EP2628895A1 - Verfahren und System zur Materialzersetzungserkennung in einem Objekt durch Analyse von Schallschwingungsdaten - Google Patents

Verfahren und System zur Materialzersetzungserkennung in einem Objekt durch Analyse von Schallschwingungsdaten Download PDF

Info

Publication number
EP2628895A1
EP2628895A1 EP12000950.1A EP12000950A EP2628895A1 EP 2628895 A1 EP2628895 A1 EP 2628895A1 EP 12000950 A EP12000950 A EP 12000950A EP 2628895 A1 EP2628895 A1 EP 2628895A1
Authority
EP
European Patent Office
Prior art keywords
time
acoustic vibration
frequency
vibration data
feature
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.)
Withdrawn
Application number
EP12000950.1A
Other languages
English (en)
French (fr)
Inventor
Joachim Prof.Dr.-Ing. Rudolph
Andreas Prof. Dr. Schütze
Dr. Dr. Strauss Daniel Prof.
Stefan Prof. Dr.-Ing Seelecke
Douglas Herrn Espin
Jürgen Prof. Dr.-Ing. Griebsch
Stefan Prof. Dr.-Ing Diebels
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.)
Zentrum fur Mechatronik und Automatisierungstechnik Ggmbh
Original Assignee
Zentrum fur Mechatronik und Automatisierungstechnik Ggmbh
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 Zentrum fur Mechatronik und Automatisierungstechnik Ggmbh filed Critical Zentrum fur Mechatronik und Automatisierungstechnik Ggmbh
Priority to EP12000950.1A priority Critical patent/EP2628895A1/de
Publication of EP2628895A1 publication Critical patent/EP2628895A1/de
Withdrawn legal-status Critical Current

Links

Images

Classifications

    • E—FIXED CONSTRUCTIONS
    • E21—EARTH OR ROCK DRILLING; MINING
    • E21B—EARTH OR ROCK DRILLING; OBTAINING OIL, GAS, WATER, SOLUBLE OR MELTABLE MATERIALS OR A SLURRY OF MINERALS FROM WELLS
    • E21B47/00—Survey of boreholes or wells
    • E21B47/006—Detection of corrosion or deposition of substances

Definitions

  • the present invention relates to a method and a system for material degradation detection in an object by analyzing acoustic vibration data. Particularly, the invention is used to detect material degradation caused by corrosion in down-hole pipes.
  • Corrosion means the disintegration of an engineered material into its constituent atoms due to chemical reactions with its surroundings. In the most common use of corrosion, it means electrochemical oxidation of metals in reaction with an oxidant such as oxygen. Formation of an oxide of iron due to oxidation of the iron atoms in solid solution is a well-known example of electrochemical corrosion, commonly known as rusting. This type of damage typically produces oxides or salts of the original metal. Corrosion can also occur in materials other than metals, such as ceramics or polymers, although in this context, the term degradation is more common. In other words, corrosion is the wearing away of material due to a chemical reaction.
  • Corrosion can be concentrated locally to form a pit or crack, or it can extend across a wide area more or less uniformly corroding the surface. Because corrosion is a diffusion controlled process, it occurs on exposed surfaces.
  • the patent EP 1 097 290 B1 discloses a down-hole corrosion monitoring system comprising piezoelectric transducers, a microprocessor, an electrical power source and a conducting device, a control and instrumentation device and a display device.
  • the transducers are arranged in a fixed array, spaced longitudinally and axially from each other and affixed to the section of a well casing or tubing to be monitored.
  • the microprocessors are electrically connected to the transducers for activating the transducers, and for receiving and transmitting signals produced by the transducers.
  • the monitoring system is used for monitoring a down-hole corrosion rate in an oil well tubing and casing strings in predicting its life and to avoid failures during operation. The system permits down-hole corrosion monitoring without taking the well out of service or disrupting the flow.
  • the basic idea of the invention is to detect material degradation/material loss in objects autonomously by an analysis of material degradation correlates in evoked acoustic signals (acoustic vibration data), by a system which self-adjusts to the measurement conditions of the environment and by an energy efficient implementation.
  • the objective is realized by a hybrid adapted signal processing learning machine.
  • This hybrid learning machine extracts time-frequency features in the acoustic signals automatically such that these features are optimal for a subsequent novelty detection process.
  • Optimal means that the feature extraction and the novelty detection process minimize the very objective function and thus form a hybrid union in the sense of mathematical optimization.
  • the hybrid adapted signal processing learning machine auto-adapts to the acoustic signal on site such that it provides sensitive novelty detection, being at the same time very robust to any background noise of the environment.
  • the invention comprises a hybrid adapted signal processing - supervised machine learning approach which "learns" the conditions on site, i.e. down-hole, after setting up the object, i.e. a pipe, and detects the material degradation correlates as represented by the extracted features as novel instances.
  • machine learning schemes represent a black-box model which complements the available (general) physical models of acoustic waves in materials. The a priori information from those general physical models defines only the physically reasonable range of appropriate features.
  • the energy efficiency is provided by sparsity regarding the activation, i.e. through actuators, and spatial sampling, i.e. by a number of sensors, by an efficient activation/excitation strategy, by a multirate/lattice implementation of signal processing, and by sparsity regarding information which has to be transferred to a central unit.
  • the number of tests of the object can be adjusted to an estimated degradation rate using the hybrid learning machine, e.g., following the path of the features in the feature space. In the way, the number of tests for small degradation rates and thus the energy consumption can be reduced.
  • Novelty detection is the identification of new or unknown data or signals that a machine learning system is not aware of, during training. Novelty detection is a so-called one-class classification.
  • the known data form one class and a novelty-detection method tries to identify outliers that differ from the distribution of ordinary data, which formed the single data class.
  • one-class classification is useful if outliers are sparse compared to ordinary data.
  • a hybrid scheme offers a much higher degree of adaptivity. This is because not only the decision function of novelty detection process is optimized according to a learning rule but also the optimal features are fed to the novelty detection process. In this way, irrelevant information is directly removed, reducing the dimensionality of the problem and improving the novelty detection process.
  • the mathematical framework of such hybrid approaches with a uniform objective function is well known.
  • the objective function is formulated using statistical learning theory combined with reproducing kernel Hilbert space regularization for binary classification.
  • the advantages regarding the adaptivity and robustness of the hybrid approach with uniform objective function as compared to conventional two-stage schemes in which the feature extraction in the time-frequency domain is independent from the objective function of the learning machine, are well demonstrated.
  • the adaptivity and robustness can especially be exploited in down-hole pipe applications as there is only very limited knowledge about the acoustic response signals and an adaptive self-calibration and robustness to noise is of major importance.
  • the entire scheme can be implemented by a sparse kernel expansion for the novelty detection and a signal-adapted decomposition using the two-multiplier lattice, requiring a minimum number of floating point operations.
  • a method for material degradation detection in an object of said material by analyzing acoustic vibration data derived from acoustic signals from said object comprising:
  • the invention provides the advantage of robust and accurate material degradation detection under severe environmental conditions.
  • the method is able to detect corrosion of down-hole pipes robustly.
  • the material degradation comprises thickness degradation and/or corrosion of said object.
  • the novelty detection scheme for detecting novel values of the time-frequency feature is embedded in statistical learning theory and whereas the extraction of the time-frequency feature is optimized for novelty detection using a (highly efficient) multirate signal processing strategy in the learning machine.
  • the object is excited by acoustic vibration at a first position and whereas said acoustic vibration data is detected at a second position in distance to the first position.
  • the feature extraction maximises the distance between background acoustic noise and a response at the second position to the excitation at the first position.
  • a warning signal is generated
  • the method comprises a quantification of the material degradation. Furthermore, the energy for excitation and detection is harvested from vibrations of the object. Finally, the excitation by acoustic vibration and the detection of the acoustic vibration data are executed only at predefined points in time.
  • a system for material degradation detection in an object of said material by analyzing acoustic vibration data derived from acoustic signals from said object comprising:
  • the system is able to perform the methods according to the present invention.
  • FIG. 1 shows an illustration of a typical pipe 13 segment of a pipe which can be used in a down-hole.
  • the pipe segment 13 is equipped with an acoustic vibration excitation unit 2 at a first position P1.
  • the excitation unit 2 can comprise a piezo actuator, preferably of a stack type.
  • the excitation unit 2 is driven with a sinusoidally varying signal in the range of DC to 12 kHz. It produces an acoustic excitation in the radial direction of the pipe segment 13.
  • the pipe segment 13 is equipped with a detection unit 1 with a piezo accelerometer, i.e. preferably of a stack type.
  • the radial direction of the pipe segment 13 is also the sensitive axis of the detection unit 1.
  • the excitation unit 2 and the detection unit 1 can be located at the position of the pipe segment connectors 14.
  • an excitation unit 2 can also function as a detection unit 1 and vice versa.
  • Figure 2 shows an illustration of an example of a standing wave pattern in the tube segment 13 of figure 1 .
  • the excitation produces the standing wave pattern of which the shape (eigenmode) depends on the excitation frequency.
  • the detection unit 1 picks up the radial component of this vibration at their respective point of attachment.
  • six modes in axial direction of the tube segment 13 can be seen.
  • On the right side in figure 2 three nodes in circumferential direction are shown.
  • the wall thickness of the pipe segments are decreasing.
  • the decreasing wall thickness represents a physical model for wall-thickness degradation due to corrosion.
  • Figure 3 shows a diagram of the steady state responses/acoustic vibration data from three different pipe segments, with decreasing wall thickness from top to down (7,1 mm, 5,6 mm, 4,0 mm). These sampled signals are considered as vectors in the original data space S ⁇ R d .
  • the x-axes shows the time in ⁇ s and the y-axis the power level of the detected signal.
  • the acoustic vibration data are converted to time-frequency domain representations.
  • Figure 4 shows a diagram of the time-frequency domain representations of figure 3 .
  • the x-axis shows the time in s and the y-axis the frequency in kHz.
  • the grey level is proportional to the power level.
  • the used feature extraction provides automatically a low dimensional vector of characteristic and physically reasonable features FF.
  • the features FF are extracted by optimized basis functions in a hybrid learning theory.
  • Figures 5 to 7 show diagrams of the tight decision boundary in F .
  • This decision boundary is a back-projection from the very high dimensional induced feature space of kernel learning machines in which the decision boundary is just a sphere.
  • Figures 5 to 7 just show the tight decision boundary L (points touch the line) as no impact of background noise was present in the lab setup.
  • a number of 15 repeated measurements with very same settings in three different pipe segments with three different wall thicknesses are represented by only two time-frequency features FF (feature 1, feature 2).
  • the values of feature 1 are drawn on the x-axis and the values of feature 2 are drawn on the y-axis.
  • a thick wall is a model for non corrosion
  • a slightly thinner wall is a model for mid-corrosion
  • a thin wall is a model for corrosion.
  • a wireless transmission unit 9 the values of the extracted time-frequency features are transmitted to a supervised learning machine 5.
  • the learning machine 5 detects if the values of the extracted time-frequency features of the time-frequency domain representation are novel compared to the values of the time-frequency feature of the acoustic vibration data without material degradation.
  • the values without material degradation are determined during a training phase where the object is not degraded.
  • a wired transmission can be used.
  • the learning machine 5 is connected to a warning signal unit 10. If the values of the extracted time-frequency features of the time-frequency domain representation are novel compared to the values of the time-frequency features without material degradation and if the values of the extracted time-frequency features of the time-frequency representation reach a predefined critical value, the warning signal unit 10 generates a warning signal WS.
  • the learning machine 5 is connected to a classification and/or regression machine 6 which in addition quantifies the material degradation.
  • the detection unit, the conversion unit and the processing unit are arranged at the object and the learning machine is arranged in a distant location.
  • the learning machine comprises a novelty detection scheme for detecting novel values of the time-frequency feature which is embedded in statistical learning theory.
  • the feature extraction is optimized for novelty detection using a highly efficient multirate signal processing strategy in the learning machine.
  • Figure 9 shows a flow chart of a method for material degradation detection in an object.
  • a supervised learning machine is trained to recognize acoustic vibration data VD without material degradation by extraction of time-frequency features FF of the detected acoustic vibration data VD.
  • the object is excited with acoustic vibrations AV.
  • a second step 101 - after the training phase further acoustic vibration data VD from the object are detected.
  • the acoustic vibration data VD are converted to a time-frequency domain representation FR, i.e. using wavelet transformation.
  • step 103 the time-frequency domain representation FR is presented to the learning machine, alternatively to a processing unit.
  • step 104 the time-frequency domain representation FR is processed by the learning machine or the processing unit.
  • step 105 time-frequency features FF of the time-frequency domain representation FR which are significant for the material degradation are extracted by the learning machine or the processing unit.
  • a warning signal WS is generated if the values of the extracted time-frequency features FF of the time-frequency domain representation FR are novel compared to the values of the time-frequency features FF of the training and if the values of the extracted time-frequency feature FF (or at least of one time-frequency feature FF) of the time-frequency domain representation FR reached a predefined critical value.

Landscapes

  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Geology (AREA)
  • Mining & Mineral Resources (AREA)
  • Geophysics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Fluid Mechanics (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • Geochemistry & Mineralogy (AREA)
  • Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
EP12000950.1A 2012-02-14 2012-02-14 Verfahren und System zur Materialzersetzungserkennung in einem Objekt durch Analyse von Schallschwingungsdaten Withdrawn EP2628895A1 (de)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP12000950.1A EP2628895A1 (de) 2012-02-14 2012-02-14 Verfahren und System zur Materialzersetzungserkennung in einem Objekt durch Analyse von Schallschwingungsdaten

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP12000950.1A EP2628895A1 (de) 2012-02-14 2012-02-14 Verfahren und System zur Materialzersetzungserkennung in einem Objekt durch Analyse von Schallschwingungsdaten

Publications (1)

Publication Number Publication Date
EP2628895A1 true EP2628895A1 (de) 2013-08-21

Family

ID=45655000

Family Applications (1)

Application Number Title Priority Date Filing Date
EP12000950.1A Withdrawn EP2628895A1 (de) 2012-02-14 2012-02-14 Verfahren und System zur Materialzersetzungserkennung in einem Objekt durch Analyse von Schallschwingungsdaten

Country Status (1)

Country Link
EP (1) EP2628895A1 (de)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019087125A1 (en) * 2017-11-03 2019-05-09 IdeaCuria Inc. Systems to monitor characteristics of materials involving optical and acoustic techniques
US20220163958A1 (en) * 2021-02-04 2022-05-26 Chengdu Qinchuan Iot Technology Co., Ltd. Methods and systems for managing a pipe network of natural gas
US11713671B2 (en) 2014-10-28 2023-08-01 Halliburton Energy Services, Inc. Downhole state-machine-based monitoring of vibration

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4893286A (en) * 1987-11-04 1990-01-09 Standard Oil Company System and method for preprocessing and transmitting echo waveform information
EP1097290B1 (de) 1998-07-15 2004-07-07 Saudi Arabian Oil Company Vorrichtung und verfahren zur überwachung von korrosion in einem bohrloch
EP1467060A1 (de) 2003-04-08 2004-10-13 Halliburton Energy Services, Inc. Biegsame piezoelektrische Vorrichtung für Bohrlochmessungen, -betätigungen und -struckturüberwachung

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4893286A (en) * 1987-11-04 1990-01-09 Standard Oil Company System and method for preprocessing and transmitting echo waveform information
EP1097290B1 (de) 1998-07-15 2004-07-07 Saudi Arabian Oil Company Vorrichtung und verfahren zur überwachung von korrosion in einem bohrloch
EP1467060A1 (de) 2003-04-08 2004-10-13 Halliburton Energy Services, Inc. Biegsame piezoelektrische Vorrichtung für Bohrlochmessungen, -betätigungen und -struckturüberwachung

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
DINO ISA ET AL: "Pipeline defect detection using support vector machines", 6TH WSEAS INTERNATIONAL CONFERENCE ON CIRCUITS, SYSTEMS, ELECTRONICS,CONTROL & SIGNAL PROCESSING, 29 December 2007 (2007-12-29), Cairo, Egypt, pages 162 - 168, XP055032994, Retrieved from the Internet <URL:http://www.wseas.us/e-library/conferences/2007egypt/papers/568-369.pdf> [retrieved on 20120717] *
GERT VAN DIJCK ET AL: "Information Theory Filters for Wavelet Packet Coefficient Selection with Application to Corrosion Type Identification from Acoustic Emission Signals", SENSORS, vol. 11, no. 6, 27 May 2011 (2011-05-27), pages 5695 - 5715, XP055032996, DOI: 10.3390/s110605695 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US11713671B2 (en) 2014-10-28 2023-08-01 Halliburton Energy Services, Inc. Downhole state-machine-based monitoring of vibration
WO2019087125A1 (en) * 2017-11-03 2019-05-09 IdeaCuria Inc. Systems to monitor characteristics of materials involving optical and acoustic techniques
US20220163958A1 (en) * 2021-02-04 2022-05-26 Chengdu Qinchuan Iot Technology Co., Ltd. Methods and systems for managing a pipe network of natural gas
US11822325B2 (en) * 2021-02-04 2023-11-21 Chengdu Qinchuan Iot Technology Co., Ltd. Methods and systems for managing a pipe network of natural gas

Similar Documents

Publication Publication Date Title
KR102069266B1 (ko) 진단 장치, 컴퓨터 프로그램, 및 진단 시스템
Mohamed et al. Tool condition monitoring for high-performance machining systems—A review
Guo et al. Online process monitoring with near-zero misdetection for ultrasonic welding of lithium-ion batteries: An integration of univariate and multivariate methods
JP2022103461A (ja) 診断装置、診断方法、プログラムおよび診断システム
Ma et al. A deep coupled network for health state assessment of cutting tools based on fusion of multisensory signals
Islam et al. Discriminant feature distribution analysis-based hybrid feature selection for online bearing fault diagnosis in induction motors
US9959738B2 (en) Reciprocating machinery monitoring system and method
EP2628895A1 (de) Verfahren und System zur Materialzersetzungserkennung in einem Objekt durch Analyse von Schallschwingungsdaten
KR102575629B1 (ko) 설비 고장 예측 시스템 및 그 방법
JP4417318B2 (ja) 設備診断装置
Fahad et al. Corrosion detection in industrial pipes using guided acoustics and radial basis function neural network
CN116012681A (zh) 基于声振信号融合的管道机器人电机故障诊断方法及系统
Layouni et al. A survey on the application of neural networks in the safety assessment of oil and gas pipelines
Sattarifar et al. Emergence of machine learning techniques in ultrasonic guided wave-based structural health monitoring: a narrative review
Qi et al. A review on data-driven condition monitoring of industrial equipment
JP6939053B2 (ja) 診断装置、プログラムおよび診断システム
JP2017037052A (ja) 振動検出装置、振動検出方法および振動検出プログラム
Ying A data-driven framework for ultrasonic structural health monitoring of pipes
Mita et al. Active detection of loosened bolts using ultrasonic waves and support vector machines
KR20150104459A (ko) 기계 구조물의 결함 진단 시스템 및 그 방법
Wu et al. Contact event detection for robotic oil drilling
Feng Condition Classification in Underground Pipes Based on Acoustical Characteristics. Acoustical characteristics are used to classify the structural and operational conditions in underground pipes with advanced signal classification methods
EP3855151A1 (de) Fluidleckdiagnosevorrichtung, fluidleckdiagnosesystem, fluidleckdiagnoseverfahren und aufzeichnungsmedium mit darauf gespeichertem fluidleckdiagnoseprogramm
Ahmad et al. Centrifugal Pump Fault Diagnosis Using Discriminative Factor-Based Features Selection and K-Nearest Neighbors
US20230167732A1 (en) Detecting Defects in Tubular Structures

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

AX Request for extension of the european patent

Extension state: BA ME

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE APPLICATION IS DEEMED TO BE WITHDRAWN

18D Application deemed to be withdrawn

Effective date: 20140222