fiber reinforced laminates with a scarf angle of 5° bonded together by an adhesive film. A 3D finite element model is established to calculate the propagation of Lamb waves with an artificial defect. Based on the wave front of out-of-plane displacement at different timepoint, the debonding area is located when propagating waves convert mode or reduce phase velocity. For validation, experiments are also conducted with wave-visualization technique. Lamb waves are excited by laser ultrasonic system and received by an acoustic emission sensor. The results agree with the simulation ones, which verify that the laser ultrasonic Lamb waves are effective for debond detection on the inclined plane of scarf-repaired composite laminates.

Study on a Fault Mitigation Scheme for Rub-Impact of an Aero-Engine Based on NiTi Wires.

The aim of this study was to solve the frequently occurring rotor-stator rub-impact fault in aero-engines without causing a significant reduction in efficiency. We proposed a fault mitigation scheme, using shape memory alloy (SMA) wire, whereby the tip clearance between the rotor and the stator is adjusted. In this scheme, an acoustic emission (AE) sensor is utilized to monitor the rub-impact fault. An active control actuator is designed with pre-strained two-way SMA wires, driven by an electric current via an Arduino control board, to mitigate the rub-impact fault once it occurs. In order to investigate the feasibility of the proposed scheme, a series of tests on the material properties of NiTi wires, including heating response rate, ultimate strain, free recovery rate, and restoring force, were carried out. A prototype of the actuator was designed, manufactured, and tested under various conditions. The experimental result verifies that the proposed scheme has the potential to mitigate or eliminate the rotor-stator rub-impact fault in aero-engines.

A Comparative Analysis Applied to the Partial Discharges Identification in Dry-Type Transformers by Hall and Acoustic Emission Sensors.

Dry-type insulated transformers stand out for their higher applicability in substations, high-voltage instrumentation systems, and electrical installations. In this machine, the insulation system is constituted of dielectric materials such as epoxy resin and Nomex paper. Some critical issues in the operation of this equipment, such as overload, moisture, or heat, can induce a slow degradation of the physical–chemical properties of the dielectric materials, which can culminate in the total failure of the transformer. However, before the transformer’s shutdown, it is common to detect discharge activity in the insulation system. Based on this issue, this work proposes an experimental and comparative analysis between acoustic emission and Hall-effect sensors, aiming at differentiating discharges in epoxy resin and Nomex paper, materials that constitute the insulation of the dry-type insulated transformers. Two signal processing techniques were studied: traditional frequency analysis and discrete wavelet transform. The objective is to develop signal processing techniques to differentiate each type of discharge since different discharges require different maintenance actions. The results obtained indicate that acoustic emission sensors and Hall sensors are promising in differentiating discharge in epoxy resin and Nomex paper. Furthermore, the pattern recognition tools presented by this work, which associated the wavelet levels energies and the energy of the full signals with the average band and the equivalent bandwidth, were effective to perform feature extraction of power transformer condition.

Motor-current-based electromagnetic interference de-noising method for rolling element bearing diagnosis using acoustic emission sensors

The high sensitivity of AE sensors enables engineers to detect tiny fault signals of a bearing in advance of failure. However, this process is also easily corrupted by noise, due to the sensitivity of the sensors. Among possible noise sources, electromagnetic interference (EMI) generated by variable frequency drives (VFD) is one of the most difficult noises to address because of its highly nonstationary characteristics. This disturbs the envelope spectrum, which is the conventional method of bearing diagnosis. Thus, in this paper, a method is proposed to adaptively remove EMI from the AE signal for more accurate bearing diagnosis. The proposed method eliminates EMI peaks in the enveloped frequency spectrum, using a motor current signal. To this end, the proposed method employs empirical mode decomposition (EMD) and probabilistic filtering techniques. The proposed method is verified by examining bearing testbed data and effectively eliminates the unwanted peaks of the EMI for AE data.

A Testing Apparatus for Gas-Driven Coal and Gas Outbursts and its Application

In this study, a multi-physics coal and gas outburst experimental apparatus is developed to allow investigating the gas-driven mechanism behind the disaster caused by coal and gas outbursts in tectonic regions. The apparatus can simulate the coal and gas outbursts under different initial geo-stresses, gas pressures and temperatures. By integrating acoustic emission sensors in the holes on axial platens, acoustic signals from coal samples during the whole test can be monitored. A series of testability experiments were conducted on the developed apparatus to verify its performance. The damage characteristics and outburst occurrence of raw coal under different gas pressures were experimentally studied and that provided a more complete theoretical basis for coal and gas outburst. The degree of fragmentation of coal samples and the mass proportion distributions of the outburst pulverized coal after outbursts under different gas pressures were analyzed, as a reference for exploring the evolutionary characteristics of coal and gas outburst with gas-driven action.

Damage Evolution of Steel Fibre-Reinforced High-Performance Concrete in Low-Cycle Flexural Fatigue: Numerical Modeling and Experimental Validation.

This contribution aims to analyze the deterioration behaviour of steel fibre-reinforced high-performance concrete (HPC) in both experiments as well as numerical simulations. For this purpose, flexural tensile tests are carried out on beams with different fibre contents and suitable damage indicators are established to describe and calibrate the damage behaviour numerically using a phase-field model approach. In addition to conventional measurement methods, the tests are equipped with acoustic emission sensors in order to obtain a more precise picture of crack evolution by observing acoustic events. It is shown that, in addition to classical damage indicators, such as stiffness degradation and absorbed energy, various acoustic indicators, such as the acoustic energy of individual crack events, can also provide information about the damage progress. For the efficient numerical analysis of the overall material behaviour of fibre-reinforced HPC, a phenomenological material model is developed. The data obtained in the experiments are used to calibrate and validate the numerical model for the simulation of three-point bending beam tests. To verify the efficiency of the presented numerical model, the numerical results are compared with the experimental data, e.g., load-CMOD curves and the degradation of residual stiffness.

The Investigation of Detect Position of Partial Discharge in Cast-Resin Transformer Using High-Frequency Current Transformer Sensor and Acoustic Emission Sensor

The lifetime of a cast-resin transformer mainly depends on the condition of insulation material. Partial discharge (PD) is an important reason for insulation deterioration in cast-resin transformers. Identifying the position of PD is very necessary for damage assessment while the transformer is still operating, and the transformer is covered by housing. This paper proposes the investigation of a cast-resin transformer using an AE sensor and HFCT sensor to specify the precise source of PD. In this study, four AE sensors were used to find PD sources, and the high-frequency current transducer (HFCT) technique was used to identify the PD source and the criteria level. The experiment, in the first two parts, identified the possibility of PD, which includes the position of PD. The final part of the experiment verified the position of the PD source of a cast-resin transformer and confirmed the inspection results. AE and HFCT sensors can be used to detect the location of PD sources, confirming the position of the PD source by sensor detection. In addition, the evident partial discharge picture on the insulator surface of high voltage side. The process successfully and accurately identifies and locates the PD source.

Study on preparation and simple calibration system of acoustic emission sensor based on epoxy resin

Study on preparation and simple calibration system of acoustic emission sensor based on epoxy resin

Acoustic emission-based damage analysis of steel fibre reinforced concrete in progressive cyclic uniaxial tension tests

Although direct tension tests (DTT) on steel fibre reinforced concrete (SFRC) are difficult to perform, they are of interest because of the uniform tensile stress distribution. Therefore, this paper reports on 31 DTT under monotonic or progressive cyclic loading. All test specimens are notched and continuously monitored with acoustic emission (AE) sensors and clip gauges. The aim is a better understanding of the (cyclic) uniaxial tensile behaviour of SFRC by AE-based damage monitoring. The research shows the importance of fibre distribution and orientation on the load–displacement behaviour. Micro-cracking is detected in the loading cycle prior to fracture by means of AE analysis. Failure mode analysis of the AE signals shows more than 99% mode I damage and indicates the onset of macro-cracking. Localisation of AE events validates the fibre position, the damage stages and the main crack position. Based on AE analyses, differences in damage development are identified even when the post-peak behaviour of the DTT specimens is similar. As such, the obtained experimental data set has an added value to the limited existing research of AE monitoring during DTT on SFRC.

Strict-Feedback Backstepping Digital Twin and Machine Learning Solution in AE Signals for Bearing Crack Identification

Bearings are nonlinear systems that can be used in several industrial applications. In this study, the combination of a strict-feedback backstepping digital twin and machine learning algorithm was developed for bearing crack type/size diagnosis. Acoustic emission sensors were used to collect normal and abnormal data for various crack sizes and motor speeds. The proposed method has three main steps. In the first step, the strict-feedback backstepping digital twin is designed for acoustic emission signal modeling and estimation. After that, the acoustic emission residual signal is generated. Finally, a support vector machine is recommended for crack type/size classification. The proposed digital twin is presented in two steps, (a) AE signal modeling and (b) AE signal estimation. The AE signal in normal conditions is modeled using an autoregressive technique, the Laguerre algorithm, a support vector regression technique and a Gaussian process regression procedure. To design the proposed digital twin, a strict-feedback backstepping observer, an integral term, a support vector regression and a fuzzy logic algorithm are suggested for AE signal estimation. The Ulsan Industrial Artificial Intelligence (UIAI) Lab’s bearing dataset was used to test the efficiency of the combined strict-feedback backstepping digital twin and machine learning technique for bearing crack type/size diagnosis. The average accuracies of the crack type diagnosis and crack size diagnosis of acoustic emission signals for the bearings used in the proposed algorithm were 97.13% and 96.9%, respectively.

Design Optimization of Sensitivity-Enhanced Structure for Fiber Bragg Grating Acoustic Emission Sensor Based on Additive Manufacturing.

A four-loop shaped structure of fiber Bragg grating (FBG) acoustic emission (AE) sensor based on additive manufacturing (AM) technology is proposed in the letter. The finite element analysis (FEA) method was used to model and analyze the sensor structure. We aimed at improving the sensitivity, the static load analysis, and the dynamic response analysis of the normal FBG acoustic emission sensor and the FBG AE sensor with improved structure parameters. We constructed the FBG AE sensor experimental system based on a narrowband laser demodulation method and test on real acoustic emission signals. The results demonstrated that the response sensitivity of the FBG acoustic emission sensor was 1.47 times higher than the sensitivity of the normal FBG sensor. The sensitivity coefficient of PLA-AE-FBG2 sensor was 3.057, and that of PLA-AE-FBG1 was 2.0702. Through structural design and parameter optimization, the sensitivity and stability of the FBG AE sensor are improved. The four-loop shaped sensor is more suitable for the health monitoring in fields such as aero-engine blade, micro-crack of structure, and crack growth in bonded joints. While ensuring the sensing characteristics, sensitivity, and stability of the four-loop shaped sensor have been enhanced. It is possible to apply the FBG AE sensor in some complex engineering environments.

Multifunctional ratiometric fluorescent sensing platform constructed by grafting various response groups on carbon dots with bromine active site for biosensing and bioimaging

Multifunctional sensing platform with the advantages of simplicity, convenience and efficiency is critical to the improvement of application in biosensing and bioimaging. However, the lack of easily modified scaffold materials seriously hinders its various responsive sensing development. To solve this issue, a novel kind of carbon dots (namely rCDs) with easily modified bromine substitution site was developed by one-step solvothermal treatment of citric acid, ethylenediamine and 4-bromo-1,8-naphthalic anhydride. Benefiting from the maintained structure of naphthalimide structure with bromine substitution site to contribute responsive yellow-green fluorescence emission and the stable blue fluorescence emission from citric acid and ethylenediamine, dual emissive ratiometric sensors based on rCDs could be conveniently fabricated by avoiding the combination of two components with distinct emission wavelength together. As proof, three ratiometric fluorescent probes for sensing formaldehyde (rCDs-FA), HSO3- (rCDs-HSO3-) and pH (rCDs-pH) featuring high stability, selectivity and biocompatibility were simply prepared. Moreover, the as-obtained probes were successfully applied to monitor dynamic variations of formaldehyde, HSO3- and pH changes in living cells. Taken together, the established multifunctional ratiometric fluorescent sensing platform could be simply and effectively regulated through changing the grafted response groups. This work would provide a universal method to design more biosensing or bioimaging platforms by adjusting response groups in the future.

Structural damage acoustic emission information enhancement through acoustic black hole mechanism

To obtain weak structure damage information, existing methods basically enhance the signal detected by an acoustic emission sensor. There is a risk that the sensor cannot obtain the structural damage information. In response to this problem, this study presents a novel method to enhance the acoustic emission information before it reaches the sensor based on acoustic black holes. First, the enhancement effect of acoustic black holes on acoustic emission signals was determined by simulations. Then, the enhancement effects of the geometric parameters of the acoustic black holes on acoustic emission signals were studied in detail, and the selection method of the best geometric parameters in practical applications was obtained. Finally, broken-lead experiments were performed on the plate to verify the ability and regularity of the proposed method. The results show that the presented method has good prospects for enhancing acoustic emission information.

Real-time estimation of machine cutting tool wear

ABSTRACT Status of the cutting tool has a great impact on the quality of the final product during the machining processes. However, the cutting tool condition monitoring (TCM) system mostly stays at the stage of research and development in the literature. In this research task, a combination of multiple sensors including three-axis accelerometer and acoustic emission (AE) sensor and driver current detector of the spindle, is used to collect respective data of the milling cutter during machining process of the CNC machines. The information is used indirectly to justify the tool wear level. During the machining process, two types of the cutter with different materials are considered with cutting paths classified into three categories: straight cutting path, square cutting path, and circle cutting path. We also propose a signal feature extraction method combined with an ANN for the TCM system. It can be used to perceive the difference between tool materials and wear level in three standard types of cutting path. Finally, online analysis supported by the TCM system is developed to show the identification accuracy of tool wear.

機械学習を用いたデジタル打音検査によるジベル施工状態の予測

Digital hammering inspection using acoustic emission (AE) sensor is expected to be a method to investigate for improper construction of stud dowel in concrete structures. AE sensor measure vibration waveforms using mechanical resonance of piezoelectric elements. Since there is no established method for the predicting the state of the stud dowel using the waveforms obtained from AE sensor, machine learning, such as random forests and XGBoost, is used to make predictions. The features used for prediction are the decay time and the frequency and amplitude of the waveform when it was converted to the frequency domain by the fast Fourier transform. Binary classification is used to predict whether an abnormality of stud dowel is present or not. It is possible to improve the accuracy by dividing the data according to the state of the stud dowel.

Prediction of Burr Types in Drilling of Al-7075 Using Acoustic Emission and Convolution Neural Networks

The formation of exit burrs during the drilling of ductile metals such as aluminum is critical in precision manufacturing and manufacturing automation. Because drilling burrs are difficult to remove, methods to predict various burr types and/or implement burr minimization schemes that consider the various attributes of burrs must be devised. In this study, not only drilling process conditions, including feed, cutting speed, and drill diameter, but also an artificial neural network is implemented to predict the formation of burrs during the drilling of aluminum-7075, which is widely used in the aerospace and automobile industries. Based on the drilling conditions, the main exit burr characteristics, such as burr size and type, were classified experimentally. Three different types of exit burrs (uniform burrs, uniform burrs with caps, and transient burrs) were observed from the aluminum 7075 workpieces. The classification results were further analyzed using burr control charts and an empirical equation, which enables the understanding of the overall influence of the drilling conditions over the burr types. Moreover, acoustic emission (AE) sensor monitoring scheme was utilized to sample the sensitive time-series signals during drilling burr formation. Subsequently, burr types were predicted using artificial intelligence techniques, namely machining learning and deep learning. First, backpropagation (BP) neural networks were constructed using the drilling conditions and AE signals as input vectors. For a comparative prediction, a convolution neural network (CNN) was implemented to obtain spectrogram image inputs from the sampled AE data. The proposed scheme is useful in predicting drilling burr types by employing a sensitive sensor monitoring setup and advanced artificial intelligence techniques, where both prediction results are well matched with experimental results. In addition, the CNN model shows effectiveness for a commonly practiced manufacturing process as it predicts the burr types with better accuracy than the BP network model (0.9375 over 0.8571).

Layout Optimization of Acoustic Emission Sensors and Identification of Damage Patterns During Static Loading of Concrete Hollow Slab

Layout Optimization of Acoustic Emission Sensors and Identification of Damage Patterns During Static Loading of Concrete Hollow Slab

Influence of Propagation Distance on Characteristic Parameters of Acoustic Emission Signals in Concrete Materials Based on Low‐Frequency Sensor

Acoustic emission is a nondestructive testing technology based on the propagation of transient elastic waves captured by acoustic emission sensors. The acoustic emission signal depends not only on the distance and quality of the propagation path of the transient elastic wave but also on the sensitivity and frequency bandwidth of the receiving sensor that converts the transient elastic wave into a voltage signal. The frequency range of damage signals in concrete materials is generally in the low‐frequency band. If high‐frequency sensors are used, the low sensitivity to low‐frequency signals will cause measurement errors, while the bandwidth of general commercial acoustic emission sensors is relatively narrow. Therefore, a high‐sensitivity, low‐frequency acoustic emission sensor is proposed, whose bandwidth is almost four times that of commercial sensors. Based on the customized sensor, we quantitatively analyzed the influence of propagation distance on the characteristic parameters of acoustic waves propagating in concrete. The results show that the different propagation modes of acoustic waves in concrete have different attenuation with the propagation distance, related to the position relationship between the acoustic source and the sensor and the propagation path and path quality. This result gives us a better understanding of the propagation mechanism of acoustic emission signals in concrete materials.

Real-time polymer flow state monitoring during fused filament fabrication based on acoustic emission

Fused filament fabrication (FFF) has been widely used in various manufacturing situations and has shown that it is flexible and convenient in complex customize manufacturing scenarios. It is worth noting that the flow state of polymer melt inside the nozzle of the FFF process is closely related to the quality of the printed product, however, it is an unsolved problem to obtain effective information about this critical flow state by an in-situ and nondestructive method. In this work we proposed a strategy for identifying the polymer melt flow state inside the nozzle in the FFF process. Firstly, we summarized three types of printing products of different quality which was drawn by three different flow state, specifically an acoustic emission sensor was adopted to collect the information of flow state due to its real-time and nondestructive capability, the experiment of collecting acoustic emission (AE) signals under three different flow conditions was established, then an deep learning based pattern recognize model named AETCN was adopted to identify difference flow state without complex data preprocessing, and the accuracy rate is more than 98%. Results of our works can provide a novel perspective for FFF quality monitoring and benefit to the development of an FFF advanced monitoring system.

Influence of the Backing Layer on the Non-Metallic Encapsulated Acoustic Emission Sensor for Concrete Monitoring

Influence of the Backing Layer on the Non-Metallic Encapsulated Acoustic Emission Sensor for Concrete Monitoring