EP4533052A1 - Procédé et système de surveillance de palier lisse - Google Patents

Procédé et système de surveillance de palier lisse

Info

Publication number
EP4533052A1
EP4533052A1 EP23730018.1A EP23730018A EP4533052A1 EP 4533052 A1 EP4533052 A1 EP 4533052A1 EP 23730018 A EP23730018 A EP 23730018A EP 4533052 A1 EP4533052 A1 EP 4533052A1
Authority
EP
European Patent Office
Prior art keywords
data
autoencoder
plain bearing
borne sound
borne
Prior art date
Legal status (The legal status 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 status listed.)
Pending
Application number
EP23730018.1A
Other languages
German (de)
English (en)
Inventor
Florian König
Georg Jacobs
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Rheinisch Westlische Technische Hochschuke RWTH
Original Assignee
Rheinisch Westlische Technische Hochschuke RWTH
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 Rheinisch Westlische Technische Hochschuke RWTH filed Critical Rheinisch Westlische Technische Hochschuke RWTH
Publication of EP4533052A1 publication Critical patent/EP4533052A1/fr
Pending legal-status Critical Current

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
    • G01M13/045—Acoustic or vibration analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/14—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object using acoustic emission techniques
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/36—Detecting the response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/42—Detecting the response signal, e.g. electronic circuits specially adapted therefor by frequency filtering or by tuning to resonant frequency
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4481—Neural networks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/46—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by spectral analysis, e.g. Fourier analysis or wavelet analysis
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2291/00—Indexing codes associated with group G01N29/00
    • G01N2291/26—Scanned objects
    • G01N2291/269—Various geometry objects
    • G01N2291/2696—Wheels, Gears, Bearings

Definitions

  • condition monitoring can be difficult and unreliable due to dynamically changing operating conditions and vibrations.
  • analysis of large amounts of data may be necessary, which means that the monitoring itself can be very complex.
  • the warehouse monitoring can be done, for example, on the basis of temperature monitoring.
  • this form of monitoring is only suitable for some warehouse types. For example, condition monitoring of plain bearings with a polymer running layer can be very unreliable because the plastics used there have very poor thermal conductivity. As a result, failure-relevant areas with excess temperatures cannot be detected or can only be detected very poorly and, despite monitoring, the plain bearing can fail without warning.
  • the structure-borne noise sensor can be designed, for example, as a piezoelectric element and/or as a contact microphone.
  • the structure-borne sound sensor can be attached to a part of the plain bearing or to another component contacted with it. By attaching it to the plain bearing, the signal quality can be particularly good.
  • the structure-borne sound sensor can be arranged on one of the bearing pins of the planetary gear of the wind turbine or on a gear housing of the planetary gear of the wind turbine.
  • the method has a step of identifying anomalies in the transformed structure-borne sound signal using an autoencoder.
  • an autoencoder By using the autoencoder, complex analytical filtering of the structure-borne sound signal, for example in order to separate parts of the structure-borne sound signal with information regarding a storage condition from parts of the structure-borne sound signal that correspond to noise or are caused by irrelevant environmental influences, may be unnecessary.
  • An anomaly can, for example, be an operating state that does not correspond to a normal state and/or an operating state with increased wear.
  • An anomaly can also be a condition in which the plain bearing has failed and/or is beginning to wear severely. The anomaly can be a wear-critical and/or fatigue-critical condition of the plain bearing.
  • the autoencoder can enable the plain bearing to be automatically monitored efficiently. Only small amounts of data and little computing power are required. For example, complex human evaluation, human monitoring and analytical data evaluation of the structure-borne sound signal are not necessary.
  • the autoencoder can be particularly efficient at evaluating signals in the time-frequency domain. In addition, it is not necessary to provide features in structure-borne sound signals that can be used to identify anomalies. Rather, the autoencoder can detect and/or learn anomalies based on known normal states. No extraction of features from the data set itself is necessary to identify anomalies. The autoencoder can automatically extract relevant information from the transformed structure-borne sound signal, despite many interference signals.
  • damage detection is possible using the following options in descending order: structure-borne sound-based, vibration-based, bearing oil contamination-based, airborne sound-based, temperature-based, smoke-based, and based on an actual failure. It can be seen that with structure-borne noise-based monitoring, a particularly early warning of an impending failure is possible, especially earlier than with other methods.
  • the autoencoder is trained to identify deviations from a system-specific normal state as an anomaly.
  • the normal state can be a state that is not critical for wear and/or fatigue of the plain bearing.
  • the autoencoder can therefore be trained, for example, directly after the wind turbine or other device with the plain bearing to be monitored is put into operation with the first minutes of operation, hours of operation, days of operation or even weeks of operation.
  • the frequency range can also be specified, for example, directly when the structure-borne sound signal is detected by filtering. For example, signals outside the frequency range are no longer fed to the autoencoder as input data.
  • the structure-borne sound sensor can also be designed to only detect structure-borne sound signals in the desired frequency range.
  • a measurement duration is 0.1 to 64 milliseconds, in particular 1 to 32 milliseconds.
  • a measurement period is sufficient to reliably detect wear-critical and fatigue-critical conditions in the detected structure-borne sound signal.
  • this short measurement period reduces the possibility that changes in the operating state, for example due to gusts, falsely indicate an anomaly.
  • the method has a step of a wear calculation and/or a fatigue calculation of the plain bearing using a recurrent neural network.
  • the recurrent neural network can receive the generated anomaly data as input data and generate a wear characteristic value and/or a fatigue characteristic value as output data.
  • a wear calculation can, for example, be designed to output the remaining service life of the plain bearing.
  • a fatigue calculation can, for example, be designed to output a probability of failure due to material fatigue of the plain bearing.
  • a forecast can be made as to when the plain bearing will fail, in particular with what probability. On this basis, maintenance and/or replacement of the plain bearing can be planned to enable cost-efficient and reliable operation.
  • the training method may include a step of generating a training data set.
  • the training data set may have the provided anomaly data as input data.
  • the training data set can have the provided wear characteristics and/or the provided fatigue characteristics as output data.
  • the training method may include a step of training the recurrent neural network to learn a relationship between the anomaly data and the wear characteristics and/or fatigue characteristics.
  • the transformation of the structure-borne sound signal takes place locally, in particular by a local computing device.
  • the local computing device can be, for example, a computer that is connected to the Wind turbine, steam turbine or the compressor is installed and / or is connected directly by cable to sensors and / or control elements of this system or device.
  • the structure-borne sound signal can be compressed, for example, which makes it easy to transmit data for evaluation in another computing device.
  • further evaluation can be carried out by a central server, in particular for a plurality of plain bearings and also from different systems or devices. This makes central monitoring and/or maintenance planning easy.
  • the digitization can also take place locally, in particular by the local computing device.
  • the data transmission device can, for example, transmit respective outputs of the local computing device to the local server, for example transformed structure-borne sound signals, anomaly data and/or wear characteristics and/or fatigue characteristics. Respective data may be stored on the data storage device.
  • the data storage device can, for example, have a storage module of the central server and/or a further storage module of the local computing device. Data can only be stored locally or centrally or redundantly locally and centrally.
  • step 12 a structure-borne sound signal from the plain bearing 50 is detected by means of a structure-borne sound sensor 52 attached to it, the structure-borne sound sensor 52 also being illustrated schematically in FIG. 2.
  • the detected structure-borne sound signal is digitized in a step 14 of the method 10.
  • the digitized structure-borne sound signal is transformed in a step 16 of the method 10 into a time-frequency domain using a continuous wavelet transformation.
  • anomalies in the transformed structure-borne noise signal are identified using an autoencoder.
  • anomaly data is generated in a step 20 of the method 10. This generated anomaly data is made available to a recurrent neural network as input data.
  • the system 40 schematically illustrates a system 40 for monitoring the plain bearing 50.
  • the system 40 is designed to carry out the method 10.
  • the system 40 accordingly has the structure-borne sound sensor 52.
  • the system also has 40 Computing system 42.
  • the computing system 40 includes a computing device 44 installed locally on the wind turbine, the steam turbine or the compressor.
  • the local computing device 44 has a module 54 for receiving the detected structure-borne sound signal, which is communicatively connected to the structure-borne sound sensor 52, for example by radio or cable.
  • the local computing device 44 has a module 56, by means of which the structure-borne noise signal recorded and received by the local computing device 44 is digitized.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Immunology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Pathology (AREA)
  • Signal Processing (AREA)
  • Acoustics & Sound (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

La présente invention concerne un procédé (10) de surveillance d'un palier lisse (50) d'une éolienne, d'une turbine à vapeur ou d'un compresseur. Le procédé comprend au moins les étapes suivantes : la détection (12) d'un signal de bruit de structure du palier lisse (50) au moyen d'un capteur de bruit de structure (52) ; la transformation (16) du signal de bruit de structure détecté dans un domaine temps-fréquence, en particulier par transformée en ondelettes continue ; l'identification (18) des anomalies dans le signal de bruit de structure transformé au moyen d'un autoencodeur ; la génération (20) de données d'anomalie sur la base des anomalies identifiées. L'invention concerne également un système (40) de surveillance d'un palier lisse (50).
EP23730018.1A 2022-06-01 2023-05-26 Procédé et système de surveillance de palier lisse Pending EP4533052A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102022113888.7A DE102022113888A1 (de) 2022-06-01 2022-06-01 Verfahren und System zur Überwachung eines Gleitlagers
PCT/EP2023/064209 WO2023232687A1 (fr) 2022-06-01 2023-05-26 Procédé et système de surveillance de palier lisse

Publications (1)

Publication Number Publication Date
EP4533052A1 true EP4533052A1 (fr) 2025-04-09

Family

ID=86760384

Family Applications (1)

Application Number Title Priority Date Filing Date
EP23730018.1A Pending EP4533052A1 (fr) 2022-06-01 2023-05-26 Procédé et système de surveillance de palier lisse

Country Status (3)

Country Link
EP (1) EP4533052A1 (fr)
DE (1) DE102022113888A1 (fr)
WO (1) WO2023232687A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118583498B (zh) * 2024-08-02 2024-12-10 常熟理工学院 一种滚动轴承剩余使用寿命预测方法及系统
CN119783009B (zh) * 2025-03-10 2025-06-10 山东神力索具有限公司 基于人工智能的安全挂钩索具热疲劳评估方法及装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CZ306833B6 (cs) 2012-12-20 2017-08-02 Doosan Ĺ koda Power s.r.o. Způsob detekce a lokalizace částečného kontaktu rotor-stator při provozu turbíny
EP3330818A1 (fr) 2016-11-30 2018-06-06 Siemens Aktiengesellschaft Procédé et dispositif de surveillance de l'état des composants d'une installation technique
JP7044333B1 (ja) 2020-09-30 2022-03-30 株式会社荏原製作所 機械学習装置、摺動面診断装置、推論装置、機械学習方法、機械学習プログラム、摺動面診断方法、摺動面診断プログラム、推論方法、及び、推論プログラム
CN113158814B (zh) * 2021-03-26 2022-06-03 清华大学 一种基于卷积自编码器的轴承健康状态监测方法
CN114154743B (zh) 2021-12-13 2023-04-07 四川大学 基于vetmrrn的空间滚动轴承剩余寿命预测方法

Also Published As

Publication number Publication date
WO2023232687A1 (fr) 2023-12-07
DE102022113888A1 (de) 2023-12-07

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