Differential Diagnosis Based on Multivariable Monitoring to Assess Induction Machine Rotor Conditions

Concari, Carlo; Franceschini, G.; Tassoni, C.

doi:10.1109/tie.2008.2003212

Cited by 68 publications

(33 citation statements)

References 20 publications

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“…Usually MCSA is used to detect a high levels of both eccentricity types, it mean when the static and dynamic eccentricity exist together, but it is very difficult to detect such faults if they appear individually [13]- [5], for those authors the fault signatures are presented in stator current spectrum only if the two kinds of eccentricity exist together and it is not easy to distinguish them. In addition, it will be difficult to diagnose the low level static eccentricity with traditional MCSA technique, for them other signals such as noise, bearing vibration and temperature may have to be used in these cases for detecting fault signature [14]. Many research works head to use others induction motor quantities to detect SE fault signatures such air gap magnetic flux density [13]- [15], terminal voltages of induction machines at switch-off [5].…”

Section: Fig 1 Types Of Eccentricity Faults (A) Concentric Rotor mentioning

confidence: 99%

“…The usability of analysis of external magnetic field to detect faults in induction motor have proved by several papers, the majority of those papers are interested on diagnosis of broken rotor bar [17]- [18]- [14]- [15], stator winding short-circuits [19,20], net voltage asymmetry [21] wound rotor phase disconnection [22], dynamic eccentricity [13]. This paper heads to diagnose low levels of purely SE in induction motor using TSFE method, short description of this technique modeling will be presented in first part of paper.…”

Section: Fig 1 Types Of Eccentricity Faults (A) Concentric Rotor mentioning

confidence: 99%

See 1 more Smart Citation

Detection of Static Eccentricity Fault in Saturated Induction Motors by Air-Gap Magnetic Flux Signature Analysis Using Finite Element Method

Halem

Zouzou

Srairi

et al. 2015

J. Fundam and Appl Sci.

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Unfortunately, motor current signature analysis (MCSA) cannot detect the small degrees of the purely static eccentricity (SE) defects, while the air-gap magnetic flux signature analysis (FSA) is applied successfully. The simulation results are obtained by using time stepping finite elements (TSFE) method. In order to show the impact of magnetic saturation upon the diagnosis of SE fault, the analysis is carried out for saturated induction motors. The index signatures of static eccentricity fault around fundamental and PSHs are detected successfully for saturated motor.

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Section: Fig 1 Types Of Eccentricity Faults (A) Concentric Rotor mentioning

confidence: 99%

Section: Fig 1 Types Of Eccentricity Faults (A) Concentric Rotor mentioning

confidence: 99%

Detection of Static Eccentricity Fault in Saturated Induction Motors by Air-Gap Magnetic Flux Signature Analysis Using Finite Element Method

Halem

Zouzou

Srairi

et al. 2015

J. Fundam and Appl Sci.

View full text Add to dashboard Cite

show abstract

“…Therefore, this paper uses the electrical detection method to analyze rotor and eccentric faults of a motor and assess the operating state of the motor with such faults [1,2]. Motor current signature analysis (MCSA) is currently one of the most popular techniques for monitoring the condition of medium-voltage induction motors online in an industrial environment [3][4][5][6][7][8]. When a fault occurs, the magnetic flux changes.…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Diagnostic System Based on Electrical Detection Method and Fuzzy Algorithm

Chang

Lin

Kuo

et al. 2016

Int. J. Fuzzy Syst.

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This study develops an electrical detection method for the diagnosis and fault detection of induction motors. An experiment constructs two types of defect models: broken bar and dynamic eccentricity. Electrical signals acquired during the operation of a motor are transformed through a fast Fourier transform to obtain the feature frequency components for identifying the type of motor fault. Subsequently, the Clark-Concordia transform is used to compare the stator current Concordia pattern between faulty and healthy motors. Finally, a fuzzy inference system is designed for assessing the severity of motor faults. The proposed method not only can diagnose the type of motor fault, but can also assess the operational state of a motor. The method is suitable for preparing a maintenance program for induction motors and for reducing their excessive maintenance cost.

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“…This type of data acquisition system deals with the parameter related sample values and those samples values are generally collected in a PC through its USB or serial port. The PC software (MATLAB, Lab-VIEW based) performs the required analysis to identify any incipient fault through various techniques out of which current signature analysis (CSA) using FFT [4,5], neural networked CBM [6], multi variable supervision system [7], FPGA with fuzzy logic [8], inter turn fault using wavelet transform [9], Hilbert transform [10] are remarkable. Due to its predictive nature the use of condition monitoring allows maintenance to be scheduled or other action to be taken to prevent failure and avoid its consequences.…”

Section: Introductionmentioning

confidence: 99%

Channel Efficiency with Security Enhancement for Remote Condition Monitoring of Multi Machine System Using Hybrid Huffman Coding

Datta

Chowdhuri

Bera

2015

J. Inst. Eng. India Ser. B

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This paper presents a novel scheme of remote condition monitoring of multi machine system where a secured and coded data of induction machine with different parameters is communicated between a state-of-the-art dedicated hardware Units (DHU) installed at the machine terminal and a centralized PC based machine data management (MDM) software. The DHUs are built for acquisition of different parameters from the respective machines, and hence are placed at their nearby panels in order to acquire different parameters cost effectively during their running condition. The MDM software collects these data through a communication channel where all the DHUs are networked using RS485 protocol. Before transmitting, the parameter's related data is modified with the adoption of differential pulse coded modulation (DPCM) and Huffman coding technique. It is further encrypted with a private key where different keys are used for different DHUs. In this way a data security scheme is adopted during its passage through the communication channel in order to avoid any third party attack into the channel. The hybrid mode of DPCM and Huffman coding is chosen to reduce the data packet length. A MATLAB based simulation and its practical implementation using DHUs at three machine terminals (one healthy three phase, one healthy single phase and one faulty three phase machine) proves its efficacy and usefulness for condition based maintenance of multi machine system. The data at the central control room are decrypted and decoded using MDM software. In this work it is observed that Chanel efficiency with respect to different parameter measurements has been increased very much.

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Differential Diagnosis Based on Multivariable Monitoring to Assess Induction Machine Rotor Conditions

Cited by 68 publications

References 20 publications

Detection of Static Eccentricity Fault in Saturated Induction Motors by Air-Gap Magnetic Flux Signature Analysis Using Finite Element Method

Detection of Static Eccentricity Fault in Saturated Induction Motors by Air-Gap Magnetic Flux Signature Analysis Using Finite Element Method

Induction Motor Diagnostic System Based on Electrical Detection Method and Fuzzy Algorithm

Channel Efficiency with Security Enhancement for Remote Condition Monitoring of Multi Machine System Using Hybrid Huffman Coding

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