Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Arellano-Espitia, Francisco; Delgado-Prieto, Miguel; Martinez-Viol, Victor; Saucedo-Dorantes, Juan José; Osornio-Ríos, Roque Alfredo

doi:10.3390/s20143949

Cited by 34 publications

(16 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DAE architecture proposed by [ 47 ], trained layer by layer is adopted in this pa-per. The DAE hyperparameters, such as, the coefficient for the L2 regularization term, the coefficient for the sparsity regularization term and, the parameter for sparsity proportion, as well as the number of neurons in each hidden layer, are established from the search for the optimal configuration using a genetic algorithm (GA).…”

Section: Methodsmentioning

confidence: 99%

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Arellano-Espitia

Delgado-Prieto

Gonzalez-Abreu

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.

show abstract

Section: Methodsmentioning

confidence: 99%

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Arellano-Espitia

Delgado-Prieto

Gonzalez-Abreu

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…So, the input of the anomaly detection model is the information contained in the features, and the output is an anomaly score that determines how different is the new measurement analyzed compared to those that has been trained (the reference). Full details of how to build the SAE model can be found in [15]. Since the DAE tends to perform a poor reconstruction with data different from those used for its training, the reconstruction error of the input data is expected to be a score of anomaly detection.…”

Section: Methodsmentioning

confidence: 99%

Anomaly Detection in Electromechanical Systems by means of Deep-Autoencoder

Arellano-Espitia

Delgado-Prieto

Martinez-Viol

et al. 2021

2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

Self Cite

View full text Add to dashboard Cite

Anomaly detection in manufacturing processes is one of the main concerns in the new era of the Industry 4.0 framework. The detection of uncharacterized events represents a major challenge within the operation monitoring of electrical rotatory machinery. In this regard, although several machine learning techniques have been classically considered, the recent appearance of deep-learning approaches represents an opportunity in the field to increase the anomaly detection capabilities in front of complex electromechanical systems. However, each anomaly detection technique considers its own data interpretability and modelling strategy, which should be analyzed in front of the specificities of the data generated in an industrial environment and, specifically, by an electromechanical actuator. Thus, in this study, a comparison framework is considered including multiple fault scenarios in order to analyze the performance of four representative anomaly detection techniques, that is, one-class support vector machine, k-nearest neighbor, Gaussian mixture model and, finally, deep-autoencoder. The experimental results suggest that the use of the deep-autoencoder in the task of detecting anomalies of operation in electromechanical systems has a higher performance compared to the state of the art methods.

show abstract

“…Result showed that the GDCCN provides better performance than conventional DL methods even in noisy enviroments and different workloads. Espitia et al 2020 64 & Espitia and Soto. 2020 65 used SAE based deep learning methodology for effective fault diagnosis in electromechanical systems.…”

Section: Based Motor Fault Diagnosismentioning

confidence: 98%

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Gangsar¹,

Bajpei²,

Porwal³

2022

Noise & Vibration Worldwide

View full text Add to dashboard Cite

Rotating machine faults are unavoidable; thus, early diagnosis is essential to avoid further damage to the machine or other machine attached to it. Various signal analysis based conventional techniques have been developed and used in the industries to identify various defects in the rotating machines. In last two decades, researchers have shifted their focus to automated or intelligent fault diagnosis based on Artificial Intelligence (AI) techniques due to a variety of issues in conventional fault analysis techniques, such as a dependence on machine operating circumstances, human interference, and expert abilities. In AI based techniques, various machine learning (ML) and deep learning (DL) techniques have been successfully applied for fault diagnosis of various rotating machines. From last half decade DL have been gaining popularity due to its attractive characteristic of automated feature learning and solving big data, unbalanced data, big computational burden and over-fitting problems of conventional ML techniques. Advances in DL methodologies have prompted interest in DL based intelligent fault diagnosis in the industry in the last five to 6 years. This review paper summarizes recent research and developments on DL based fault diagnosis in the last five to 6 years for various critical rotating machineries in industry such as electric motors, rotor-bearing systems, gear and gearbox, wind turbines, pumps, and compressors.

show abstract

Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems

Cited by 34 publications

References 45 publications

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems

Anomaly Detection in Electromechanical Systems by means of Deep-Autoencoder

A review on deep learning based condition monitoring and fault diagnosis of rotating machinery

Contact Info

Product

Resources

About