Papers by Miguel Delgado Prieto
2018 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)
Condition-based maintenance plays an important role to ensure the working condition and to increa... more 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 diagnose and identify 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 experimentally dataset acquired from a laboratory electromechanical system.
Applied Sciences
Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among bui... more Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among buildings’ equipment. Reliable fault detection and diagnosis schemes can effectively reduce their energy consumption and maintenance costs. In this respect, data-driven approaches have shown impressive results, but their accuracy depends on the availability of representative data to train the models, which is not common in real applications. For this reason, transfer learning is attracting growing attention since it tackles the problem by leveraging the knowledge between datasets, increasing the representativeness of fault scenarios. However, to date, research on transfer learning for heating, ventilation and air-conditioning has mostly been focused on learning algorithmic, overlooking the importance of a proper domain similarity analysis over the available data. Thus, this study proposes the design of a transfer learning approach based on a specific data selection methodology to tackle dis...
2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS)
Cyber-physical systems are the response to the adaptability, scalability and accurate demands of ... more Cyber-physical systems are the response to the adaptability, scalability and accurate demands of the new era of manufacturing called Industry 4.0. They will become the core technology of control and monitoring in smart manufacturing processes. In this regard, the complexity of industrial systems implies a challenge for the implementation of monitoring and diagnosis schemes. Moreover, the challenges that is presented in technological aspects regarding connectivity, data management and computing are being resolved through different IT-OT (information technology and operational technology) convergence proposals. These solutions are making it possible to have large computing capacities and low response latency. However, regarding the logical part of information processing and analysis, this still requires additional studies to identify the options with a better complexity-performance trade-off. The emergence of techniques based on artificial intelligence, especially those based on deep-learning, has provided monitoring schemes with the capacity for characterization and recognition in front of complex electromechanical systems. However, most deep learning-based schemes suffer from critical lack of interpretability lying to low generalization capabilities and overfitted responses. This paper proposes a study of two of the main deep learning-based techniques applied to fault diagnosis in electromechanical systems. An analysis of the interpretability of the learning processes is carried out, and the approaches are evaluated under common performance metrics.
Fault Diagnosis and Detection
Dealing with industrial applications, the implementation of condition monitoring schemes must ove... more Dealing with industrial applications, the implementation of condition monitoring schemes must overcome a critical limitation, that is, the lack of a priori information about fault patterns of the system under analysis. Indeed, classical diagnosis schemes, in general, outdo the membership probability of a measure in regard to predefined operating scenarios. However, dealing with noncharacterized systems, the knowledge about faulty operating scenarios is limited and, consequently, the diagnosis performance is insufficient. In this context, the novelty detection framework plays an essential role for monitoring systems in which the information about different operating scenarios is initially unavailable or restricted. The novelty detection approach begins with the assumption that only data corresponding to the healthy operation of the system under analysis is available. Thus, the challenge is to detect and learn additional scenarios during the operation of the system in order to complement the information obtained by the diagnosis scheme. This work has two main objectives: first, the presentation of novelty detection as the current trend toward the new paradigm of industrial condition monitoring and, second, the introduction to its applicability by means of analyses of different novelty detection strategies over a real industrial system based on rotatory machinery.
2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA), 2018
Aquesta és una còpia de la versió author's final draft d'un article publicat a Institute of Elect... more Aquesta és una còpia de la versió author's final draft d'un article publicat a Institute of Electrical and Electronics Engineers (IEEE).
Traditionally, for primary flight control surfaces only rotary and linear electro-hydraulic actua... more Traditionally, for primary flight control surfaces only rotary and linear electro-hydraulic actuators (EHA) have been considered, however the later trend is to replace them by electro mechanical actuators (EMA). An EMA has no internal hydraulic fluid, instead using electric motors to directly drive the ram through a mechanical gearbox. Compared to an EHA, the EMA has certain advantages. There is a new issue related to the aforesaid new trend with the aim of improve system reliability. This is the fault detection and diagnosis of the failures that may take place in both, mechanics and electric components. So, it is well assumed that the safety is the main issue for the EMAs development. The motor failures, the damaged bearings, and the eccentricities existing in the drive train, affects on one hand the air gap flux distribution and on the other leads to current and voltage unbalances. However, it is difficult to examine EMA faults by analysing only specific fault harmonics due to fau...
Renewable Energy and Power Quality Journal, 2021
Complex disturbance patterns take place over the corresponding power supply networks due to the i... more Complex disturbance patterns take place over the corresponding power supply networks due to the increased complexity of electrical loads at industrial plants. Such complex patterns are the result of a combination of simpler standardized disturbances. However, their detection and identification represent a challenge to current power quality monitoring systems. The detection of disturbances and their identification would allow early and effective decision-making processes towards optimal power grid controls or maintenance and security operations of the grid. In this regard, this paper presents an evaluation of the four main techniques for novelty detection: k-Nearest Neighbor, Gaussian Mixture Models, One-Class Support Vector Machine, and Stacked Autoencoder. A set of synthetic signals have been considered to evaluate the performance and suitability of each technique as an anomaly detector applied to power quality disturbances. A set of statistical features have been considered to cha...
2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 2020
The increasing energy consumption of heating, ventilation and air conditioning (HVAC) systems is ... more The increasing energy consumption of heating, ventilation and air conditioning (HVAC) systems is one of the main concerns in the building sector. Fault detection technologies are now indispensable for energy efficiency and performance improvement. In this paper, a methodology for the robust and reliable fault detection and diagnosis is presented as a two-stage framework composed by an offline stage where the models are built and an online stage that is constantly receiving new samples. The system includes a novelty detection scheme developed using one-class support vector machines (OC-SVM) and a classifier built using SVM. The proposed strategy is applied to a dataset for a single-zone constant air volume air handling unit. The experimental results show that the novelty detection stage adds robustness layer to the typical classification scheme.
Sensors, 2020
In this work, we numerically investigate the diffraction management of longitudinal elastic waves... more 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 fo...
ISA Transactions, 2019
Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de ... more Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista ISA Transactions.
IEEE Access, 2019
Acoustic emission (AE) analysis is a powerful potential characterization method for fracture mech... more Acoustic emission (AE) analysis is a powerful potential characterization method for fracture mechanism analysis during metallic specimen testing. Nevertheless, identifying and extracting each event when analyzing the raw signal remains a major challenge. Typically, the 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. In this paper, to address these limitations, advanced methodologies for detecting AE hits have been developed. The most prominent methodologies 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 non-cyclostationary 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 paper, with the aim of making aware of the drawbacks of the traditional threshold approach, a comprehensive analysis of its limiting factors when taking into regard the AE waveform behavior is presented. In addition, 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 analyzing first, AE waves generated from a standardized Hsu-Nielsen test and second, a data frame of a highly active signal derived from a tensile test. In this paper, with the aim to quantify the performance with which these AE detection methodologies work, for the first time, time features as the endpoint and duration accuracies, as well as statistical metrics as accuracy, precision, and false detection rates, are studied. INDEX TERMS Acoustic emission, materials testing, AE thresholding method, short-term average to longterm average ratio, instantaneous amplitude, Akaike information criterion, wavelet analysis, Otsu's method. I. INTRODUCTION High demands are placed on safety and reliability specifications in the design and manufacturing of metallic materials, particularly in the transportation sector, where the lifetime, performance and cost of structural parts are critical aspects. This has led to extensive scientific and technical study of the mechanical properties of metallic components [1], [2]. Characterisation of the mechanical properties of metallic components commonly requires estimations of the post-yield The associate editor coordinating the review of this manuscript and approving it for publication was Dusmanta Kumar Mohanta.
Sensors, 2019
Nondestructive testing of metallic objects that may contain embedded defects of different sizes i... more 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 ultras...
Sensors, 2019
Laser-generated ultrasound is a modern non-destructive testing technique. It has been investigate... more 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 appli...
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2017
Strategies for condition monitoring are relevant to improve the operation safety and to ensure th... more Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extract...
Shock and Vibration, 2016
This paper presents an adaptive novelty detection methodology applied to a kinematic chain for th... more This paper presents an adaptive novelty detection methodology applied to a kinematic chain for the monitoring of faults. The proposed approach has the premise that only information of the healthy operation of the machine is initially available and fault scenarios will eventually develop. This approach aims to cover some of the challenges presented when condition monitoring is applied under a continuous learning framework. The structure of the method is divided into two recursive stages: first, an offline stage for initialization and retraining of the feature reduction and novelty detection modules and, second, an online monitoring stage to continuously assess the condition of the machine. Contrary to classical static feature reduction approaches, the proposed method reformulates the features by employing first a Laplacian Score ranking and then the Fisher Score ranking for retraining. The proposed methodology is validated experimentally by monitoring the vibration measurements of a ...
Vibration Analysis and Control - New Trends and Developments, 2011
IEEE Transactions on Industrial Informatics, 2020
The detection of uncharacterized events during electromechanical systems operation represents one... more The detection of uncharacterized events during electromechanical systems operation represents one of the most critical data challenges dealing with condition-based monitoring under the Industry 4.0 framework. Thus, the detection of novelty conditions and the learning of new patterns are considered as mandatory competencies in modern industrial applications. In this regard, this study proposes a novel multi-fault detection and identification scheme, based on machine learning, information data-fusion, novelty-detection and incremental learning. First, statistical time-domain features estimated from multiple physical magnitudes acquired from the electrical motor under inspection are fused under a feature-fusion level scheme. Second, a selforganizing map structure is proposed to construct a data-based model of the available conditions of operation. Third, the incremental learning of the condition-based monitoring scheme is performed adding self-organizing structures and optimizing their projections through a linear discriminant analysis. The performance of the proposed scheme is validated under a complete set of experimental scenarios and results compared with a classical approach.
2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
In the modern industry framework, the application of condition monitoring schemes over electromec... more In the modern industry framework, the application of condition monitoring schemes over electromechanical systems is being subjected to demanding requirements. Currently, the massive digitalization of industrial assets allows the investigation towards multiple monitoring strategies capable of emphasize deviations over the nominal system operation. However, the most prominent techniques, such as Machine Learning, present great challenges in complex systems. In this regard, the proposed study presents the analysis of the diagnostic capabilities resulting from the classical approaches based on machine learning facing to complex electromechanical systems that implies a working environment subject to different operation condition, configurations with multiple components and the presence of faults of different nature (mechanical, electrical, electromagnetic), under isolated or combined scenarios. Discriminative feature extraction capabilities and classification accuracy will be analyzed as performance measures.
2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
Condition monitoring applied to power quality involves several techniques and procedures for the ... more Condition monitoring applied to power quality involves several techniques and procedures for the assessment of the electrical signal. Data-driven approaches are the most common methodologies supported on data and signal processing procedures. Electrical systems in factory automation become more complex with the increase of multiple load profiles connected, and unexpected electrical events can occur causing the appearance of power quality disturbances. However, emerging technologies in the techniques related to the detection and identification of power quality disturbances are analyzed and compared according to the complexity of the current electrical system, that is, including simple and combined disturbances. These new technologies allow developing more cyber-physical systems to process the new methodologies for condition monitoring. Thus, in this study, a deep learning-based approach for the identification of power quality disturbances is implemented and their performance analyzed in front of classical disturbances defined by the International standards considered in the related literature.
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Papers by Miguel Delgado Prieto