PSI - Issue 17

Volume 17 • 201 9

ISSN 2452-3216

ELSEVIER

3rd International Conference on Structural Integrity, ICSI 2019, 2-5 September 2019, Funchal, Madeira, Portugal

Guest Editors: P edro M . G .P. Moreira P aulo J. S . T avares

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Procedia Structural Integrity 17 (2019) 1–4 ICSI 2019 The 3rd International Conference on Structural Integrity Editorial Pedro Moreira*, Paulo J. Tavares INEGI – Institute of Scienc and Innovation in Mechanical and Industrial Engineering, Porto, Portugal ICSI 2019 The 3rd International Conference on Structural Integrity Editorial Pedro Moreira*, Paulo J. Tavares INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal ICSI 2019 The 3rd International Conference on Structural Integrity Editorial Pedro Moreira*, Paulo J. Tavares INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers.

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. In 2019, ICSI made an effort to return to the delegates a part of their dedication and enthusiastic support from the previous editions, in the shape of increased visibility to the conference and scientific impact. ICSI2019 therefore launched a number of invitations to prominent researchers all over the globe to lecture on their own research fields, such as Prof. Prof. Xiaosu Yi; Prof. Aleksander Sedmak; Prof. Alexopoulos Nikolaos; Prof. Constantinos Soutis, and; Prof. Øystein Grong. In view of the success of the ICSI2017 thematic symposia, ICSI2019 has also been organized into a general track and thematic symposia. Apart from the publication of the proceedings in Procedia Structural Integrity and a special issue in Theoretical and Applied Fracture Mechanics, special issues were also offered in Applied Composite Materials and Engineering Failure Analysis, highly relevant journals in the field of Structural Integrity. The response to these efforts has been outstanding: Nine symposia were proposed and accepted for organization; the number of abstract submissions was kept at a similar level to 2017 with 197 communications approved for oral communication from a total of 229 received abstracts, and 131 full papers accepted for publication in the conference proceedings. Research activity in Structural Integrity has seen an emerging increase in recent years and spread throughout a number of exciting areas. ICSI focuses on all aspects and scales of structural integrity. This ranges from basics to future trends, with special emphasis on multi-scale and multi-physics approaches, and applications to new materials and challenging environments. Current research topics in the realm of Structural Integrity targeted by ICSI2019 include, but are not limited to Fracture and Fatigue, Stress Analysis, Damage Tolerance, Durability, Crack Closure, Joining Technologies, Nanomechanics and Nanomaterials, Ageing, Coatings Technology, Environmental Effects, Structural Health Monitoring, New materials, Surface Engineering, Structural Integrity in Biomechanics and many other exciting research topics. In 2019, ICSI made an effort to return to the delegates a part of their dedication and enthusiastic support from the previous editions, in the shape of increased visibility to the conference and scientific impact. ICSI2019 therefore launched a number of invitations to prominent researchers all over the globe to lecture on their own research fields, such as Prof. Prof. Xiaosu Yi; Prof. Aleksander Sedmak; Prof. Alexopoulos Nikolaos; Prof. Constantinos Soutis, and; Prof. Øystein Grong. In view of the success of the ICSI2017 thematic symposia, ICSI2019 has also been organized into a general track and thematic symposia. Apart from the publication of the proceedings in Procedia Structural Integrity and a special issue in Theoretical and Applied Fracture Mechanics, special issues were also offered in Applied Composite Materials and Engineering Failure Analysis, highly relevant journals in the field of Structural Integrity. The response to these efforts has been outstanding: Nine symposia were proposed and accepted for organization; the number of abstract submissions was kept at a similar level to 2017 with 197 communications approved for oral communication from a total of 229 received abstracts, and 131 full papers accepted for publication in the conference proceedings. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. Research activity in Structural Integrity has seen an emerging increase in recent years and spread throughout a number of excitin areas. ICSI focuses on all aspects and scales of structural integrity. This ranges from basics to fut re trends, with special emphasis on multi-scale and multi-physics approaches, and applications to new materials and challenging nvironments. Current research topics in the realm of Structural Integrity targeted by ICSI2019 include, but are not limited t Fracture and Fatigue, Stress Analysis, Damage T leranc , D rability, Crack Closure, Joining T chnologies, Nanom chanics and Nanomaterials, Ageing, Coatings Technology, Environmental Effects, Stru tural Health M nitoring, New materials, Surf ce Engineering, Structural Integrity in Biomechanics and many other exciting research topics. In 2019, ICSI made an effort to ret rn to the delegates a part of their dedication and enthusiastic support from the previous editions, in the shape of increa ed visibility to t conference and scientific impact. ICSI2019 therefore launched a numb r of invitations to promi ent researchers all over the globe to lecture on their own research fields, such as Prof. Prof. Xi osu Yi; Prof. Aleksander Sedmak; Prof. Alexopoulos Nikolaos; Prof. Constantinos Soutis, and; Prof. Øystein Grong. In view of the success of the ICSI2017 thematic symposia, ICSI2019 has also been organized into a general track and thematic symposia. Ap rt from the publication of the pr ceedings in Procedia Structur l Integrity and a special issue in Theoretical and Applied Fracture Mechanics, special issues were also offered in Applied C mposite Materials and Engineering Failure Analysis, highly relevant journals in the field of Structural Integrity. The response to these efforts has been outstanding: Nine symposia were proposed and accepted for organization; the number of abstract submissions was kept at a similar level to 2017 with 197 communications approved for oral communication from a total of 229 received abstracts, and 131 full papers accepted for publication in the conference proceedings. Research activity in Structural Integrity has seen an emerging increase in recent years and spread throughout a number of exciting areas. ICSI focuses on all aspects and scales of structural integrity. This ranges from basics to future trends, with special emphasis on multi-scale and multi-physics approaches, and applications to new materials and challenging environments. Current research topics in the realm of Structural Integrity targeted by ICSI2019 include, but are not limited to Fracture and Fatigue, Stress Analysis, Damage Tolerance, Durability, Crack Closure, Joining Technologies, Nanomechanics and Nanomaterials, Ageing, Coatings Technology, Environmental Effects, Structural Health Monitoring, New materials, Surface Engineering, Structural Integrity in Biomechanics and many other exciting research topics.

2452-3216 © 2019 The Authors. Published by Elsevier B.V. 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. 2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. * Corresponding author. Tel.: +351 225 082 151; fax: +351 229 537 352. E-mail address: pmoreira@inegi.up.pt * Corresponding author. Tel.: +351 225 082 151; fax: +351 229 537 352. E-mail address: pmoreira@inegi.up.pt * Corresponding author. Tel.: +351 225 082 151; fax: +351 229 537 352. E-mail address: pmoreira@inegi.up.pt

2452-3216  2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. 10.1016/j.prostr.2019.08.001

Pedro Moreira et al. / Procedia Structural Integrity 17 (2019) 1–4 Pedro Moreira; Paulo Tavares / Structural Integrity Procedia 00 (2019) 000 – 000

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The biennial ICSI conferences, at the end of summer, were planned to be a referential source of inspiration for the researchers in the field that want to keep updated on the latest developments from reference researchers around the globe. The conference has seen an unprecedented growth in volume and quality and we welcome the reader to judge the excellence of the conference by himself and whether he should attend the next ICSI in 2021. Above all, the organizers believe the ICSI conferences disseminate excellent research and share worthwhile and beneficial knowledge for the enhancement of science and the prosperity of our society, and therefore actively contribute to the preservation and sustainability of our world.

Conference Chairs,

Pedro M. G. P. Moreira Paulo J. S. Tavares INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering

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Committees Chairmen

Pedro Moreira, INEGI, Portugal Paulo Tavares, INEGI, Portugal Organizing Committee Carmen Sguazzo, INEGI, Portugal Virginia Infante, IST, Portugal Luís Reis, IST, Portugal Paulo Lobo, UMA, Portugal Luís Borrego, IPC, Portugal Local Organizing Committee Lino Maia, UMA, Portugal Behzad Farahani, INEGI, Portugal Daniel Braga, IDMEC, Portugal Nuno Viriato, INEGI, Portugal Shayan Eslami, INEGI, Portugal Andreia Flores, INEGI, Portugal Joana Machado, INEGI, Portugal

International Scientific Committee Abilio de Jesus, University of Porto, Portugal

Aleksandar Sedmak, University of Belgrade, Serbia Alexopoulos Nikolaus, University of Aagen, Greece Alfonso Fernandez Canteli, University of Oviedo, Spain Andrea Carpinteri, University of Parma, Italy António Arêde, University of Porto, Portugal Antonio Martin Meizoso, CEIT IK4, Spain António Torres Marques, University of Porto, Portugal Carlos Rebelo, University of Coimbra, Portugal Carmen Sguazzo, INEGI, Portugal Carmine Pappalettere, Politecnico di Bari, Italy Claudia Barile, Politecnico di Bari, Italy Constantinos Soutis, The University of Manchester, UK Daniel Kujawski, Western Michigan University, USA Dariusz Rozumek, Opole University of Technology, Poland

Donka Angelova, University of Chemical Technology and Metallurgy, Bulgaria Filippo Berto, Norwegian University of Science and Technology, Norway Francesco Iacoviello, Università di Cassino e del Lazio Meridionale, Italy Grzegorz Lesiuk, Wroclaw University of Technology and Science, Poland Hannes Körber, Technical University of Munich, Germany Hernani Lopes, Instituto Superior de Engenharia do Porto, Portugal Humberto Varum, University of Porto, Portugal Igor Varfolomeev, Fraunhofer IWM, GermanyPedro Camanho, University of Porto, Portugal J Gordon G Williams, Imperial College London, UK

Jesus Toribio, University of Salamanca, Spain Jidong Kang, CanmetMATERIALS, Canada João Custódio, LNEC, Portugal

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John W. Hutchinson, Harvard University, USA José Correia, INEGI, Portugal José L. Ocaña, Centro Láser UPM, Spain José Xavier, University of Trás-os-Montes e Alto Douro, Portugal

Leslie Banks-Sills, Tel Aviv University, Israel Lino Maia, Universidade da Madeira, Portugal

Luca Susmel, University of Sheffield, UK Lucas da Silva, University of Porto, Portugal Luis Borrego, Instituto Superior de Engenharia de Coimbra, Portugal Luis Reis, Instituto Superior Técnico, Portugal Luis Simões da Silva, University of Coimbra, Portugal Malgorzata Kujawinska, Warsaw University of Technology, Poland Manuel Freitas, Instituto Superior Técnico, Portugal Marcelo Moura, University of Porto, Portugal Marcos Pereira, PUC, Brasil Mário Vaz, University of Porto, Portugal Martins Ferreira, University of Coimbra, Portugal Mieczyslaw Szata, Wroclaw University of Science and Technology, Poland Nikolai Kashaev, Helmholtz-Zentrum Geesthacht, Germany Paulo Tavares, INEGI, Portugal Paulo Lobo, University of Madeira, Portugal Pedro Areias, University of Évora, Portugal Pedro Camanho, University of Porto, Portugal Pedro Moreira, INEGI, Portugal Per Stahle, Lund Institute of Technology, Sweden Peter Horst, Technische Universität Braunschweig, Germany Raj Das, University of Auckland, New Zeland Rui Calçada, University of Porto, Portugal Rui Miranda Guedes, University of Porto, Portugal Sabrina Vantadori, University of Parma, Italy Satish kumar Velaga, Indira Gandhi Centre for Atomic Research, India Spiros Pantelakis, University of Patras, Greece Stefan Pastrama, University Politehnica of Bucharest, Romania Stéphane Sire, Université de Bretagne Occidentale, France Thierry Grosdidier, CNRS UMR, France Thierry Palin- Luc, Ecole Nationale Supérieure d’Arts et Métiers, France Uwe Zerbst, BAM, Germany Valery Shlyannikov, Kazan National Research Technical University, Russia Virginia Infante, Instituto Superior Técnico, Portugal Volnei Tita, Universidade de São Paulo, Brasil Weidong Zhu, University of Maryland, USA Zhiliang Zhang, Norwegian University of Science and Technology, Norway

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Procedia Structural Integrity 17 (2019) 238–245

ICSI 2019 The 3rd International Conference on Structural Integrity Acoustic Emission-Based Similarity Analysis: A Baseline Convergence Algorithm ICSI 2019 The 3rd International Conference on Structural Integrity Acoustic Emission-Based Similarity Analysis: A Baseline Convergence Algorithm

Andra Gabriela Stancu a *, Linghao Zhou a , Slim Soua a a TWI Ltd, Granta Park, Great Abington, Cambridge, CB21 6AL, United Kingdom Andra Gabriela Stancu a *, Linghao Zhou a , Slim Soua a a TWI Ltd, Granta Park, Great Abington, Cambridge, CB21 6AL, United Kingdom

Abstract Condition monitoring has been widely employed to monitor critical components for drivetrains of machinery, whose malfunctioning will inevitably cause unexpected downtime and an increase of maintenance costs. This paper presents a practical condition monitoring technique, which uses a procedure consisting of a baseline definition process, similarity analysis collated with bathtub curve and maintenance decision-making support to enhance the reliability of the machinery. The proposed condition monitoring procedure features the benefits of being scalable and adaptable to multiple sensory technologies including Vibration, Acoustic Emission and Audible Acoustics. Validation of the convergence methodology is performed on a low-speed rotating mechanism. The outcome defines a process of baseline generation and identification of deviations from normal operating conditions. Abstract Condition monitoring has been widely em loyed to monitor critical components for drivetrains of machinery, whose malfunctioning will inevitably cause u expected downtime and an increase of maintenance costs. This paper resents a practical condition monitoring tec nique, which uses a procedure consisting of a baseline definition process, similarity analysis collated with bathtub curve and maintenance decision-making support to enhance the reliability of the achinery. The proposed condition monitoring procedure features the benefits of being scalable and adaptable to multiple sensory technologies including Vibration, Acoustic Emission and Audible Ac ustics. Validation of the convergence methodology is perf rmed on a low-speed rotating mechanism. The outcome defines a process of baseline generation and identification of deviations from normal operating conditions.

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers.

Keywords: Condition Monitoring; Similarity Analysis; Baseline; Acoustic Emission; Reliability; Keywords: Condition Monitoring; Similarity Analysis; Baseline; Acoustic Emission; Reliability;

* Tel.: +44-1223-940-406. E-mail address: andra.stancu@twi.co.uk * Tel.: +44-1223-940-406. E-mail address: andra.stancu@twi.co.uk

2452-3216 © 2019 The Authors. Published by Elsevier B.V. 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.

2452-3216  2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. 10.1016/j.prostr.2019.08.032

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1. Introduction

1.1. Background

Mechanical integrity of drivetrains in complex machineries is critical to maintain, ensuring their availability and reducing unexpected downtime. Successfully applying condition-based maintenance (CBM) brings the benefits of increasing the operators’ awareness of the whole systems, hence scheduling maintenance strate gies that are proactive and targeted to resolve incipient faults before propagation. Being researched and practiced for more than 30 years [1], condition monitoring is one of the most adopted maintenance strategies to guarantee the safety operations of key facilities [2]. Predominantly, condition monitoring applies techniques to collect and translate the key system information through installed sensors, enabling continuous assessment with the help of Digital Signal Processing (DSP) and tracking of the condition of the monitored machinery. This principle has seen application widely in industrial rotating electric machines as reviewed in [1-5]. The cost reduction benefit of condition monitoring is well recognized, enhanced by the rapid development of available sensory technologies and their corresponding signal analysis techniques. Fault detection techniques and reliable monitoring methods for early fault diagnosis have been studied extensively, generating a continuous advancement in statistical analysis algorithms, as well as functional integration and performance of available monitoring products. However, there is still a continuous effort allocated to new developments which can offer better pattern analysis capabilities and lower-cost alternatives to available products and services. The type of the machine failure will dictate most of the time the way to prevent it from happening. Among well-established techniques, vibration analysis is widely used for condition monitoring and fault identification. It is supported by various commercially available accelerometers, offering wide selections of dynamic ranges, utilities and sensitivities. The studies on vibration-based condition monitoring are innumerable. To name a few, in [2] and [5], the authors reviewed vibration-based condition monitoring technologies, while in [6] the authors have explained theoretical methods and summarized applicable vibration processing techniques. While vibration monitoring is the most regularly used technique in rotating machinery applications, monitoring machines operating at very low rotational speed has been reported to negatively influence the effectiveness of vibration analysis [2], [7]. This is due to the compromised vibration signal-to-noise ratio (SNR) when under slow rotation, as well as the limitation of the sensor’s measurable frequency range. Acoustic Emission (AE) is another well-known and promising technology used for a wide range of applications. AE signal is a high frequency elastic wave emitted due to surface or internal deformation of the component being monitored. Typical AE sources could be fault mechanisms including cracks, corrosion, fibers breaking, increased friction, leakage, etc. While AE has been studied extensively for structural integrity and monitoring including bridges or other concrete structures, in recent decades an increasing trend of applying AE-based monitoring for rotating machinery can be observed [8] [9] [10]. It is reported that through appropriate signal processing, AE signals can be used to detect small-scale degradation, incipient defects in both low speed and high speed machines. On the other hand, as pointed out in [11], AE signals are more susceptible to complicated transmission paths, rapid amplitude attenuation as well as demanding in advanced processing techniques. Other monitoring technologies including but not limited to audible acoustics, temperature, infrared, oil debris, current signatures, etc., all have specific applications under certain conditions. 1.2. Condition Monitoring Techniques

1.3. Inspection and condition monitoring applicability

Planned inspections alone cannot capture the degradation mechanism from a one-time evaluation due to signal fluctuation and the coherent noise. Monitoring, on the other hand, addresses these challenges by instantaneously capturing a wide range of parameters and keeping track of their trend. Condition monitoring allows the machinery to be monitored autonomously for a long period, establishing a baseline of the normal operating conditions and giving warnings when signs of degradation are captured. It is therefore, better

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suited for long-term in-situ maintenance schemes, compared to one-time inspections. With the concept and benefits of condition monitoring introduced, there are still challenges remain to be addressed. To begin with, modularization and extraction of the signs of degradation, i.e. parameters, are essential to establish a normal or healthy condition as a baseline. Moreover, to mitigate the fluctuation in the parameters induced by change of environmental condition, optimal number of recordings should be selected for signal conditioning. Finally, the interpretation of the parameters should be discussed. This paper addresses the aforementioned challenges using a combination of signature extraction, baseline convergence and similarity analysis, which will be elaborated in the following sections. The continuous monitoring technology described in this paper aims to generate warnings at the early stages of damage, allowing enough time for the operators to develop and apply predictive maintenance measures. The methodology used is based on a pattern recognition algorithm for machinery operating under healthy conditions, generating a baseline for the identification of arising deviations from the normal operation. The main objective is to create an alarm when the signals captured from the running process deviate from their mean or when the signal variability increases. The condition monitoring process comprises mainly of three components, namely: Signal Acquisition, Baseline Analysis and Similarity Analysis. The core concept is to utilize raw signals captured from different sensory technologies that are applicable, to develop a system signature, defined as a collection of descriptive signal patterns and relevant parameters of the signal. Once the signature is established, the baseline analysis will be conducted to statistically determine, how many signature states can be collectively taken into consideration as a stable system condition, i.e., a baseline. A sample of data can be regarded as a baseline only when it gathers sufficient information about the mechanism’s running state and being stable enough (i.e. its standard deviation is small) for allowing subtle changes to be detected. Finally, from the establishment of the baseline condition onward, the condition monitoring process will be performed using similarity analysis to assess the deviations of the current condition from the baseline. This process is continuously performed for all subsequent conditions, which by nature should have the same size (i.e. number of states) as the established baseline condition. As described in the previous section, although it is commonly recognized that machinery in operation constantly generates various kinds of system information, such as vibration, noise, increase of temperature, etc., this raw data is either unnoticeable or incomprehensible directly to operators. Sensory technologies are applied to capture such system information, which will then be digitalized and processed using DSP methods and translated into meaningful indications and intuitive trends. The first step of the translation process is to acquire a set of parameters from raw signals to establish the state of the monitored system. Assuming that a series of raw signals with predetermined length L has been captured, and processed to acquire N types of parameters {P i | i = 1, 2…N} for the n th time, then the current (n th ) state of the system can be described as a signature set S n : = [ (1) The set S contains the system ’ s historical state (S 1 to S n-1 ), and the current state S n . The value of recording length, L, for each series of signals is determined to cover a span of at least one period of the lowest rotating speed of the machinery system. The selection of the parameters N, should be investigated on a case-by-case basis. 2.1. System parameters and signature set 1 2 3 ⋮ ] 2. Methodology

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2.2. Baseline convergence

By setting up the signature set S, the condition of the system can be established by evaluating the statistical impact of new system states to the historical state sets. To achieve this, the convergence algorithm is implemented. To evaluate the statistical impact of the current state S n on the previous state {S 1 , S 2 , … S n-1 }, firstly the mean value of the state set is defined as: | >1 = ∑ =1 = 1 ∑ =1 [ 1 2 ⋮ ] = [ ( 1 : ), ( 2 : ), ( : )] (2) Where μ n is a matrix of size N x 1. N is the number of types of P, and E is the expectation operator. Therefore, the statistical difference D of two consecutive sets of data, namely {S 1 , S 2 , … S n-1 } and {S 1 , S 2 , … S n } can be estimated by applying the method below: ( , −1 ) = 100% × √ 1 ∑ =1 (1 − ( 1 : ) ( 1 : −1 )) ) 2 (3) Converges when: ( , −1 ) <= Convergence rate (4) In practice, it is beneficial to collect as many relevant parameters as possible. However, their magnitude is generally expressed on different scales. Simply amalgamate them will cause the parameters with higher magnitude to dominante. To produce an overall indicator, reasonably considering the impact of all parameters, the deviation of each parameters in μ n is evaluated in % . Equation 3 describes the distance between two sets of observations in N-dimension. As the observation number n is continuously growing, it is anticipated that D will decrease in an exponential trend, until it converges at n = c. A convergence criterion is used for the evaluation of the signature sets and the parameters contained. If increasing the number of signature sets does not have any sensible effect on the calculated D after a certain n = c, then it is considered that signature set {S 1 , S 2 , … S c } can be seen as a baseline condition Base , containing c observations each with N types of parameters, i.e.: Base = {S 1 , S 2 , … S c }, where c meets the convergence criteria (5) The convergence criterion must be carefully chosen, as it affects the monitoring process. It should guarantee a practically achievable convergence time (which does not take too long to satisfy) and avoids D converging too quickly, failing to capture the baseline condition. Theoretically, c should be determined as the minimum number that satisfies the criteria, since a high value of c will result in subtle defects in the signatures being averaged out. The convergence criteria depends on how complex the machinery monitored is. Generally, it is considered that a recording time of at least 20 times the slowest rotating shaft speed should make a meaningful processing data length [3]. Therefore, the convergence rate should be selected so that it allows at least these amount of data to be recorded for one measurement, and a rule of thumb based on practical experience demands the rate to be 5% - 10%.

2.3. Similarity analysis

The Base condition established in section 2.2 is used in identifying potential defects at later stages of operation. The subsequent conditions, i.e. signature sets, measured are defined in a similar manner and containing an identical number of observations to the Base condition, to be sensibly compared.

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The condition monitoring process assumes the comparison of each signature with the baseline, in which case the process M is similarly defined as Equation 2 and 3, as follows: = [ ( × +1:( +1)× 1 ) ( × +1:( +1)× 2 ) ⋮ ( × +1:( +1)× ) ] (6)

( , ) = 100% × √ 1 ∑ =1 [1 − ( +1:( +1)× ) ( 1 : )) ] 2

(7)

Deviations from the baseline can be plotted as a trend of systematic progression and are calculated using Equations (6) and (7). Assuming the system operates fault free, it is anticipated that M will remain a steady horizontal trend.

3. Case study and algorithm validation

The programme of work carried out involves monitoring of slow speed rotating machinery components (shaft and bearing), using AE. Over the last years, other research studies have focused on the use of AE for this particular application, such as [12], [13]. The development and validation of the algorithm was performed on a practical case study, involving an escalator bearing, which operates constantly at a low speed of 12RPM. The mechanism was regularly inspected using vibration analysis. In spite of the inspection programme in place, unexpected failures occur, without any symptoms detected in advance, hence the motivation for conducting this research. While vibration monitoring is the most regularly used technique in rotating machinery applications, at very low RPM it is difficult to diagnose damage or degradation. One reason is the background noise in the fault signals, being complicated and of the small amount of energy and preventing conventional vibration testing methods from being sensitive enough. Acoustic Emission is a high frequency elastic wave emitted due to structural damage. Fundamentally, the frequency range of acoustic emission signals is broader in comparison with vibration signals and can detect small-scale degradation, such as friction and material loss at very early stages. The use of acoustic emission enables inhibiting the noise interferences and therefore improve diagnostics accuracy. For this reason, this technique is applicable to slow speed rotating machinery and is able to detect small energy release rates. Furthermore, the broader frequency spectrum (1 - 100 MHz) in comparison with vibration signal facilitates various high-frequency or structural resonance related signal processing techniques. 3.1. Technique selection

Figure 1. Acoustic emission signals: representation of continuous and burst type emission.

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Two types of signals can be distinguished in acoustic emission as shown in Figure 1, based on the energy released when a component undergoes stress, namely: burst type and continuous type. The first is more applicable to identifying defects such as cracks, corrosion, fibres breaking, which trigger the acquisition of the signal based on a pre-defined threshold. A continuous AE signal can be associated with yield rather than fracture mechanics. It captures variations in amplitude and frequency, being continuously recorded for a defined period of time, suitable for showing progressive damage and can be better correlated to dislocation avalanching at the leading edge of discontinuities, and discontinuity surfaces rubbing. For the present case study, the encountered damage mechanism can be described mainly by the lack of lubrication causing metal-to-metal contact of the shaft and inner bearing surface. Thus, the energy released in this case would be more sensitively captured in the form of continuous AE type of signals.

3.2. Analysis

The AE data adopted for validation analysis is sampled at 7.2 MHz rate continuously on a rotating machinery. The data analysis was performed as presented in Section 2. The convergence criterion implemented for baseline definition is shown in Figure 2 where the effect of the sample size reaches a standard deviation of 1.091% at approximately 300 measurements, corresponding to 30 minutes of continuous monitoring. Once the baseline condition is identified, the subsequent signatures are gathered, continuously updating the condition monitoring process using similarity analysis,

Figure 2. Baseline analysis, convergence at approximately 300 measurements; Mean (red line) and standard deviation (green line) of the baseline.

Figure 3. Similarity analysis results; Deviation from the baseline remains under 5%.

shown in Figure 3. In Figure 3, the measurements in percentage on the y-axis depict the deviations of the monitoring conditions (x axis) to the predetermined baseline, remaining steadily below 5%. Combining with the fact that no progressively increase trend is observed, it evidently suggests that the machinery being monitored is in healthy state. The percentage reference in this case can only be representative of how high the change is at the monitoring time. Finally, the operator establishes a limit of the deviation allowed, based on previous maintenance experience and observations of the machinery system’s working conditions, as well as the failure incidents history. This threshold is utilized to give out warnings once breached. A generally used model describing the likely failure rates of technologies and products, such as the bathtub curve is used to show the lifetime over a certain period of time. The monitoring process can be employed during any given phase if the three-part timeline presented in Figure 4, correlating any measured deviations from the normal operating conditions to the probability of failure characterized by the life stage of the machinery. It suggests that during useful lift period, the failure rate of the machinery is expected to remain a low and steady horizontal line. This is comparable to the results demonstrated in Figure 3, where the

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deviation of continuous monitoring dropped quickly before entering a horizontally steady line, indicating a low failure rate for the machinery.

Figure 4. Bathtub Curve: the variation of failure rate of components during their life [14].

4. Conclusion and remarks

A long-term monitoring approach was adopted and implemented by means of acoustic emission technique. The collected data was processed using a baseline and signature definition algorithm, followed by a similarity-based analysis. In summary, the baseline definition process requires, in this case, continuous data acquisition during a period of approximately 30 minutes, which will then be used as reference for the assessment of the established signatures. Minor deviation from the baseline was observed during the early stage of monitoring which increases the confidence in the use of the defined baseline. Specifically for rotating machinery, the solution presented was demonstrated successfully, using baseline acquisition and similarity analysis for long-term operation without any interruption. However, the proposed algorithm is intended generally for condition monitoring of various machineries and its use can be extended to a wide range of techniques. For any particular case, a customized approach will require identifying the relevant long-term remote condition monitoring parameters. Furthermore, other physical parameters need further consideration, which could be integrated in the monitoring process, including loading cases, operation schedule, state of lubrication, etc. Establishing a relationship between the likelihood of failure of the machinery of interest with the monitored deviation will enable operators to address efficiently their maintenance planning schemes by evaluating the remaining lifetime. Therefore, future efforts will be taken towards linking the rate of failure to the similarity analysis performed.

Andra Gabriela Stancu et al. / Procedia Structural Integrity 17 (2019) 238–245 A. G. Stancu et al./ Structural Integrity Procedia 00 (2019) 000 – 000

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[1] P. J. Tavner, “Review of condition monitoring of rotating electrical machines,” IET Electric Power Applications 2 (4), p. 215 – 247, 2008. [2] C. Liu and F. Wang, “A review of current condition monitoring and fault diagnosis methods for low -speed and heavy- load slewing bearing,” in 9th International Conference on Modelling, Identification and Control (ICMIC) , 2017. [3] E. P. Carden and P. Fann ing, “Vibration based condition monitoring: a review,” Structural health monitoring 3 (4), pp. 355-377, 2004. [4] M. J. Gómez, C. Castejón and J. C. García- Prada, “Automatic condition monitoring system for crack detection in rotating machinery,” Reliability Engineering & System Safety 152, p. 239 – 247, 2016. [5] R. B. Randall, “Vibration - based condition monitoring: industrial, aerospace and automotive applications,” John Wiley & Sons, 2011. [6] Y. Han and Y. Song, “Condition monitoring techniques for electrical equipment- a literature survey,” IEEE Transactions on Power delivery 18 (1), p. 4 – 13, 2003. [7] E. Y. Kim, A. C. Tan, J. Mathew and B.- S. Yang, “Condition monitoring of low speed bearings: A comparative study of the ultrasound technique versu s vibration measurements,” Australian Journal of Mechanical Engineering 5 (2), pp. 177-189, 2008. [8] D. Mba and R. B. Rao, “Development of acoustic emission technology for condition monitoring and diagnosis of rotating machines; bearings, pumps, gearbo xes, engines and rotating structures.,” The Shock and Vibration Digest Vol 38 (1), pp. 3-16, 2006. [9] F. Elasha, M. Greaves, D. Mba and A. Addali, “Application of acoustic emission in diagnostic of bearing faults within a helicopter gearbox,” in Procedia CIRP 38 , 2015. [10] D. Mba, “Applicability of acoustic emissions to monitoring the mechanical integr ity of bolted structures in low speed rotating machinery: case study,” Ndt & e International 35 (5), pp. 293-300, 2002. [11] J. Sikorska and D. Mba, “Challenges and obstacles in the application of acoustic emission to process machinery,” Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, vol. 222, no. 1, pp. 1-19, 2008. [12] T. J. Holroyd, “Condition Monitoring of Very Slowly Rotating Machinery,” in COMADEM , Manchester, 2001. [13] M. A. Elforj ani, “Condition Monitoring of Slow Speed Rotating Machinery Using,” Cranfield University, Cranfield, 2010. [14] D. J. Smith, “Reliability, Maintainability and Risk (Ninth Edition),” Practical Methods for Engineers, pp. 15 30, 24 March 2017.

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Procedia Structural Integrity 17 (2019) 13–20 ICSI 2019 The 3rd International Conference on Structural Integrity Acquiring in situ Fatigue Crack Growth Curves by a Compliance Method for Micro Bending Beams to Reveal the Interaction of Fatigue Cracks with Grain Boundaries Patrick Gruenewald a , Jonas Rauber a , Michael Marx a , Christian Motz a , Florian Schaefer a, * a Dep. Materials Science and Methods, Saarland University, Campus D2 3, 66123 Saarbruecken, Germany To understand the interaction of dislocations with microstructural obstacles it is necessary to find test methods which are able to resolve the interaction of only a single or few defects with obstacles like interfaces. Therefore, the investigation of micro specimens has been established over the years as a suitable method to test the influence of microstructural features on the mechanical response. While quasi-static loading of micro specimens has been carried out extensively in the past decade and has given powerful insights on the mechanical behavior at small scales, cyclic loading and fatigue crack growth experiments still provide a challenge. In order to check the possibility to systematically initiate and monitor fatigue crack growth rates in micro specimens, we cyclically loaded micro bending beams made of a nickelbase superalloy. Furthermore, for grain boundaries of differing types we checked if the crack growth curves are suitable to measure crack - microstructure interactions. The fatigue cracks showed a deceleration when approaching the grain boundaries followed by an abrupt re-acceleration, which is in accordance to macroscopic experiments and connected to dislocation or slip transfer from the plastic zone of the crack through the grain boundary. Furthermore, we observed a dependency of the deceleration on the grain boundary type and the crystallographic orientation of the neighboring grains. A 3D HR-orientation gradient map was gathered by HR-EBSD using the software CrossCourt in combination with a self-provided MATLAB tool to reveal detailed information about strain localization at the grain boundary in the process zone near the crack tip. ICSI 2019 The 3rd International Conference on Structural Integrity Acquiring in situ Fatigue Crack Growth Curves by a Compliance Method for Micro Bending Beams to Reveal the Interaction of Fatigue Cracks with Grain Boundaries Patrick Gruenewald a , Jonas Rauber a , Michael Marx a , Christian Motz a , Florian Schaefer a, * a Dep. Materials Science and Methods, Saarland University, Campus D2 3, 66123 Saarbruecken, Germany Abstract To understand the interaction of dislocations with microstructural obstacles it is necessary to find test methods which are able to resolve the interaction of only a single or few defects with obstacles like interfaces. Therefore, the investigation of micro specimens has been established over the years as a suitable method to test the influence of microstructural features on the mechanical response. While quasi-static loading of micro specimens has been carried out extensively in the past decade and has given powerful insights on the mechanical behavior at small scales, cyclic loading and fatigue crack growth experiments still provide a challenge. In order to check the possibility to systematically initiate and monitor fatigue crack growth rates in micro specimens, we cyclically loaded micro bending beams made of a nickelbase superalloy. Furthermore, for grain boundaries of differing types we checked if the crack growth curves are suitable to measure crack - microstructure interactions. The fatigue cracks showed a deceleration when approaching the grain boundaries followed by an abrupt re-acceleration, which is in accordance to macroscopic experiments and connected to dislocation or slip transfer from the plastic zone of the crack through the grain boundary. Furthermore, we observed a dependency of the deceleration on the grain boundary type and the crystallographic orientation of the neighboring grains. A 3D HR-orientation gradient map was gathered by HR-EBSD using the software CrossCourt in combination with a self-provided MATLAB tool to reveal detailed information about strain localization at the grain boundary in the process zone near the crack tip. Abstract © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers.

© 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers.

* Florian Schaefer. Tel.: +49-681-302-5172; fax: +49-681-302-5015. E-mail address: f.schaefer@matsci.uni-sb.de

2452-3216 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the ICSI 2019 organizers. * Florian Schaefer. Tel.: +49-681-302-5172; fax: +49-681-302-5015. E-mail address: f.schaefer@matsci.uni-sb.de

2452-3216 © 2019 The Authors. Published by Elsevier B.V. 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.2019.08.003

Patrick Gruenewald et al. / Procedia Structural Integrity 17 (2019) 13–20 Author name / Structural Integrity Procedia 00 (2019) 000 – 000

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Keywords: small fatigue cracks, dislocation grain boundary interaction, micro bending beam, compliance method, micro specimen

Nomenclature a

crack length N ye’s tensor Burgers vector Young’s modulus

α b E F K κ N R ρ σ θ ω J

deformation gradient tensor stress intensity factor

curvature tensor

J-integral

number of load cycles

stress ratio

density of geometrically necessary dislocations

stress tensor

orientation vector rotation tensor coefficients of fit

m,C

1. Introduction

Interfaces dominate the initiation and damage evolution in many materials. The interaction of cracks and the plastic zone with internal interfaces such as grain boundaries is one of the main mechanisms for an increase in lifetime of components if the interaction leads to a deceleration of the crack propagation during fatigue. However, this effect can be ambivalent as grain boundaries are also known to be main crack initiation sites during the fatigue of metals (Lang et al. (2017)). A detailed study of the interaction of fatigue cracks with grain boundaries is necessary to understand the mechanisms that determine whether grain boundaries act as factors that increase or as factors that decrease lifetime depending on their geometry (Knorr et al. 2015), constitution or deformation localization in their vicinity (Zhang et al. (1999)). Understanding the interaction of cracks and grain boundaries requires a detailed knowledge of the interplay between the cracks themselves, the cracks' plastic zone and the grain boundaries. One experimental strategy is to acquire data from macroscopic fatigue tests by zooming in the process zone by high-resolution microscopy or high-resolution analysis methods like electron microscopy or focused ion beam tomography (FIB) (Kacher and Robertson (2012), Schaef et al. (2010)). Another way to gain insight is to downsize the experiment to exclude confounding factors for the evaluation and measurements and to achieve information that is inaccessible for tests on the macro scale such as the local grain boundary morphology or incompatibility stresses at grain boundaries in a polycrystalline compound of elastic anisotropic grains (Klusemann et al. 2013, Tiba et al. (2015)). The aim of this investigation is to study how extremely small fatigue cracks with lengths of less than 10 µm interact with grain boundaries under bending deformation to get a high-resolution view of the dislocation - grain boundary interaction in the plastic zone. Therefore crack growth curves are gathered in situ using bicrystalline micro bending beams of a polycrystalline modification of the nickel base superalloy CMSX-4, prepared by FIB milling, in the scanning electron microscope (SEM). The interaction of the crack with the grain boundary leaves a typical fingerprint of a deceleration followed by a re-acceleration as known from macro fatigue tests with microstructural short fatigue cracks (Brueck et al. (2018), Krupp et al. (2010)). These micro crack growth curves provide detailed information on fatigue crack growth in the regime of the intrinsic fatigue crack propagation threshold proposed by Zerbst et al. (2016) and a new testing method for the quantification of the crack grain boundary interaction (Schaefer et al. 2017).