Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today's Industry. A wide range of algorithms are being used to monitor industrial proces...
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Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today's Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance;thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.
The advent of Industry 4.0 introduced new ways for businesses to evolve by implementing maintenance policies leading to advancements in terms of productivity, efficiency, and financial performance. In line with the gr...
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The advent of Industry 4.0 introduced new ways for businesses to evolve by implementing maintenance policies leading to advancements in terms of productivity, efficiency, and financial performance. In line with the growing emphasis on sustainability, industries implement predictive techniques based on Artificial Intelligence for the purpose of mitigating machine and equipment failures by predicting anomalies during their production process. In this work, a new dataset that was made publicly available, collected from an industrial blower, is presented, analyzed and modeled using a Sequence-to-Sequence Stacked Sparse Long Short-Term Memory autoencoder. Specifically the right and left mounted ball bearing units were measured during several months of normal operational condition as well as during an encumbered operational state. An anomaly detection model was developed for the purpose of analyzing the operational behavior of the two bearing units. A stacked sparse Long Short-Term Memory autoencoder was successfully trained on the data obtained from the left unit under normal operating conditions, learning the underlying patterns and statistical connections of the data. The model was evaluated by means of the Mean Squared Error using data from the unit's encumbered state, as well as using data collected from the right unit. The model performed satisfactorily throughout its evaluation on all collected datasets. Also, the model proved its capability for generalization along with adaptability on assessing the behavior of equipment similar to the one it was trained on.
Dimension reduction is one of the key data transformation techniques in machine learning and knowledge discovery. It can be realized by using linear and nonlinear transformation techniques. An additive autoencoder for...
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Dimension reduction is one of the key data transformation techniques in machine learning and knowledge discovery. It can be realized by using linear and nonlinear transformation techniques. An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and analyzed. Compared to the classical model, adding an explicit linear operator to the overall transformation and considering the nonlinear residual estimation in the original data dimension significantly improves the data reproduction capabilities of the proposed model. The computational experiments confirm that an autoencoder of this form, with only a shallow network to encapsulate the nonlinear behavior, is able to identify an intrinsic dimension of a dataset with low autoencoding error. This observation leads to an investigation in which shallow and deep network structures, and how they are trained, are compared. We conclude that the deeper network structures obtain lower autoencoding errors during the identification of the intrinsic dimension. However, the detected dimension does not change compared to a shallow network. As far as we know, this is the first experimental result concluding no benefit from a deep architecture compared to its shallow counterpart.& COPY;2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
In wind engineering, to accurately estimate the nonlinear dynamic response of structures while considering uncertainties of hurricanes, a suite of wind records representing the hurricane hazards of a given location is...
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In wind engineering, to accurately estimate the nonlinear dynamic response of structures while considering uncertainties of hurricanes, a suite of wind records representing the hurricane hazards of a given location is of great interest. Such a suite generally consists of a large number of hurricane wind records, which may lead to highly computational cost for structural analysis. To reduce the computational demand while still preserving the accuracy of the uncertainty quantification process, this paper proposes a machine learning approach to select a representative subset of all collected hurricane wind records for a location. First, hurricane wind records, which are expressed as time series with information that includes both wind speed and direction, are collected from a synthetic hurricane catalog. The high dimensional hurricane wind records are then compressed into a set of low dimensional latent feature vectors using an artificial neural network, designated as an autoencoder. The latent feature vectors represent the important patterns of wind records such as duration, magnitude, and the changing of wind speeds and directions over time. The wind records are then clustered by applying the k-means algorithm on the latent features, and a subset of records is selected from each cluster. The wind records selected from each cluster are those whose latent feature points are closest to the centroid of all latent feature points in that cluster. In order to do regional analysis while taking into account that the hurricane wind records are site-specific, this paper suggests that a region can be discretized into a set of grids, with the proposed hurricane selection approach applied to each grid. This procedure is demonstrated using Massachusetts as a testbed.
Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the P...
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Partial differential equation (PDE)-constrained inverse problems are some of the most challenging and computationally demanding problems in computational science today. Fine meshes required to accurately compute the PDE solution introduce an enormous number of parameters and require large-scale computing resources such as more processors and more memory to solve such systems in a reasonable time. For inverse problems constrained by time-dependent PDEs, the adjoint method often employed to compute gradients and higher order derivatives efficiently requires solving a time-reversed, so-called adjoint PDE that depends on the forward PDE solution at each timestep. This necessitates the storage of a high-dimensional forward solution vector at every timestep. Such a procedure quickly exhausts the available memory resources. Several approaches that trade additional computation for reduced memory footprint have been proposed to mitigate the memory bottleneck, including checkpointing and compression strategies. In this work, we propose a close-to-ideal scalable compression approach using autoencoders to eliminate the need for checkpointing and substantial memory storage, thereby reducing the time-to-solution and memory requirements. We compare our approach with checkpointing and an off-the-shelf compression approach on an earth-scale ill-posed seismic inverse problem. The results verify the expected close-to-ideal speedup for the gradient and Hessian-vector product using the proposed autoencoder compression approach. To highlight the usefulness of the proposed approach, we combine the autoencoder compression with the data-informed active subspace (DIAS) prior showing how the DIAS method can be affordably extended to large-scale problems without the need for checkpointing and large memory.
When anomaly detection is applied to a product inspection process, it is often the case that the system initially has no anomaly data, but acquires anomaly data during operation. In such cases, an unsupervised learnin...
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When anomaly detection is applied to a product inspection process, it is often the case that the system initially has no anomaly data, but acquires anomaly data during operation. In such cases, an unsupervised learning-based anomaly detection model (e.g. autoencoders) using only normal data may be applied in the initial phase of operation, and a supervised learning-based anomaly detection model with additional anomaly data may be applied once anomaly data have been acquired during operation. Although supervised learning-based anomaly detection models are an extension of autoencoders to supervised learning, when they are used to update an unsupervised learning-based anomaly detection model to a supervised learning-based anomaly detection model, the problem of inconsistency in the anomaly scores before and after the model update arises. In this paper, we propose a new method called autoencoder with adaptive loss function (AEAL) to improve the detection accuracy of known anomalies while ensuring consistent anomaly scores before and after model updates. AEAL is an autoencoder-based method for learning anomaly detection models by dynamically adjusting the balance between minimizing reconstruction errors for anomaly data and maximizing reconstruction errors for anomaly data. Experimental results on multiple public image datasets show that AEAL has the effect of improving the detection accuracy of known anomalies while ensuring that anomaly scores are consistent with the anomaly detection model prior to the update.
Deep learning (DL), that is becoming quite popular for prediction and analysis of complex patterns in large amounts of data is used to investigate the safety behaviour of the nuclear plant items. This is achieved by u...
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Deep learning (DL), that is becoming quite popular for prediction and analysis of complex patterns in large amounts of data is used to investigate the safety behaviour of the nuclear plant items. This is achieved by using multiple layers of artificial neural networks to process and transform input data, allowing for the creation of highly accurate predictive models. Particularly to the aim the unsupervised machine learning approach and the digital twin concept in form of pressurized water reactor 2-loop simulator are used. This innovative methodology is based on neural network algorithm that makes capable to predict failures of plant structure, system, and components earlier than the activation of safety and emergency systems. Moreover, to match the objective of the study several scenarios of loss of cooling accident (LOCA) of different break size were simulated. To make the acquisition platform realistic, Gaussian noise was added to the input signals. The neural network has been fed by synthetic dataset provide by PCTRAN simulator and the efficiency in event identification was studied. Further, due to the very limited studies on the unsupervised anomaly detection by means of autoencoder neural networks applied for plant monitoring and surveillance, the methodology has been validated with experimental data from resonant test rig designed for fatigue testing of tubular components. The obtained results demonstrate the reliability and the efficiency of the methodology in detecting anomalous events prior the activation of safety system. Particularly, if the difference between the expected readings and the collected data goes beyond the predetermined threshold, then the anomalous event is identified, e.g., the model detected anomalies up to 38 min before the reactor scram intervention.
Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of ...
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Long non-coding RNAs (lncRNAs) play important roles by regulating proteins in many biological processes and life activities. To uncover molecular mechanisms of lncRNA, it is very necessary to identify interactions of lncRNA with proteins. Recently, some machine learning methods were proposed to detect lncRNA-protein interactions according to the distribution of known interactions. The performances of these methods were largely dependent upon: (1) how exactly the distribution of known interactions was characterized by feature space;(2) how discriminative the feature space was for distinguishing lncRNA-protein interactions. Because the known interactions may be multiple and complex model, it remains a challenge to construct discriminative feature space for lncRNA-protein *** resolve this problem, a novel method named DFRPI was developed based on deep autoencoder and marginal fisher analysis in this paper. Firstly, some initial features of lncRNA-protein interactions were extracted from the primary sequences and secondary structures of lncRNA and protein. Secondly, a deep autoencoder was exploited to learn encode parameters of the initial features to describe the known interactions precisely. Next, the marginal fisher analysis was employed to optimize the encode parameters of features to characterize a discriminative feature space of the lncRNA-protein interactions. Finally, a random forest-based predictor was trained on the discriminative feature space to detect lncRNA-protein interactions. Verified by a series of experiments, the results showed that our predictor achieved the precision of 0.920, recall of 0.916, accuracy of 0.918, MCC of 0.836, specificity of 0.920, sensitivity of 0.916 and AUC of 0.906 respectively, which outperforms the concerned methods for predicting lncRNA-protein interaction. It may be suggested that the proposed method can generate a reasonable and effective feature space for distinguishing lncRNA-protein interactions accurately. T
Unsupervised Learning is widely used approach for outlier detection because non-availability of training dataset in various domains (especially, in evolving domains). Approaches like clustering-based, distance-based, ...
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Unsupervised Learning is widely used approach for outlier detection because non-availability of training dataset in various domains (especially, in evolving domains). Approaches like clustering-based, distance-based, density-based outlier detection methods have been proposed over the last several years. Recently, outlier detection using deep learning has drawn attention of researchers. Deep learning-based unsupervised techniques (autoencoder) minimize the reconstruction error using each data instance in the dataset and subsequently, data points with higher reconstruction error are treated as outlier points. However, autoencoder based model overestimates the reconstruction error for normal points whereas it is underestimated for outlier points. As a result, genuine outliers are missed by this approach. We propose two techniques to address the issue of reconstruction error stated earlier. Main idea of our techniques is to compute reconstruction error only using 'normal points'. In the proposed techniques, we identify probable outliers utilizing the clustering approaches intelligently and subsequently, we do not include them in the minimization process of reconstruction error. We exploit recently recognized clustering approach Density Peak Clustering (DPC) to identify the probable outlier points based on density and distance to the higher density points. However, DPC has inherent drawback of setting threshold which plays important role in deciding density. Therefore, Self Organizing Map (SOM) is exploited as another clustering approach in this article. Subsequently, we conducted experiments on synthetic as well as real world datasets and the results show that the proposed technique outperforms the popular existing deep learning model like RandNet, Boosting-based autoencoder Ensemble method (BAE), One Class Support Vector Machine (OCSVM) and density-based algorithms like LOF, LDOF, INFLO, and RDOS.
Ultraviolet Visible (UV-Vis) spectroscopy detection technology has been widely used in quantitative analysis for its advantages of rapid and non-destructive determination. However, the difference of optical hardware s...
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Ultraviolet Visible (UV-Vis) spectroscopy detection technology has been widely used in quantitative analysis for its advantages of rapid and non-destructive determination. However, the difference of optical hardware severely restricts the development of spectral technology. Model transfer is one of the effective methods to establish models on different instruments. Due to the high dimension and nonlinearity of spectral data, the existing methods cannot effectively extract the hidden differences in spectra of different spectrometers. Thus, based on the necessity of spectral calibration model transfer between the traditional large spectrometer and the micro-spectrometer, a novel model transfer method based on improved deep autoencoder is proposed to realize spectral reconstruction between different spectrometers. Firstly, two autoencoders are used to train the spectral data of the master and slave instrument, respectively. Then, the hidden variable constraint is added to enhance the feature representation of the autoencoder, which makes the two hidden variables equal. Combined with a Bayesian optimization algorithm for the objective function, the transfer accuracy coefficient is proposed to characterize the model transfer performance. The experimental results show that after model transfer, the spectrum of the slave spectrometer is basically coincident with the master spectrometer and the wavelength shift is eliminated. Compared with the two commonly used direct standardization (DS) and piecewise direct standardization (PDS) algorithms, the average transfer accuracy coefficient of the proposed method is improved by 45.11% and 22.38%, respectively, when there are nonlinear differences between different spectrometers.
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