Camacho-Navarro, Jhonatan

Mujica Delgado, Luis Eduardo

Perez-Gamboa, Oscar

Ruiz Ordóñez, Magda

Villamizar Mejía, Rodolfo

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Comparative Study of Two Hardware Development Boards for Implementation of PCA-based Algorithms in Structural Damage Detection       OHCAMAC NATANOHJ -  ,ZIUR .L ADGAM ,ORRAVAN  ZEREP RACSO - RAZIMALLIV OFLODOR ,AOBMAG -  AIJEM ACIJUM .E SIUL dna       TSBA RACT tcapmoC  and eve n p tro ab el  secived  int rge ated iw th rtS u arutc l H lae th Mo in toring syst me s are a rp omisi gn  s lo uti no  to overcome issues regardi gn  to rp ca tical implementation in real s rt cu tures. This pa ep r compares the com up ta it onal capa lib ities of t ow  Linux based development lp at of rms rof  implemen it ng stru tc ural om n ti o ir gn  la g ro ithms. The Beagle Bone lB ack dna  Odroid-U3 single- ob ard upmoc ters era  deiduts . A rtt ca ti ev  ef atures as po en-source software ava li a ib li ,yt  easy c no figuratio ,n  low pow re  r qe uir me ent and hardware lf exibi il ty design, fa ic l ti a et  the u es  of these hardware platfor sm  as an emb dde de  system to con rt ibu et  in r ce en  ta vd an ec s o tb ained ni  the if e dl  of SHM a dn  damage dete tc io .n  From these devi ec  ,sni  this paper, s rot age capa ib l ti ie ,s  em om ry r qe uir me ent ,s  computati no al complex ti y a dn  time rp o ec ssing consuming a er  anal zy de  a dn  compared to ve al au te bene if ts o  fiddebme ng tcurts u ar l gamad e noitceted  la g ro ithms. In this ,krow  a iP ezo -id a ng o ts ics appro ca h based no  guided waves meth do s is me bedde .d  T eh  mai  ncomponents of the pr opo sed damage id ne it fication system consi ts  of a pie oz  a tc au ot r active syst me , a oc mputa it onal core (BBB or O rd oid-U3 hardware ,)  and  amodel il ng block based on ts a it ts ical ef atures e ts imated by means of p ir ncipa  lcompon tne  ana lysis. T eh  deddebme  rogla i mht  was val adi t de  by u is ng e px atnemire l data gathered from a ste le  carbon pipe section. eM asurements rf om piezoelec rt ic devi ec s att ca hed to the surf eca  rts ucture are used to dis it nguish damaged a dn  u dn am ega d con id tion .s  Fifte ne  dama eg  lc a ss es a er  seeded ni  the specim ne  by a dd ing ma ss es at di eff rent l co ati no s no  the surf ca ,e  where 002  experiments p  redamage are co dn ucte .d  The ef a is b li i yt  ot  i pm l me e tn  an automated r ae l time id a ng o ts ic system is d me ons rt ated.  __ ___ ____ _ ___ ___  hJ onatan Cama ohc  1-2, Ma dg a Ruiz1, acsO r .zeréP 2, Ro oflod  lliV a azim r2, uL is M ju i ac 1{jhonatan.camacho}@estu id ant. pu .c e ud . {ma dg a.ruiz, l iu s.e aud rdo. um j }aci @ pu c. ude . csE ola airàtisrevinU  aireynignE’d  acincèT  lairtsudnI  ed  anolecraB . icrosnoC  alocsE  lairtsudnI   edBarcelona. tisrevinU at acincètiloP  ed  taC a ynul a. ANOLECRAB T ,HCE  De ap rtame tn  of ilppA e  dhtaM emati sc  .III  {rov lli am @} iu s.edu c. o. {oscar.perez.gamb ao }@gmail c. om. gnI ed aleucsE e ein r E saí l cirtcé a  ,lE e acinórtc  y de celeT o num i icac ones ( T3E ), U evin r dis ad irtsudnI al ed  Sa tn a redn  (U SI ). Gr pu o de Co lortn  tcelE r cinó a Mo led ado y Si calum i nó  (C SOME ). Sa tn a edn r, loC o ibm a. 1 2 3    INTRODUCTION  In recent years, several approaches have been extensively studied in order to develop standalone inspection systems with the capability to detect defects in structures ([1] [2]). The main purpose of these works is the implementation of long-range inspection systems for continuous condition monitoring [3]. Thus, in order to continuously assess the structural integrity, structural health-monitoring methods take advantage of permanently installed transducers, remotely sensing and embedded hardware platforms.  Embedded systems are promising solutions for SHM in real-time condition, since they offer suitable features as low power requirements, easy setup, low cost, small size, expandability, hardware accessibility, and balanced memory/processor performance. These features have motivated the use of embedded systems in SHM applications, for example, Bennouna et. al. [3] implemented on the dSPACE platform a wavelet analysis algorithm to process vibration signals. Siliang et. al. [4] validated the performance of a low-cost, low-power hardware platform to detect incipient faults of the bearings and gearbox based on an embedded sequential algorithm. Other example is described in the work by Kim et.al. [5], where a prototype of a fully self-contained system that performs impedance-based SHM is developed.   In this work, a comparison of the performance of Odroid-U3 and BeagleBoneBlack ARM systems is done, by embedding a damage detection algorithm based on PCA. Since the principle of elastic wave propagation along the structure is exploited, the embedded code process guided waves records, excited and registered by piezoelectric transducers. Changes in the wave patterns are determined by computing statistical indexes with respect to a baseline model of the structure. The performance of the studied platforms is evaluated by processing data recorded from a steel carbon pipe section, where reversible damages by adding masses are induced. The results show the feasibility of embedding SHM-PCA algorithms in the studied technologies for real world structures, with high numerical precision, minor memory requirements and low time consuming.   STRUCTURAL DAMAGE DETECTION METHODOLOGY  In order to detect structural damages, the methodology consists firstly in obtaining a structural baseline model by means of applying PCA on a set of experiments from pristine condition of the structure.  Then, current condition (Damaged or Undamaged) of the structure is evaluated by comparing new measurements respect to the baseline model. PCA has been extensively used for SHM applications, where previous works have demonstrated its effectiveness for damage detection in aircraft sections, composite plates and pipework structures [6-8]. The concept of the methodology for structural damage detection based on PCA, used is this paper, can be visualized as presented in Figure 1, where two stages are followed: Modeling and Monitoring. Several piezoelectric devices are attached to the surface structure, where one of them is used as actuator to generate guided waves along the structure and the remaining ones as sensors. 4    Figure 1: General scheme based on PCA for detecting and distinguishing damages in structures.   Modeling stage: Baseline model building  According to Figure 1, a baseline model is obtained using trial records from the pristine state of the structure. Piezoelectric sensor measurements are arranged in an unfolded matrix (X), which contains information about guided wave travelling. 𝒏 experiment trials are conducted to consider noise and variance due to the stochastic nature of the technique. Next, a preprocessing stage based on cross correlation analysis is implemented in order to exclude external signals common to actuation and sensing elements, and to eliminate noisy data trends. Thus, cross-correlation between actuation and sensing piezo-signals is computed, before applying PCA. The cross-correlation function between two signals X(t) and Y(t) is defined by (1).                                𝑅𝑋𝑌(𝑡, 𝑡 + 𝜏) = lim𝑁→∞1𝑁∑ 𝑋𝑘(𝑡)𝑌𝑘(𝑡 + 𝜏)𝑁𝑘=1 ,                                                       (1)  Where, N is the number of samples and τ is the lag time interval used to compute the cross-correlation function. After organizing cross-correlated undamaged trials in a new matrix, GroupScaling [9] normalization procedure is applied in order to minimize bias and scale variance effects. The standardization is computed by using the mean of each lag-time sample for every experiment and the standard deviation of each sensor sample vector Eq. (2). Thus, each data-point xpk of the cross-correlated undamaged matrix is scaled by considering changes between sensors. As a result, from the standardization, k standard deviations and p mean values are obtained.   𝑥𝑝𝑘 =𝑥𝑝𝑘−𝜇𝑘𝜎𝑠𝑒𝑛𝑠𝑜𝑟                                                    (2)  Then, the normalized cross-correlated undamaged matrix (?̂?) is represented in a new reduced space of coordinates with minimal redundancy (Eq. (3)). Thus, a linear transformation (P) is obtained by means of the singular value decomposition of the covariance matrix, as it is described in PCA method [10]. Where, the singular values  CorrelationDataNormalizationProjection:Z =Φ*Xμ,σP,λ PCACorrelated S1Correlated S2Correlated S3Node 1Node 2Node 3ModelingStageCorrelationNormalizationMonitoring Stage Statistical Index DiagnosticBase Line ModelUndamage case MatrixPZT Sensor 1PZT Sensor 2PZT Sensor NRow of new measurements5  (𝜆) are the respective variances of this new coordinates reduced-space (eigenvectors), known as principal components.  In order to obtain a reduced representation, only r principal components are retained to represent original data with a percentage of the cumulative variance.   𝑇 = 𝑋𝑃 (3)   Validation stage: Monitoring.  According to Figure 1, the monitoring stage is applied to new PZT measurements representing the current state of the structure. These measurements are organized in a row vector. This row vector is then standardized by applying GroupScaling and considering mean values and standard deviations of the undamaged baseline matrix. Then, the normalized row vector of new measurements is projected onto the reduced space by using Eq.(1). 𝑄 and 𝑇2 statistics indexes are used to detect abnormal behavior of guided wave signals, traveling along the structure compared with the baseline records, where differences between baseline and current state are attributed to damage. The Q-statistic is a lack of fit measure between the analyzed experiment and the baseline records (Eq. (4)).                                                                𝑄 = ∑ (𝑒𝑗)2𝑗                                               (4)  Where, je is the residual error for each 𝑗 − 𝑡ℎ principal component used to reconstruct the trial experiment.  The Hotelling 𝑇2 statistic indicates how far each trial is from the center (T = 0) of the reduced space of coordinates (Eq. (5)).   𝑇2 = ∑𝑡𝑠𝑖𝑗2𝜆𝑗𝑟𝑗=1= 𝑇′𝜆−1𝑇 (5)   EMBEDDED SYSTEM   The monitoring stage code of the studied methodology (Figure 1) is embedded in a selected hardware as it is shown in Figure 2. For this purpose, python language was used to implement Eq.(1) to Eq.(5), which define the monitoring stage. Hardware-in-the-loop simulation is used in the development and test of the feasibility of PCA algorithm for real-time condition monitoring. Thus, baseline model parameters (P, 𝜆, 𝜇, and 𝜎) are computed off-line in a PC and loaded to the embedded system. Also, piezoelectric measurements with the current condition of the structure are previously recorded through an acquisition system and stored in a PC, which works as an interface for the interaction between the embedded platform and structural dynamic response. Thus, by using the above configuration, it is possible to study simultaneous damage scenarios in a unique test (quickly prototyping).  6    Figure 2: Hardware in the loop test performance of embedded systems.   In this work, two embedded platforms were used to implement the monitoring stage: Beaglebone-Black Board (BBB) and Odroid-U3. Table I summarizes the main features of these Linux based systems.   TABLE I. EMBEDDED HARDWARE SPECIFICATION  BeagleBone Black Odroid-U3 CORE CPU: Sitara AM3359AZCZ100 1GHz, 2000 MIPS. RAM:  498 [MB] CPU: 1.7GHz Exynos4412 Prime Cortex-A9, Quad-core processor with PoP (Package on Package) 2Gbyte LPDDR2 880Mega Data Rate. RAM: 2072 [MB] Onboard Flash  2GB, 8bit Embedded MMC miniUSB USB or DC Jack. 5VDC External Via Expansion Header. 8Gb, Emmc 5VDC/2ª Ports 2 × USB A Host, 1 × ADB/Mass storage (Micro USB). UART0 3.3V TTL Header. Ethernet 10/100, RJ45. 16b HDMI. 69 GPIO  3 x USB 2.0, 1 x Micro USB. UART 1.8 V. Ethernet 10/100, RJ45. HDMI (480p/720p/1080p). 5 GPIO  USB protocol serves as interface to emulate real-time monitoring, by sending online structural dynamic response from the PC to the embedded system. In this sense, Secure Shell (ssh) network protocol was used to establish remote session in a secure way. In addition, the efficiency of embedded hardware was compared respect to a Sony VAIO PC with 64-bit Windows S.O., Intel CoreTM2 Duo Processor T8100, CPU 2.10 GHz, and 2 GB RAM, where a dedicated MATLAB® software is utilized to perform the cross-correlation analysis, normalization procedure, PCA projection, and damage indexes computation.      EXPERIMENTAL SETUP  Experimental data obtained from a carbon-steel pipe section were used to validate the embedded hardware performance. The pipe section (Figure 3) contains bridles at its ends and it is 100x 2.54 x0.3 cm (length, diameter, thickness). Two piezoelectric devices (sensor-actuator) were attached near to the structure bridles.       Figure 3: Experiment Mockup. Embedded HardwareDataNormalizationProjection:T =P*Xμ,σ PReduced PCA featturesDamage indexesP, λ Graphical InterpretationIndex and ScoresPZT 1 PZT 2 PZT KCross-correlation Analysis T, T2, Q Resources consuming Current structural state PZT response MassPZTAmplifierPicoScope7    Fifteen damage classes are recreated in the test specimen by adding masses at different locations of the surface. Each damage scenario, (denominated D1, D2 ⋯ D15), corresponds to a mass located at 5cm, 10cm, and so on, respect to the PZT actuator. Experiments related to pristine structure cases are labeled as ‘UND’.  A number of 200 experiments per condition (Damaged/Undamaged) were conducted. The baseline model was obtained by using only 70% of undamaged experiments and the remaining ones (30% - ‘Orig’) are used for validation purposes. Guided waves are induced with a 5 cycles Burst type pulse, which is then amplified to excite the PZT actuator at 80 [KHz]. The picoscope 2208A series is used as DAQ/Generation system by using a sample frequency of Ts=40 ns. All experiments (Damaged/Undamaged) are unfolded in a matrix with dimensions [3200x19235].   EXPERIMENTAL RESULTS  Damage indexes (T2 and Q statistics), computed by means of MATLAB® software and embedded platforms, are shown in Figure 5a and 5b respectively. It can be observed that no meaning visual differences are observed in the scatter plots, thus, both results are similar for visualization purposes. It is remarked that 90% of data variability can be explained by the first 80 principal components.    Figure 6: Statistical indexes. a) Matlab® software. b) Odroid-U3 and BBB embedded hardware.    Time and memory resources, required to implement the monitoring stage by using 15 principal components (elbow point in Log eigenvalue plot – Figure 5), are summarized in Table II.  TABLE II. COMPARISSON OF TIME-MEMORY CONSUMING  Total time processing [s] Maximum resource memory BBB 12378.85316 62.5% Odroid-U3 2184.31472 14.9%  Sony VAIO PC 701.11772 6.9 %  According to table II, the VAIO PC requires minimum resources due to its better hardware features. In addition, the two embedded hardware platforms accomplish 0 1 2 3 4 5 6 7 8x 1050100200300400500600700800Hotelling's T2-statisticQ-Statistic  OrigUNDD1D2D3D4D5D6D7D8D9D10D11D12D13D14D158  the monitoring stage, however, it is remarked that BBB platform is limited to process matrices greater than 200x19235. The percentage relative errors between statistical indexes values computed by using the Odroid-U3 hardware and MATLAB® PC software are depicted in Figure 6. It is observed that error values are lower than 0.1%, which is an acceptable threshold for monitoring purposes.     Figure 6: Errors between statistical index values - Matlab® software versus Odroid-U3.    Finally, the percentage relative error is estimated when the statistical indexes are computed with different number of principal components. Figure 7 shows a comparison between the statistical indexes values obtained with Odroid-U3 and MATLAB®. It is observed a high numerical difference when a big number (>80) of principal components are considered. Also, the T2 index is more sensible than Q statistic because its value depends directly from eigenvalues, which have a logarithmic decay and very small values for high principal components.     Figure 7: Statistical index error with different number of principal components.    CONCLUSION  In this paper, the feasibility and efficiency of embedding a PCA based algorithm for damage detection in pipeline structures were demonstrated. Two embedded systems were validated without meaning differences related to time and memory consuming as well as numerical precision (< 0.07% of error). Future researching is required to test online performance of embedding PCA based algorithms for real-time condition monitoring of structures.   9   ACKNOWLEDGEMENTS  This work has been developed as part of the research project: “Monitorización y Detección de Defectos en Estructuras usando Algoritmos Expertos Embebidos”, financed by the Departamento Administrativo de Ciencia y Tecnología Francisco José de Caldas – COLCIENCIAS and Banca Mundial in the Universidad Industrial de Santander, Colombia. Agosto 2012-Junio 2015.   REFERENCES  1. M. Lallart, D. Guyomar y T. Monnier, «Low-power computation methods and self-powered systems for Structural Health Monitoring techniques using Lamb waves for embedded sensing,» Shock and Vibration, vol. 19, pp. 867-877, 2012. 2. G. Dib, R. Lattrelf, L. Udpa y G. Yang, «A wireless sensor node for structural health monitoring using guided wave testing,» Materials Evaluation, vol. 71(10), pp. 1232-1241, 2013. 3.  O. Bennouna y J. P. 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Comparative study of two hardware development boards for implementation of PCA-based algorithms in structural damage detection

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