In this research, an algorithm is presented for predicting the remaining useful life (RUL) of aircraft engines from a set of predictor variables measured by several sensors located in the engine. RUL ...prediction is essential for the safety of those aboard, but also to reduce engine maintenance and repair costs. The algorithm combines time series analysis methods to forecast the values of the predictor variables with machine learning techniques to predict RUL from those variables. First, an auto-regressive integrated moving average (ARIMA) model is used to estimate the values of the predictor variables in advance. Then, we use the result of the previous step as the input of a support vector regression model (SVM), where RUL is the response variable. The validity of the method was checked on an extensive public database, and the results compared with those obtained using a vector auto-regressive moving average (VARMA) model. Our algorithm showed a high prediction capability, far greater than that provided by the VARMA model.
•A method to forecast the remaining useful life of aircraft engines is proposed.•The predictor variables were obtained from sensors located in the engine.•The proposed method combines ARIMA and SVM models.•Results of our method unsurpassed those obtained using a VARMA model.
Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of ...the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.
Many of the next generation of adaptive optics systems on large and extremely large telescopes require tomographic techniques in order to correct for atmospheric turbulence over a large field of ...view. Multi-object adaptive optics is one such technique. In this paper, different implementations of a tomographic reconstructor based on a machine learning architecture named "CARMEN" are presented. Basic concepts of adaptive optics are introduced first, with a short explanation of three different control systems used on real telescopes and the sensors utilised. The operation of the reconstructor, along with the three neural network frameworks used, and the developed CUDA code are detailed. Changes to the size of the reconstructor influence the training and execution time of the neural network. The native CUDA code turns out to be the best choice for all the systems, although some of the other frameworks offer good performance under certain circumstances.
ABSTRACT
Latency in the control loop of adaptive optics (AO) systems can severely limit performance. Under the frozen flow hypothesis linear predictive control techniques can overcome this; however, ...identification and tracking of relevant turbulent parameters (such as wind speeds) is required for such parametric techniques. This can complicate practical implementations and introduce stability issues when encountering variable conditions. Here, we present a non-linear wavefront predictor using a long short-term memory (LSTM) artificial neural network (ANN) that assumes no prior knowledge of the atmosphere and thus requires no user input. The ANN is designed to predict the open-loop wavefront slope measurements of a Shack–Hartmann wavefront sensor (SH-WFS) one frame in advance to compensate for a single-frame delay in a simulated 7 × 7 single-conjugate adaptive optics system operating at 150 Hz. We describe how the training regime of the LSTM ANN affects prediction performance and show how the performance of the predictor varies under various guide star magnitudes. We show that the prediction remains stable when both wind speed and direction are varying. We then extend our approach to a more realistic two-frame latency system. AO system performance when using the LSTM predictor is enhanced for all simulated conditions with prediction errors within 19.9–40.0 nm RMS of a latency-free system operating under the same conditions compared to a bandwidth error of 78.3 ± 4.4 nm RMS.
This paper presents a new fault detection system in hypnotic sensors used for general anesthesia during surgery. Drug infusion during surgery is based on information received from patient monitoring ...devices; accordingly, faults in sensor devices can put patient safety at risk. Our research offers a solution to cope with these undesirable scenarios. We focus on the anesthesia process using intravenous propofol as the hypnotic drug and employing a Bispectral Index (BIS
) monitor to estimate the patient's unconsciousness level. The method developed identifies BIS episodes affected by disturbances during surgery with null clinical value. Thus, the clinician-or the automatic controller-will not take those measures into account to calculate the drug dose. Our method compares the measured BIS signal with expected behavior predicted by the propofol dose provider and the electromyogram (EMG) signal. For the prediction of the BIS signal, a model based on a hybrid intelligent system architecture has been created. The model uses clustering combined with regression techniques. To validate its accuracy, a dataset taken during surgeries with general anesthesia was used. The proposed fault detection method for BIS sensor measures has also been verified using data from real cases. The obtained results prove the method's effectiveness.
Adaptive optics reconstructors are needed to remove the effects of atmospheric distortion in optical systems of large telescopes. The use of reconstructors based on neural networks has been proved ...successful in recent times. Some of their properties require a specific characterization. A procedure, based in time series clustering algorithms, is presented to characterize the relationship between temporal structure of inputs and outputs, through analyzing the data provided by the system. This procedure is used to compare the performance of a reconstructor based in Artificial Neural Networks, with one that shows promising results, but is still in development, in order to corroborate its suitability previously to its implementation in real applications. Also, this procedure could be applied with other physical systems that also have evolution in time.
► A major fallacy of some of the prior research on bankruptcy prediction is the manner in which the sample is drawn and the model accuracy is defined. In these studies, each one of the bankrupt ...companies is matched with a known non-bankrupt company from the same time period. Then, a model that predicts better than 50% (the assumed rate of chance) is thought to outperform random guessing. ► We considered a real setting. That is, we used a database made up of the annual accounts of 59,474 Spanish firms, 138 of them bankrupt. Therefore only a 0.232% of the companies went bankrupt. ► The present research presents a hybrid approach using fuzzy c-means clustering and Multivariate Adaptive Regression Splines (MARS). In a first stage, clusters are created using fuzzy c-means. The clusters are classified into two groups: those that contain bankrupted companies and those that not. Then, a MARS model is created using such clusters as a part of the input information. ► The performance of the proposed model is better than those obtained with the following benchmark techniques: MARS, discriminant analysis and neural networks.
During the last years, hybrid models have proven to be a promising approach for the design of classification systems for the forecasting of bankruptcy. In the present research we propose a hybrid system which combines fuzzy clustering and MARS. Both models are especially suitable for the bankruptcy prediction problem, due to their theoretical advantages when the information used for the forecasting is drawn from company financial statements. We test the accuracy of our approach in a real setting consisting of a database made up of 59,336 non-bankrupt Spanish companies and 138 distressed firms which went bankrupt during 2007. As benchmarking techniques we used discriminant analysis, MARS and a feed-forward neural network. Our results show that the hybrid model outperforms the other systems, both in terms of the percentage of correct classifications and in terms of the profit generated by the lending decisions.
This article proposes a new missing data imputation method based on genetic algorithms. The algorithm presented in this paper is a useful tool for the completion of missing data in knowledge and ...skills tests. This algorithm uses both Bayesian and Akaike’s information criterions as fitness functions and applies them to the classical item response theory models of one, two and three parameters. The results obtained by this new algorithm have been compared with those achieved by means of the Multivariate Imputation by Chained Equations (MICE) algorithm. For all the missing data ratios checked, the average incorrect imputation percentages obtained with the GA algorithm were, statistically, significantly lower than the results obtained with the MICE method. The most favorable frameworks for the use of the algorithm developed in the present research are those questionnaires in which missing answers would be considered as missing completely at random (MCAR). In other words, those questionnaires in which the same questions are present for all the examinees, but not necessarily in the same order.
•A genetic algorithm for missing data imputation is proposed.•The algorithm is tested in the context of the item response theory.•Optimum parameters of the algorithm are analyzed.•The proposed algorithm performs better than MICE algorithm.
ABSTRACT
We present 24 new dense light curves of the near-Earth asteroids (3103) Eger, (161989) Cacus, (2100) Ra-Shalom, and (12711) Tukmit, obtained with the Instituto Astrofísico Canarias 80 and ...Telescopio Abierto Remoto 2 telescopes at the Teide Observatory (Tenerife, Spain) during 2021 and 2022, in the framework of projects visible NEAs observations survey and NEO Rapid Observation, Characterization and Key Simulations. The shape models and rotation state parameters (P, λ, β) were computed by applying the light curve inversion method to the new data altogether with the archival data. For (3013) Eger and (161989) Cacus, our shape models and rotation state parameters agree with previous works, though they have smaller uncertainties. For (2100) Ra-Shalom, our results also agree with previous studies. Still, we find that a Yarkovsky–O’Keefe–Radzievskii–Paddack acceleration of υ = (0.223 ± 0.237) × 10−8 rad d−2 slightly improves the fit of the light curves, suggesting that (2100) Ra-Shalom could be affected by this acceleration. We also present for the first time a shape model for (12711) Tukmit, along with its rotation state parameters (P = 3.484900 ± 0.000031 h, λ = 27° ± 8°, β = 9° ± 15°).
Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a ...data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.
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