Development of A Low-Cost FPGA-Based Measurement System for Real-Time Processing of Acoustic Emission Data: Proof of Concept Using Control of Pulsed Laser Ablation in Liquids

Today, the demand for continuous monitoring of valuable or safety critical equipment is increasing in many industrial applications due to safety and economical requirements. Therefore, reliable in-situ measurement techniques are required for instance in Structural Health Monitoring (SHM) as well as process monitoring and control. Here, current challenges are related to the processing of sensor data with a high data rate and low latency. In particular, measurement and analyses of Acoustic Emission (AE) are widely used for passive, in-situ inspection. Advantages of AE are related to its sensitivity to different micro-mechanical mechanisms on the material level. However, online processing of AE waveforms is computationally demanding. The related equipment is typically bulky, expensive, and not well suited for permanent installation. The contribution of this paper is the development of a Field Programmable Gate Array (FPGA)-based measurement system using ZedBoard devlopment kit with Zynq-7000 system on chip for embedded implementation of suitable online processing algorithms. This platform comprises a dual-core Advanced Reduced Instruction Set Computer Machine (ARM) architecture running a Linux operating system and FPGA fabric. A FPGA-based hardware implementation of the discrete wavelet transform is realized to accelerate processing the AE measurements. Key features of the system are low cost, small form factor, and low energy consumption, which makes it suitable to serve as field-deployed measurement and control device. For verification of the functionality, a novel automatically realized adjustment of the working distance during pulsed laser ablation in liquids is established as an example. A sample rate of 5 MHz is achieved at 16 bit resolution.

[1]  Siegfried Fouvry,et al.  Identification of fretting fatigue crack propagation mechanisms using acoustic emission , 2010 .

[2]  D. Leea,et al.  Precision manufacturing process monitoring with acoustic emission , 2005 .

[3]  David P. Thambiratnam,et al.  Effective Discrimination of Acoustic Emission Source Signals for Structural Health Monitoring , 2012 .

[4]  David Mba,et al.  Development of Acoustic Emission Technology for Condition Monitoring andDiagnosis of Rotating Machines; Bearings, Pumps, Gearboxes, Engines and RotatingStructures. , 2006 .

[5]  René Streubel,et al.  Size control of laser-fabricated surfactant-free gold nanoparticles with highly diluted electrolytes and their subsequent bioconjugation. , 2013, Physical chemistry chemical physics : PCCP.

[6]  G. Possa,et al.  Introduction to acoustic emission , 1983 .

[7]  Álisson Rocha Machado,et al.  A new approach for detection of wear mechanisms and determination of tool life in turning using acoustic emission , 2015 .

[8]  K. Worden,et al.  The application of machine learning to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[9]  Julie Varley,et al.  The uses of passive measurement of acoustic emissions from chemical engineering processes , 2001 .

[10]  Janez Grum,et al.  Acoustic Emission Detection of Macro-Cracks on Engraving Tool Steel Inserts during the Injection Molding Cycle Using PZT Sensors , 2013, Sensors.

[11]  David He,et al.  Low speed bearing fault diagnosis using acoustic emission sensors , 2016 .

[12]  Wendy Flores-Fuentes,et al.  Optoelectronic instrumentation enhancement using data mining feedback for a 3D measurement system , 2016 .

[13]  Mathieu Feuilloy,et al.  Acoustic emission pattern recognition approach based on Hilbert–Huang transform for structural health monitoring in polymer-composite materials , 2013 .

[14]  A. Raghavan,et al.  Guided-wave signal processing using chirplet matching pursuits and mode correlation for structural health monitoring , 2007 .

[15]  Wendy Flores-Fuentes,et al.  Optical cyber-physical system embedded on an FPGA for 3D measurement in structural health monitoring tasks , 2018, Microprocess. Microsystems.

[16]  Mohammed Bahoura,et al.  FPGA-Implementation of Discrete Wavelet Transform with Application to Signal Denoising , 2012, Circuits Syst. Signal Process..

[17]  Craig A. Rogers,et al.  Coupled Electro-Mechanical Analysis of Adaptive Material Systems — Determination of the Actuator Power Consumption and System Energy Transfer , 1994 .

[18]  Keith Worden,et al.  An introduction to structural health monitoring , 2007, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[19]  E. Pomponi,et al.  Wavelet based approach to signal activity detection and phase picking: Application to acoustic emission , 2015, Signal Process..

[20]  J. H. Thomas,et al.  Damage characterization of polymer-based composite materials: Multivariable analysis and wavelet transform for clustering acoustic emission data , 2008 .

[21]  Dirk Söffker,et al.  Wear detection by means of wavelet-based acoustic emission analysis , 2015 .

[22]  Yongfeng Lu,et al.  Laser ablation of solid substrates in a water-confined environment , 2001 .

[23]  Wendy Flores-Fuentes,et al.  Experimental image and range scanner datasets fusion in SHM for displacement detection , 2017 .

[24]  Wendy Flores-Fuentes,et al.  Combined application of Power Spectrum Centroid and Support Vector Machines for measurement improvement in Optical Scanning Systems , 2014, Signal Process..

[25]  Dirk Söffker,et al.  Implementation of Frequency-Based Classification of Damages in Composites Using Real-Time FPGA-Based Hardware Framework , 2017 .

[26]  Bilal Gökce,et al.  Fluence Threshold Behaviour on Ablation and Bubble Formation in Pulsed Laser Ablation in Liquids. , 2017, Chemphyschem : a European journal of chemical physics and physical chemistry.

[27]  Mohammed Bahoura,et al.  Real-time implementation of discrete wavelet transform on FPGA , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[28]  S. Barcikowski,et al.  Laser Synthesis and Processing of Colloids: Fundamentals and Applications. , 2017, Chemical reviews.

[29]  Alan Hase,et al.  The relationship between acoustic emission signals and cutting phenomena in turning process , 2014 .