Automatic diagnosis of electromechanical faults in induction motors based on the transient analysis of the stray flux via MUSIC methods

Zamudio-Ramírez, Israel; Ramirez-Nunez, Juan A.; Antonino-Daviu, Jose A.; Osornio-Ríos, Roque Alfredo; Quijano-López, Alfredo; Razik, Hubert; Romero-Troncoso, René de Jesús

doi:10.1109/tia.2020.2988002

Cited by 34 publications

(18 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mathematical model [ 7 ] for the output y in each neuron in an FNN is given as

where

and b are the weights and bias applied to the inputs

, and

is the activation function. The rectified linear unit (Relu) activation function was selected.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

Attestog

Senanayaka

Khang

et al. 2022

Sensors

View full text Add to dashboard Cite

This paper aims to classify local demagnetisation and inter-turn short-circuit (ITSC) on position sensorless permanent magnet synchronous motors (PMSM) in transient states based on external stray flux and learning classifier. Within the framework, four supervised machine learning tools were tested: ensemble decision tree (EDT), k-nearest neighbours (KNN), support vector machine (SVM), and feedforward neural network (FNN). All algorithms are trained on datasets from one operational profile but tested on other different operation profiles. Their input features or spectrograms are computed from resampled time-series data based on the estimated position of the rotor from one stray flux sensor through an optimisation problem. This eliminates the need for the position sensors, allowing for the fault classification of sensorless PMSM drives using only two external stray flux sensors alone. Both SVM and FNN algorithms could identify a single fault of the magnet defect with an accuracy higher than 95% in transient states. For mixed faults, the FNN-based algorithm could identify ITSC in parallel-strands stator winding and local partial demagnetisation with an accuracy of 87.1%.

show abstract

“…The mathematical model [ 7 ] for the output y in each neuron in an FNN is given as

where

and b are the weights and bias applied to the inputs

, and

is the activation function. The rectified linear unit (Relu) activation function was selected.…”

Section: Methodsmentioning

confidence: 99%

“…al. [ 7 ] proposed also smart sensors for the online detection of individual and combined faults in induction motors, such as broken rotor bar and misalignment. The sensor solution consists of three hall sensors oriented towards the three Cartesian directions.…”

Section: Introductionmentioning

confidence: 99%

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

Attestog

Senanayaka

Khang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…� Time domain [36,37] � Frequency domain [2][3][4]13,14] � Time-frequency domain [22,24,34,38] In this section, statistical tools such as mean value, STD, energy, frequency domain and time-frequency domain…”

Section: Signal Processingmentioning

confidence: 99%

“…Fault detection based on unprocessed signal is a difficult task since the variation of the signal does not give meaningful information regarding the machine condition. Several signal processing tools are used to extract useful patterns inside the signals for fault detection that can be divided into three categories: Time domain [36, 37] Frequency domain [2–4, 13, 14] Time–frequency domain [22, 24, 34, 38] …”

Section: Signal Processingmentioning

confidence: 99%

Static and dynamic eccentricity fault diagnosis of large salient pole synchronous generators by means of external magnetic field

Ehya

Nysveen

Nilssen

et al. 2021

IET Electric Power Appl

View full text Add to dashboard Cite

Although synchronous generators are robust and long-lasting equipment of power plants, consistent electricity production depends on their health conditions. Static and dynamic eccentricity faults are among the prevalent faults that may have a costly effect. Although several methods have been proposed in the literature to detect static and dynamic eccentricity faults in salient pole synchronous generators (SPSGs), they are non-sensitive to a low degree of failure and require a predefined threshold to recognise the fault occurrence that may vary based on machine configuration. This article presents a detailed magnetic analysis of the SPSGs with static and dynamic eccentricity faults by focusing on the external magnetic field. The external magnetic field was measured using two search coils installed on the backside of the stator yoke. Also, advanced signal processing tools based on wavelet entropy were used to analyse the induced electromotive force (emf) in search coils to extract the fault index. The proposed index required no threshold to recognise the starting point of fault occurrence and was sensitive to a low degree of fault. It was also non-sensitive to load variation and noise that may induce a false alarm. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

“…In recent years, a technique called the MUltiple Signal Classification (MUSIC) algorithm has provided promising results for analyzing induction motors [ 31 , 32 ], evaluating the behavior of civil structures [ 33 , 34 , 35 ], and impact-source-localization in composite structures under deformation conditions [ 36 ], among other applications. This technique presents diverse advantages such as noise immunity and high resolution and does not require a large amount of experimental information to estimate the frequencies contained in the analyzed signal with high accuracy [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm

Martínez-Cruz

Amezquita-Sanchez

Pérez-Soto

et al. 2021

Sensors

View full text Add to dashboard Cite

In this paper, the natural frequencies (NFs) identification by finite element method (FEM) is applied to a two degrees-of-freedom (2-DOF) planar robot, and its validation through a novel experimental methodology, the Multiple Signal Classification (MUSIC) algorithm, is presented. The experimental platforms are two different 2-DOF planar robots with different materials for the links and different types of actuators. The FEM is carried out using ANSYS™ software for the experiments, with vibration signal analysis by MUSIC algorithm. The advantages of the MUSIC algorithm against the commonly used fast Fourier transform (FFT) method are also presented for a synthetic signal contaminated by three different noise levels. The analytical and experimental results show that the proposed methodology identifies the NFs of a high-resolution robot even when they are very closed and when the signal is embedded in high-level noise. Furthermore, the results show that the proposed methodology can obtain a high-frequency resolution with a short sample data set. Identifying the NFs of robots is useful for avoiding such frequencies in the path planning and in the selection of controller gains that establish the bandwidth.

show abstract

Automatic diagnosis of electromechanical faults in induction motors based on the transient analysis of the stray flux via MUSIC methods

Cited by 34 publications

References 35 publications

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

Mixed Fault Classification of Sensorless PMSM Drive in Dynamic Operations Based on External Stray Flux Sensors

Static and dynamic eccentricity fault diagnosis of large salient pole synchronous generators by means of external magnetic field

Natural Frequencies Identification by FEM Applied to a 2-DOF Planar Robot and Its Validation Using MUSIC Algorithm

Contact Info

Product

Resources

About