WO2019167086A1 - Système d'évaluation de multiples défauts dans des moteurs à induction - Google Patents

Système d'évaluation de multiples défauts dans des moteurs à induction Download PDF

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Publication number
WO2019167086A1
WO2019167086A1 PCT/IN2019/050181 IN2019050181W WO2019167086A1 WO 2019167086 A1 WO2019167086 A1 WO 2019167086A1 IN 2019050181 W IN2019050181 W IN 2019050181W WO 2019167086 A1 WO2019167086 A1 WO 2019167086A1
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WO
WIPO (PCT)
Prior art keywords
motor
faults
fault
estimator
frequency
Prior art date
Application number
PCT/IN2019/050181
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English (en)
Inventor
Aurobinda Routray
Arunava Naha
Anik Kumar SAMANTA
Amey PAWAR
Chandrasekhar SAKPAL
Original Assignee
Aurobinda Routray
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 Aurobinda Routray filed Critical Aurobinda Routray
Publication of WO2019167086A1 publication Critical patent/WO2019167086A1/fr

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Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K11/00—Structural association of dynamo-electric machines with electric components or with devices for shielding, monitoring or protection
    • H02K11/20—Structural association of dynamo-electric machines with electric components or with devices for shielding, monitoring or protection for measuring, monitoring, testing, protecting or switching
    • H02K11/27—Devices for sensing current, or actuated thereby
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
    • G01R23/16—Spectrum analysis; Fourier analysis
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02K—DYNAMO-ELECTRIC MACHINES
    • H02K11/00—Structural association of dynamo-electric machines with electric components or with devices for shielding, monitoring or protection
    • H02K11/20—Structural association of dynamo-electric machines with electric components or with devices for shielding, monitoring or protection for measuring, monitoring, testing, protecting or switching
    • H02K11/21—Devices for sensing speed or position, or actuated thereby
    • H02K11/215—Magnetic effect devices, e.g. Hall-effect or magneto-resistive elements

Definitions

  • Another object of the invention is to provide a system and apparatus for remote assessment of faults of a 3 -phase squirrel cage induction motor using a cloud-based server. Data from the induction motor can be assessed directly by the system, or it can be uploaded into a cloud-based server for processing.
  • the slip and the supply frequency are found non-invasively from the stator current for forming adaptive fault frequency search bands required for the spectral estimation. Spectral estimation over small multiple bands reduced the computational burden extensively. The peak magnitude of the spectrum is utilized to quantify the fault severity. As all the methods require only the single -phase stator current, the reliability is increased, and the complexity and the cost of using additional sensors is reduced.
  • the faults under consideration comprise broken rotor bar (BRB), broken end ring (BER), inter-tum short circuit (ITSC), eccentricity related faults, and bearing faults.
  • the bearing faults are classified as outer raceway fault, inner raceway fault, and rolling element fault.
  • the software is capable of generating an automated report in document formats having all the motor fault information and graphs in it.
  • a band of frequency is searched around the f brb component. If a peak is found in the band around the theoritical f brb , then the motor is said to have broken rotor bar.
  • the searchband of 2 Hz has been fixed for finding the fault component around the vicinity of the f brb . If the f ber is found to be within the 2 Hz band around the theoritical f ber , then the motor is said to have broken end ring. For this, a motor current sensor is deployed to sense motor current.
  • a motor current sensor is deployed to sense motor current.
  • an input mechanism facilitates input of number of rotor slots of the motor, number of bars of the motor, and number of pole-pairs of the motor.
  • Inter turn short circuit f i sc l ⁇ -*-(l- s) f (eq. If)
  • the Rayleigh-quotient-based spectral estimator is then used to find whether a fault component is present the in the band. In case the fault frequency component is present, the amplitude of the fault component decides the severity of the fault. The above-mentioned procedure is valid for detecting all the faults.
  • the spectral estimator can reliably quantify the degree of damage.
  • the normalized peak magnitude of sideband frequency components with respect to the fundamental is considered as the discriminating feature for fault detection and quantification.
  • An embedded hardware platform is developed for online fault diagnosis and RT fault simulation.
  • the system is capable of storing the history of the fault components. As a result, a user can find out the progression of each fault using this feature.
  • the threshold of the faults are found using statistics of faulty and healthy data. Further, reliability is achieved by pairing the threshold with historical data of the motor once the system starts monitoring the motors.
  • the system provides an apparatus for continuous or an intermittent monitoring of squirrel cage induction motor in their inception with only a single -phase stator current.
  • the system provides for estimating the fundamental frequency supplied to the motor using only the single-phase stator current input.
  • the system provides for EKF-based input signal conditioning for detection of weak failure modes of the induction motor.
  • the system provides for normalization of the fault magnitude using the magnitude of the fundamental component.
  • said spectral estimator is configured to determine eccentric specific faults, in that, a static eccentricity fault, a dynamic eccentricity fault, and a mixed eccentricity fault, in terms of spectral signature information of the motor, is determined by computing fundamental supply frequency (f) of the motor obtained by sensing motor current by means of a motor current sensor, sensing rotor slots (R) using an input mechanism associated with this system, rotational frequency ( ) of the motor by means of a rotational frequency sensor, slip (s) of the motor estimated by a slip estimator, number of bars of the motor input using an input mechanism associated with this system, number of pole-pairs (p) of the motor using an input mechanism associated with this system.
  • Figure 1 illustrates steps involved in operation of the fault detection system.
  • the acquired signal consists of a high magnitude fundamental component, fault frequency components, and noise. Presence of the high- amplitude fundamental component makes it difficult for the system to detect low-magnitude fault components.
  • the spectral estimator estimates the spectrum of the conditioned stator current in the specified spectral band and sends it to the peak detector. Description of the spectral estimator is provided in Figure (2).
  • the peak detector (112) finds the fault peak locations form the spectrum, and the amplitude estimator
  • Figure 3 illustrates implementation schematics of the cloud based 3 -phase induction motor incipient fault detection and failure prognosis system.
  • FIG. 3 shows the overall implementation of the scheme for a cloud based assessment of faults for multiple motors.
  • multiple induction motors (300) can transmit data using current transformer connected with a WiFi module (301).
  • the transmitted data are received by a WiFi hub (302) and sent to the cloud server (303) for data processing.
  • the output of the fault diagnostic module can be accessed using cell phone apps in the shop floor for further action.
  • the TECHNICAL ADVANCEMENT of this invention lies in providing a system which uses only a single stator current as input in order to detect multiple faults. Additionally, the system is configured to detect faults under adverse running conditions like low loading. Additionally, in the system, the spectral estimator can detect closely spaced sinusoids; as a result, closely spaced fault components can be resolved. Furthermore, the spectral estimator, of this invention, can estimate the amplitude of the fault component accurately and hence the severity. Still additionally, the detectability of the closely spaced fault components is further improved by using the signal conditioning unit.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Microelectronics & Electronic Packaging (AREA)
  • Power Engineering (AREA)
  • Tests Of Circuit Breakers, Generators, And Electric Motors (AREA)

Abstract

Cette invention concerne un système d'évaluation de multiples défauts dans des moteurs à induction, ledit moteur étant alimenté par une alimentation triphasée (100) à l'aide d'un variateur fréquence (VFD), ledit système comprenant : un capteur de courant (102) pour détecter un courant de stator monophasé ; un filtre anti-repliement (105) pour une conversion ultérieure du courant détecté en format numérique à l'aide d'un convertisseur analogique-numérique (106), ledit signal acquis étant constitué d'une composante fondamentale de grande amplitude, de composantes de fréquence de défaut et d'un bloc d'estimation de bruit et d'amplitude (107) pour l'estimation d'un signal fondamental conditionné de plus avec un signal d'entrée pour un moteur afin d'obtenir un signal d'entrée conditionné pour une estimation du glissement et de la vitesse du moteur pour déterminer une bande de recherche de défaut corrélative à des composantes de fréquence de défaut, lesdites composantes de fréquence de défaut étant estimées au moyen d'un estimateur spectral pour déterminer si ledit moteur comprend des défauts de rotor, des défauts spécifiques d'excentrique, des défauts de palier et/ou des défauts de stator.
PCT/IN2019/050181 2018-03-01 2019-03-01 Système d'évaluation de multiples défauts dans des moteurs à induction WO2019167086A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
IN201831003744 2018-03-01
IN201831003744 2018-03-01

Publications (1)

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WO2019167086A1 true WO2019167086A1 (fr) 2019-09-06

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111537881A (zh) * 2020-05-26 2020-08-14 华润智慧能源有限公司 异步电机的故障诊断方法、装置、设备及可读存储介质
CN113002307A (zh) * 2021-02-18 2021-06-22 广州橙行智动汽车科技有限公司 一种故障检测方法、装置和车辆
CN114035043A (zh) * 2021-10-18 2022-02-11 辽宁科技大学 基于预知指向最佳分辨方法的鼠笼电机断条故障诊断方法
WO2022035423A1 (fr) 2020-08-11 2022-02-17 General Electric Company Systèmes et procédés de détection améliorée d'anomalies pour machines tournantes
WO2022107100A1 (fr) * 2020-11-23 2022-05-27 Rayong Engineering And Plant Service Co., Ltd. Procédé et système d'auto-détection de défaut de moteur à induction
CN115993138A (zh) * 2023-03-23 2023-04-21 中国人民解放军火箭军工程大学 一种多表冗余激光惯组故障诊断方法及系统
US11733301B2 (en) 2021-05-13 2023-08-22 General Electric Technology Gmbh Systems and methods for providing voltage-less electrical signature analysis for fault protection
CN117851873A (zh) * 2024-03-07 2024-04-09 唐智科技湖南发展有限公司 一种基于动态接触角的轴承运行状态评估方法及系统

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4761703A (en) * 1987-08-31 1988-08-02 Electric Power Research Institute, Inc. Rotor fault detector for induction motors
US20130049733A1 (en) * 2011-08-29 2013-02-28 General Electric Company Fault detection based on current signature analysis for a generator

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4761703A (en) * 1987-08-31 1988-08-02 Electric Power Research Institute, Inc. Rotor fault detector for induction motors
US20130049733A1 (en) * 2011-08-29 2013-02-28 General Electric Company Fault detection based on current signature analysis for a generator

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111537881A (zh) * 2020-05-26 2020-08-14 华润智慧能源有限公司 异步电机的故障诊断方法、装置、设备及可读存储介质
WO2022035423A1 (fr) 2020-08-11 2022-02-17 General Electric Company Systèmes et procédés de détection améliorée d'anomalies pour machines tournantes
WO2022107100A1 (fr) * 2020-11-23 2022-05-27 Rayong Engineering And Plant Service Co., Ltd. Procédé et système d'auto-détection de défaut de moteur à induction
CN113002307A (zh) * 2021-02-18 2021-06-22 广州橙行智动汽车科技有限公司 一种故障检测方法、装置和车辆
US11733301B2 (en) 2021-05-13 2023-08-22 General Electric Technology Gmbh Systems and methods for providing voltage-less electrical signature analysis for fault protection
CN114035043A (zh) * 2021-10-18 2022-02-11 辽宁科技大学 基于预知指向最佳分辨方法的鼠笼电机断条故障诊断方法
CN114035043B (zh) * 2021-10-18 2023-06-09 辽宁科技大学 基于预知指向最佳分辨方法的鼠笼电机断条故障诊断方法
CN115993138A (zh) * 2023-03-23 2023-04-21 中国人民解放军火箭军工程大学 一种多表冗余激光惯组故障诊断方法及系统
CN117851873A (zh) * 2024-03-07 2024-04-09 唐智科技湖南发展有限公司 一种基于动态接触角的轴承运行状态评估方法及系统
CN117851873B (zh) * 2024-03-07 2024-05-28 唐智科技湖南发展有限公司 一种基于动态接触角的轴承运行状态评估方法及系统

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