A new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis

Defdaf, Mabrouk; Berrabah, Fouad; Chebabhi, Ali; Chérif, Bilal Djamal Eddine

doi:10.1002/2050-7038.12807

Cited by 21 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some recent applications of the DWT are, fault detection in electrical machines [ 31 , 32 ] and analysis of electroencephalographic signals (EEG) from patients with diseases such as Parkinson’s [ 33 ].…”

Section: Materials and Methodsmentioning

confidence: 99%

Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators

Galan-Uribe

Amezquita-Sanchez

Morales-Velazquez

2023

Sensors

View full text Add to dashboard Cite

Robotic systems are a fundamental part of modern industrial development. In this regard, they are required for long periods, in repetitive processes that must comply with strict tolerance ranges. Hence, the positional accuracy of the robots is critical, since degradation of this can represent a considerable loss of resources. In recent years, prognosis and health management (PHM) methodologies, based on machine and deep learning, have been applied to robots, in order to diagnose and detect faults and identify the degradation of robot positional accuracy, using external measurement systems, such as lasers and cameras; however, their implementation is complex in industrial environments. In this respect, this paper proposes a method based on discrete wavelet transform, nonlinear indices, principal component analysis, and artificial neural networks, in order to detect a positional deviation in robot joints, by analyzing the currents of the actuators. The results show that the proposed methodology allows classification of the robot positional degradation with an accuracy of 100%, using its current signals. The early detection of robot positional degradation, allows the implementation of PHM strategies on time, and prevents losses in manufacturing processes.

show abstract

Section: Materials and Methodsmentioning

confidence: 99%

Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators

Galan-Uribe

Amezquita-Sanchez

Morales-Velazquez

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…A decomposition rate is used in [18] for CM of electric drives based on WT. RMS and Kurtosis are calculated for the WT coefficients for broken bar fault detection in electric drives and combined with a neural network for fault classification in [19]. Bearing fault classification was performed with an SVM based on WT in combination with singular value decomposition for dimensionality reduction in [20].…”

Section: Time-frequency-based Health Indicatorsmentioning

confidence: 99%

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Nentwich

Reinhart

2021

Robotics

View full text Add to dashboard Cite

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. In this context, we propose a new method to evaluate health indicators for this application and suggest a new health indicator (HI) based on vibration data measurements, Short-time Fourier transform and Z-scores. By executing the method, we find that the proposed health indicator can detect varying faults better, has lower temperature sensitivity and works better in instationary velocity regimes compared to several state-of-the-art HIs. A discussion of the validity of the results concludes our contribution.

show abstract

“…Due to some restrictions and uncertainty, the model-based approach cannot be an appropriate method for the fault detection process. erefore, the data-driven methods, which consider measured signals such as vibration, acoustics, voltage, and current, are more likely to be considered for the fault detection process [6,7]. Since some of these signals such as vibration and acoustics have an invasive nature, motor current signature analysis (MCSA) is considered for this purpose.…”

Section: Introductionmentioning

confidence: 99%

Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks

Mashayekhi

Borzadaran

Marzebali

2022

International Transactions on Electrical Energy Systems

View full text Add to dashboard Cite

Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the long short-term memory (LSTM) and fully convolutional neural networks (FCNs) in a conjoined manner. The authors use the FCN architecture for feature extraction from the time-series signal and augment it with LSTM to improve classification performance. This structure has not been previously applied for fault severity detection in induction machine systems. The authors avoid manual feature engineering and, by eliminating the preprocessing phase, directly use time series of electrical signals for fault detection and classifications. The experimental results have been carried out in different fault severities and loads. The analysis of the results and comparison with other deep and classical methods show that the faulty cases can be separated based on severity and load levels with a high accuracy (98.92%), which shows that the adopted architecture is successful in automatically extracting discriminative features from the signal.

show abstract

A new transform discrete wavelet technique based on artificial neural network for induction motor broken rotor bar faults diagnosis

Cited by 21 publications

References 19 publications

Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators

Supervised Machine-Learning Methodology for Industrial Robot Positional Health Using Artificial Neural Networks, Discrete Wavelet Transform, and Nonlinear Indicators

A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks

Contact Info

Product

Resources

About