WO2011006528A1 - Détection de défauts dans une machine électrique tournante - Google Patents

Détection de défauts dans une machine électrique tournante Download PDF

Info

Publication number
WO2011006528A1
WO2011006528A1 PCT/EP2009/058906 EP2009058906W WO2011006528A1 WO 2011006528 A1 WO2011006528 A1 WO 2011006528A1 EP 2009058906 W EP2009058906 W EP 2009058906W WO 2011006528 A1 WO2011006528 A1 WO 2011006528A1
Authority
WO
WIPO (PCT)
Prior art keywords
frequency band
electrical machine
rotating electrical
frequency
computer program
Prior art date
Application number
PCT/EP2009/058906
Other languages
English (en)
Inventor
Pedro Rodriguez
Original Assignee
Abb Research Ltd
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 Abb Research Ltd filed Critical Abb Research Ltd
Priority to PCT/EP2009/058906 priority Critical patent/WO2011006528A1/fr
Publication of WO2011006528A1 publication Critical patent/WO2011006528A1/fr

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/34—Testing dynamo-electric machines
    • G01R31/343—Testing dynamo-electric machines in operation
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
    • H02P29/00—Arrangements for regulating or controlling electric motors, appropriate for both AC and DC motors
    • H02P29/02—Providing protection against overload without automatic interruption of supply
    • H02P29/024—Detecting a fault condition, e.g. short circuit, locked rotor, open circuit or loss of load
    • H02P29/0241—Detecting a fault condition, e.g. short circuit, locked rotor, open circuit or loss of load the fault being an overvoltage

Definitions

  • the present invention relates generally to rotating electrical machines, such as motors and generators, and more particularly to fault detection of electrical machines .
  • MCSA motor current signature analysis
  • the monitoring method is based on the behaviour of the current at the side band associate with the fault.
  • STFFT Short Time Fast Fourier Transform
  • An object of the present invention is to provide a solution to fault detection which can detect several different fault conditions.
  • a method for detecting faults in a rotating electrical machine comprises the steps of: selecting at least one frequency band to analyse, each frequency band being a frequency band of a measured entity to analyse; obtaining a plurality of magnitude measurements over time, making up a frequency band series, for each of the at least one frequency bands; and evaluating the frequency band series to determine the presence or absence of a plurality of different fault conditions.
  • measurements may utilise discrete wavelet transform.
  • the step of evaluating may use a weight function to evaluate the frequency band series.
  • the step of evaluating may use a different weight
  • each of the weight functions evaluates a plurality of frequency bands .
  • the measured entity may be the stator current of the rotating electrical machine.
  • the measured entity may be a physical vibration of the rotating electrical machine.
  • the measurements may be taken during a phase when
  • the phase may be a start-up phase of the rotating
  • the fault conditions may be at least two conditions selected from the group consisting of a broken rotor bar, static air gap eccentricities, dynamic air gap
  • the at least one frequency bands may all be sub-bands within the range of 0 to 5 kHz.
  • a second aspect of the invention is an apparatus for detecting faults in a rotating electrical machine.
  • the apparatus comprises: a frequency band selector arranged to select at least one frequency band to analyse, each frequency band being a frequency band of a measured entity to analyse a measurer arranged to obtain a
  • the apparatus may further comprise a current measuring device arranged to measure a stator current of the rotating electrical machine.
  • the apparatus may further comprise a vibration sensor arranged to measure a vibration of the rotating
  • a third aspect of the invention is a computer program for a fault detecting apparatus, the computer program
  • a fourth aspect of the invention is a computer program product comprising a computer program according to the third aspect and a computer readable means on which the computer program is stored.
  • the invention is based on the following.
  • a signal e.g. stator current or mechanical vibration
  • This signal is divided into frequency bands, in a similar way to band pass filters, e.g. using discrete wavelet transform.
  • band pass filters e.g. using discrete wavelet transform.
  • a plurality of fault conditions can be identified; i.e. not only one fault condition as known in the art.
  • the frequency bands can be 0-8, 8- 16, 16-32, 32-64, 64-128, 128-256, and 256-512 Hz.
  • eccentricity and/or broken rotor bars can be identified this way.
  • the analysis can for example be performed during start-up of the machine.
  • the frequency bands can be evaluated as they progress during a transient (start or stop), e.g. using the wavelet analysis. Since the frequency bands due to the different root causes evolve differently, this method allows discriminating with high certainty.
  • the solution has been found to be able to detect e.g. broken rotor bars and eccentricity faults and distinguishing it with respect to other failures and load torque oscillations.
  • Fig Ia is a schematic diagram showing an environment where the present invention could be applied using stator current measurements
  • Fig Ib is a schematic diagram showing an environment where the present invention could be applied using vibration measurements
  • Fig 2 is a schematic diagram showing modules of the fault detecting apparatus of Figs Ia and/or b,
  • Fig 3 is a flow chart illustrating the use of the fault detecting apparatus of Figs Ia and/or b,
  • Fig 4 is a graph of a discrete wavelet decomposition of a starting current of a healthy motor
  • Fig 5 is a graph of a discrete wavelet decomposition of a starting current of a motor with one broken rotor bar
  • Fig 6 is a graph of a discrete wavelet decomposition of a starting current of a motor with 10 per cent
  • Fig 7 is a graph of a discrete wavelet decomposition of a starting current of a motor with two broken bars and at the same time load torque oscillations
  • Fig 8 is a diagram illustrating a sub-band coding algorithm of a discrete wavelet transform
  • Fig 9 is a graph illustrating discrete wavelet transform filtering performed with the Mallat algorithm
  • Fig 10 shows one example of a computer program product comprising computer readable means
  • Fig 11 is a schematic graph of sidebands of a starting current of a motor in an embodiment
  • Fig 12 is a graph showing rotor slip and left side band component frequency of a starting current of a motor with one broken rotor bar
  • I Fig 133- is a graph showing rotor slip and left side band component frequency of a starting current of a motor with 10 per cent dynamic eccentricity.
  • Fig Ia is a schematic diagram showing an environment where the present invention could be applied to stator current measurements.
  • a rotating electrical machine such as a motor or
  • stator 3 and a rotor 2. These are only displayed schematically here; the actual
  • stator 3 is
  • a current to/from the stator flows through a cable and is measured with a current measuring device 4, such as an ammeter.
  • the ammeter provides a measurement signal to a fault
  • the fault detector can detect a plurality of faults of the rotating electrical machine 1.
  • Fig Ib is a schematic diagram showing an environment where the present invention could be applied to vibration measurements.
  • a vibration sensor 6 measures
  • the vibration sensor can for example comprise an accelerometer, a velocity sensor, a displacement sensor.
  • the vibration sensor can send measurement values wirelessly to the fault detecting apparatus 5. The vibration sensor thus provides a
  • stator current and vibration may be combined with flux measurements, e.g.
  • Fig 11 is a schematic graph of sidebands of a starting current of a motor. The graph shows peaks in frequency domain diagram of the current signal. Only peaks are shown, as indicated by arrows, not the entire graph.
  • a main frequency is indicated by a relatively large arrow 61.
  • a left first sideband, indicated by arrow 60 is a peak at a frequency fi which is the peak which is closest in frequency to the main frequency, which is below the main frequency.
  • a first right sideband is indicated by arrow 62.
  • Fig 12 is a graph showing rotor slip and left side band component frequency of a starting current of a motor with one broken rotor bar.
  • the upper graph 70 shows the rotor slip over time, while the lower graph 72 shows the frequency of the left sideband 60 (Fig 11) when the motor with a broken bar is started.
  • the left side band frequency drops all the way to zero before it returns to a steady state at around 45 Hz.
  • Fig 13 is a graph showing rotor slip and left side band component frequency of a starting current of a motor with 10 per cent dynamic eccentricity.
  • the upper graph 74 shows the rotor slip over time, while the lower graph 76 shows the frequency of the left sideband 60 (Fig 11) when the motor with eccentricity issues is started.
  • the left side band frequency remains at a frequency of about 28 Hz, which is far from the main frequency (here 50Hz) .
  • Embodiments of the present invention are directed to utilising the characteristics of the left sideband which differ for different motor problems. This is done by examining lower frequencies of the current in the
  • Fig 2 is a schematic diagram showing modules of the fault detecting apparatus 5 of Figs Ia and/or b.
  • the various modules 7-9 can be implemented by means of software and/or hardware. It is also to be noted that the modules may share some hardware components such as controllers and memory 10.
  • a controller (not explicitly shown but can be used to implement part or all of the modules) is provided using any suitable central processing unit
  • the memory 10 can be any combination of read and write memory (RAM) and read only memory (ROM) .
  • the memory 10 also comprises persistent storage.
  • the persistent memory can be any single one or combination of magnetic memory, optical memory, or solid state memory.
  • a frequency band selector 7 selects the frequency bands to analyse.
  • a measurer 8 receives the measurement data to analyse, e.g. vibration data or stator current data.
  • An evaluator 9 evaluates the measured data for the selected frequency bands to evaluate whether one or more faults have occurred in the rotating electrical machine 1, as will be explained in more detail below.
  • the fault detecting apparatus 5 may be connected to other systems (not shown) for further handling when faults are detected.
  • the fault detecting apparatus can be connected to a monitoring and alarm system, or it can be arranged to autonomously stop the rotating electrical machine 1.
  • the fault detecting apparatus 5 may further be provided with a user interface (not shown), e.g.
  • a speaker comprising a display and a keypad or keyboard, mouse or trackball, etc.
  • a speaker can also be
  • the fault detecting apparatus 5 can be implemented using a general purpose computer such as a personal computer with appropriate input for the measurements, analog and/or digital.
  • Fig 3 is a flow chart illustrating the use of the fault detecting apparatus of Figs Ia and/or b, in a method to detect faults of the rotating electrical machine 1.
  • the method is processed during the start-up phase of the rotating electrical machine.
  • the method can be run during a shut-down phase.
  • the method can be started automatically when monitored machine is turned on, or using signals from a control system such as a motoro control system.
  • the method can be run
  • suitable frequency bands are selected.
  • This selection can be suitably configured, whereby this step, for example, can select frequency bands 0-8 Hz, 8-16 Hz, 16-32 Hz, 32-64 Hz, 64-128 Hz, 128-256 Hz, and 256 - 512 Hz.
  • This selection is just an example and other suitable frequency bands can be selected. In one embodiment, only frequency bands under 5 kHz are selected.
  • an obtain magnitude measurements step 22 a plurality of magnitude measurements are obtained over time for each of the frequency bands, thus making up a frequency band series.
  • This can be implemented using discrete wavelet transform.
  • the measurement signal is received and the frequency components of the signal are extracted according to the frequency bands of the
  • the discrete wavelet transform decomposition provides a set of wavelet signals (approximation and details) .
  • Each one of those signals contains the time evolution of the components within the original measurement signal that are included within its corresponding frequency band, according to the band expressions shown above.
  • the analysis of those signals can allow the detection of some patterns caused by the evolution of the components associated with the fault. For the analyses performed,
  • Daubechies wavelets can be employed, although other types of wavelet families, such as Morlet or biorthogonal, also provide satisfactory results.
  • the use of such a high- order wavelet is justified by the decrease in the overlap between bands.
  • an evaluate signal for faults step 24 the frequency bands are analysed to detect any of a plurality of potential faults. The details of this evaluation will be explained in more detail with reference to Figs 4-7 below.
  • step 26 the process ends if there are no faults. However, if there are faults, the process continues to a react to faults step 28.
  • the process reacts to the detected fault or faults.
  • one or more corresponding alarm signals can be sent to an operation management system.
  • an alert or alarm can be displayed on the fault detecting apparatus 5.
  • an audible signal can be
  • the method is started again whenever suitable fault detection should be performed.
  • Figs 4 to 8 are graphs of a discrete wavelet
  • the starting current was obtained from simulations of a motor with details as follows:
  • the topmost graph in Figs 4-8 shows the stator current s during the start-up phase.
  • Fig 4 is a graph of a discrete wavelet
  • Fig. 5 shows the decomposition for the case of one broken bar for the same motor.
  • the dio band, 8-16 Hz has a significantly different appearance compared to the healthy motor. The signal is stronger and lasts longer than in the healthy motor.
  • the lower frequency band harmonic due to the broken bar evolves, first the signal is in ds, and then after the first 0.3 s it appears in dg, after the signals continues to the detail signal dio, then, to the approximation signal aio, after 0.5 s, it appears in dio again. After that, it moves to dg and to the main signal sub-band (ds) when the transient disappears. Consequently, the fault of the broken rotor can be detected by analysing any one or more of these frequency bands. For optimal detection accuracy, all of the affected frequency bands can
  • Fig 7 is a graph of a discrete wavelet decomposition of a starting current of a motor with two broken bars and at the same time load torque oscillations occurring at low frequencies (from 50 to 15 Hz) . It is clear that the patterns produced by the broken bars, see ellipse 34, are distinguishable from the pattern due to the torque oscillations, see ellipse 36. It has thus been shown that with the inventive method and apparatus presented herein, individual faults can be detected and identified, event when a plurality of faults are present.
  • FFT Fast Fourier Transform
  • Short Time Fast Fourier Transform is better in this aspect, but implies some constraints regarding the selection of the optimum window size for data analysis.
  • the wavelet theory is here used as a tool for analysing signals with
  • frequency spectrum varying in time It allows a time- localization of the frequency components occurring within the signal, being able to extract their time evolution. This property enables the detection of characteristic patterns within the evolution of those components, which can be related to the occurrence of certain phenomena.
  • the discrete wavelet transform performs the decomposition of a sampled signal s (t) (si, S2, S3 ... S n ) onto an approximation signal at a certain decomposition level n (a n (t) ) and n detail signals (d D (t) with j varying from 1 to n) .
  • n a n (t)
  • d D n detail signals
  • Each signal is the product of the corresponding coefficients (approximation coefficients for a n and wavelet coefficients for d D ) and the scaling function or the wavelet function at each level, respectively.
  • ⁇ " ⁇ ⁇ J are the scaling and wavelet coefficients, respectively
  • ⁇ "(t), ⁇ (t) are the scaling function at level n and wavelet function at level j , respectively
  • n is the decomposition level
  • a n is the approximation signal at level n
  • d D is the detail signal at level j .
  • Mallat's algorithm or sub- band coding algorithm the approximation signal behaves as a low-pass filter whereas each wavelet signal behaves as a pass-band filter, extracting the time evolution of the components of the original signal included within its corresponding frequency band.
  • the sampling frequency is typically between 1 and 20 kHz, depending on the main frequency and hardware and software capabilities. It is shown how the original sampled signal S [n] is passed firstly through a half-band high-pass filter g[n] and a low pass filter h[n] .
  • a down- sampling by two can be performed, obtaining, for
  • the detail d D contains the information concerning the signal components whose frequencies are included in the interval [2 ⁇ D+1) f s , 2 ⁇ J f s ] .
  • the approximation signal a n includes the low-frequency components of the signal, belonging to the interval [0, 2 "(n+1) f s ] .
  • n d The number of decomposition levels (n d ) is related to the sampling frequency of the signal being analysed (f s ) .
  • approximation signal Alternatively, this can be achieved by simply using the approximation coefficients.
  • One particular example is to use an appropriately configured weight function, based on weighing the approximation and details signal in the neighbourhood of the moment in time when the motor velocity reaches half of the steady state speed. This point is selected because when the lower side band component due to broken bars reach the lower
  • the number of levels in the discrete wavelet transform decomposition depends on the sampling frequency. One example is to select 10 levels of decomposition, in order to have at least three sub-bands below the sub-band that contain the fundamental frequency (50 or 60 Hz) . From the calculated weight and taking into account the rms value of the current in the steady state, five indicators are calculated for five different time periods. Each of the indicators are a result of a particular weight function for that time period, which takes into consideration particular frequency patterns for the various fault conditions. In one embodiment there are different sets of weight functions to evaluate different fault conditions, wherein each set of weight functions is adapted to detect a particular fault condition . The analysis of these indicators determined the motor condition.
  • Fig. 6 shows a block diagram of the detection algorithm. The preset values are calculated from the machine in the healthy condition .
  • Fig 10 shows one example of a computer program product comprising computer readable means 50.
  • a computer program can be stored, which computer program can cause a computer to execute the method according to embodiments described herein.
  • the computer program product is an optical disc, such as a CD (compact disc) , a DVD (digital
  • the computer readable means can also be solid state memory, such as flash memory or a software package distributed over a network, such as the Internet.
  • the computer readable means can hold a computer program for methods for the fault
  • eccentricities dynamic air gap eccentricities, opening of stator coil, short circuit of stator coil, abnormal connection in stator winding, cracked end rings, bent shaft, short circuit in rotor field windings, bearing failures, and gearbox failures.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Tests Of Circuit Breakers, Generators, And Electric Motors (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

L'invention porte sur un procédé pour détecter des défauts dans une machine électrique tournante. Le procédé comporte les étapes suivantes : la sélection d'au moins une bande de fréquence à analyser, chaque bande de fréquence étant une bande de fréquence d'une entité mesurée à analyser ; l’obtention d'une pluralité de mesures de grandeur au cours du temps, constituant une série de bande de fréquence, pour la ou chacune des bandes de fréquence, et l’évaluation de la série de bande de fréquence afin de déterminer la présence ou l'absence d'une pluralité de conditions de défaut différentes. L'invention porte également sur un appareil de détection de défauts, un programme d'ordinateur et un produit de programme d'ordinateur correspondants.
PCT/EP2009/058906 2009-07-13 2009-07-13 Détection de défauts dans une machine électrique tournante WO2011006528A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
PCT/EP2009/058906 WO2011006528A1 (fr) 2009-07-13 2009-07-13 Détection de défauts dans une machine électrique tournante

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/EP2009/058906 WO2011006528A1 (fr) 2009-07-13 2009-07-13 Détection de défauts dans une machine électrique tournante

Publications (1)

Publication Number Publication Date
WO2011006528A1 true WO2011006528A1 (fr) 2011-01-20

Family

ID=42102078

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2009/058906 WO2011006528A1 (fr) 2009-07-13 2009-07-13 Détection de défauts dans une machine électrique tournante

Country Status (1)

Country Link
WO (1) WO2011006528A1 (fr)

Cited By (26)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102279343A (zh) * 2011-07-23 2011-12-14 华北电力大学（保定） 一种基于esprit-saa的笼型异步电动机转子断条故障在线检测方法
ITRM20110440A1 (it) * 2011-08-12 2013-02-13 Uni Degli Studi I Roma La Sapienza Metodo per la valutazione on-line del guasto per eccentricita' rotorica in generatori/motori sincroni tramite la misura delle correnti interne dell'avvolgimento statorico e l'analisi spettrale di segnali analitici da esse derivati tramite trasformazi
ITRM20110441A1 (it) * 2011-08-12 2013-02-13 Univ Roma Metodo per la misura della riduzione di traferro in macchine sincrone trifasi tramite -tracciamento delle ampiezze massime e minime - di traiettorie di vettori spaziali multipolari ricavati dalle correnti interne statoriche
US20140049285A1 (en) * 2011-04-29 2014-02-20 Pedro Rodriguez Method For Monitoring Demagnetization
US20140109679A1 (en) * 2011-06-29 2014-04-24 Pedro Rodriguez Method For Identifying A Fault In An Electrical Machine
WO2016143882A1 (fr) * 2015-03-10 2016-09-15 Mitsubishi Electric Corporation Détection de défauts dans des moteurs à induction en se basant sur une analyse de signature de courant
US9541606B2 (en) 2012-12-17 2017-01-10 General Electric Company Fault detection system and associated method
CN106547988A (zh) * 2016-11-14 2017-03-29 太原理工大学 多故障耦合模拟实验的笼型异步电动机结构设计方法
EP3220120A1 (fr) * 2016-03-17 2017-09-20 ABB Schweiz AG Procédé, dispositif et système de diagnostic permettant de déterminer des états défectueux dans une machine électrique
JP6293388B1 (ja) * 2016-06-21 2018-03-14 三菱電機株式会社 負荷の異常検出装置
EP3306330A1 (fr) * 2016-10-05 2018-04-11 Rolls-Royce plc Défaut d'enroulement de stator de générateur synchrone sans balai
DE102016222660A1 (de) * 2016-11-17 2018-05-17 Volkswagen Aktiengesellschaft Verfahren und Vorrichtung zum Erkennen von Veränderungen in einem elektrisch betriebenen Antrieb
US10403116B2 (en) 2017-06-20 2019-09-03 General Electric Company Electrical signature analysis of electrical rotating machines
US10495693B2 (en) 2017-06-01 2019-12-03 General Electric Company Wind turbine fault detection using acoustic, vibration, and electrical signals
WO2019229687A1 (fr) * 2018-05-31 2019-12-05 Abb Schweiz Ag Dispositif de surveillance et de protection d'état de machines électriques tournantes, et procédé associé
JP2020008337A (ja) * 2018-07-04 2020-01-16 株式会社明電舎 設備の異常診断装置及び異常診断方法
US10663372B2 (en) 2018-05-21 2020-05-26 Caterpillar Inc. Bearing failure detection in a hydraulic fracturing rig
CN111208424A (zh) * 2020-01-14 2020-05-29 华能四川水电有限公司 发电机定转子间隙不均故障自动检测预警方法及装置
RU2724988C1 (ru) * 2019-07-09 2020-06-29 федеральное государственное бюджетное образовательное учреждение высшего образования "Ивановский государственный энергетический университет имени В.И. Ленина" (ИГЭУ) Способ выявления оборванных стержней в короткозамкнутой обмотке ротора асинхронного электродвигателя
CN112325932A (zh) * 2020-10-28 2021-02-05 广东寰球智能科技有限公司 基于交流电机的监测方法及监测装置
US10928814B2 (en) 2017-02-24 2021-02-23 General Electric Technology Gmbh Autonomous procedure for monitoring and diagnostics of machine based on electrical signature analysis
CN112526339A (zh) * 2020-11-24 2021-03-19 辽宁科技大学 基于多项式-相位变换的鼠笼电机转子断条故障诊断方法
JP2021085820A (ja) * 2019-11-29 2021-06-03 株式会社日立製作所 診断装置および診断方法
CN113030727A (zh) * 2021-03-30 2021-06-25 华北电力大学（保定） 一种发电机转子动态匝间短路故障模拟装置及其方法
CN113391235A (zh) * 2021-06-04 2021-09-14 华北电力大学（保定） 一种同步发电机转子动态匝间短路故障检测系统及方法
CN116381489A (zh) * 2023-04-20 2023-07-04 华北电力大学（保定） 一种非侵入式大容量发电机三维气隙偏心故障的检测方法

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5995910A (en) * 1997-08-29 1999-11-30 Reliance Electric Industrial Company Method and system for synthesizing vibration data
WO2002089305A1 (fr) * 2001-05-01 2002-11-07 Square D Company Detection de palier moteur defectueux par analyse par ondelettes de l'onde mobile du courant de demarrage
US7539549B1 (en) * 1999-09-28 2009-05-26 Rockwell Automation Technologies, Inc. Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5995910A (en) * 1997-08-29 1999-11-30 Reliance Electric Industrial Company Method and system for synthesizing vibration data
US7539549B1 (en) * 1999-09-28 2009-05-26 Rockwell Automation Technologies, Inc. Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis
WO2002089305A1 (fr) * 2001-05-01 2002-11-07 Square D Company Detection de palier moteur defectueux par analyse par ondelettes de l'onde mobile du courant de demarrage

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
CHAO-MING CHEN, KENNETH A. LOPARO: "ELECTRIC FAULT DETECTION FOR VECTOR-CONTROLLED INDUCTION MOTORS USING THE DISCRETE WAVELET TRANSFORM", AMERICAN CONTROL CONFERENCE, vol. 6, 21 June 1998 (1998-06-21) - 26 June 1998 (1998-06-26), pages 3297 - 3301, XP002578459, ISBN: 0-7803-4530-4 *
DOUGLAS H ET AL: "Detection of broken rotor bars in induction motors using wavelet analysis", ELECTRIC MACHINES AND DRIVES CONFERENCE, 2003. IEMDC'03. IEEE INTERNAT IONAL JUNE 1-4, 2003, PISCATAWAY, NJ, USA,IEEE, vol. 2, 1 June 2003 (2003-06-01), pages 923 - 928, XP010643461, ISBN: 978-0-7803-7817-9 *
FERNANDO BRIZ ET AL: "Broken Rotor Bar Detection in Line-Fed Induction Machines Using Complex Wavelet Analysis of Startup Transients", IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS, IEEE SERVICE CENTER, PISCATAWAY, NJ, US, vol. 43, no. 3, 1 May 2008 (2008-05-01), pages 760 - 768, XP011214873, ISSN: 0093-9994 *

Cited By (41)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140049285A1 (en) * 2011-04-29 2014-02-20 Pedro Rodriguez Method For Monitoring Demagnetization
US9823308B2 (en) * 2011-04-29 2017-11-21 Abb Schweiz Ag Method for monitoring demagnetization
US9255837B2 (en) * 2011-06-29 2016-02-09 Abb Research Ltd. Method for identifying a fault in an electrical machine
US20140109679A1 (en) * 2011-06-29 2014-04-24 Pedro Rodriguez Method For Identifying A Fault In An Electrical Machine
CN102279343B (zh) * 2011-07-23 2013-08-07 华北电力大学（保定） 一种基于esprit-saa的笼型异步电动机转子断条故障在线检测方法
CN102279343A (zh) * 2011-07-23 2011-12-14 华北电力大学（保定） 一种基于esprit-saa的笼型异步电动机转子断条故障在线检测方法
ITRM20110440A1 (it) * 2011-08-12 2013-02-13 Uni Degli Studi I Roma La Sapienza Metodo per la valutazione on-line del guasto per eccentricita' rotorica in generatori/motori sincroni tramite la misura delle correnti interne dell'avvolgimento statorico e l'analisi spettrale di segnali analitici da esse derivati tramite trasformazi
WO2013024500A1 (fr) * 2011-08-12 2013-02-21 Universita' Degil Studi Di Roma "La Sapienza" Procédé pour l'évaluation en ligne de panne due à des générateurs à excentricité rotorique et des moteurs synchrones
WO2013024499A1 (fr) * 2011-08-12 2013-02-21 Universitad' Degli Studi Di Roma "La Sapienza" Procédé pour la mesure de la réduction d'entrefer magnétique dans les machines synchrones triphasées
ITRM20110441A1 (it) * 2011-08-12 2013-02-13 Univ Roma Metodo per la misura della riduzione di traferro in macchine sincrone trifasi tramite -tracciamento delle ampiezze massime e minime - di traiettorie di vettori spaziali multipolari ricavati dalle correnti interne statoriche
US9541606B2 (en) 2012-12-17 2017-01-10 General Electric Company Fault detection system and associated method
WO2016143882A1 (fr) * 2015-03-10 2016-09-15 Mitsubishi Electric Corporation Détection de défauts dans des moteurs à induction en se basant sur une analyse de signature de courant
US9618583B2 (en) 2015-03-10 2017-04-11 Mitsubishi Electric Research Laboratories, Inc Fault detection in induction motors based on current signature analysis
EP3220120A1 (fr) * 2016-03-17 2017-09-20 ABB Schweiz AG Procédé, dispositif et système de diagnostic permettant de déterminer des états défectueux dans une machine électrique
WO2017157627A1 (fr) * 2016-03-17 2017-09-21 Abb Schweiz Ag Procédé, dispositif de diagnostic et système pour déterminer des conditions de panne dans une machine électrique
JP6293388B1 (ja) * 2016-06-21 2018-03-14 三菱電機株式会社 負荷の異常検出装置
US10725107B2 (en) 2016-10-05 2020-07-28 Rolls-Royce Plc Brushless synchronous generator stator winding fault
EP3306330A1 (fr) * 2016-10-05 2018-04-11 Rolls-Royce plc Défaut d'enroulement de stator de générateur synchrone sans balai
CN106547988B (zh) * 2016-11-14 2019-05-28 太原理工大学 多故障耦合模拟实验的笼型异步电动机结构设计方法
CN106547988A (zh) * 2016-11-14 2017-03-29 太原理工大学 多故障耦合模拟实验的笼型异步电动机结构设计方法
DE102016222660A1 (de) * 2016-11-17 2018-05-17 Volkswagen Aktiengesellschaft Verfahren und Vorrichtung zum Erkennen von Veränderungen in einem elektrisch betriebenen Antrieb
US10928814B2 (en) 2017-02-24 2021-02-23 General Electric Technology Gmbh Autonomous procedure for monitoring and diagnostics of machine based on electrical signature analysis
US10495693B2 (en) 2017-06-01 2019-12-03 General Electric Company Wind turbine fault detection using acoustic, vibration, and electrical signals
US10403116B2 (en) 2017-06-20 2019-09-03 General Electric Company Electrical signature analysis of electrical rotating machines
US10663372B2 (en) 2018-05-21 2020-05-26 Caterpillar Inc. Bearing failure detection in a hydraulic fracturing rig
WO2019229687A1 (fr) * 2018-05-31 2019-12-05 Abb Schweiz Ag Dispositif de surveillance et de protection d'état de machines électriques tournantes, et procédé associé
US11656280B2 (en) 2018-05-31 2023-05-23 Abb Schweiz Ag Device for condition monitoring and protection of rotating electrical machines, and a method thereof
JP7002417B2 (ja) 2018-07-04 2022-01-20 株式会社明電舎 設備の異常診断装置及び異常診断方法
JP2020008337A (ja) * 2018-07-04 2020-01-16 株式会社明電舎 設備の異常診断装置及び異常診断方法
RU2724988C1 (ru) * 2019-07-09 2020-06-29 федеральное государственное бюджетное образовательное учреждение высшего образования "Ивановский государственный энергетический университет имени В.И. Ленина" (ИГЭУ) Способ выявления оборванных стержней в короткозамкнутой обмотке ротора асинхронного электродвигателя
JP2021085820A (ja) * 2019-11-29 2021-06-03 株式会社日立製作所 診断装置および診断方法
JP7191807B2 (ja) 2019-11-29 2022-12-19 株式会社日立製作所 診断装置および診断方法
CN111208424A (zh) * 2020-01-14 2020-05-29 华能四川水电有限公司 发电机定转子间隙不均故障自动检测预警方法及装置
CN111208424B (zh) * 2020-01-14 2021-09-07 华能四川能源开发有限公司 发电机定转子间隙不均故障自动检测预警方法及装置
CN112325932A (zh) * 2020-10-28 2021-02-05 广东寰球智能科技有限公司 基于交流电机的监测方法及监测装置
CN112526339A (zh) * 2020-11-24 2021-03-19 辽宁科技大学 基于多项式-相位变换的鼠笼电机转子断条故障诊断方法
CN112526339B (zh) * 2020-11-24 2023-08-22 辽宁科技大学 基于多项式-相位变换的鼠笼电机转子断条故障诊断方法
CN113030727A (zh) * 2021-03-30 2021-06-25 华北电力大学（保定） 一种发电机转子动态匝间短路故障模拟装置及其方法
CN113391235A (zh) * 2021-06-04 2021-09-14 华北电力大学（保定） 一种同步发电机转子动态匝间短路故障检测系统及方法
CN116381489A (zh) * 2023-04-20 2023-07-04 华北电力大学（保定） 一种非侵入式大容量发电机三维气隙偏心故障的检测方法
CN116381489B (zh) * 2023-04-20 2023-11-17 华北电力大学（保定） 一种非侵入式大容量发电机三维气隙偏心故障的检测方法

Similar Documents

Publication Publication Date Title
WO2011006528A1 (fr) Détection de défauts dans une machine électrique tournante
JP5546545B2 (ja) 電気機械のロータの動作中に電気機械を監視および／または分析する方法および電気機械を動作中に監視および／または分析する装置
US10267860B2 (en) Fault detection in induction machines
DK2565658T3 (en) Fault detection based on current signature analysis of a generator
US7117744B2 (en) Apparatus and method for detecting vibrations of the shaft assembly in an electrical machine
US5739698A (en) Machine fault detection using slot pass frequency flux measurements
Bessous et al. A comparative study between the MCSA, DWT and the vibration analysis methods to diagnose the dynamic eccentricity fault in induction motors
JP5565120B2 (ja) 転がり軸受部振動データの高周波電磁振動成分除去方法および高周波電磁振動成分除去装置、回転機械の転がりの軸受診断方法および軸受診断装置
US10408879B2 (en) Method and apparatus for diagnosing a fault condition in an electric machine
Mehala et al. A comparative study of FFT, STFT and wavelet techniques for induction machine fault diagnostic analysis
CN102279341B (zh) 一种基于esprit与psa的笼型异步电动机转子断条故障检测方法
JP2005538371A (ja) 動作中に電気機械を監視および／または分析するための装置および方法
DK2761315T3 (en) METHOD FOR DETERMINING STATIONARY SIGNALS FOR DIAGNOSTICATION OF AN ELECTROMECHANICAL SYSTEM
JP7109656B2 (ja) 電動機設備の異常診断装置、電動機設備の異常診断方法、および電動機設備の異常診断システム
JP2013526724A (ja) 機械振動の監視
KR20230054455A (ko) 기계 상태 모니터링 방법 및 기계 상태 모니터링 시스템
EP2851698B1 (fr) Procédé de détection d'une panne d'une machine électrique
CN102279364A (zh) 一种基于music与psa的笼型异步电动机转子断条故障检测方法
Mehala et al. Condition monitoring methods, failure identification and analysis for Induction machines
JP6316510B1 (ja) 電動機の診断装置
Ahamed et al. Novel diagnosis technique of mass unbalance in rotor of induction motor by the analysis of motor starting current at no load through wavelet transform
JP2007236170A (ja) 回転電機の回転数測定方法及び回転電機の回転数測定装置
Chikkam et al. High-resolution-based electrical fault diagnosis of induction motor using gabor analysis of quadrature stator current at variable speed regime
Yeolekar et al. Outer race bearing fault identification of induction motor based on stator current signature by wavelet transform
Ghadirinezhd et al. High-resolution rotor fault diagnosis of wound rotor induction machine based on stator current signature analyse

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: 09780498

Country of ref document: EP

Kind code of ref document: A1

DPE1 Request for preliminary examination filed after expiration of 19th month from priority date (pct application filed from 20040101)
NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 09780498

Country of ref document: EP

Kind code of ref document: A1