WO2015178820A1 - Procédé et dispositif pour déterminer des propriétés d'un roulement - Google Patents

Procédé et dispositif pour déterminer des propriétés d'un roulement Download PDF

Info

Publication number
WO2015178820A1
WO2015178820A1 PCT/SE2015/050505 SE2015050505W WO2015178820A1 WO 2015178820 A1 WO2015178820 A1 WO 2015178820A1 SE 2015050505 W SE2015050505 W SE 2015050505W WO 2015178820 A1 WO2015178820 A1 WO 2015178820A1
Authority
WO
WIPO (PCT)
Prior art keywords
features
signal
steady
rotating machine
state
Prior art date
Application number
PCT/SE2015/050505
Other languages
English (en)
Inventor
Kim ALBERTSSON
Jens Eliasson
Joakim Nilsson
Fredrik SANDIN
Sergio MARTIN DEL CAMPO BARRAZA
Original Assignee
Aktiebolaget Skf
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Aktiebolaget Skf filed Critical Aktiebolaget Skf
Publication of WO2015178820A1 publication Critical patent/WO2015178820A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • G01M13/04—Bearings

Definitions

  • the present invention relates to a method and a device for determining operational properties of a rotating machine.
  • the present invention relates to a method and device for determining vibrational properties of a bearing in a rotating machine.
  • Rolling bearings provide low friction rotation by means of the rolling elements arranged between the outer and inner race of the bearing.
  • Model-based fault detection and diagnosis methods are commonly used and functions well when an accurate model that remains valid over time can be engineered, which however means that experts need to be involved in the development and deployment of such methods.
  • a difficulty with that approach is that the solution is application-specific and can be invalidated if system components change, which limits the general applicability of the approach.
  • Fault detection and diagnosis systems for rotating machinery often include vibration analysis, and sometimes also acoustic-emission (AE) analysis.
  • Frequency analysis can enable early detection of low-amplitude high-frequency signal components that are associated with emerging faults.
  • AE acoustic-emission
  • Such methods discard time-domain patterns in the signal and have limited sensitivity to broadband and stochastic patterns.
  • Several components are required to monitor these modalities, including the sensor (typically of piezoelectric type), preamplifier, signal-processing system and fault detection and diagnosis methods.
  • the signal-processing system can be a
  • the pre-processed signal is analyzed in an automated fashion with methods like those described above, or manually by humans.
  • condition monitoring signals require a high sampling rate, which is
  • vibration and acoustic emission signals require sampling rates on the order of several kHz up to the MHz range.
  • a method for condition monitoring of a bearing in a rotating machine comprising; receiving vibration data or acoustic emission data from the rotating machine; identifying time-domain features in the data using a self- learning algorithm to form a set of features, each feature being represented by a unique waveform, until a converged set of features is reached; storing the converged set of features as a steady-state set of features; in a
  • monitoring state identifying time-domain features in the data through the self learning algorithm and updating the set of features based on the features indentified in the monitoring state; comparing the updated set of features to the steady-state set of features; and if the updated set of features is different from the steady-state set of features, determining that the conditions of the rotating machine has changed.
  • vibration or acoustic emission data should in the initial state be understood as receiving data for a period of time at least such that a converging set of features is reached.
  • vibration or acoustic emission data is received so that an updated set of features can be determined. Data may for example be received continuously. Vibrations and acoustic emissions are highly interrelated as vibrations in a component may give rise to acoustic emissions.
  • the rotating machine may be any rotating arrangement that generates detectable vibrations or acoustic emission during operation.
  • the rotating machine may comprise one or more bearings and the vibration or acoustic emission data received may be related to one or more specific bearings within the rotating machine.
  • a time-domain feature may also be referred to as an atom, where each identified feature, atom or time-domain waveform represents a particular vibrational or acoustic-emission property of the rotating machine.
  • the present invention is based on the realization that by using a self- learning algorithm to characterize a rotating machine through a set of time- domain features, a change in the condition of the rotating machine may be identified by observing if the set of features change.
  • the automated detection of novel features in a signal can enable preventive actions and improved maintenance planning in a broad range of applications.
  • changes can be detected without the need for fault modeling.
  • indentifying a change in the set of features instead of comparing each stored feature individually to an updated feature, also the addition or removal of features in the set can readily be identified.
  • the method comprises an initial state in which a steady-state set of features is identified, and a monitoring state where the rotating machine is monitored and features are identified to determine if vibrational or acoustic-emission properties are changing over time.
  • a monitoring state where the rotating machine is monitored and features are identified to determine if vibrational or acoustic-emission properties are changing over time.
  • an updated set of features is formed which is compared to the steady-state set of features.
  • the updated set of features is typically continuously updated and compared to the steady state set of features.
  • the updated set of features is different from the converged steady-state set of features if at least one of the features has changed in frequency, shape, and/or amplitude, and if the change is larger than a predetermined threshold value.
  • a change in any of the aforementioned parameters larger than a predetermined threshold value may be the result of a change of the condition of a component of the rotating machine. If a detected difference is smaller than the predetermined threshold value, it may be determined that the difference is the result of noise or that the difference is within the range of known measurement uncertainties.
  • the set of features is different from the steady-state set of features if at least one feature has been added to or removed from the steady-state set of features.
  • the addition of a feature to the steady-state set of features is indicating that properties of the rotating system have changed.
  • the removal of a feature is an indication of change, and further analysis may be performed to determine if the change is a potential cause for failure.
  • the method may comprise generating an output to a host system only when a difference between the updated set of features and the steady-state set of features is identified.
  • the vibration or acoustic-emission data may advantageously be received in the form of an analog signal, the method further comprising the step of converting the analog signal to a digital signal.
  • the self-learning algorithm may advantageously start from a randomized initial state.
  • unsupervised learning methodology based on a randomized initial state, only a minimum of configuration is required prior to use, and the method may be applied to a wide range of applications without any adaptation or prior knowledge of the application at hand.
  • the method may further comprise determining a cross correlation between each feature in the steady state set of features and a corresponding feature in the updated set of features.
  • the method may further comprise determining a signal-to-noise ratio (SNR) between each feature in the updated set of features and said received signal.
  • SNR signal-to-noise ratio
  • a condition monitoring device comprising; an analog input configured to receive a signal indicative of vibrational or acoustic-emission properties of a rotating machine; an amplifier configured to amplify the signal; a low pass filter configured to block frequencies of the signal higher than a predetermined cutoff frequency; an analog to digital converter configured to sample the analog signal to form a digital signal; a control unit configured to identify vibrational features in the signal using a self-learning algorithm to form a set of features, each feature being represented by a unique waveform, until a converged set of features is reached; a storage unit configured to store the converged set of features as a steady-state set of features; wherein the control unit is further configured to continuously identify time-domain features in the signal and to update the set of features, a change detector configured to compare the updated set of features to the steady-state set of features; and if the set of features is different from the steady-state set of features, determine that the conditions of the rotating machine has changed.
  • the device integrates an amplifier with a general-purpose signal processing and condition-monitoring system for change detection in terms of learned additive features. Thereby, the signal from a vibration sensor or acoustic sensor is fed directly to the device and no further data processing or communication is required during normal operation. Because the device is general and not designed for one particular condition monitoring application the design can be refined and optimized (in FPGA's) and then realized in the form of an ASIC, which is power efficient and suitable for embedded and wireless application.
  • the adaptive feature detection device models the received signal in terms of waveforms with compact support that is learned from the signal using an unsupervised statistical optimization algorithm.
  • the device adapts the shape and length of the waveforms, herein also referred to as features where each feature is represented by a unique waveform, to a specific signal from a specific rotating machine.
  • the signal is modeled as a linear
  • the device enables unsupervised learning of features present in a signal, detection of changes of the features, detection of new features in the signal, and detection of removal of features in the signal.
  • identified features may be requested by and communicated to a host system for further analysis.
  • the device reports significant changes to the set of learned features or residual of the signal model.
  • the device does not generate any output data during normal operation, i.e. when the input signal corresponds to a superposition of the historically learned features comprised in the steady- state set of features, and noise. Nor does it require any further information processing or human intervention in that case. Therefore, the device can significantly reduce the data rate that needs to be communicated and analyzed. Because the device can adapt the features to a particular signal and machine, the condition monitoring process can potentially be simplified. Given a set of features, the signal model is similar to a matched filter.
  • the signal to noise ratio is high in the presence of additive noise.
  • new features in the signal can potentially be detected before they could have been detected with standard methods like spectral analysis.
  • the unsupervised learning, high data reduction and high signal to noise ratio that are enabled by the device can be useful for early detection of faults in rotating machines.
  • the device can simplify and enable continuous condition monitoring of rotating machines as it enables wireless condition monitoring with minimal bandwidth requirements.
  • the low pass filter of the condition- monitoring device may advantageously comprise an analog low pass filter, an analog-to-digital (AD) converter, and a digital low pass filter.
  • AD analog-to-digital
  • the advantages of both types of filters are achieved.
  • the cut-off frequency of the analog filter may be set with respect to the sampling frequency of the AD-converter such that the signal does not comprise frequencies too high to be accurately sampled.
  • condition monitoring arrangement comprising a condition monitoring device as described above and a sensor configured to detect vibrations of a rotating machine and to convert the vibrations into an electrical signal.
  • condition monitoring arrangement may in one embodiment comprise an acoustic emission sensor configured to detect acoustic emissions from a rotating machine and to convert the acoustic emissions into an electrical signal.
  • the electrical signal representing the vibrations or acoustic emissions can then be coupled to the condition- monitoring device.
  • Fig. 1 is a flow chart outlining the general steps according to an embodiment of the invention.
  • FIG. 2 schematically illustrates a system according to an embodiment of the invention.
  • FIG. 3 schematically illustrates exemplary features in an embodiment of the invention. Detailed Description of Preferred Embodiments of the Invention
  • Fig. 1 is a flow chart outlining the general method steps of the invention steps, the method illustrated in Fig. 1 will be discussed with reference to Fig. 2 schematically illustrating a condition-monitoring device 200 capable of performing the method.
  • a first step 102 data is received continuously in the form of an electrical signal representing analog acoustic emissions or vibrations from the rotating machine through an analog input 202.
  • the acoustic emissions or vibrations are converted into an electronic signal by a sensing device (not shown) arranged in connection with the rotating machine and provided from the sensor to the analog input 202.
  • the sensing device may be any suitable sensing device such as a piezoelectric of MEMS-based sensor.
  • an amplifier 204 is preferably included to ensure that the amplitude of the signal is sufficient for further treatment of the signal. Accordingly, a condition-monitoring device 200 comprising an amplifier 204 may be used with different types of sensing devices.
  • the input signal is low-pass filtered in an analog low-pass filter 206 in order to avoid aliasing effects.
  • the analog signal is converted to a digital signal in an analog-to-digital converter 208 (ADC).
  • ADC analog-to-digital converter
  • a digital low-pass filter may be arranged after the ADC.
  • a clock 210 synchronizes the digital signal processing and a buffer 210 is used to facilitate further signal processing after AD conversion.
  • a band-pass filter to filter out lower-frequency vibration signals which may otherwise dominate over the much lower-amplitude acoustic emission at higher frequencies.
  • time-domain features from the vibration or acoustic emission signal are identified using a self-learning algorithm implemented in a signal decomposition block 214 communicating with a feature optimization block 216. Based on the result of signal decomposition, the gain of the amplifier 204 may be controlled via a gain control module 218.
  • the signal is modeled as a linear superposition of noise and features.
  • features are identified until the number and shape of identified features converge to form a steady-state set of features. Each feature corresponds to a waveform having a finite length.
  • the steady-state set of features is stored 106 to enable comparison with later identified features. Identification of the steady-state set of features is typically done in an initial state where it is known that the rotating machine is operating under normal conditions.
  • the vibration or acoustic-emission data is continuously monitored and signal decomposition and feature optimization is performed to identify 108 features and to update the set of features.
  • the updated set of features is continuously compared 1 10 with the stored steady- state set of features to determine if the updated set of features is different from the steady-state set of features.
  • the change detector 220 If it is determined that the set of features has changed, in the change detector 220, this may be reported as an event at the device output 222. Thereby, communication with a host system is only required when it has been observed that a change of properties of the rotating machine has occurred. From the nature of the reported feature, or set of features, it is often possible to determine the nature of the defect, for example based on empirical data or models.
  • decomposition and feature optimization algorithm works with a signal window that is at least twice as long as the longest feature identified in the steady- state set of features. A longer window is preferable to avoid edge effects.
  • the sampling frequency of the ADC 208 is based on the sensing device connected to the input 202.
  • the sampling frequency may be on the order of a few tens of kHz whereas for an acoustic emission sensor the sampling frequency may be on the order of several MHz.
  • a sampling frequency of a few hundreds of Hz may be sufficient.
  • the method as such is not limited to any particular frequencies, but is instead generally applicable to in principle any signal comprising information about the vibrational or acoustic-emission properties of a rotating machine.
  • determining if there is a difference between the updated set of features and the steady state set of features may involve observing the cross-correlation between corresponding features as a measure of the change of each feature per time unit. After convergence of the features, i.e. after the steady-state set of features had been identified and the method is in the monitoring state, only a very small change is expected during normal operation and a cross-correlation value should thus be close to one. The determination that the set of features has changed may thus be based on the observed cross-correlation value where a suitably selected threshold value lower than one may indicate a change of features. If a defect is introduced which alters the properties of an existing feature, the cross-correlation value for the relevant feature will decrease rapidly. The rapid decrease can be readily detected and may be reported as an event.
  • the respective signal-to-noise ratio (SNR) between each feature and the full signal may be determined and monitored. Also here, a change in one of the features will result in a rapid change of the SNR, which may be detected.
  • SNR signal-to-noise ratio
  • the analog input 202 and event output terminals 222 are externally accessible, whereas the remaining blocks of the condition monitoring device 200 may be integrated in a single electronic circuit or package for example in the form of an ASIC or FPGA.
  • FIG. 3 schematically illustrates exemplary identified features
  • an impulse-like waveform detected in the vibration data is typically the result of a defect bearing where a damaged race or ball in the bearing typically gives rise to an impulse having a high frequency (in comparison to the operating frequency of the rotating machine) and an exponential decay in amplitude. Similar features may be found for defect cogs and the like.
  • an impulse waveform representing a feature grows in length until a steady-state condition is reached, for example when the amplitude of the tail of the waveform is below a predetermined threshold value.
  • the harmonic waveform 304 is typically related to the operating frequency and vibrational modes of the rotating machine during normal operation and is found in all rotating machines. Both the fundamental frequency and higher order harmonics may be observed. Imbalance or misalignment of components in the rotating machine, such as in a shaft, may be observed as an increase in amplitude in one or more of the harmonic features.
  • the conditioning monitoring device yet being able to perform the functionality of the present invention.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)

Abstract

L'invention concerne un procédé de surveillance de la condition d'un roulement dans une machine tournante, le procédé comportant ; l'étape consistant à recevoir en continu des données se rapportant aux vibrations ou à l'émission acoustique en provenance de la machine tournante ; l'étape consistant à identifier des caractéristiques de domaine temporel dans les données à l'aide d'un algorithme d'auto-apprentissage pour former un ensemble de caractéristiques, chaque caractéristique étant représentée par une forme d'onde unique, jusqu'à ce qu'un ensemble convergé de caractéristiques soit atteint ; l'étape consistant à stocker l'ensemble convergé de caractéristiques sous la forme d'un ensemble de caractéristiques à l'état stable ; dans un état de surveillance, l'étape consistant à identifier des caractéristiques de domaine temporel dans les données par l'intermédiaire de l'algorithme d'auto-apprentissage et l'étape consistant à effectuer la mise à jour dudit ensemble de caractéristiques ; l'étape consistant à comparer l'ensemble de caractéristiques à l'ensemble de caractéristiques à l'état stable ; et si l'ensemble de caractéristiques est différent de l'ensemble de caractéristiques à l'état stable, l'étape consistant à déterminer que les conditions de la machine tournante ont changé. L'invention concerne également un dispositif de mise en œuvre du procédé.
PCT/SE2015/050505 2014-05-19 2015-05-07 Procédé et dispositif pour déterminer des propriétés d'un roulement WO2015178820A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
SE1450584 2014-05-19
SE1450584-6 2014-05-19

Publications (1)

Publication Number Publication Date
WO2015178820A1 true WO2015178820A1 (fr) 2015-11-26

Family

ID=54554367

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/SE2015/050505 WO2015178820A1 (fr) 2014-05-19 2015-05-07 Procédé et dispositif pour déterminer des propriétés d'un roulement

Country Status (1)

Country Link
WO (1) WO2015178820A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105928702A (zh) * 2016-04-29 2016-09-07 石家庄铁道大学 基于形态分量分析的变工况齿轮箱轴承故障诊断方法
CN113343388A (zh) * 2021-06-22 2021-09-03 安徽容知日新科技股份有限公司 一种获取稳态振动数据的方法及计算设备

Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4366544A (en) * 1979-04-16 1982-12-28 Mitsubishi Denki Kabushiki Kaisha Judging system for detecting failure of machine
US4980844A (en) * 1988-05-27 1990-12-25 Victor Demjanenko Method and apparatus for diagnosing the state of a machine
WO1996013011A1 (fr) * 1994-10-20 1996-05-02 Ingersoll-Rand Company Dispositif de surveillance utilisant les phenomenes vibratoires
WO1997043729A1 (fr) * 1996-05-14 1997-11-20 Csi Technology, Inc. Analyse de donnees de vibrations fondee sur des parametres de formes d'ondes temporelles
WO1998036251A1 (fr) * 1997-02-18 1998-08-20 Dme Corporation Procede et dispositif destines a un diagnostic predictif de pieces mobiles de machines
EP0982579A1 (fr) * 1998-03-16 2000-03-01 Central Japan Railway Company Dispositifs pour inspecter les paliers des moteurs principaux d'un materiel roulant
US20030130810A1 (en) * 2001-12-04 2003-07-10 Smulders Adrianus J. Harmonic activity locator
US20050096873A1 (en) * 2002-12-30 2005-05-05 Renata Klein Method and system for diagnostics and prognostics of a mechanical system
US20070282545A1 (en) * 2006-05-17 2007-12-06 Curtiss-Wright Flow Control Corporation Probabilistic Stress Wave Analysis System and Method
US20100023307A1 (en) * 2008-07-24 2010-01-28 University Of Cincinnati Methods for prognosing mechanical systems
EP2365310A1 (fr) * 2010-03-12 2011-09-14 Honeywell International Inc. Procédé et système de détection des défaillances de roulements de rupture

Patent Citations (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4366544A (en) * 1979-04-16 1982-12-28 Mitsubishi Denki Kabushiki Kaisha Judging system for detecting failure of machine
US4980844A (en) * 1988-05-27 1990-12-25 Victor Demjanenko Method and apparatus for diagnosing the state of a machine
WO1996013011A1 (fr) * 1994-10-20 1996-05-02 Ingersoll-Rand Company Dispositif de surveillance utilisant les phenomenes vibratoires
WO1997043729A1 (fr) * 1996-05-14 1997-11-20 Csi Technology, Inc. Analyse de donnees de vibrations fondee sur des parametres de formes d'ondes temporelles
WO1998036251A1 (fr) * 1997-02-18 1998-08-20 Dme Corporation Procede et dispositif destines a un diagnostic predictif de pieces mobiles de machines
EP0982579A1 (fr) * 1998-03-16 2000-03-01 Central Japan Railway Company Dispositifs pour inspecter les paliers des moteurs principaux d'un materiel roulant
US20030130810A1 (en) * 2001-12-04 2003-07-10 Smulders Adrianus J. Harmonic activity locator
US20050096873A1 (en) * 2002-12-30 2005-05-05 Renata Klein Method and system for diagnostics and prognostics of a mechanical system
US20070282545A1 (en) * 2006-05-17 2007-12-06 Curtiss-Wright Flow Control Corporation Probabilistic Stress Wave Analysis System and Method
US20100023307A1 (en) * 2008-07-24 2010-01-28 University Of Cincinnati Methods for prognosing mechanical systems
EP2365310A1 (fr) * 2010-03-12 2011-09-14 Honeywell International Inc. Procédé et système de détection des défaillances de roulements de rupture

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
"Analysis and Interpretation of SKF Acoustic Emission Enveloping (AEE) Measurements", SKF APPLICATION NOTE CM3155/1 EN, 1 August 2013 (2013-08-01), XP055110811 *
DEL CAMPO ET AL.: "FPGA Prototype Of Machine Learning Analog-To-Feature Converter For Event-Based Succinct Representation Of Signals", IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP2013), 22 September 2013 (2013-09-22) - 25 September 2013 (2013-09-25), Southampton, UK, XP055237812 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105928702A (zh) * 2016-04-29 2016-09-07 石家庄铁道大学 基于形态分量分析的变工况齿轮箱轴承故障诊断方法
CN113343388A (zh) * 2021-06-22 2021-09-03 安徽容知日新科技股份有限公司 一种获取稳态振动数据的方法及计算设备

Similar Documents

Publication Publication Date Title
Liao Discovering prognostic features using genetic programming in remaining useful life prediction
Shi et al. Rolling bearing initial fault detection using long short-term memory recurrent network
Wang et al. Theoretical and experimental investigations on spectral Lp/Lq norm ratio and spectral Gini index for rotating machine health monitoring
Camci et al. Feature evaluation for effective bearing prognostics
CN113400652B (zh) 基于振动信号的3d打印机监测与诊断知识库装置、系统
KR101482509B1 (ko) 베어링 결함 진단 시스템 및 그 진단 방법
CN110501631B (zh) 一种在线间歇故障检测与诊断方法
US20130297256A1 (en) Method and System for Predictive and Conditional Fault Detection
JP2019067197A (ja) 故障予兆検知手法
JP2001318031A (ja) 装置の異常診断方法
US20140058615A1 (en) Fleet anomaly detection system and method
WO2015178820A1 (fr) Procédé et dispositif pour déterminer des propriétés d'un roulement
JP2010266327A (ja) 設備診断装置及び設備診断方法
KR20210077496A (ko) 초음파 대역의 음향 신호를 수신하는 다채널 감지 센서를 구비한 설비 고장 예측 시스템
CN114813124A (zh) 一种轴承故障的监测方法及装置
KR102545672B1 (ko) 기계고장 진단 방법 및 장치
WO2020162425A1 (fr) Dispositif d'analyse, procédé d'analyse et programme
CN104062673B (zh) 核分析仪自诊断系统
Perez-Ramirez et al. Fractal dimension theory-based approach for bearing fault detection in induction motors
KR101752298B1 (ko) 회전익 진동 기반 건전성 감시 장치 및 이를 이용하는 감시 방법
KR102325439B1 (ko) 사출 성형 설비의 고장 예측 시스템 및 그 방법
CN113168739B (zh) 用于检查至少一台车辆的方法以及电子计算装置
Satija et al. Development of rolling bearing health diagnosis and prediction system using MEMS accelerometer vibration sensing module
JP4049331B2 (ja) 診断対象物の評価方法および評価装置
Alekseev et al. Data measurement system of compressor units defect diagnosis by vibration value

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15795877

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 15795877

Country of ref document: EP

Kind code of ref document: A1