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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.
Francisco Arellano-Espitia; Miguel Delgado-Prieto; Artvin-Darien Gonzalez-Abreu; Juan Jose Saucedo-Dorantes; Roque Alfredo Osornio-Rios. Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems. Sensors 2021, 21, 5830 .
AMA StyleFrancisco Arellano-Espitia, Miguel Delgado-Prieto, Artvin-Darien Gonzalez-Abreu, Juan Jose Saucedo-Dorantes, Roque Alfredo Osornio-Rios. Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems. Sensors. 2021; 21 (17):5830.
Chicago/Turabian StyleFrancisco Arellano-Espitia; Miguel Delgado-Prieto; Artvin-Darien Gonzalez-Abreu; Juan Jose Saucedo-Dorantes; Roque Alfredo Osornio-Rios. 2021. "Deep-Compact-Clustering Based Anomaly Detection Applied to Electromechanical Industrial Systems." Sensors 21, no. 17: 5830.
Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.
Juan Jose Saucedo-Dorantes; Francisco Arellano-Espitia; Miguel Delgado-Prieto; Roque Alfredo Osornio-Rios. Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings. Sensors 2021, 21, 5832 .
AMA StyleJuan Jose Saucedo-Dorantes, Francisco Arellano-Espitia, Miguel Delgado-Prieto, Roque Alfredo Osornio-Rios. Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings. Sensors. 2021; 21 (17):5832.
Chicago/Turabian StyleJuan Jose Saucedo-Dorantes; Francisco Arellano-Espitia; Miguel Delgado-Prieto; Roque Alfredo Osornio-Rios. 2021. "Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings." Sensors 21, no. 17: 5832.
Monitoring electrical power quality has become a priority in the industrial sector background: avoiding unwanted effects that affect the whole performance at industrial facilities is an aim. The lack of commercial equipment capable of detecting them is a proven fact. Studies and research related to these types of grid behaviors are still a subject for which contributions are required. Although research has been conducted for disturbance detection, most methodologies consider only a few standardized disturbance combinations. This paper proposes an innovative deep learning-based diagnosis method to be applied on power quality disturbances, and it is based on three stages. Firstly, a domain fusion approach is considered in a feature extraction stage to characterize the electrical power grid. Secondly, an adaptive pattern characterization is carried out by considering a stacked autoencoder. Finally, a neural network structure is applied to identify disturbances. The proposed approach relies on the training and validation of the diagnosis system with synthetic data: single, double and triple disturbances combinations and different noise levels, also validated with available experimental measurements provided by IEEE 1159.2 Working Group. The proposed method achieves nearly a 100% hit rate allowing a far more practical application due to its capability of pattern characterization.
Artvin-Darien Gonzalez-Abreu; Miguel Delgado-Prieto; Roque-Alfredo Osornio-Rios; Juan-Jose Saucedo-Dorantes; Rene-De-Jesus Romero-Troncoso. A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances. Energies 2021, 14, 2839 .
AMA StyleArtvin-Darien Gonzalez-Abreu, Miguel Delgado-Prieto, Roque-Alfredo Osornio-Rios, Juan-Jose Saucedo-Dorantes, Rene-De-Jesus Romero-Troncoso. A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances. Energies. 2021; 14 (10):2839.
Chicago/Turabian StyleArtvin-Darien Gonzalez-Abreu; Miguel Delgado-Prieto; Roque-Alfredo Osornio-Rios; Juan-Jose Saucedo-Dorantes; Rene-De-Jesus Romero-Troncoso. 2021. "A Novel Deep Learning-Based Diagnosis Method Applied to Power Quality Disturbances." Energies 14, no. 10: 2839.
In this work, we numerically investigate the diffraction management of longitudinal elastic waves propagating in a two-dimensional metallic phononic crystal. We demonstrate that this structure acts as an “ultrasonic lens”, providing self-collimation or focusing effect at a certain distance from the crystal output. We implement this directional propagation in the design of a coupling device capable to control the directivity or focusing of ultrasonic waves propagation inside a target object. These effects are robust over a broad frequency band and are preserved in the propagation through a coupling gel between the “ultrasonic lens” and the solid target. These results may find interesting industrial and medical applications, where the localization of the ultrasonic waves may be required at certain positions embedded in the object under study. An application example for non-destructive testing with improved results, after using the ultrasonic lens, is discussed as a proof of concept for the novelty and applicability of our numerical simulation study.
Hossam Selim; Rubén Picó; Jose Trull; Miguel Delgado Prieto; Crina Cojocaru. Directional Ultrasound Source for Solid Materials Inspection: Diffraction Management in a Metallic Phononic Crystal. Sensors 2020, 20, 6148 .
AMA StyleHossam Selim, Rubén Picó, Jose Trull, Miguel Delgado Prieto, Crina Cojocaru. Directional Ultrasound Source for Solid Materials Inspection: Diffraction Management in a Metallic Phononic Crystal. Sensors. 2020; 20 (21):6148.
Chicago/Turabian StyleHossam Selim; Rubén Picó; Jose Trull; Miguel Delgado Prieto; Crina Cojocaru. 2020. "Directional Ultrasound Source for Solid Materials Inspection: Diffraction Management in a Metallic Phononic Crystal." Sensors 20, no. 21: 6148.
Juan Jose Saucedo-Dorantes; Miguel Delgado-Prieto; Roque Alfredo Osornio-Rios; Rene De Jesus Romero-Troncoso. Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults. IEEE Transactions on Industrial Informatics 2020, 16, 5985 -5995.
AMA StyleJuan Jose Saucedo-Dorantes, Miguel Delgado-Prieto, Roque Alfredo Osornio-Rios, Rene De Jesus Romero-Troncoso. Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults. IEEE Transactions on Industrial Informatics. 2020; 16 (9):5985-5995.
Chicago/Turabian StyleJuan Jose Saucedo-Dorantes; Miguel Delgado-Prieto; Roque Alfredo Osornio-Rios; Rene De Jesus Romero-Troncoso. 2020. "Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults." IEEE Transactions on Industrial Informatics 16, no. 9: 5985-5995.
Fault diagnosis in manufacturing systems represents one of the most critical challenges dealing with condition-based monitoring in the recent era of smart manufacturing. In the current Industry 4.0 framework, maintenance strategies based on traditional data-driven fault diagnosis schemes require enhanced capabilities to be applied over modern production systems. In fact, the integration of multiple mechanical components, the consideration of multiple operating conditions, and the appearance of combined fault patterns due to eventual multi-fault scenarios lead to complex electromechanical systems requiring advanced monitoring strategies. In this regard, data fusion schemes supported with advanced deep learning technology represent a promising approach towards a big data paradigm using cloud-based software services. However, the deep learning models’ structure and hyper-parameters selection represent the main limitation when applied. Thus, in this paper, a novel deep-learning-based methodology for fault diagnosis in electromechanical systems is presented. The main benefits of the proposed methodology are the easiness of application and high adaptability to available data. The methodology is supported by an unsupervised stacked auto-encoders and a supervised discriminant analysis.
Francisco Arellano-Espitia; Miguel Delgado-Prieto; Víctor Martínez-Viol; Juan Jose Saucedo-Dorantes; Roque Alfredo Osornio-Rios. Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems. Sensors 2020, 20, 3949 .
AMA StyleFrancisco Arellano-Espitia, Miguel Delgado-Prieto, Víctor Martínez-Viol, Juan Jose Saucedo-Dorantes, Roque Alfredo Osornio-Rios. Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems. Sensors. 2020; 20 (14):3949.
Chicago/Turabian StyleFrancisco Arellano-Espitia; Miguel Delgado-Prieto; Víctor Martínez-Viol; Juan Jose Saucedo-Dorantes; Roque Alfredo Osornio-Rios. 2020. "Deep-Learning-Based Methodology for Fault Diagnosis in Electromechanical Systems." Sensors 20, no. 14: 3949.
Classical methods for monitoring electromechanical systems lack two critical functions for effective industrial application: management of unexpected events and the incorporation of new patterns into the knowledge database. This study presents a novel, high-performance condition-monitoring method based on a four-stage incremental learning approach. First, non-stationary operation is characterised using normalised time-frequency maps. Second, operating novelties are detected using multivariate kernel density estimators. Third, the operating novelties are characterised and labelled to increase the knowledge available for subsequent diagnosis. Fourth, operating faults are diagnosed and classified using neural networks. The proposed method is validated experimentally with an industrial camshaft-based machine under a variety of operating conditions.
J.A. Cariño; M. Delgado-Prieto; D. Zurita; A. Picot; J.A. Ortega; R.J. Romero-Troncoso. Incremental novelty detection and fault identification scheme applied to a kinematic chain under non-stationary operation. ISA Transactions 2020, 97, 76 -85.
AMA StyleJ.A. Cariño, M. Delgado-Prieto, D. Zurita, A. Picot, J.A. Ortega, R.J. Romero-Troncoso. Incremental novelty detection and fault identification scheme applied to a kinematic chain under non-stationary operation. ISA Transactions. 2020; 97 ():76-85.
Chicago/Turabian StyleJ.A. Cariño; M. Delgado-Prieto; D. Zurita; A. Picot; J.A. Ortega; R.J. Romero-Troncoso. 2020. "Incremental novelty detection and fault identification scheme applied to a kinematic chain under non-stationary operation." ISA Transactions 97, no. : 76-85.
The proper sequencing and optimal loading of chillers is one of the major avenues for energy efficiency improvement in existing heating, ventilating and air conditioning installations. The main enabler for the success of such applications is the access to accurate chiller performance maps that allow to operate the equipment in optimal conditions. However, current solutions are excessively reliant on maps obtained through suboptimal means, such as manufacturer datasheets, extensive instrumentation campaigns or burdensome modelling and simulation methodologies. Furthermore, recent studies show that strategies based on model-predictive control may lead to increased savings by anticipating the future cooling demand and scheduling the operation of the chillers, selecting the optimal operation configuration and extending the remaining life by reducing switching. In this regard, this study presents a novel data-driven and multi-criteria chiller orchestration strategy that combines a chiller performance characterization stage for obtaining performance maps based on a neural network-based learning methodology and a state-of-the-art hybrid load forecasting scheme for calculating the future load profiles. The effectiveness of the proposed methodology is tested with experimental data from a multi-chiller installation in a tertiary sector building, where nearly a 20% average performance increase is achieved compared to the standard real-time controller of the HVAC installation.
Enric Sala-Cardoso; Miguel Delgado-Prieto; Konstantinos Kampouropoulos; Luis Romeral. Predictive chiller operation: A data-driven loading and scheduling approach. Energy and Buildings 2019, 208, 109639 .
AMA StyleEnric Sala-Cardoso, Miguel Delgado-Prieto, Konstantinos Kampouropoulos, Luis Romeral. Predictive chiller operation: A data-driven loading and scheduling approach. Energy and Buildings. 2019; 208 ():109639.
Chicago/Turabian StyleEnric Sala-Cardoso; Miguel Delgado-Prieto; Konstantinos Kampouropoulos; Luis Romeral. 2019. "Predictive chiller operation: A data-driven loading and scheduling approach." Energy and Buildings 208, no. : 109639.
Laser-generated ultrasound represents an interesting nondestructive testing technique that is being investigated in the last years as performative alternative to classical ultrasonic-based approaches. The greatest difficulty in analyzing the acoustic emission response is that an in-depth knowledge of how acoustic waves propagate through the tested composite is required. In this regard, different signal processing approaches are being applied in order to assess the significance of features extracted from the resulting analysis. In this study, the detection capabilities of internal defects in a metallic sample are proposed to be studied by means of the time-frequency analysis of the ultrasonic waves resulting from laser-induced thermal mechanism. In the proposed study, the use of the wavelet transform considering different wavelet variants is considered due to its multi-resolution time-frequency characteristics. Also, a significant time-frequency technique widely applied in other fields of research is applied, the synchrosqueezed transform.
Hossam Selim; Fernando Piñal-Moctezuma; Miguel Delgado Prieto; José Francisco Trull; Luis Romeral Martínez; Crina Cojocaru. Wavelet Transform Applied to Internal Defect Detection by Means of Laser Ultrasound. Wavelet Transform and Complexity 2019, 1 .
AMA StyleHossam Selim, Fernando Piñal-Moctezuma, Miguel Delgado Prieto, José Francisco Trull, Luis Romeral Martínez, Crina Cojocaru. Wavelet Transform Applied to Internal Defect Detection by Means of Laser Ultrasound. Wavelet Transform and Complexity. 2019; ():1.
Chicago/Turabian StyleHossam Selim; Fernando Piñal-Moctezuma; Miguel Delgado Prieto; José Francisco Trull; Luis Romeral Martínez; Crina Cojocaru. 2019. "Wavelet Transform Applied to Internal Defect Detection by Means of Laser Ultrasound." Wavelet Transform and Complexity , no. : 1.
This work envisages a detailed study of two-dimensional defect localization and reconstruction, using laser generated ultrasound and its application as a remotely controlled non-destructive testing method. As an alternative to full ultrasonic or full optical approaches, we propose a hybrid configuration where ultrasound is generated by impact of laser pulses, while the detection is done with conventional transducers. We implement this approach for defect reconstruction in metallic elements and show that it combines advantages of both photonic and ultrasonic devices, reducing the drawbacks of both methods. We combine our experimental results with a high-resolution signal processing procedure based on the synthetic aperture focusing technique for the benefit of the final two-dimensional visualization of the defects.
Hossam Selim; Miguel Delgado-Prieto; Jose Trull; Rubén Picó; Luis Romeral; Crina Cojocaru. Defect reconstruction by non-destructive testing with laser induced ultrasonic detection. Ultrasonics 2019, 101, 106000 .
AMA StyleHossam Selim, Miguel Delgado-Prieto, Jose Trull, Rubén Picó, Luis Romeral, Crina Cojocaru. Defect reconstruction by non-destructive testing with laser induced ultrasonic detection. Ultrasonics. 2019; 101 ():106000.
Chicago/Turabian StyleHossam Selim; Miguel Delgado-Prieto; Jose Trull; Rubén Picó; Luis Romeral; Crina Cojocaru. 2019. "Defect reconstruction by non-destructive testing with laser induced ultrasonic detection." Ultrasonics 101, no. : 106000.
Acoustic emission (AE) analysis is a powerful potential characterisation method for fracture mechanism analysis during metallic specimen testing. Nevertheless, identifying and extracting each event when analysing the raw signal remains a major challenge. Typically, AE detection is carried out using a thresholding approach. However, though extensively applied, this approach presents some critical limitations due to overlapping transients, differences in strength and low signal-to-noise ratio. To address these limitations, advanced methodologies for detecting AE hits have been developed in the literature. The most prominently used are instantaneous amplitude, the short-term average to long-term average ratio, the Akaike information criterion and wavelet analysis, each of which exhibits satisfactory performance and ease of implementation for diverse applications. However, their proneness to errors in the presence of noncyclostationary AE wavefronts and the lack of thorough comparison for transient AE signals are constraints to the wider application of these methods in non-destructive testing procedures. In this study with the aim of make aware about the drawbacks of the traditional threshold approach, a comprehensive analysis of its limiting factors when taking in regard the AE waveform behaviour is presented. Additionally in a second section, a performance analysis of the main advanced representative-methods in the field is carried out through a common comparative framework, by analysing first, AE waves generated from a standardised Hsu-Nielsen test and second, a data frame of a highly active signal derived from a tensile test. With the aim to quantify the performance with which these AE detection methodologies work, for the first time in literature, time features as the endpoint and duration accuracies, as well as statistical metrics as accuracy, precision and false detection rates, are studied.
Fernando Pinal Moctezuma; Miguel Delgado Prieto; Luis Romeral Martinez. Performance Analysis of Acoustic Emission Hit Detection Methods Using Time Features. IEEE Access 2019, 7, 71119 -71130.
AMA StyleFernando Pinal Moctezuma, Miguel Delgado Prieto, Luis Romeral Martinez. Performance Analysis of Acoustic Emission Hit Detection Methods Using Time Features. IEEE Access. 2019; 7 (99):71119-71130.
Chicago/Turabian StyleFernando Pinal Moctezuma; Miguel Delgado Prieto; Luis Romeral Martinez. 2019. "Performance Analysis of Acoustic Emission Hit Detection Methods Using Time Features." IEEE Access 7, no. 99: 71119-71130.
Strategies of condition monitoring applied to electric motors play an important role in the competitiveness of multiple industrial sectors. However, the risk of faults coexistence in an electric motor and the overlapping of their effects in the considered physical magnitudes represent, currently, a critical limitation to provide reliable diagnosis outcomes. In this regard, additional investigation efforts are required towards high-dimensional data fusion schemes, particularly over the features calculation and features reduction, which represent two decisive stages in such data-driven approaches. In this study, a novel multiple-fault detection and identification methodology supported by a feature-level fusion strategy and a Self-Organizing Maps (SOM) hierarchical structure is proposed. The condition diagnosis as well as the corresponding estimated probability are obtained. Moreover, the proposed method allows the visualization of the results while preserving the underlying physical phenomenon of the system under monitoring. The proposed scheme is performed sequentially; first, a set of statistical-time based features is estimated from different physical magnitudes. Second, a hybrid feature reduction method is proposed, composed by an initial soft feature reduction, based on sequential floating forward selection to remove the less informative features, and followed by a hierarchical SOM structure which reveals directly the diagnosis and probability assessment. The effectiveness of the proposed detection and identification scheme is validated with a complete set of experimental data including healthy and five faulty conditions. The accuracy’s results are compared with classical condition monitoring approaches in order to validate the competency of the proposed method.
Juan Jose Saucedo-Dorantes; Miguel Delgado-Prieto; René De Jesús Romero-Troncoso; Roque Alfredo Osornio-Rios. Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine. Applied Soft Computing 2019, 81, 105497 .
AMA StyleJuan Jose Saucedo-Dorantes, Miguel Delgado-Prieto, René De Jesús Romero-Troncoso, Roque Alfredo Osornio-Rios. Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine. Applied Soft Computing. 2019; 81 ():105497.
Chicago/Turabian StyleJuan Jose Saucedo-Dorantes; Miguel Delgado-Prieto; René De Jesús Romero-Troncoso; Roque Alfredo Osornio-Rios. 2019. "Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine." Applied Soft Computing 81, no. : 105497.
Nondestructive testing of metallic objects that may contain embedded defects of different sizes is an important application in many industrial branches for quality control. Most of these techniques allow defect detection and its approximate localization, but few methods give enough information for its 3D reconstruction. Here we present a hybrid laser–transducer system that combines remote, laser-generated ultrasound excitation and noncontact ultrasonic transducer detection. This fully noncontact method allows access to scan areas on different object’s faces and defect details from different angles/perspectives. This hybrid system can analyze the object’s volume data and allows a 3D reconstruction image of the embedded defects. As a novelty for signal processing improvement, we use a 2D apodization window filtering technique, applied along with the synthetic aperture focusing algorithm, to remove the undesired effects due to side lobes and wide-angle reflections of propagating ultrasound waves, thus enhancing the resulting 3D image of the defect. Finally, we provide both qualitative and quantitative volumetric results that yield valuable information about defect location and size.
Hossam Selim; José Trull; Miguel Delgado Prieto; Rubén Picó; Luis Romeral; Crina Cojocaru. Fully Noncontact Hybrid NDT for 3D Defect Reconstruction Using SAFT Algorithm and 2D Apodization Window. Sensors 2019, 19, 2138 .
AMA StyleHossam Selim, José Trull, Miguel Delgado Prieto, Rubén Picó, Luis Romeral, Crina Cojocaru. Fully Noncontact Hybrid NDT for 3D Defect Reconstruction Using SAFT Algorithm and 2D Apodization Window. Sensors. 2019; 19 (9):2138.
Chicago/Turabian StyleHossam Selim; José Trull; Miguel Delgado Prieto; Rubén Picó; Luis Romeral; Crina Cojocaru. 2019. "Fully Noncontact Hybrid NDT for 3D Defect Reconstruction Using SAFT Algorithm and 2D Apodization Window." Sensors 19, no. 9: 2138.
Non-destructive testing of metallic objects that may contain embedded defects of different sizes is an important application in many industrial branches for quality control. Most of these techniques allow defect detection and its approximate localization, but very few give enough information for its 3D reconstruction. Here we present a hybrid laser – transducer system that combines remote laser-generated ultrasound excitation and non-contact ultrasonic transducer detection. This fully non-contact method gives access to separating scan areas on different object’s faces and defect details from different angles/perspectives can be analysed. This hybrid system can analyse the whole object’s volume data and allow a 3D reconstruction image of the embedded defects. As a novelty for the signal processing improvement, we use a 2D apodization window filtering technique, applied along with the synthetic aperture focusing algorithm in order to remove the undesired effects of side lobes and wide-angle reflections of propagating ultrasound waves, thus, enhancing the resulting 3D image of the defect. We provide both qualitative and quantitative volumetric results with high accuracy and resolution compared with conventional techniques.
Hossam Selim; José Trull; Miguel Delgado Prieto; Rubén Picó; Luis Romeral; Crina Cojocaru. Fully Non-Contact Hybrid NDT Inspection for 3D Defect Reconstruction Using an Improved SAFT Algorithm. 2019, 1 .
AMA StyleHossam Selim, José Trull, Miguel Delgado Prieto, Rubén Picó, Luis Romeral, Crina Cojocaru. Fully Non-Contact Hybrid NDT Inspection for 3D Defect Reconstruction Using an Improved SAFT Algorithm. . 2019; ():1.
Chicago/Turabian StyleHossam Selim; José Trull; Miguel Delgado Prieto; Rubén Picó; Luis Romeral; Crina Cojocaru. 2019. "Fully Non-Contact Hybrid NDT Inspection for 3D Defect Reconstruction Using an Improved SAFT Algorithm." , no. : 1.
Condition monitoring and fault identification have become important aspects to ensure the proper operating condition of rotating machinery in industrial applications. In this sense, gearbox transmission systems and induction motors are important rotating elements due to they are probably the most used in industrial sites. Thus, from an industrial perspective, the occurrence of vibrations is inherent to the working condition in any rotating machine. To overcome this issue, condition monitoring strategies have to be developed aiming to avoid unnecessary cost and downtimes; thereby, condition-based maintenance based on vibration analysis has become as the most reliable approach with condition monitoring and fault identification purposes. In this regard, this work proposes a spectral analysis of the nonlinear vibration effects produced by worn gears and damaged bearings during the condition monitoring and fault assessment in an electromechanical system. The analysis is based on the spectral estimation from the available vibration and stator current signals; furthermore, the theoretical fault-related frequency components are estimated for being located in such estimated spectra. Consequently, the identification of different levels of uniform wear is performed by comparing the amplitude increase of those theoretical frequency components. Finally, through time-frequency maps is proved that an incipient fault, such as wear in gears and damage in bearings, may produce nonlinear frequency components that affect the proper operating condition of the electromechanical system. The proposed analysis is validated under a complete experimentally dataset acquired from a real laboratory electromechanical system.
J. J. Saucedo-Dorantes; M. Delgado-Prieto; R. A. Osornio-Rios; R. J. Romero-Troncoso. Spectral Analysis of Nonlinear Vibration Effects Produced by Worn Gears and Damaged Bearing in Electromechanical Systems: A Condition Monitoring Approach. Mechanical Engineering and Materials 2019, 293 -320.
AMA StyleJ. J. Saucedo-Dorantes, M. Delgado-Prieto, R. A. Osornio-Rios, R. J. Romero-Troncoso. Spectral Analysis of Nonlinear Vibration Effects Produced by Worn Gears and Damaged Bearing in Electromechanical Systems: A Condition Monitoring Approach. Mechanical Engineering and Materials. 2019; ():293-320.
Chicago/Turabian StyleJ. J. Saucedo-Dorantes; M. Delgado-Prieto; R. A. Osornio-Rios; R. J. Romero-Troncoso. 2019. "Spectral Analysis of Nonlinear Vibration Effects Produced by Worn Gears and Damaged Bearing in Electromechanical Systems: A Condition Monitoring Approach." Mechanical Engineering and Materials , no. : 293-320.
Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigated over recent years as an alternative to classical ultrasonic methods, mainly in industrial maintenance and quality control procedures. In this study, the detection and reconstruction of internal defects in a metallic sample is performed by means of a time-frequency analysis of ultrasonic waves generated by a laser-induced thermal mechanism. In the proposed methodology, we used wavelet transform due to its multi-resolution time frequency characteristics. In order to isolate and estimate the corresponding time of flight of eventual ultrasonic echoes related to internal defects, a density-based spatial clustering was applied to the resulting time frequency maps. Using the laser scan beam’s position, the ultrasonic transducer’s location and the echoes’ arrival times were determined, the estimation of the defect’s position was carried out afterwards. Finally, clustering algorithms were applied to the resulting geometric solutions from the set of the laser scan points which was proposed to obtain a two-dimensional projection of the defect outline over the scan plane. The study demonstrates that the proposed method of wavelet transform ultrasonic imaging can be effectively applied to detect and size internal defects without any reference information, which represents a valuable outcome for various applications in the industry.
Hossam Selim; Miguel Delgado Prieto; José Trull; Luis Romeral; Crina Cojocaru. Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples. Sensors 2019, 19, 573 .
AMA StyleHossam Selim, Miguel Delgado Prieto, José Trull, Luis Romeral, Crina Cojocaru. Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples. Sensors. 2019; 19 (3):573.
Chicago/Turabian StyleHossam Selim; Miguel Delgado Prieto; José Trull; Luis Romeral; Crina Cojocaru. 2019. "Laser Ultrasound Inspection Based on Wavelet Transform and Data Clustering for Defect Estimation in Metallic Samples." Sensors 19, no. 3: 573.
Condition-based maintenance plays an important role to ensure the working condition and to increase the availability of the machinery. The feature calculation and feature extraction are critical signal processing that allow to obtain a high-performance characterization of the available physical magnitudes related to specific working conditions of machines. Aiming to overcome this issue, this research proposes a novel condition monitoring strategy based on the spectral energy estimation and Linear Discriminant Analysis for diagnosing and identifying different operating conditions in an induction motor-based electromechanical system. The proposed method involves the acquisition of vibration signals from which the frequency spectrum is computed through the Fast Fourier Transform. Subsequently, such frequency spectrum is segmented to estimate a feature matrix in terms of its spectral energy. Finally, the feature matrix is subjected to a transformation into a 2-dimentional base by means of the Linear Discriminant Analysis and the final diagnosis outcome is performed by a NN-based classifier. The proposed strategy is validated under a complete experimental dataset acquired from a laboratory electromechanical system.
Mayra Ramirez-Chavez; Juan Jose Saucedo-Dorantes; Arturo Yosimar Jaen-Cuellar; Roque Alfredo Osornio Rios; Rene De Jesus Romero-Troncoso; Miguel Delgado Prieto. Condition Monitoring Strategy Based on Spectral Energy Estimation and Linear Discriminant Analysis Applied to an Induction Motor. 2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) 2018, 1 -6.
AMA StyleMayra Ramirez-Chavez, Juan Jose Saucedo-Dorantes, Arturo Yosimar Jaen-Cuellar, Roque Alfredo Osornio Rios, Rene De Jesus Romero-Troncoso, Miguel Delgado Prieto. Condition Monitoring Strategy Based on Spectral Energy Estimation and Linear Discriminant Analysis Applied to an Induction Motor. 2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC). 2018; ():1-6.
Chicago/Turabian StyleMayra Ramirez-Chavez; Juan Jose Saucedo-Dorantes; Arturo Yosimar Jaen-Cuellar; Roque Alfredo Osornio Rios; Rene De Jesus Romero-Troncoso; Miguel Delgado Prieto. 2018. "Condition Monitoring Strategy Based on Spectral Energy Estimation and Linear Discriminant Analysis Applied to an Induction Motor." 2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) , no. : 1-6.
An industrial machinery condition monitoring methodology based on ensemble novelty detection and evolving classification is proposed in this study. The methodology contributes to solve current challenges dealing with classical electromechanical system monitoring approaches applied in industrial frameworks, that is, the presence of unknown events, the limitation to the nominal healthy condition as starting knowledge, and the incorporation of new patterns to the available knowledge. The proposed methodology is divided into four main stages: 1) a dedicated feature calculation and reduction over available physical magnitudes to increase novelty detection and fault classification capabilities; 2) a novelty detection based on the ensemble of one-class support vector machines to identify not previously considered events; 3) a diagnosis by means of eClass evolving classifiers for patterns recognition; and 4) re-training to include new patterns to the novelty detection and fault identification models. The effectiveness of the proposed fault detection and identification methodology has been compared with classical approaches, and verified by experimental results obtained from an automotive end-of-line test machine.
Jesus A. Carino; Miguel Delgado-Prieto; Jose Antonio Iglesias; Araceli Sanchis; Daniel Zurita; Marta Millan; Juan Antonio Ortega Redondo; Rene Romero-Troncoso. Fault Detection and Identification Methodology Under an Incremental Learning Framework Applied to Industrial Machinery. IEEE Access 2018, 6, 49755 -49766.
AMA StyleJesus A. Carino, Miguel Delgado-Prieto, Jose Antonio Iglesias, Araceli Sanchis, Daniel Zurita, Marta Millan, Juan Antonio Ortega Redondo, Rene Romero-Troncoso. Fault Detection and Identification Methodology Under an Incremental Learning Framework Applied to Industrial Machinery. IEEE Access. 2018; 6 ():49755-49766.
Chicago/Turabian StyleJesus A. Carino; Miguel Delgado-Prieto; Jose Antonio Iglesias; Araceli Sanchis; Daniel Zurita; Marta Millan; Juan Antonio Ortega Redondo; Rene Romero-Troncoso. 2018. "Fault Detection and Identification Methodology Under an Incremental Learning Framework Applied to Industrial Machinery." IEEE Access 6, no. : 49755-49766.
This study is focused on the current challenges dealing with electromechanical system monitoring applied in industrial frameworks, that is, the presence of unknown events and the limitation to the nominal healthy condition as starting knowledge. Thus, an industrial machinery condition monitoring methodology based on novelty detection and classification is proposed in this study. The methodology is divided in three main stages. First, a dedicated feature calculation and reduction over each available physical magnitude. Second, an ensemble structure of novelty detection models based on one-class support vector machines to identify not previously considered events. Third, a diagnosis model supported by a feature fusion scheme in order to reach high fault classification capabilities. The effectiveness of the fault detection and identification methodology has been compared with classical single model approach, and verified by experimental results obtained from an electromechanical machine.
Miguel Delgado Prieto; J. A. Carino; J. J. Saucedo- Dorantes; Roque A Osornio-Rios; L. Romeral; R. J. Romero Troncoso. Novelty Detection based Condition Monitoring Scheme Applied to Electromechanical Systems. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 2018, 1, 1213 -1216.
AMA StyleMiguel Delgado Prieto, J. A. Carino, J. J. Saucedo- Dorantes, Roque A Osornio-Rios, L. Romeral, R. J. Romero Troncoso. Novelty Detection based Condition Monitoring Scheme Applied to Electromechanical Systems. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). 2018; 1 ():1213-1216.
Chicago/Turabian StyleMiguel Delgado Prieto; J. A. Carino; J. J. Saucedo- Dorantes; Roque A Osornio-Rios; L. Romeral; R. J. Romero Troncoso. 2018. "Novelty Detection based Condition Monitoring Scheme Applied to Electromechanical Systems." 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 1, no. : 1213-1216.
A great deal of investigations are being carried out towards the effective implementation of the 4.0 Industry new paradigm. Indeed, most of the machinery involved in industrial processes are intended to be digitalized aiming to obtain enhanced information to be used for an optimized operation of the whole manufacturing process. In this regard, condition monitoring strategies are being also reconsidered to include improved performances and functionalities. Thus, the contribution of this research work lies in the proposal of an incremental learning framework approach applied to the condition monitoring of electromechanical systems. The proposed strategy is divided in three main steps, first, different available physical magnitudes are characterized through the calculation of a set of statistical-time based features. Second, a modelling of the considered conditions is performed by means of self-organizing maps in order to preserve the topology of the data; and finally, a novelty detection is carried out by a comparison among the quantization error value achieved in the data modelling for each of the considered conditions. The effectiveness of the proposed novelty fault identification condition monitoring methodology is proved by means of the evaluation of a complete experimental database acquired during the continuous working conditions of an electromechanical system.
J. J. Saucedo-Dorantes; Miguel Delgado Prieto; J. A. Carino-Corrales; Roque A Osornio-Rios; L. Romeral-Martinez; R. J. Romero-Troncoso. Incremental Learning Framework-based Condition Monitoring for Novelty Fault Identification Applied to Electromechanical Systems. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 2018, 1, 1359 -1364.
AMA StyleJ. J. Saucedo-Dorantes, Miguel Delgado Prieto, J. A. Carino-Corrales, Roque A Osornio-Rios, L. Romeral-Martinez, R. J. Romero-Troncoso. Incremental Learning Framework-based Condition Monitoring for Novelty Fault Identification Applied to Electromechanical Systems. 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA). 2018; 1 ():1359-1364.
Chicago/Turabian StyleJ. J. Saucedo-Dorantes; Miguel Delgado Prieto; J. A. Carino-Corrales; Roque A Osornio-Rios; L. Romeral-Martinez; R. J. Romero-Troncoso. 2018. "Incremental Learning Framework-based Condition Monitoring for Novelty Fault Identification Applied to Electromechanical Systems." 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) 1, no. : 1359-1364.