Papers by Diego Flores Cabrera
Earth Science Informatics
Journal of Computing and Information Science in Engineering
Most of the approaches of feature extraction for data-driven rotating machinery fault diagnosis a... more Most of the approaches of feature extraction for data-driven rotating machinery fault diagnosis assume characteristics of periodicity and seasonality typically inherent to linear signals obtained from different sensors. Nevertheless, the behavior of rotating machinery is not necessarily linear when a failure occurs. Thus, new techniques based on the theory of chaos and nonlinear systems are needed to extract proper features of signals. This article introduces the use of features extracted from the Poincaré plot (PP), which are computed over vibration and current signals measured on a gearbox powered by an induction motor. A comparison between the performance of classic statistical features and PP features is developed by applying feature analysis based on analysis of varaince (ANOVA) and cluster validity assessment to rank and select the subset of best features. K-nearest-neighbor (KNN) algorithm is used to test the performance of the selected feature set for fault severity classifi...
IEEE Access, 2022
Nowadays, intelligent models can correctly detect faults by analysing signals from rotating machi... more Nowadays, intelligent models can correctly detect faults by analysing signals from rotating machinery. However, most of the studies are run in controlled environments and the performance in industrial real-world environments is not yet fully validated. Hence, a suitable tool to implement fault diagnosers is transfer learning, this topic is under development and challenges persist. This paper proposes a framework for creating accurate 1D-CNN based fault classifiers that can be transferred between different rotating machines and working conditions. Multiple Bayesian processes select architecture parameters and hyperparameters, which minimize a loss function related to their transferability to other machines and to the same machine under different operating conditions (such as load and engine speed). The resulting model is fitted to heterogeneous fault diagnosis data resulting in a 1D-CNN ensemble that improves the performance of the unitary model. Subsequently, the transfer learning capability of the ensemble is analyzed on two source data sets using function and parameter based transfer. The results are compared with classical fault diagnosis classifiers. Finally, additional transfer operations on five target domain datasets validate our framework on limited labeled samples and allow interpretation of the ensemble results. The ultimate goal is to find an ensemble that can generalize fault diagnosis on rotating machinery for easy implementation and update in industrial settings.
Mathematical and Computational Applications, 2022
Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then,... more Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection of the possible failure modes and their severity assessment in such devices is an important field of research. Data-driven approaches usually require an exhaustive development of pipelines including models’ parameter optimization and feature selection. This paper takes advantage of the recent Auto Machine Learning (AutoML) tools to propose proper feature and model selection for three failure modes under different severity levels: broken tooth, pitting and crack. The performance of 64 statistical condition indicators (SCI) extracted from vibration signals under the three failure modes were analyzed by two AutoML systems, namely the H2O Driverless AI platform and TPOT, both of which include feature engineering and feature selection mechanisms. In both cases, the systems converged to differ...
There are growing demands for condition-based monitoring of gearboxes, and techniques to improve ... more There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The
Applied Sciences, 2020
Railway safety is a matter of importance as a single failure can involve risks associated with ec... more Railway safety is a matter of importance as a single failure can involve risks associated with economic and human losses. The early fault detection in railway axles and other railway parts represents a broad field of research that is currently under study. In the present work, the problem of the early crack detection in railway axles is addressed through condition-based monitoring, with the evaluation of several condition indicators of vibration signals on time and frequency domains. To achieve this goal, we applied two different approaches: in the first approach, we evaluate only the vibrations signals captured by accelerometers placed along the longitudinal direction and, in the second approach, a data fusion technique at the condition indicator level was conducted, evaluating six accelerometers by merging the indicator conditions according to the sensor placement. In both cases, a total of 54 condition indicators per vibration signal was calculated and selecting the best features...
Applied Sciences, 2020
Data-driven machine learning techniques play an important role in fault diagnosis, safety, and ma... more Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random ...
Applied Sciences, 2020
Prognostics and Health Management technologies are useful for early fault detection and optimizat... more Prognostics and Health Management technologies are useful for early fault detection and optimization of reliability in mechanical systems. Reciprocating compressors units are commonly used in industry for gas pressurization and transportation, and the valves in compressors are considered vulnerable parts susceptible to failure. Then, early detection of faults is important for avoiding catastrophic accidents. A feasible approach for fault detection consists in measuring the vibration signal for extracting useful features enabling fault detection and classification. In this research, a test-bed composed by two-stage reciprocating compressor was used for simulating a set of 13 different conditions of combined faults in valves and roller bearings. Three accelerometers were used for collecting the vibration signals for extracting three different types of features. These features were analyzed furthermore by using two random forest models to classifying the different faults. The first set...
Sensors, 2020
Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques hav... more Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time–frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique w...
Fuzzy Sets and Systems, 2016
Rotating machinery is an important device supporting manufacturing processes, and a wide research... more Rotating machinery is an important device supporting manufacturing processes, and a wide research works are devoted to detecting and diagnosing faults in such machinery. Recently, prognosis and health management in rotating machinery have received high attention as a research area, and some advances in this field are focused on fault severity assessment and its prediction. This paper applies a fuzzy transition based model for predicting fault severity conditions in helical gears. The approach combines Mamdani models and hierarchical clustering to estimate the membership degrees to fault severity levels of samples extracted from historical vibration signals. These membership degrees are used to estimate the weighted fuzzy transitions for modelling the evolution along the fault severity states over time, according to certain degradation path. The obtained fuzzy model is able of predicting the one step-ahead membership degrees to the severity levels of the failure mode under study, by using the current and the previous membership degrees to the severity levels of two available successive input samples. This fuzzy predictive model was validated by using real data obtained from a test bed with different damages of tooth breaking in the helical gears. Results show adequate predictions for two scenarios of fault degradation paths.
Mundo Agrario Revista De Estudios Rurales, Aug 20, 2014
El desarrollo de las denominaciones de origen (DO) de los vinos es uno de los temas pendientes en... more El desarrollo de las denominaciones de origen (DO) de los vinos es uno de los temas pendientes en la industria vitivinícola de Argentina y de Chile. Dada la fuerte tendencia a la concentración de la industria del vino en estos dos países, es relevante estudiar las DO pues representan un mecanismo adecuado para reducir la brecha y favorecer las posibilidades de las pymes. ¿Por qué no se han desarrollado las DO en Argentina y Chile? El presente artículo examina las causas que inhibieron el desarrollo de las DO locales en la vitivinicultura regional.
Contador Público AuditorCuenc
ISA Transactions, 2016
Healthy rolling element bearings are vital guarantees for safe operation of the rotating machiner... more Healthy rolling element bearings are vital guarantees for safe operation of the rotating machinery. Time-frequency (TF) signal analysis is an effective tool to detect bearing defects under time-varying shaft speed condition. However, it is a challenging work dealing with defective characteristic frequency and rotation frequency simultaneously without a tachometer. For this reason, a technique using the generalized synchrosqueezing transform (GST) guided by enhanced TF ridge extraction is suggested to detect the existence of the bearing defects. The low frequency band and the resonance band are first chopped from the Fourier spectrum of the bearing vibration measurements. The TF information of the lower band component and the resonance band envelope are represented using short-time Fourier transform, where the TF ridge are extracted by harmonic summation search and ridge candidate fusion operations. The inverse of the extracted TF ridge is subsequently used to guide the GST mapping the chirped TF representation to the constant one. The rectified TF pictures are then synchrosqueezed as sharper spectra where the rotation frequency and the defective characteristic frequency can be identified, respectively. Both simulated and experimental signals were used to evaluate the present technique. The results validate the effectiveness of the suggested technique for the bearing defect detection.
Sensors, 2015
There are growing demands for condition-based monitoring of gearboxes, and techniques to improve ... more There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness and accuracy for fault diagnosis are considered valuable contributions. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance in the diagnosis system. The main aim of this research is to propose a multi-stage feature selection mechanism for selecting the best set of condition parameters on the time, frequency and time-frequency domains, which are extracted from vibration signals for fault diagnosis purposes in gearboxes. The selection is based on genetic algorithms, proposing in each stage a new subset of the best features regarding the classifier performance in a supervised environment. The selected features are augmented at each stage and used as input for a neural network classifier in the next step, while a new subset of feature candidates is treated by the selection process. As a result, the inherent exploration and exploitation of the genetic algorithms for finding the best solutions of the selection problem are locally focused. The
Frontiers of Mechanical Engineering, 2015
This paper addresses the development of a random forest classifier for the multi-class fault diag... more This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal's condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients' energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters' space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.
Se evaluó el crecimiento de la especie pionera nativa Verbesina crassiramea Blake sobre suelos en... more Se evaluó el crecimiento de la especie pionera nativa Verbesina crassiramea Blake sobre suelos en diferente estado de alteración en los predios aledaños al embalse de Chisacá. Se utilizó un diseño completamente aleatorizado en parcelas divi-didas con 4 suelos (tratamientos), cada uno con 20 individuos juveniles tras-plantados. Se midieron 6 variables respuesta: altura, cobertura, diámetro basal y sus respectivas tasas de crecimiento relativo (TCR). Se aplicaron MANOVAS de medidas repetidas en el tiempo para las 3 primeras y ANOVAS para las TCR con el fin de evaluar diferencias en el crecimiento de esta especie entre trata-mientos y su comportamiento a lo largo del muestreo, además de regresiones lineales simples con el fin de evaluar la relación entre crecimiento observado y las variables fisicoquímicas del suelo. Los resultados indican diferencias signi-ficativas de crecimiento entre los suelos estudiados, que se manifiestan paulati-namente conforme avanza el muestreo y se evidenci...
2-Aminopyridines were identified from phenotypic whole cell high-throughput screening of a com. a... more 2-Aminopyridines were identified from phenotypic whole cell high-throughput screening of a com. available SoftFocus kinase library as promising selective in vitro antiplasmodial hits. The selected hits were validated through re-synthesis, retesting, physico-chem. and in vitro metab. screens and showed attractive properties for a "Hit to Lead" medicinal chem. program. MMV017007, was identified as a lead compd. possessing good in vivo efficacy in mice Pf SCID (ED90 3.6 mg/kg) and pharmacokinetics properties in rat (F = 83% and half-life 8.7 h). However, MMV017007 showed potential cardiovascular risks through hERG inhibition (IC50 5.5 μM) and a high predicted human dose. MMV017007 was subjected to a lead optimization program resulting in a late Lead and pre-clin. candidate, compd. MMV390048. This pre-clin. candidate showed impressive in vitro Pf activity and overcame cardiovascular risks having low hERG inhibition (IC50 >11 μM). MMV390048 completely cured P. berghei-infect...
Idesia (Arica), 2014
Entre los siglos XVII y XIX el Norte Chico de Chile se convirtió en un dinámico polo de producció... more Entre los siglos XVII y XIX el Norte Chico de Chile se convirtió en un dinámico polo de producción y exportación de aguardientes. El aguardiente se destinaba al mercado de Potosí, tanto por vía marítima como por vía terrestre, gracias al servicio regular de transporte de carga que ofrecían los arrieros. Surgió así la ruta del aguardiente, que contribuyó a establecer estrechos lazos económicos, sociales y culturales entre las ciudades, villas y localidades rurales de las actuales Chile, Argentina y Bolivia. Así se echaron las bases para el desarrollo de la Denominación de Origen Pisco, delimitada en 1931. Palabras clave: aguardiente, rutas comerciales, vino, pisco, denominaciones de origen, industria vitivinícola.
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Papers by Diego Flores Cabrera