Virtual fault simulation for diagnosis of shaft misalignment of rotating machine

Behera, Dipti Prakash; Behera, Rashmi Ranjan; Naikan, V. N. A.

doi:10.1109/icacci.2014.6968225

Cited by 8 publications

(3 citation statements)

References 3 publications

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“…O desalinhamento pode ser radial, angular ou em muitos casos os dois combinados, seja no plano horizontal ou no vertical. (Behera et al, 2014) • Desalinhamento angular ou axial acontece quando as linhas de centro do eixo das máquinas se interceptam, formando um ângulo. Fortes vibrações axiais na frequência de rotação do rotor (fr) representam o desalinhamento angular, que também podem vir seguidas de altos níveis múltiplos da rotação.…”

Section: Desalinhamentounclassified

Detecção de desequilíbrio de tensão e de desalinhamento no motor de indução através da análise do fluxo de dispersão

Araújo

Filho

Baccarini

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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Os motores de indução trifásicos são largamente empregados no setor industrial devido sua versatilidade, robustez e baixo custo. No entanto, estas máquinas estão sujeitas a diversos tipos de falhas que comprometem o seu funcionamento e a integridade de seus componentes. Dentre essas falhas estão as de desalinhamento e as de desequilíbrio de tensão. Se não detectadas podem causar danos irreversíveis ao motor e perdas no processo industrial. Assim, a importância da confiabilidade e da disponibilidade exigidas para os motores elétricos crescem constantemente. Neste cenário, o presente trabalho apresenta a Análise da Assinatura de Fluxo de Dispersão (SFSA) para detecção de falha de desalinhamento e desequilíbrio de tensão no motor de indução trifásico comparando-a com uma técnica tradicional, Análise da Assinatura de Corrente do Motor (MCSA). Testes experimentais no Laboratório de Máquinas e Transformadores (LAMET) da Universidade Federal de São João del-Rei (UFSJ) foram usados para validar a eficácia dessa ferramenta. Os resultados obtidos mostraram a análise dos sinais de fluxo como uma confiável e simples alternativa para detecção de falha de desalinhamento e de desequilíbrio de tensão, evitando testes invasivos e paradas inesperadas para manutenção da máquina.

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Section: Desalinhamentounclassified

Detecção de desequilíbrio de tensão e de desalinhamento no motor de indução através da análise do fluxo de dispersão

Araújo

Filho

Baccarini

et al. 2020

Anais Do Congresso Brasileiro De Automática 2020

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“…Misalignment is a condition wherever the centrelines of coupled shafts do not coincide. In general, there are two types of misalignment: parallel and angular (Behera et al 2014). These types are shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

Pietrzak

Wolkiewicz

2021

Power Electronics and Drives

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This paper deals with the selected methods of detecting angular misalignment in drive systems with a permanent magnet synchronous motor (PMSM), which are based on the analysis of the stator phase current signal, as well as their experimental verification and comparison. The proposed and compared methods are spectral analysis and wavelet analysis of the stator current, stator current envelope, stator current space vector module. Furthermore, the influence of power supply frequency and load torque on the performance of the proposed diagnostic methods is also discussed. The experimental tests were carried out for an undamaged motor and for two levels of angular misalignment. The article discusses the question of exactly what damage symptoms can be extracted from each of the methods. In the case of spectral analyses, it is demonstrated which multiplicities of the failure frequency are the most sensitive to misalignment and the least sensitive to changes in motor operating condition, which may be considered novel in the case of drive systems with permanent magnet motors. It is also proven that discrete wavelet transform (DWT) of the envelope and monitoring of the value of the relevant components allows the detection of misalignment with the availability of measuring current only in one phase in various motor operating conditions.

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“…Coupling misalignment is a condition where the shafts of the driver machine and driven machine are not on the same center line. Misalignment may be either due to two categories namely: parallel, and angular or presence of a combination of both [3]. Possible causes of misalignment include improper installation, wearing of the machine components and damage of the installed bases due to vibration from machines [4].…”

Section: Introductionmentioning

confidence: 99%

AR model of the torque signal for mechanical induction motor faults detection and diagnosis

Haroun

Seghir

Hamdani

2015

2015 3rd International Conference on Control, Engineering &Amp; Information Technology (CEIT)

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Mechanical faults present a large portion of induction motor failures, if left undetected, it can lead to partial or total breakdown of the machine. This paper propose a scheme to detect and diagnose mechanical faults in an induction motor by the AR model coefficients of the Torque signal. First, the torque signal obtained from experiment in different conditions: healthy condition, motor with dynamic eccentricity fault, and motor with misalignment fault are normalized to exclude the load effect. Then the AR model coefficients are extracted as features to reduce the dimension data while keeping the effective information. Finally, the Self Organizing Map neural network is used for classification of the different conditions. The experimental results show the effectiveness of the proposed method, were both eccentricity and misalignment faults might be easily detected and discriminated from each other.

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Virtual fault simulation for diagnosis of shaft misalignment of rotating machine

Cited by 8 publications

References 3 publications

Detecção de desequilíbrio de tensão e de desalinhamento no motor de indução através da análise do fluxo de dispersão

Detecção de desequilíbrio de tensão e de desalinhamento no motor de indução através da análise do fluxo de dispersão

Application of Spectral and Wavelet Analysis of Stator Current to Detect Angular Misalignment in PMSM Drive Systems

AR model of the torque signal for mechanical induction motor faults detection and diagnosis

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