Available online at www.sciencedirect.com Available online at www.sciencedirect.com
Available Availableonline onlineatatwww.sciencedirect.com www.sciencedirect.com Procedia Manufacturing 00 (2017) 000–000
ScienceDirect ScienceDirect
www.elsevier.com/locate/procedia
Procedia Manufacturing (2017) 000–000 Procedia Manufacturing 24 00 (2018) 319–324 Procedia Manufacturing 00 (2017) 000–000
www.elsevier.com/locate/procedia 4th International Conference on System-Integrated Intelligence www.elsevier.com/locate/procedia
Wireless4thOnline Impact Source LocalizationIntelligence on a Composite International Conference on System-Integrated a
a
a
Sarah Aguasvivas Manzano , Dana Hughes , Nikolaus Correll Wireless Online Impact Source Localization on a Composite a University
of Colorado Boulder, 1111 Engineering Drive, Boulder, CO 80309, USA
a a 28-30 June Manufacturing Engineering Society International 2017, MESIC 2017, Sarah Aguasvivas Manzano , DanaConference Hughesa , Nikolaus Correll 2017,Boulder, Vigo1111 (Pontevedra), Spain a University of Colorado Engineering Drive, Boulder, CO 80309, USA Abstract
Costing models for capacity optimization in Industry 4.0: Trade-off between used capacity and operational efficiency
We describe a self-sensing composite capable of making online estimations on the location of an impact within its structure Abstract without the need for wires or offline data processing. We embed a network of piezoelectric sensors in the material during manufacturing and connect each sensor to a dedicated andestimations power source. Triangulation by each in We describe a self-sensing composite capablemicro-controller of making online on the location of isanperformed impact within its sensor structure a a,* b b the network through wireless distributed computation and communication. By making the computation of the sensors distributed, without the need for wires or offline data processing. We embed a network of piezoelectric sensors in the material during manu-
A. Santana , P. Afonso , A. Zanin , R. Wernke
the systemand is scalable number of sensors and robust with regard to Triangulation individual failure. One of the facturing connect with each regard sensor to the a dedicated micro-controller and power source. is performed by key eachchallenges sensor in a University of Minho, 4800-058 Guimarães, Portugal of distributed structural health monitoring is the need for multiple sensor signals in order to calculate the impact’s location. This b the network through wireless distributed computation and89809-000 communication. Unochapecó, Chapecó,By SC,making Brazil the computation of the sensors distributed, requires thatis every sensor of neighboring whose signals willfailure. be used during thekey computation. the system scalable withmakes regardantoappropriate the numberchoice of sensors and robust sensors with regard to individual One of the challenges Wedistributed address this problemhealth by performing pairing of a given sensor with in theorder neighbors that it the believes provide the most of structural monitoringselective is the need for multiple sensor signals to calculate impact’s location. This reliable data at a given time. This reliability depends on how close in time the event detection occurred in other sensors compared requires that every sensor makes an appropriate choice of neighboring sensors whose signals will be used during the computation. Abstract to the given sensor. Experimental results on a composite with embedded sensors show that this that approach is robust against We address this problem by performing selective pairing of 12 a given sensor with the neighbors it believes provide thefailure most of individual sensors and that it is accurate to provide aclose goodinguess, but notpushed andetection exacttolocation of the when the sensors Under the of "Industry 4.0",enough production processes willthe beevent be increasingly interconnected, reliable data concept at a given time. This reliability depends on how time occurred in impact other sensors compared information basedExperimental on a real time basis necessarily, moresensors efficient. this are toogiven closesensor. together. to the results on aand, composite with 12much embedded showIn that thiscontext, approachcapacity is robust optimization against failure
goes beyondsensors the traditional of capacity maximization, contributing alsoanfor organization’s value. of individual and that itaim is accurate enough to provide a good guess, but not exact location of theprofitability impact whenand the sensors Indeed, lean management and continuous improvement approaches suggest capacity optimization instead of c 2018 The Authors. are too close together. Published by Elsevier Ltd. maximization. The study of capacity optimization and costing models is an important research topic that deserves This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) © 2018 The Authors. Published by Elsevier contributions from both the practical andB.V. theoretical perspectives. This paper presents and discusses a mathematical Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on System-Integrated c 2018 The Authors. Published by Elsevier Ltd. This is for an open accessmanagement article under the CC BY-NC-ND (https://creativecommons.org/licenses/by-nc-nd/4.0/) model capacity based on differentlicense costing models (ABC and TDABC). A generic model has been Intelligence. Peer-review under responsibility of the committee of the 4th International Conference on System-Integrated Intelligence. This is an open access article the scientific CC license (https://creativecommons.org/licenses/by-nc-nd/4.0/) developed and it was used tounder analyze idleBY-NC-ND capacity and to design strategies towards the maximization of organization’s Selection and peer-review under responsibility the the 4th International and Conference on System-Integrated value. The trade-off capacity maximization vsscientific operational efficiency is highlighted it is shown that capacity Keywords: structural health monitoring, distributedofsensors, wireless,committee internet of of things; Intelligence. might hide operational inefficiency. optimization © 2017 The Authors. Published by Elsevier B.V. Keywords: structural health monitoring, distributed sensors, wireless, internet of things; Peer-review under responsibility of the scientific committee of the Manufacturing Engineering Society International Conference 1. Introduction 2017.
We wish to investigate the benefits of distributing the computation across wireless sensors in a structure in order to perform non-destructive evaluation. An important advantage of making structural health monitoring wireless is the We wishoftohaving investigate the benefits of distributing themalfunctioning computation across wirelesssensors sensorsasinwell a structure in order possibility self-sensing materials that allow for of individual as expansion of 1. Introduction to perform non-destructive evaluation. An important advantage of making structural health monitoring wireless is the possibility of having self-sensing materials that allow for malfunctioning of individual sensors as well as expansion of E-mail address:
[email protected] The cost of idle capacity is a fundamental information for companies and their management of extreme importance in modern production systems. In general, it is defined as unused capacity or production potential and can be measured in several ways:
[email protected] tons of production, available hours of manufacturing, etc. The management of the idle capacity E-mail address:
Keywords: Cost Models; ABC; TDABC; Capacity Management; Idle Capacity; Operational Efficiency 1. Introduction
* Paulo Afonso. Tel.: +351 253 510 761; fax: +351 253 604 741 c 2018 The 2351-9789 Authors. Published by Elsevier Ltd. E-mail address:
[email protected] This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Selection peer-review under responsibility of the scientific 2351-9789 © Published B.V. c 2017 2351-9789and 2018The TheAuthors. Authors. Publishedby byElsevier Elsevier Ltd. committee of the 4th International Conference on System-Integrated Intelligence. Peer-review under responsibility of the scientificbycommittee of the Manufacturing Engineering Society International Conference 2017. 2351-9789 © 2018 The Authors. Published Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of the scientific committee of International the 4th International Conference on System-Integrated Intelligence. Peer-review under responsibility of the scientific committee of the 4th Conference on System-Integrated Intelligence. 10.1016/j.promfg.2018.06.021
320 2
Sarah Aguasvivas Manzano et al. / Procedia Manufacturing 24 (2018) 319–324 Sarah Aguasvivas-Manzano / Procedia Manufacturing 00 (2017) 000–000
the sensor network with no need to re-wire the system [4]. The resulting material can feel when a damage or an event happens in its own structure in order to not only raise attention on the defect, but also provide details that will better describe it, such as the defect’s location. The material in question may change the amount of sensors in its structure during operation since the proposed approach is independent of the size and shape of the material and the number of sensors in the network. 1.1. Related Work A self-contained material that can robustly perform non-destructive evaluation on itself through resilient sensing, actuation, computation and communication is highly demanded in fields such as civil engineering [16], aerospace engineering [1][7], robotics [14] and arguably most engineering and industrial fields. The localization of impacts or defects within a structure had been performed for many years using ultrasound methods with machines external to the material, such as lasers and imaging devices that require not only calibration but also trained personnel operating expensive machines. A survey on NDE [15] acknowledges Fig. 1. Picture of the composite test bed with the test points to be used in this work indicated as red stars. the need for a more self-contained method to do these kinds of testing. Distributed source localization may be a plausible solution to that challenge. This solution has been approached from many angles, ranging from odor detection [6] to distributed impact using microphones [3] [9]. Multiple distributed algorithms have been formulated such as a distributed bearing angle method [13] or more complicated methods such as the ESPIRIT algorithm [8]. A common risk of the methods that have been formally proposed is that they require advanced sensing to perform the computation. This reduces the scalability of the solution and increases the cost. The proposed solution in this work only requires information about the position of the sensor and its Lamb wave propagation measurement in the form of a raw voltage reading from a PZT sensor. The process of pairing the sensors with neighboring distributed sensors is a key challenge in this scope, especially when the only signal provided by the sensors is the vibration in one direction. This problem has been approached by [11], where a theoretical framework of prioritizing certain areas within the structure in order to pair sensors through the use of a Genetic Algorithm (GA) is introduced. The limitations of this approach on this particular problem is that it requires convergence steps to select the best neighbors. This convergence, moreover, is not guaranteed and the error is not expected to descend. Therefore, performing a complete GA optimization for the sensor selection can compromise the time it takes to estimate the source localization. 2. Materials and Methods We test a network of 12 piezoelectric sensors embedded in a composite flat plate as seen in Figure 1.1. Each sensor is connected to a Sparkfun ESP32 Thing, whose dual core 32-bit processor is 6x6mm2 in size and contains capabilities for wireless communication, operation through Real Time Operating System (RTOS) and data collection and transmission at large sampling rates. Currently, the sensors communicate via a TCP/IP network to a common sink that catches their data packages for processing on a central computer. This approach allows us to study and debug the
Sarah Aguasvivas Manzano et al. / Procedia Manufacturing 24 (2018) 319–324 Sarah Aguasvivas-Manzano / Procedia Manufacturing 00 (2017) 000–000
321 3
proposed distributed algorithm. However, this computation will be performed in the micro-controller’s circuit to make the system fully distributed in the future. The sensors collect data at a sampling rate of 2kHz. The method that will be tested for distributed source localization is the triangulation method, due to the simplicity of computation required by this method. In order to distribute the triangulation method, it is necessary to decide for each sensor that detects an impact which other neighboring sensor will provide the best signal at a given time as fast as possible. The proposed method performs a distributed source localization by making one weighted guess on the other sensors in the network to be paired with. If that guess is successful, the result will fall within a reasonable threshold, allowing for it to be considered during the consensus; if the guess is unsuccessful, the result will not converge at the number of iterations set due to the time delay difference. This will make the solution to be discarded. When the connection to the client is established, the client collects the signal of the sensors in order to process the data in parallel (one process per sensor). All the sensors share a common log that contains the last time they detected an event read from the client’s clock. The source localization problem was approached using three modules for each sensor that run concurrently with the data collection on the ESP32’s real time operating system. Module 1 is the event detection module. It analyzes the wavelet decomposed signal window in order to detect whether an event happened in the structure. Module 2 is the pairing module. This assigns a fitness value for each sensor in the log in order to determine the pair that will be used to estimate the source. Once the pairing is done, the time delays between sensor signals is computed through cross correlation. Module 3 is the source localization module, which uses a Jacobian-Free Newton Krylov method with GMRES [2] to detect the source localization in less than 10 iteration steps. 2.1. Event Detection Module This module uses a continuous wavelet transform [17] of the raw signal and the last time where sensor i detected an event using Python’s scipy.signal.cwt function. A threshold in the gradient of the wavelet decomposed signal is applied to further examine that window of the signal. Then, if the difference between the current time and the last time sensor i detected an event is large enough, then sensor i logs the window of data where the signal was detected into the common log. Figure 2.1 shows a wavelet decomposed signal after 10 taps in the same point in the board from one sensor. Through numerical differentiation in one direction of this matrix, the gradient was computed and the last time the sensor detected an event is obtained from the common log. Figure 2.1 illustrates the processed signal that is evaluated in order to detect whether an event happened.
Fig. 2. Example of the wavelet decomposition applied to the raw signal for 10 taps in one point.
The reason why the last event time is also taken into consideration is because it is possible that the gradient in the signal at the next time step stays within the allowable threshold, making the method detect multiple taps when only one tap occurred.
Sarah Aguasvivas Manzano et al. / Procedia Manufacturing 24 (2018) 319–324 Sarah Aguasvivas-Manzano / Procedia Manufacturing 00 (2017) 000–000
322 4
2.2. Pairing Module In this module, the sensor i starts with the certainty that an event happened within the structure and the window of data where the event was detected. It is still uncertain that the data reported by the other sensors in the log is not data from a past event. The choice of candidates will be performed by iterating through the existing common log and calculating how fit a candidate sensor j is for source localization. The fitness value used in this work is how close in time the events announced by two sensors are, meaning how small |T last,i − T last, j | is. If the difference is too large, that means that the other sensor has not detected the same event and that the data reading from the common log provides information about a past event. |T last,i − T last, j |−1 wi j = (1) −1 j |T last,i − T last, j |
The resulting weights wi j does not necessarily have to have the length equals to the number of sensors in the network, because it is possible that some sensors do not contain data or are disconnected. These weights will be then used to establish how relevant a candidate sensor is in order to make a random sampling without repeats weighted by this metric. 2.3. Source Localization Module
The arithmetics to be performed for the triangulation method for sensors that only have one signal is found in [12]. Each sensor i is trying to find a distance between itself and the point where the source was localized. This distance can be approximated to be the Euclidean distance di = (xi − x0 )2 + (yi − y0 )2 in this case since the problem to be solved is in a flat plate. This method can be extended to curved geometries with some caveats. Equation 2 describes
the cost function to optimize for the flat plate model, where ti j represents the time delay in seconds between the signal
in sensor i and the signal in sensor j. E(x0 , y0 ) =(t23 (d1 − d2 ) − t12 (d2 − d3 ))2 + (t31 (d2 − d3 ) − t23 (d3 − d1 ))2 + (t12 (d3 − d1 ) − t31 (d1 − d2 ))2
(2)
It is necessary to optimize both for x0 and for y0 . This is why the function to optimize will be the first derivative in x0 and in y0 of the cost function without any constraints. ∂E(x0 ,y0 ) ∂x0 F = ∂E(x =0 0 ,y0 )
(3)
∂y0
This work employs Newton-Krylov for optimization, which finds an approximation of the Jacobian for the cost without having to compute the real Jacobian by introducing a parameter as seen in Equation 4. J(u)v is then used for preconditioning. A Jacobian-free preconditioner is passed to the GMRES function until F is finally optimized for each iteration step. Overall, it takes less than 10 iteration steps to optimize F. F(u + v) − F(u) J(u)v ≈
(4)
The source localization method will only be fired for the active sensors that detected that an event happened somewhere in the structure, this is why in Figure 3 not all the 11 sensors’ convergences are listed in the legend. 3. Results Figure 3 displays the result of applying source localization using a grid of distributed sensors at three test points after 100 taps on the board. Between taps, a waiting time of about 2 seconds was done to allow the common log to
Sarah Aguasvivas Manzano et al. / Procedia Manufacturing 24 (2018) 319–324 Sarah Aguasvivas-Manzano / Procedia Manufacturing 00 (2017) 000–000
323 5
refresh itself in case it has data from a previous event and avoid junk results. A circle of a radius of the norm of one standard deviation was drawn around the mean of the predicted points.
Fig. 3. Mean and standard deviation of sensor predictions.
Figure 3 represents the convergence steps done by the Jacobian-free Newton Krylov method in the remaining active sensors. A cutoff of 10 iteration steps was imposed in the algorithm. Notice that not all of the 11 sensors are included in the plot. This is because some sensors from the network were purposely disconnected to show the convergence despite failing sensors.
Fig. 4. Example of the distributed convergence of the Jacobian-Free Newton Krylov for active sensors.
4. Discussion The source localization results are not always accurate due to the proximity of the sensors to the test points. However, this method tends to be more accurate when the taps occur toward the center of the board since a higher number of sensors can enclose the test point inside a triangle to perform the optimization. From Figure 3 we can see that the Newton Method highly depends on the initial guess. This brings the need for a new distributed method to calculate the source. From Figure 3 the robustness of the distributed implementation of the triangulation method is implied. When a few sensors were unplugged from the network or simply had legitimate failures, it did not stop the client from trying to
Sarah Aguasvivas Manzano et al. / Procedia Manufacturing 24 (2018) 319–324 Sarah Aguasvivas-Manzano / Procedia Manufacturing 00 (2017) 000–000
324 6
find the source of the impact. This proves that the method is robust and accounts for missing connections. If a sensor failed after performing some computations, its connection simply stopped and other sensors would not choose that sensors because of the relatively old data that the sensor left in the log. 5. Conclusions and future work The experiments showed that it is possible to fabricate a robust self-sensing composite that is wireless and capable of estimating the source of an impact online without the need for having all the sensors be fully-functional at all times. The numerical results show relatively low error for regions in which there were sufficient sensors to enclose a triangle with but high errors in regions where there weren’t enough sensors to form a triangle. The presented method, however, shows limitations such as dependency on initial guess of the position due to the Newton optimization. While these results do not yet prove reliability of the method, they prove that it is possible to build a robotic material capable of wireless online self sensing. There were preexisting challenges associated with this method. The first one is the sensor synchronization in a distributed fashion. This was not achieved to its true potential. A Firefly-based algorithm [5] will be evaluated in order to perform this synchronization. Another future work item to be completed is to replace the hand-coded algorithm here using supervised learning across the wireless channel as proposed in [10]. Acknowledgments This research has been supported by the Airforce Office of Scientific Research, we are grateful for this support. References [1] A. G. Anisimov, B. Muller, J. Sinke, and R. M. Groves. Strain characterization of embedded aerospace smart materials using shearography. volume 9435, pages 943524–943524–10. SPIE, 2015. [2] J. Brown. Algebraic solvers. https://github.com/cucs-numpde/class/blob/master/AlgebraicSolvers.ipynb, 2017. [3] A. Canclini, F. Antonacci, A. Sarti, and S. Tubaro. Acoustic source localization with distributed asynchronous microphone networks. IEEE Transactions on Audio, Speech, and Language Processing, 21(2):439–443, 2013. [4] Nikolaus Correll, Prabal Dutta, Richard Han, and Kristofer Pister. New directions: Wireless robotic materials. 2017. [5] M. Grzenda, A. I. Awad, J. Furtak, and J. Legierski. Advances in network systems: architectures, security, and applications, volume 461. Springer, Cham, 2017. [6] A. T. Hayes, A. Martinoli, and R. M. Goodman. Distributed odor source localization. IEEE Sensors Journal, 2(3):260–271, Jun 2002. [7] I Hostage, G. M. and L. R. Broadwell. Resilient command and control: The need for distributed control. Joint Force Quarterly : JFQ, (74):38–43, Third 2014. Copyright - Copyright National Defense University Third Quarter 2014; Last updated - 2014-10-01. [8] A. Hu, T. Lv, H. Gao, Z. Zhang, and S. Yang. An esprit-based approach for 2-d localization of incoherently distributed sources in massive mimo systems. IEEE Journal of Selected Topics in Signal Processing, 8(5):996–1011, Oct 2014. [9] D. Hughes and N. Correll. Texture recognition and localization in amorphous robotic skin. 10(5):055002, 2015. [10] D. Hughes and N. Correll. Distributed machine learning in materials that couple sensing, actuation, computation and communication. 2016. [11] T. E. Kalayci and A. Uur. Genetic algorithmbased sensor deployment with area priority. Cybernetics and Systems, 42(8):605–620, 2011. [12] T. Kundu. Acoustic source localization. Ultrasonics, 54(1):25 – 38, 2014. [13] C. Lin, Z. Lin, R. Zheng, G. Yan, and G. Mao. Distributed source localization of multi-agent systems with bearing angle measurements. IEEE Transactions on Automatic Control, 61(4):1105–1110, April 2016. [14] M. A. McEvoy and N. Correll. Shape-Changing Robotic Materials Using Variable Stiffness Elements and Distributed Control. PhD thesis, 2017. [15] Carosena Meola. Recent advances in non-destructive inspection. Nova Science Publishers, New York, 2010. [16] S. Rana, P. Subramani, R. Fangueiro, and A. Gomes Correia. A review on smart self-sensing composite materials for civil engineering applications. AIMS Materials Science, 3(matersci-03-00357):357, 2016. [17] K. Teotrakool, M. J. Devaney, and L. Eren. Adjustable-speed drive bearing-fault detection via wavelet packet decomposition. IEEE Transactions on Instrumentation and Measurement, 58(8):2747–2754, 2009.
