In this work we present a Structural Health Monitoring (SHM) system for the inspection of metallic plates using Lamb waves. This system is based on a PWAS (Piezoelectric Wafer Active Sensor) network used for mode selective excitation with in-house equipment for both signal generation and signal measurement. The measured data is processed using the topological derivative, a scalar function that measures the sensitivity of a certain functional to domain perturbations. It will serve as an indicator function to detect the presence of defects. We propose an efficient implementation of the topological derivative algorithm which benefits from the fact that the same equipment is used for excitation and data acquisition, allowing for real-time data processing. We will show a couple of numerical examples of the algorithm’s functioning as an initial proof-of-concept validation of the system.

The ITER interlock control system (ICS) assumes a crucial role in the tokamak operation to protect the investment against machine protection hazards. Consequently, it must be developed in compliance with the most challenging requirements. The National Instruments CompactRIO (NI cRIO) technology was chosen by ITER as the field-programmable gate array (FPGA)-based platform to develop the investment protection functions, with strict time constraints. This contribution focuses on the specific requirements for the ITER advanced protection system (APS) where the disruption mitigation control function requires a sequenced release of hydrogen ice pellets with an accuracy of less than 1 ms. These requirements are an evolution of previous protection system designs based on CompactRIO (cRIO) and motivated this feasibility study. The cRIO platform used at ITER is the NI9159, which provides an MXIe interface between a Virtex 5 LX110 FPGA and a host computer running a Linux preemptive kernel. ITER decided to improve two important requirements: 1) the MXIe interface communication latency's jitter by redesigning the ITER National Instruments (NI)-RIO Linux device driver and 2) the ability to timestamp events in the FPGA logic by designing a specific firmware module based on the precision time protocol (PTP) as used in the ITER time communication network (TCN). This contribution reports the design methodology that is followed, the firmware and software elements implemented, and the time-keeping performance obtained from the approach.

The ITER International Fusion Experiment Organization is implementing the real-time framework (RTF) to facilitate the development, deployment, and execution of instrumentation and control (I&C) applications optimized for real-time performance using the GNU/Linux-based ITER CODAC Core System (CCS) software distribution. This contribution examines the feasibility of using hardware acceleration techniques with field-programmable gate arrays (FPGAs) to implement real-time applications in the RTF that requires specific compute-intensive functions. By combining the use of languages such as high-level synthesis (HLS) and open computing language (OpenCL) with FPGA devices, specific hardware architectures can be implemented to solve certain computational problems to gain performance and limit latency. This work shows the methodology used to integrate HLS and OpenCL in the ITER CCS and the results obtained in terms of execution time for two common processing operations, vector addition and matrix multiplication, using a commercial off-the-shelf FPGA-based device.

The utilization of Machine Learning (ML) techniques for Structural Health Monitoring (SHM) has increased in the last years. Using ML techniques for signal reconstruction has been explored in the literature of this field. In this work, a database of Ultrasonic Guided Waves (UGW), propagated through a thermoplastic composite plate, and generated and acquired with piezoelectric transducers (PZT), has been preprocessed, organized, and analyzed to reconstruct signals by using Convolutional Autoencoders (cAE) neural networks. This special type of autoencoders (AE) is based on the utilization of convolutional stages, which are formed by deep-learning layers that are especially useful for their utilization on discretized time-series signals, due to their ability to convolve different sections of the signal, thus extracting different features which are automatically learned by the network. The cAE, which have a bottleneck section in their middle part (between the encoder and the decoder sections), have the ability to reduce the dimensionality of the input data, thus allowing the extraction of a minimum number of relevant features of the signals. Summarizing, this paper presents a neural network designed as a cAE which is able to reconstruct data from a reduced number of features, reaching a correlation value between the real and the artificial data higher than 98%.

iSUM (Integrated Smart Ultrasonic Monitoring) System is an integrated embedded ultrasonic guided-waves-based system consisting of methodologies and algorithms performed over data obtained by iSUM_ES devices to obtain damage detection, positioning, and characterization . i_SUM_ES device ruled by ISUM_SW performs tests and transmits the collected data to the controller device where it is processed . Through algorithms and AI models developed “ad-hoc” for each field of application, iSUM_APP provides clear, concise, and easily interpretable information of the current state of structures. iSUM System allows structural integrity inspection tests to be carried out by a minimally trained operator. It can also run scheduled tests autonomously and deliver alerts, reports, data raw on-demand or in the case of structural damage detection . This system aims to provide useful information about the most typical damages on engineering structures, reducing the time of inspection, and avoiding systems of complex installation and operation. Moreover, their use is intended to reduce inspection costs during manufacturing and operation, increasing the quality, safety, and durability of the systems composed of the target structures. This paper presents the industry needs that motivate this development, the system architecture, and the applications for which it has been conceived.

Damage localization is one of the most challenging topics within Structural Health Monitoring (SHM) in aeronautics, especially when the structure is manufactured out of carbon fiber-reinforced composite materials. Using ultrasonic guided waves (particularly Lamb waves), generated and recorded with piezoelectric transducers, is also challenging in this type of material. Otherwise, traditional methods used for this task are subjected to physics-based knowledge of the problem, such as damage imaging algorithms like delay-and-sum and RAPID. This paper presents an entirely data-driven approach, based on the ability of Deep Learning (DL) techniques (particularly those based on Convolutional Neural Networks – CNNs –) to extract features of interest for damage imaging from a pre-dataset. In this work, the selected feature to be estimated is the normal distance from the propagation path of the guided wave to a simulated damage, which allows, in combination with an especially designed positioning algorithm, to locate with high accuracy defects, even in different positions than the used for the training of the network (a fixed grid of points over the analysis zone). This paper presents the application of the method to a real composite material specimen, as well as the recorded results obtained from additional datasets recorded with the simulated damage (a piece of blu-tack) attached to different random positions other than those of the training grid.

The ITER interlock control system (ICS) requires the application of the IEC 61508 standard for all mission-critical (known as investment protection) control functions. Such functions must detect the events of the integrated physical processes and distribute them to the actuators with hard real-time constraints on the order of milliseconds or even microseconds. Systems able to achieve these timing requirements are often bespoke field-programmable gate array (FPGA)-based solutions, which are a well-known challenge to IEC 61508 processes. However, to minimize the variety of components and simplify the procurement process for an international supplier base, ITER decided to standardize the use of commercial off-the-shelf (COTS) devices. The COTS selected for the ICS was the FPGA-based CompactRIO National Instruments (NI) 9159 chassis (and several adapter I/O modules), provided by NI. This COTS requires the use of a high-level language (LabVIEW-FPGA) and the associated integrated development tools to develop the FPGA functionality. Therefore, it is necessary to ensure the required assurance that a COTS device is of sufficient quality, fit for purpose, and can be properly integrated into an investment protection control loop with the necessary level of systematic capability during the development process. This article describes in detail the method ITER uses to perform the verification and validation according to the IEC 61508 standard recommendations, for the logic configuration generated by LabVIEW-FPGA for these COTS, after the compilation of high-level language sources designed during the development.

The detection and localization of structural damage in a stiffened skin-to-stringer composite panel typical of modern aircraft construction can be addressed by ultrasonic-guided wave transducer arrays. However, the geometrical and material complexities of this part make it quite difficult to utilize physics-based concepts of wave scattering. A data-driven deep learning (DL) approach based on the convolutional neural network (CNN) is used instead for this application. The DL technique automatically selects the most sensitive wave features based on the learned training data. In addition, the generalization abilities of the network allow for detection of damage that can be different from the training scenarios. This article describes a specific 1D-CNN algorithm that has been designed for this application, and it demonstrates its ability to image damage in key regions of the stiffened composite test panel, particularly the skin region, the stringer’s flange region, and the stringer’s cap region. Covering the stringer’s regions from guided wave transducers located solely on the skin is a particularly attractive feature of the proposed SHM approach for this kind of complex structure.

Nondestructive evaluation of carbon fiber reinforced material structures has received special attention in the last decades. Usage of Ultrasonic Guided Waves (UGW), particularly Lamb waves, has become one of the most popular techniques for damage location, due to their sensitivity to defects, large range of inspection, and good propagation in several material types. However, extracting meaningful physical features from the response signals is challenging due to several factors, such as the multimodal nature of UGW, boundary conditions and the geometric shape of the structure, possible material anisotropies, and their environmental dependency. Neural networks (NN) are becoming a practical and accurate approach to analyzing the acquired data using data-driven methods. In this paper, a Convolutional-Neural-Network (CNN) is proposed to predict the distance-to-damage values from the signals corresponding to a transmitter-receiver path of transducers. The NN input is a 2D image (time-frequency) obtained as the Wavelet transform of the acquired experimental signals. The distances obtained with the NN are the input of a novel damage location algorithm which outputs a bidimensional image of the structure's surface showing the estimated damage locations with a deviation of the actual position lower than 15 mm.

The development of high-performance data acquisition (DAQ) and processing systems is crucial for the next-generation diagnostics used in big science experiments. In the ITER experiment, the instrumentation, control hardware, and software architecture selected for this type of application is called a fast controller. The core element of a fast controller is a chassis based on the use of the PCIe eXtension for Instrumentation (PXIe) or Micro Telecommunication Computing Architecture (MTCA). This paper presents a software framework named IRIO-OpenCL that was developed using the ITER CODAC Core System (CCS) Linux-based distribution, oriented toward the development of field-programmable gate array (FPGA)-based DAQ systems using OpenCL. State-of-the-art DAQ-FPGA systems are developed using hardware description languages (HDLs). The approach used in IRIO-OpenCL simplifies DAQ to enable the user to write C-like processing algorithms with OpenCL, minimizing the use of HDLs. The software has been implemented in C++ following ITER’s Nominal Device Support v3 (NDSv3) model that abstracts and generalizes the development of software device drivers and simplifies the interface with the Experimental Physics and Industrial Control System (EPICS). The framework has been validated in an ITER fast controller including an MTCA.4 chassis with an advanced mezzanine card (AMC) module using an Arria 10 FPGA from Intel FPGA and an FPGA mezzanine card (FMC) DAQ module from Analog Devices. The developed application solves the DAQ and processing problems associated with the neutron flux measurement and achieves a sampling rate of 1 GS/s using approximately 40 % of the FPGA resources. The methodology proposed in this paper reduces the development time of these systems while maintaining high performance.

Structural Health Monitoring (SHM) of Carbon Fiber Reinforced Polymers (CFRP) has become, recently, in a promising methodology for the field of Non-Destructive Inspection (NDI), specially based on Ultrasonic Guided Waves (UGW), particularly Lamb waves using Piezoelectric Transducers (PZT). However, the Environmental and Operational Conditions (EOC) perform an important role on the physical characteristics of the waves, mainly the temperature. Some of these effects are phase shifting, amplitude changes and time of flight (ToF) variations. In this paper, a compensation method for evaluating and compensating the effects of the temperature is carried out, performing a data-driven methodology to calculate the features from a dataset of typical temperature values obtained from a thermoset matrix pristine plate, with a transducer network attached. In addition, the methodology is tested on the same sample after an impact damage is carried out on it, using RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) and its geometrical variant (RAPID-G) to calculate the location of the damage.

Aircraft structures, which are manufactured increasingly using composite materials, present several difficulties for the location of damages using Structural Health Monitoring (SHM) techniques due to their mechanical properties (anisotropy, wave propagation, stiffness, etc.). In this study, the RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) algorithm was used to detect and locate damages in a small surface thermoplastic composite material sample, using Lamb waves generated and recorded with piezoelectric transducers (PZT). Some types of damage cause inaccurate predictions, mainly due to their size and location, in this case, especially when the damage is close to a direct path between transducers, and provides a masked detection. Our new proposal for the enhanced RAPID algorithm solves this issue, by performing a geometrical modification which corrects the elliptical distribution on the intersection points. (C) 2019 The Authors. Published by Elsevier B.V.

12th edition of the IAEA Technical Meeting on Control, Data Acquisition and Remote Participation for Fusion Research (CODAC 2019) 13th to 17st May 2019

Structural Health Monitoring (SHM) of aircraft structures, which are manufactured more and more using composite material, is a well-established technique for increasing reliability and reducing maintenance costs. Implementing damage inspection algorithms that run in dedicated electronic systems helps reduce big data processing time with improved results. RAPID (Reconstruction Algorithm for Probabilistic Inspection of Damage) algorithm, developed some years ago, provides a technique to identify and characterize damages both in metallic and composite materials. However, this algorithm sometimes provides inaccurate predictions of the damage location, caused mainly by the influence of path intersection points among transducers, that can mask the current location of damages. This work presents a geometrical modification of standard RAPID algorithm (RAPID-G) that mitigates the influence of the intersection point between sensor paths. The results highlight that the location of damages improves significantly with this modification of the algorithm.

The Linear IFMIF (International Fusion Materials Irradiation Facility) Prototype Accelerator (LIPAc) injector consists of a 140 mA proton/deuteron source, its associated low energy beam transport line (LEBT) as well as ancillaries such as water cooling skid, vacuum groups, High Voltage Power Supplies (HVPS), etc. A specific element, the beam "Chopper", was included in the LEBT to generate short ((similar to)100 mu s) and sharp-edged beam pulses ((similar to)10 mu s) and allow the use of interceptive diagnostics in the high energy part of the LIPAc during commissioning phases of the Radio Frequency Quadrupole RFQ (5 MeV) and the Superconducting Radio Frequency SRF Linac (9 MeV). The chopper was designed to operate in pulsed mode with very sharp rise and fall times, meaning the chopper will be used to "cut" the long rise time of the source as well as the fall time of the beam pulse. The chopper thermal screen has not been designed to withstand very high beam power (i.e., beam length and duty cycle need to be monitored); in addition, the chopper HVPS needs to be monitored in real time to detect a possible trip and extract the beam before downstream devices are damaged. For these applications, standard PLC based interlocks are too slow; therefore, faster solutions are envisaged. The proposed solution for the required interlock system is based on COTS technology with XILINX FPGAs using RIO (Reconfigurable Input/Output) technology from National Instruments (CompactRIO platform). The paper discusses the implementation of the interlock system, the response times of the proposed architecture and the fitness of the technology. Additionally, the system can be integrated into the IFMIF control system using EPICS as a standalone solution.

The structures made of composite material show two well-known difficulties for monitoring systems using ultrasonic waves. The first one is the need of a signal of such power that it can excite properly the piezoelectric transducer stuck on composite material. In fact, the composite material shows high mechanical resistance and high acoustic impedance. This paper shows two ways to increase the power given to piezoelectric transducers for ultrasound tests: to increase the current and/or the voltage applied to the transducers. The second difficulty is the need of high sensitivity electronic systems due to the high power loss that ultrasonic waves suffer when they are propagating on a structure of composite material. The paper introduces the acquisition of signals from piezoelectric transducers arranged on composite material during the test set. The tests summarized in the paper quantify the attenuation of ultrasonic waves with the distance in composite material structures.

Analog data acquisition used in diagnostics and control of large physics experiments require high sampling rates and real-time functionalities. Field Programmable Gate Array (FPGA) devices allow efficient implementation of such solutions. Currently, large scientific facilities are using middleware platforms to simplify systems integration. EPICS (Experimental Physics and Industrial Control System) is one of the most extended middleware for this purpose. Heterogeneous hardware integration in these middleware is a complex task, and different approaches attempt to standardize. One of these approaches is areaDetector. An open source module for EPICS that is mainly used for image acquisition. areaDetector simplifies integration of heterogeneous image systems, has also been used with some analog signals like quadEM. This paper presents the integration of an acquisition and processing solution in a PXIe platform using FlexRIO technology via a hardware model that solves acquisition and processing in a FPGA and a software model implemented in C++ with the IRIO library (open source solution for RIO devices in the Linux environment) to get to EPICS through areaDetector with analog data.

Interlocks are the instrumented functions of ITER that protect the machine against failures of the plant system components or incorrect machine operation. Regarding I&C, the Interlock Control System ensures that no failure of the conventional ITER controls can lead to severe damage of the machine integrity or availability. ITER Interlock System incorporates several Plant Systems that require response times below 1 ms to perform the required protection actions. Systems like the Coil Power Supply System (CPSS), Ion Cyclotron Heating and Current Drive (ICH&CD), Neutral Beam Injectors and Current Drive (NBI&CD), Disruption Mitigation System (DMS), and others. ITER developed a methodology to create the fast architecture for the Plant Interlock Systems (PIS) that ensures high integrity on the final solution. The functional safety standard IEC 61508 has been used to define a methodology for the configuration of the RIO (Reconfigurable Input/Output) platform from National Instruments (compactRIO platform). This work describes in detail this methodology, valid for most cases just selecting different input/output signal types to be handled and choosing different numbers of interlock functions to be performed. In addition, this paper describes the development of the interlock system for the ITER Poloidal Field and Central Solenoid Coil's Power Converter protection, a special case due to its unique configuration, the most complex implemented up to date. The development of this special case "puts to a test" the methodology's approach. Finally, the discussion of the fitness of the methodology and the performance of the system is presented.

One of the major problems in present tokamaks is the presence of disruptions. If disruptions are not mitigated, they can produce serious damage to the device. Therefore, disruption predictors are needed in order to apply the mitigation techniques in time. In this paper, the real-time implementation in JET of a new type of disruption predictor is presented. The new predictor, single signal predictor based on anomaly detection (SPAD), predicts disruptions detecting anomalies in the locked mode signal. The implementation is based on the Multi-threaded Application Real-Time executor framework. Analysis over JET's ITER-like wall campaigns (C28-C34) shows that SPAD was able to predict 83.57% of the disruptions with enough time to apply mitigation techniques. The average anticipation time was 389 ms. In this paper, the real-time implementation will be discussed, as well as the optimizations developed to make the algorithm suitable for real-time processing. Performance results and possible improvements will also be analyzed.

Real-time (RT) disruption prediction (DP) is essential to trigger mitigation actions that avoid irreversible damage to the devices. This paper deals with disruption mitigation alarms and performs the RT implementation of a probabilistic predictor. The RT implementation has been carried out with a fast controller with DAQ FPGA-based data acquisition devices corresponding to ITER catalogue (in particular, a reconfigurable Input/Output platform has been used). Up to three input signals have been used and relevant information for the prediction is extracted from the temporal and the frequency domains. The signals are read from the JET database. Then D/A conversions are carried out and used as inputs to the real time system. In this way, the whole process of digitization, data analysis and prediction is performed. The computation time for each prediction takes less than 200 mu s.