- Email: [email protected]
Lighting Pole Health Monitoring for Predictive Maintenance
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Structural Integrity Procedia 00 (2019) 000–000 Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2019) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Structural Integrity 17 (2019) 799–805
ICSI 2019 The 3rd International Conference on Structural Integrity ICSI 2019 The 3rd International Conference on Structural Integrity
Lighting Pole Health Monitoring for Predictive Maintenance Lighting Pole Health Monitoring for Predictive Maintenance Pavel Steinbaueraa, Zdenek Neusseraa, Ivo Bukovskyaa, Milos Nerudabb* Pavel Steinbauer , Zdenek Neusser , Ivo Bukovsky , Milos Neruda *
Czech Technical University in Prague, Faculty of Mechanical Engineering, Technická 4, Praha 6, Czech Republic b Czech Technical University in Prague, Faculty of1010/14, Mechanical Technická 4, Praha 6, Czech Republic Eltodo, a.s., Novodvorská 142Engineering, 00 Praha 4, Czech Republic b Eltodo, a.s., Novodvorská 1010/14, 142 00 Praha 4, Czech Republic
a a
Abstract Abstract The structural health of lighting poles is of great interest. Corrosive deterioration of lighting poles is dangerous and difficult to The structural lighting poles is of great Corrosive deterioration of lighting polesreplacement is dangerousofand difficult to forecast due to health varyingofoperating conditions of theinterest. lighting poles. The standard prevention is regular lighting poles. forecast due to varying operating conditions of the lighting poles. The standard prevention is regular lighting poles. Such solution is expensive, workforce demanding and unreliable. That’s why the methods forreplacement predictive of maintenance are Such solution is expensive, workforce and only unreliable. why but the also methods predictive maintenance are developed, leading to smart pole concept.demanding Such poles not monitorThat’s themselves, learn for about their initial properties and developed, leading to smart poleThe concept. Such poles notinto onlynetworks monitor with themselves, but alsoso learn their initial and compare with their actual state. poles are connected low bandwidth theyabout can report about properties their changes. compare with theiractions actual state. The poles are connected into networks lowThe bandwidth so they can report about their vibration changes. The maintenance can be then directed into deteriorated areaswith only. monitoring is based on continuous The maintenance actionsproperties can be then directed into deteriorated areas only. The monitoring is based on continuous vibration measurement and modal estimation. measurement and modal properties estimation. © 2019 The Authors. Published by Elsevier B.V. © 2019 Published by Elsevier B.V. B.V. © 2019The TheAuthors. Authors. Published by Peer-review under responsibility of Elsevier the ICSI organizers. Peer-review under responsibility of the ICSI 2019 2019 organizers. Peer-review under responsibility of the ICSI 2019 organizers. Keywords: Lighting pole; modal properties; predictive maintenance; continuous monitoring Keywords: Lighting pole; modal properties; predictive maintenance; continuous monitoring
1. Introduction 1. Introduction Extensive corrosive damage of lighting poles may lead to unexpected falls of poles, which may cause dangerous Extensive corrosive damage of lighting poles may leadStraßenbeleuchtung to unexpected falls of poles, which may cause dangerous injuries and accidents. The Roch “Infrastrukturprojekt 2000” (Infrastructure Project: Street injuries and accidents. The Roch “Infrastrukturprojekt Straßenbeleuchtung 2000” (Infrastructure Project: Lighting 2000) study on the structural stability of pole systems, which is representative on a Germany-wide Street scale, Lighting onpoles the structural stability(Roch of pole systems, which(2019)). is representative on a prevention Germany-wide scale, found that2000) 3.3%study of all pose a hazard Services GmbH, The standard is regular found that 3.3% of all poles pose a hazard (Roch Services GmbH, (2019)). The standard prevention is regular replacement of lighting poles (e.g. Prague standard in Czech Republic is replacement of each pole every 15 years). replacement of lighting poles (e.g. Prague standard in Czech Republic is replacement of each pole every 15 years).
* Corresponding author. Tel.: +420-224357568. * Corresponding Tel.: +420-224357568. E-mail address:author. E-mail:[email protected] E-mail address: E-mail:[email protected] 2452-3216 © 2019 The Authors. Published by Elsevier B.V. 2452-3216 2019responsibility The Authors. of Published Elsevier B.V. Peer-review©under the ICSIby 2019 organizers. Peer-review under responsibility of the ICSI 2019 organizers.
2452-3216 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. 10.1016/j.prostr.2019.08.106
800 2
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
But it has been shown that some poles are deteriorated well before this period (Fig. 1) while the other poles are in perfect state after 50 years of operation. 2. Motivation There are already many non-destructive methods to determine actual state of construction, e.g. based on eddy currents, ultrasound, roentgen, static loading, frequency response functions (FRF) etc. Two categories of measurement methods can be distinguished.
Fig. 1. Typical extensive erosive damage of the pole, covered by painting
Many NDT methods measure properties of the construction (eddy currents, ultrasound etc.) at specific point only. It is therefore necessary to either investigate whole structure step-by-step or at least have previously determined critical points for regular measurement. In case of lighting poles, the most dangerous are pole heel area (both above and under the ground) and pole boom which is usually several meters above the ground. So, the measurement itself require special equipment, highly skilled worker and ability to correctly process tremendous amounts of output data (including proper linkage to the geometrical position of measured point). The static pole deflection and FRF measurement are global methods. The local changes or deterioration of the structure health are detected by one measurement or only few measured quantities. For example, commercially available Roch Test method (Roch Services GmbH, (2019)) is based on pole compliance measurement by sophisticated device, which regularly travels around pole set, loads the pole by hydraulic actuator and measures pole deflection. Such approach can estimate pole state, but it is time consuming, must be also performed by skilled worker, require installation of special and very expensive equipment, access to the pole and often provide local information only (related to the position of measurement). The experimental modal analyses (EMA) or frequency response function measurement (FRF) should reveal the change of the construction state in determined modal parameters (Salawu, O. S. (1997), Adams, R. D., Cawley, P., Pye, C. J., & Stone, B. J. (1978), Wang, Z., Lin, R. M., & Lim, M. K. (1997)) due to change of mechanical properties. However, the EMA method requires accurate measurement. It means both expensive equipment, highly skilled and trained worker for measurement and subsequent data evaluation. It is also very time demanding and access to the structure must be granted. The source of excitation force must be placed to more flexible parts of the construction, in case of the pole it means to higher, less accessible parts of the pole. So it is feasible to use these methods for large, expensive and exposed constructions and machines, not for relatively inexpensive lighting poles. Although their stability and safety is of great public interest. On the other hand, basic measurement means (MEMS transducers, AD converters, data storage and transfer) are currently getting very cheap. Huge computational power is available on demand within cloud based services and in
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
801 3
low cost processors as well. The cheap communication infrastructure is built within initiative INDUSTRY 4.0 and Internet of Things (IoT), although available bandwidth is very low, so transferred data amounts are limited. 3. Introductory experiments Large sets of lighting poles were investigated. The experimental modal analyses and force-deflection measurement were carried out, together with pole dimension measurement. The deflection was measured using harmonic, low frequency force excitation, which does not require absolute support construction and point (Steinbauer, P., & Valášek, M. (2010)). Several interesting conclusions could have been drawn. The pole is relatively cheap product. The production tolerances are not set very strictly. In addition, in many cases the production did use completely different materials. Thus the pole dimension variation is large, even among the sets with same manufacturer specification. The measurement conclusions must be drawn with respect to each particular pole separately.
Fig. 2 Pole’s FRFs and deflection measurementa and resulting deflection curvesb using laser Doppler vibrometer
The pole compliance measurement is reliable method to determine pole deterioration (Fig. 3 b). Due to the mechanical parameter variations within pole sets, it can be used only to compare repeated measurements of one pole. Comparison between several poles can be done only if the dimensions and other mechanical parameters were verified to be within tolerance. Experimental modal analyses has shown that eigen frequency shift is very small, almost negligible at lower eigen frequencies, even for quite extensive damage. Higher eigen frequencies exhibit still small, but usually measurable change (Fig. 3). The differences in higher eigen frequencies could be clearly determined. That’s why, if the experimental modal analyses or at least one frequency response function could be measured regularly, the change of selected eigen frequency will indicated deterioration of pole state of health.
Fig. 3. Pole’s frequency response function differences (fixed in concretea, fixed in sand bedb)
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Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
The modal damping is difficult to be used for construction change assessment. Determination of modal damping is erroneous and usually covered by ambient influences (e.g. ground, sand damping and their changes due temperature and humidity changes). Frequency response function measured at suitable point of the pole (outside nodes of the structure) is sufficient to determine pole damage. The conclusions can only be made using repeated measurements of pole’s FRFs. Eigen frequency differences between similar, healthy poles are significant. Unfortunately, realization of experimental modal analyses measurement cannot be done by unskilled worker even for simple cases. It is also even more time demanding then pole loading by static force 4. Device Concept The paper introduces pole health detection method, based on eigen frequency shift determination using continuous acceleration measurement of the pole motion using only ambient excitation (mostly coming from the wind or traffic). The measured acceleration data are evaluated directly by local processor. The processor is integrated with sensitive MEMS accelerometer into one unit installed on the top of the pole. The measurements are carried out in selected intervals during whole pole lifetime. In the beginning, the system is initialized and trained. The markers of particular pole are stored. Later on, actual markers are regularly evaluated and compared to initial markers. Thus the methods is independent of the pole type, does not need excitation, special calibration or installation.
Fig. 4 Measurement system structurea and data flowb
The concept is integrated into smart sensor (Fig. 4). The sensor is equipped with sensitive MEMS accelerometer. It carries out long term acceleration time series measurement and performs frequency spectrum calculation using Fast Fourier Transform (FFT). Data are evaluated based on eigen frequency shift and other quantities. The long time series enables to determine frequency spectra with very small frequency step. FFT is computationally demanding, but can be performed in longer time periods, so powerful processor is not needed. The change of pole markers is aggregated into 8bit output key value, which reports state of the smart sensor’s algorithm itself and the pole’s state change. The smart sensor is connected via wireless network grid (IoT Sigfox, IQRF etc.) to the central monitoring server or cloud. The pole regularly reports its state to central server. Inspection and replacement can be then aimed at problematic areas only. The sensor grid reports are also evaluated and compared on central level, so the areas with higher amounts of change reports can be inspected personally. The smart pole replacement based on predictive maintenance principles is more appropriate and enables to achieve both higher safety and significantly decrease maintenance costs. However, there is huge number of poles to be treated (about 1 mil. poles in Czech Republic only). .
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
803 5
5. Evaluation algorithm The pole health state is evaluated using several methods, which are finally aggregated. The first method simply checks, whether static values of the acceleration vector have changed from initialization. The pole tilt caused by severe damage, ground distortion, traffic accident or similar is safely detected. The acceleration is measured in two axis into vectors y1 and y2. 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑔𝑔𝑙𝑙𝑙𝑙𝑙𝑙 = |
asin(𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆) 𝑔𝑔
−
asin([𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦1 ); 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦2 )]) 𝑔𝑔
(1)
|
Frequency analyses are performed only in case of sufficient motions, remarkable excitation of the pole (2)
max(𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦1 ) − 𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦1 ) , 𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦2 ) − 𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦2 )) < 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑛𝑛
The eigenfrequency shift detection is based on peak detection and correlation analysis. The time domain data are processed into frequency domain using fast fourier transform algorithm and treated by coarse graining to remove random outliers
psd t = ( f ) y ( f ) y ( f − f : f + f ) f [ Hz ]
(3)
The spectra is then averaged from several measurements to suppress measurement noise.
pro k
2= N : Taglong = 1, Tag short = 0, Tagtest 0
PSDT == PSDT
( PSD
T
k −1
+ psd t )
1 , k
k= k + 1
(4)
The peak detection is based search algorithm detecting significant peaks in the PSD spectra. The 50Hz region is filtered from the averaged spectra PSDT at first to remove electromagnetic interference. Then the averaged spectra PSDT is iteratively clipped from the maximum to minimum. In each iteration, newly detected frequencies are added into peak set for further analyses. For PSDi = max(PSDT) : min(PSDT), fpeak=find(PSDT>PSDi); PEAK=[PEAK, fpeak]; If length(PEAK)>=Npeakmax, Exit; end;
(5)
where Npeakmax is selected maximum of evaluated peaks. Using correlation analysis, in analogy to cross entropy concept (e.g. RAMOS, Daniel, et al. (2018)), the probability of random quantity is equivalent to statistical frequency of excitated eigen frequencies
p( xi ) PSDTb ( fi ) q( xi ) psd ab ( fi ) Where i = 1K N
b
(6) a N
b
is number of frequency intervals and
PSDTb ( fi ) a psd ab ( f i ) are statistical
frequencies of excitated eigen frequencies in the i-th frequency interval, obtained by accumulation of values binarized
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
804 6
frequency spectra as follows
PSDTb ( fi ) =
1
N PSDT
psd ab ( fi ) =
1 N psda
1 if PSDT ( f ) PSDmin pro f fi − 0.1, fi + 0.1) jinak 0 1 if psda ( f ) PSDmin pro f fi − 0.1, fi + 0.1) jinak 0
(7)
Then entropy analogy for excitated spectra, excluding absolute signal power amplitudes follows
(
)
(
)
H PSDTb ( fi ) = − PSDTb ( fi ) log 2 PSDTb ( fi ) ,
(
i
)
(
)
H PSDTb ( fi ) , psdab ( fi ) = − PSDTb ( fi ) log 2 psdab ( fi ) . i
Difference of reference and actual excited spectra is then obtained as
(
)
(
)
(
PSDTb ( fi ) , psdab ( fi ) = H PSDTb ( fi ) − H PSDTb ( fi ) , psd ab ( fi )
) bit
(8)
6. Results The method was tested on acceleration data measured in the field pole laboratory. Each pole was equipped by MEMS accelerometer ADXL330 glued on the top of the pole (Fig. 5a). The experimental modal analysis was performed to obtain reference modal data of each pole.
Fig. 5 Pole laboratorya, prototype software in Simulinkb
For acceleration measurement, the poles were excited by ambient wind only. Data were processed by prototype software developed in Matlab/Simulink environment, which generated C-code for target platform directly (Fig. 5b). Eigen frequencies detected by the algorithm are shown on the Fig. 6b.
Author name / Structural Integrity Procedia (2019) 000–000 Pavel Steinbauer et al. / Procedia Structural00 Integrity 17 (2019) 799–805
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Fig. 6 Example of windowed measured accelerations on excited polea and corresponding preprocessed frequency spectrumb
7. Conclusions The pole health detection method based on eigen frequency shift determination from acceleration measurement only using ambient excitation (mostly coming from the wind or traffic) was developed. The method was tested on acceleration data measured in the field laboratory installation of poles. The smart sensor has been developed. It is equipped with sensitive MEMS accelerometer, carries out long term acceleration series measurement, performs data evaluation based on eigen frequency shift and other quantities and it is also connected into wireless network grid. The pole thus regularly reports its state to central server. Inspection and replacement can be then aimed at problematic areas only. The sensor grid can be easily extend by additional functionality and sensors. The predictive maintenance will be enabled even for relatively low cost devices like lighting poles. The price of the final smart sensor is comparable to the price of additional protective pole coating or painting. Acknowledgements The support of TACR Epsilon project #TH02010770 INDIVO and TACR project #TN01000071 National Competence Centre of Mechatronics and Smart Technologies for Mechanical Engineering (MESTEC) are greatly acknowledged. References Salawu, O. S. (1997). Detection of structural damage through changes in frequency: a review. Engineering structures, 19(9), 718-723. Adams, R. D., Cawley, P., Pye, C. J., & Stone, B. J. (1978). A vibration technique for non-destructively assessing the integrity of structures. Journal of Mechanical Engineering Science, 20(2), 93-100. Wang, Z., Lin, R. M., & Lim, M. K. (1997). Structural damage detection using measured FRF data. Computer methods in applied mechanics and engineering, 147(1-2), 187-197. Russo, S. (2016). Integrated assessment of monumental structures through ambient vibrations and ND tests: The case of Rialto Bridge. Journal of Cultural Heritage, 19, 402-414. Steinbauer, P., & Valášek, M. (2010). Mechatronic Lighting Pole Testing Device. In Recent Advances in Mechatronics (pp. 127-132). Springer, Berlin, Heidelberg. Roch Services GmbH, (2019). [online], Roch Test Method, [cited 14.4. 2019], https://www.roch-services.co.uk/standsicherheit/rochverfahren RAMOS, Daniel, et al. (2018) Deconstructing cross-entropy for probabilistic binary classifiers. Entropy, 2018, 20.3: 208.
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Structural Integrity Procedia 00 (2019) 000–000 Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2019) 000–000
ScienceDirect
www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia
Procedia Structural Integrity 17 (2019) 799–805
ICSI 2019 The 3rd International Conference on Structural Integrity ICSI 2019 The 3rd International Conference on Structural Integrity
Lighting Pole Health Monitoring for Predictive Maintenance Lighting Pole Health Monitoring for Predictive Maintenance Pavel Steinbaueraa, Zdenek Neusseraa, Ivo Bukovskyaa, Milos Nerudabb* Pavel Steinbauer , Zdenek Neusser , Ivo Bukovsky , Milos Neruda *
Czech Technical University in Prague, Faculty of Mechanical Engineering, Technická 4, Praha 6, Czech Republic b Czech Technical University in Prague, Faculty of1010/14, Mechanical Technická 4, Praha 6, Czech Republic Eltodo, a.s., Novodvorská 142Engineering, 00 Praha 4, Czech Republic b Eltodo, a.s., Novodvorská 1010/14, 142 00 Praha 4, Czech Republic
a a
Abstract Abstract The structural health of lighting poles is of great interest. Corrosive deterioration of lighting poles is dangerous and difficult to The structural lighting poles is of great Corrosive deterioration of lighting polesreplacement is dangerousofand difficult to forecast due to health varyingofoperating conditions of theinterest. lighting poles. The standard prevention is regular lighting poles. forecast due to varying operating conditions of the lighting poles. The standard prevention is regular lighting poles. Such solution is expensive, workforce demanding and unreliable. That’s why the methods forreplacement predictive of maintenance are Such solution is expensive, workforce and only unreliable. why but the also methods predictive maintenance are developed, leading to smart pole concept.demanding Such poles not monitorThat’s themselves, learn for about their initial properties and developed, leading to smart poleThe concept. Such poles notinto onlynetworks monitor with themselves, but alsoso learn their initial and compare with their actual state. poles are connected low bandwidth theyabout can report about properties their changes. compare with theiractions actual state. The poles are connected into networks lowThe bandwidth so they can report about their vibration changes. The maintenance can be then directed into deteriorated areaswith only. monitoring is based on continuous The maintenance actionsproperties can be then directed into deteriorated areas only. The monitoring is based on continuous vibration measurement and modal estimation. measurement and modal properties estimation. © 2019 The Authors. Published by Elsevier B.V. © 2019 Published by Elsevier B.V. B.V. © 2019The TheAuthors. Authors. Published by Peer-review under responsibility of Elsevier the ICSI organizers. Peer-review under responsibility of the ICSI 2019 2019 organizers. Peer-review under responsibility of the ICSI 2019 organizers. Keywords: Lighting pole; modal properties; predictive maintenance; continuous monitoring Keywords: Lighting pole; modal properties; predictive maintenance; continuous monitoring
1. Introduction 1. Introduction Extensive corrosive damage of lighting poles may lead to unexpected falls of poles, which may cause dangerous Extensive corrosive damage of lighting poles may leadStraßenbeleuchtung to unexpected falls of poles, which may cause dangerous injuries and accidents. The Roch “Infrastrukturprojekt 2000” (Infrastructure Project: Street injuries and accidents. The Roch “Infrastrukturprojekt Straßenbeleuchtung 2000” (Infrastructure Project: Lighting 2000) study on the structural stability of pole systems, which is representative on a Germany-wide Street scale, Lighting onpoles the structural stability(Roch of pole systems, which(2019)). is representative on a prevention Germany-wide scale, found that2000) 3.3%study of all pose a hazard Services GmbH, The standard is regular found that 3.3% of all poles pose a hazard (Roch Services GmbH, (2019)). The standard prevention is regular replacement of lighting poles (e.g. Prague standard in Czech Republic is replacement of each pole every 15 years). replacement of lighting poles (e.g. Prague standard in Czech Republic is replacement of each pole every 15 years).
* Corresponding author. Tel.: +420-224357568. * Corresponding Tel.: +420-224357568. E-mail address:author. E-mail:[email protected] E-mail address: E-mail:[email protected] 2452-3216 © 2019 The Authors. Published by Elsevier B.V. 2452-3216 2019responsibility The Authors. of Published Elsevier B.V. Peer-review©under the ICSIby 2019 organizers. Peer-review under responsibility of the ICSI 2019 organizers.
2452-3216 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. 10.1016/j.prostr.2019.08.106
800 2
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
But it has been shown that some poles are deteriorated well before this period (Fig. 1) while the other poles are in perfect state after 50 years of operation. 2. Motivation There are already many non-destructive methods to determine actual state of construction, e.g. based on eddy currents, ultrasound, roentgen, static loading, frequency response functions (FRF) etc. Two categories of measurement methods can be distinguished.
Fig. 1. Typical extensive erosive damage of the pole, covered by painting
Many NDT methods measure properties of the construction (eddy currents, ultrasound etc.) at specific point only. It is therefore necessary to either investigate whole structure step-by-step or at least have previously determined critical points for regular measurement. In case of lighting poles, the most dangerous are pole heel area (both above and under the ground) and pole boom which is usually several meters above the ground. So, the measurement itself require special equipment, highly skilled worker and ability to correctly process tremendous amounts of output data (including proper linkage to the geometrical position of measured point). The static pole deflection and FRF measurement are global methods. The local changes or deterioration of the structure health are detected by one measurement or only few measured quantities. For example, commercially available Roch Test method (Roch Services GmbH, (2019)) is based on pole compliance measurement by sophisticated device, which regularly travels around pole set, loads the pole by hydraulic actuator and measures pole deflection. Such approach can estimate pole state, but it is time consuming, must be also performed by skilled worker, require installation of special and very expensive equipment, access to the pole and often provide local information only (related to the position of measurement). The experimental modal analyses (EMA) or frequency response function measurement (FRF) should reveal the change of the construction state in determined modal parameters (Salawu, O. S. (1997), Adams, R. D., Cawley, P., Pye, C. J., & Stone, B. J. (1978), Wang, Z., Lin, R. M., & Lim, M. K. (1997)) due to change of mechanical properties. However, the EMA method requires accurate measurement. It means both expensive equipment, highly skilled and trained worker for measurement and subsequent data evaluation. It is also very time demanding and access to the structure must be granted. The source of excitation force must be placed to more flexible parts of the construction, in case of the pole it means to higher, less accessible parts of the pole. So it is feasible to use these methods for large, expensive and exposed constructions and machines, not for relatively inexpensive lighting poles. Although their stability and safety is of great public interest. On the other hand, basic measurement means (MEMS transducers, AD converters, data storage and transfer) are currently getting very cheap. Huge computational power is available on demand within cloud based services and in
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
801 3
low cost processors as well. The cheap communication infrastructure is built within initiative INDUSTRY 4.0 and Internet of Things (IoT), although available bandwidth is very low, so transferred data amounts are limited. 3. Introductory experiments Large sets of lighting poles were investigated. The experimental modal analyses and force-deflection measurement were carried out, together with pole dimension measurement. The deflection was measured using harmonic, low frequency force excitation, which does not require absolute support construction and point (Steinbauer, P., & Valášek, M. (2010)). Several interesting conclusions could have been drawn. The pole is relatively cheap product. The production tolerances are not set very strictly. In addition, in many cases the production did use completely different materials. Thus the pole dimension variation is large, even among the sets with same manufacturer specification. The measurement conclusions must be drawn with respect to each particular pole separately.
Fig. 2 Pole’s FRFs and deflection measurementa and resulting deflection curvesb using laser Doppler vibrometer
The pole compliance measurement is reliable method to determine pole deterioration (Fig. 3 b). Due to the mechanical parameter variations within pole sets, it can be used only to compare repeated measurements of one pole. Comparison between several poles can be done only if the dimensions and other mechanical parameters were verified to be within tolerance. Experimental modal analyses has shown that eigen frequency shift is very small, almost negligible at lower eigen frequencies, even for quite extensive damage. Higher eigen frequencies exhibit still small, but usually measurable change (Fig. 3). The differences in higher eigen frequencies could be clearly determined. That’s why, if the experimental modal analyses or at least one frequency response function could be measured regularly, the change of selected eigen frequency will indicated deterioration of pole state of health.
Fig. 3. Pole’s frequency response function differences (fixed in concretea, fixed in sand bedb)
802 4
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
The modal damping is difficult to be used for construction change assessment. Determination of modal damping is erroneous and usually covered by ambient influences (e.g. ground, sand damping and their changes due temperature and humidity changes). Frequency response function measured at suitable point of the pole (outside nodes of the structure) is sufficient to determine pole damage. The conclusions can only be made using repeated measurements of pole’s FRFs. Eigen frequency differences between similar, healthy poles are significant. Unfortunately, realization of experimental modal analyses measurement cannot be done by unskilled worker even for simple cases. It is also even more time demanding then pole loading by static force 4. Device Concept The paper introduces pole health detection method, based on eigen frequency shift determination using continuous acceleration measurement of the pole motion using only ambient excitation (mostly coming from the wind or traffic). The measured acceleration data are evaluated directly by local processor. The processor is integrated with sensitive MEMS accelerometer into one unit installed on the top of the pole. The measurements are carried out in selected intervals during whole pole lifetime. In the beginning, the system is initialized and trained. The markers of particular pole are stored. Later on, actual markers are regularly evaluated and compared to initial markers. Thus the methods is independent of the pole type, does not need excitation, special calibration or installation.
Fig. 4 Measurement system structurea and data flowb
The concept is integrated into smart sensor (Fig. 4). The sensor is equipped with sensitive MEMS accelerometer. It carries out long term acceleration time series measurement and performs frequency spectrum calculation using Fast Fourier Transform (FFT). Data are evaluated based on eigen frequency shift and other quantities. The long time series enables to determine frequency spectra with very small frequency step. FFT is computationally demanding, but can be performed in longer time periods, so powerful processor is not needed. The change of pole markers is aggregated into 8bit output key value, which reports state of the smart sensor’s algorithm itself and the pole’s state change. The smart sensor is connected via wireless network grid (IoT Sigfox, IQRF etc.) to the central monitoring server or cloud. The pole regularly reports its state to central server. Inspection and replacement can be then aimed at problematic areas only. The sensor grid reports are also evaluated and compared on central level, so the areas with higher amounts of change reports can be inspected personally. The smart pole replacement based on predictive maintenance principles is more appropriate and enables to achieve both higher safety and significantly decrease maintenance costs. However, there is huge number of poles to be treated (about 1 mil. poles in Czech Republic only). .
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
803 5
5. Evaluation algorithm The pole health state is evaluated using several methods, which are finally aggregated. The first method simply checks, whether static values of the acceleration vector have changed from initialization. The pole tilt caused by severe damage, ground distortion, traffic accident or similar is safely detected. The acceleration is measured in two axis into vectors y1 and y2. 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑔𝑔𝑙𝑙𝑙𝑙𝑙𝑙 = |
asin(𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆) 𝑔𝑔
−
asin([𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦1 ); 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦2 )]) 𝑔𝑔
(1)
|
Frequency analyses are performed only in case of sufficient motions, remarkable excitation of the pole (2)
max(𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦1 ) − 𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦1 ) , 𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦2 ) − 𝑚𝑚𝑚𝑚𝑚𝑚(𝑦𝑦2 )) < 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑛𝑛
The eigenfrequency shift detection is based on peak detection and correlation analysis. The time domain data are processed into frequency domain using fast fourier transform algorithm and treated by coarse graining to remove random outliers
psd t = ( f ) y ( f ) y ( f − f : f + f ) f [ Hz ]
(3)
The spectra is then averaged from several measurements to suppress measurement noise.
pro k
2= N : Taglong = 1, Tag short = 0, Tagtest 0
PSDT == PSDT
( PSD
T
k −1
+ psd t )
1 , k
k= k + 1
(4)
The peak detection is based search algorithm detecting significant peaks in the PSD spectra. The 50Hz region is filtered from the averaged spectra PSDT at first to remove electromagnetic interference. Then the averaged spectra PSDT is iteratively clipped from the maximum to minimum. In each iteration, newly detected frequencies are added into peak set for further analyses. For PSDi = max(PSDT) : min(PSDT), fpeak=find(PSDT>PSDi); PEAK=[PEAK, fpeak]; If length(PEAK)>=Npeakmax, Exit; end;
(5)
where Npeakmax is selected maximum of evaluated peaks. Using correlation analysis, in analogy to cross entropy concept (e.g. RAMOS, Daniel, et al. (2018)), the probability of random quantity is equivalent to statistical frequency of excitated eigen frequencies
p( xi ) PSDTb ( fi ) q( xi ) psd ab ( fi ) Where i = 1K N
b
(6) a N
b
is number of frequency intervals and
PSDTb ( fi ) a psd ab ( f i ) are statistical
frequencies of excitated eigen frequencies in the i-th frequency interval, obtained by accumulation of values binarized
Pavel Steinbauer et al. / Procedia Structural Integrity 17 (2019) 799–805 Author name / Structural Integrity Procedia 00 (2019) 000–000
804 6
frequency spectra as follows
PSDTb ( fi ) =
1
N PSDT
psd ab ( fi ) =
1 N psda
1 if PSDT ( f ) PSDmin pro f fi − 0.1, fi + 0.1) jinak 0 1 if psda ( f ) PSDmin pro f fi − 0.1, fi + 0.1) jinak 0
(7)
Then entropy analogy for excitated spectra, excluding absolute signal power amplitudes follows
(
)
(
)
H PSDTb ( fi ) = − PSDTb ( fi ) log 2 PSDTb ( fi ) ,
(
i
)
(
)
H PSDTb ( fi ) , psdab ( fi ) = − PSDTb ( fi ) log 2 psdab ( fi ) . i
Difference of reference and actual excited spectra is then obtained as
(
)
(
)
(
PSDTb ( fi ) , psdab ( fi ) = H PSDTb ( fi ) − H PSDTb ( fi ) , psd ab ( fi )
) bit
(8)
6. Results The method was tested on acceleration data measured in the field pole laboratory. Each pole was equipped by MEMS accelerometer ADXL330 glued on the top of the pole (Fig. 5a). The experimental modal analysis was performed to obtain reference modal data of each pole.
Fig. 5 Pole laboratorya, prototype software in Simulinkb
For acceleration measurement, the poles were excited by ambient wind only. Data were processed by prototype software developed in Matlab/Simulink environment, which generated C-code for target platform directly (Fig. 5b). Eigen frequencies detected by the algorithm are shown on the Fig. 6b.
Author name / Structural Integrity Procedia (2019) 000–000 Pavel Steinbauer et al. / Procedia Structural00 Integrity 17 (2019) 799–805
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Fig. 6 Example of windowed measured accelerations on excited polea and corresponding preprocessed frequency spectrumb
7. Conclusions The pole health detection method based on eigen frequency shift determination from acceleration measurement only using ambient excitation (mostly coming from the wind or traffic) was developed. The method was tested on acceleration data measured in the field laboratory installation of poles. The smart sensor has been developed. It is equipped with sensitive MEMS accelerometer, carries out long term acceleration series measurement, performs data evaluation based on eigen frequency shift and other quantities and it is also connected into wireless network grid. The pole thus regularly reports its state to central server. Inspection and replacement can be then aimed at problematic areas only. The sensor grid can be easily extend by additional functionality and sensors. The predictive maintenance will be enabled even for relatively low cost devices like lighting poles. The price of the final smart sensor is comparable to the price of additional protective pole coating or painting. Acknowledgements The support of TACR Epsilon project #TH02010770 INDIVO and TACR project #TN01000071 National Competence Centre of Mechatronics and Smart Technologies for Mechanical Engineering (MESTEC) are greatly acknowledged. References Salawu, O. S. (1997). Detection of structural damage through changes in frequency: a review. Engineering structures, 19(9), 718-723. Adams, R. D., Cawley, P., Pye, C. J., & Stone, B. J. (1978). A vibration technique for non-destructively assessing the integrity of structures. Journal of Mechanical Engineering Science, 20(2), 93-100. Wang, Z., Lin, R. M., & Lim, M. K. (1997). Structural damage detection using measured FRF data. Computer methods in applied mechanics and engineering, 147(1-2), 187-197. Russo, S. (2016). Integrated assessment of monumental structures through ambient vibrations and ND tests: The case of Rialto Bridge. Journal of Cultural Heritage, 19, 402-414. Steinbauer, P., & Valášek, M. (2010). Mechatronic Lighting Pole Testing Device. In Recent Advances in Mechatronics (pp. 127-132). Springer, Berlin, Heidelberg. Roch Services GmbH, (2019). [online], Roch Test Method, [cited 14.4. 2019], https://www.roch-services.co.uk/standsicherheit/rochverfahren RAMOS, Daniel, et al. (2018) Deconstructing cross-entropy for probabilistic binary classifiers. Entropy, 2018, 20.3: 208.