Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques

Capolino, Gérard‐André; Cabanas, M.F.; Filippetti, F.; Bruzzese, C.; Strangas, Elias G.; Pusca, Rémus; Estima, Jorge O.; Riera-Guasp, M.; Kia, Shahin Hedayati

doi:10.1109/mie.2013.2287651

Cited by 514 publications

(329 citation statements)

References 54 publications

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“…According to Henao et al [40], the dependence of the load and the sensitivity to the variations of velocity that were the major disadvantages in carrying out the diagnosis have been overcome by recent techniques based on the approach of Park's vector. The same author mentions four techniques that have demonstrated high effectiveness even for low loads and that are simple to implement, operate independently, have low computational demand and limit the need for hardware.…”

Section: Maintenance Through Non-invasive Techniquesmentioning

confidence: 99%

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

2017

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Despite the complex mathematical models and physical phenomena on which it is based, the simplicity of its construction, its affordability, the versatility of its applications and the relative ease of its control have made the electric induction motor an essential element in a considerable number of processes at the industrial and domestic levels, in which it converts electrical energy into mechanical energy. The importance of this type of machine for the continuity of operation, mainly in industry, is such that, in addition to being an important part of the study programs of careers related to this branch of electrical engineering, a large number of investigations into monitoring, detecting and quickly diagnosing its incipient faults due to a variety of factors have been conducted. This bibliographic research aims to analyze the conceptual aspects of the first discoveries that served as the basis for the invention of the induction motor, ranging from the development of the Fourier series, the Fourier transform mathematical formula in its different forms and the measurement, treatment and analysis of signals to techniques based on artificial intelligence and soft computing. This research also includes topics of interest such as fault types and their classification according to the engine, software and hardware parts used and modern approaches or maintenance strategies.

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Section: Maintenance Through Non-invasive Techniquesmentioning

confidence: 99%

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

2017

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“…The fault management in a drive can be classified in three stages: fault detection FD [13][14][15][16][17], fault isolation FI [18][19] and implementation of a fault tolerant control (FTC) [20][21][22][23][24][25]. The implementation of these three stages in the system is necessary to ensure a suitable operation and to protect the system in post-fault situation.…”

Section: Introductionmentioning

confidence: 99%

“…Even though there are some FD methods in the multiphase drives literature, none of these methods comply with the aforementioned conditions [13]. [14] proposes a detection method of dissymmetry in the stator resistance for a seven-phase IM.…”

Section: Introductionmentioning

confidence: 99%

An Open-phase Fault Detection Method for Six-phase Induction Motor Drives

Rios-Garcia¹,

Durán²,

González-Prieto³

et al. 2024

RE&PQJ

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“…The time-frequency analysis method has been widely used in faults diagnosis, because it can provide the information in the time and frequency domain [2]. Moreover, there are many methods of artificial intelligence detection, such as statistical processing to sense [3], stray magnetic flux measurement [4], and neural networks such as support vector machine (SVM) [2].…”

Section: Introductionmentioning

confidence: 99%

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

Gao

Villecco

et al. 2017

Entropy

140

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Based on the combination of improved Local Mean Decomposition (LMD), Multi-scale Permutation Entropy (MPE) and Hidden Markov Model (HMM), the fault types of bearings are diagnosed. Improved LMD is proposed based on the self-similarity of roller bearing vibration signal by extending the right and left side of the original signal to suppress its edge effect. First, the vibration signals of the rolling bearing are decomposed into several product function (PF) components by improved LMD respectively. Then, the phase space reconstruction of the PF1 is carried out by using the mutual information (MI) method and the false nearest neighbor (FNN) method to calculate the delay time and the embedding dimension, and then the scale is set to obtain the MPE of PF1. After that, the MPE features of rolling bearings are extracted. Finally, the features of MPE are used as HMM training and diagnosis. The experimental results show that the proposed method can effectively identify the different faults of the rolling bearing.

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Trends in Fault Diagnosis for Electrical Machines: A Review of Diagnostic Techniques

Cited by 514 publications

References 54 publications

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

An Open-phase Fault Detection Method for Six-phase Induction Motor Drives

Multi-Scale Permutation Entropy Based on Improved LMD and HMM for Rolling Bearing Diagnosis

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