Characterizing the degradation process of Lithium-Ion Batteries using a Similarity-Based-Modeling Approach

This article proposes a Similarity-Based-Modeling (SBM) approach capable of characterizing the degradation process of a lithium-ion (Li-ion) battery when discharged under different current rates and different State-of-Charge (SOC) ranges. The degradation process can be represented through a biexponential model. In this regard, it is possible to determine the equivalent cycle-by-cycle efficiency which has low values at the beginning of the degradation process until it reaches a higher and steady value. The lifespan of the batteries is analyzed through the use of Monte Carlo simulations which intends to represent a more realistic way of how the batteries are used.This article proposes a Similarity-Based-Modeling (SBM) approach capable of characterizing the degradation process of a lithium-ion (Li-ion) battery when discharged under different current rates and different State-of-Charge (SOC) ranges. The degradation process can be represented through a biexponential model. In this regard, it is possible to determine the equivalent cycle-by-cycle efficiency which has low values at the beginning of the degradation process until it reaches a higher and steady value. The lifespan of the batteries is analyzed through the use of Monte Carlo simulations which intends to represent a more realistic way of how the batteries are used

Provenance-Centered Dataset of Drug-Drug Interactions

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.Comment: In Proceedings of the 14th International Semantic Web Conference (ISWC) 201

An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

Improving Sparsity in Kernel Adaptive Filters Using a Unit-Norm Dictionary

Kernel adaptive filters, a class of adaptive nonlinear time-series models, are known by their ability to learn expressive autoregressive patterns from sequential data. However, for trivial monotonic signals, they struggle to perform accurate predictions and at the same time keep computational complexity within desired boundaries. This is because new observations are incorporated to the dictionary when they are far from what the algorithm has seen in the past. We propose a novel approach to kernel adaptive filtering that compares new observations against dictionary samples in terms of their unit-norm (normalised) versions, meaning that new observations that look like previous samples but have a different magnitude are not added to the dictionary. We achieve this by proposing the unit-norm Gaussian kernel and define a sparsification criterion for this novel kernel. This new methodology is validated on two real-world datasets against standard KAF in terms of the normalised mean square error and the dictionary size.Comment: Accepted at the IEEE Digital Signal Processing conference 201

Similarity-based methods for machine diagnosis

This work presents a data-driven condition-based maintenance system based on similarity-based modeling (SBM) for automatic machinery fault diagnosis. The proposed system provides information about the equipment current state (degree of anomaly), and returns a set of exemplars that can be employed to describe the current state in a sparse fashion, which can be examined by the operator to assess a decision to be made. The system is modular and data-agnostic, enabling its use in different equipment and data sources with small modifications. The main contributions of this work are: the extensive study of the proposition and use of multiclass SBM on different databases, either as a stand-alone classification method or in combination with an off-the-shelf classifier; novel methods for selecting prototypes for the SBM models; the use of new similarity functions; and a new production-ready fault detection service. These contributions achieved the goal of increasing the SBM models performance in a fault classification scenario while reducing its computational complexity. The proposed system was evaluated in three different databases, achieving higher or similar performance when compared with previous works on the same database. Comparisons with other methods are shown for the recently developed Machinery Fault Database (MaFaulDa) and for the Case Western Reserve University (CWRU) bearing database. The proposed techniques increase the generalization power of the similarity model and of the associated classifier, having accuracies of 98.5% on MaFaulDa and 98.9% on CWRU database. These results indicate that the proposed approach based on SBM is worth further investigation.Este trabalho apresenta um sistema de manutenção preditiva para diagnóstico automático de falhas em máquinas. O sistema proposto, baseado em uma técnica denominada similarity-based modeling (SBM), provê informações sobre o estado atual do equipamento (grau de anomalia), e retorna um conjunto de amostras representativas que pode ser utilizado para descrever o estado atual de forma esparsa, permitindo a um operador avaliar a melhor decisão a ser tomada. O sistema é modular e agnóstico aos dados, permitindo que seja utilizado em variados equipamentos e dados com pequenas modificações. As principais contribuições deste trabalho são: o estudo abrangente da proposta do classificador SBM multi-classe e o seu uso em diferentes bases de dados, seja como um classificador ou auxiliando outros classificadores comumente usados; novos métodos para a seleção de amostras representativas para os modelos SBM; o uso de novas funções de similaridade; e um serviço de detecção de falhas pronto para ser utilizado em produção. Essas contribuições atingiram o objetivo de melhorar o desempenho dos modelos SBM em cenários de classificação de falhas e reduziram sua complexidade computacional. O sistema proposto foi avaliado em três bases de dados, atingindo desempenho igual ou superior ao desempenho de trabalhos anteriores nas mesmas bases. Comparações com outros métodos são apresentadas para a recém-desenvolvida Machinery Fault Database (MaFaulDa) e para a base de dados da Case Western Reserve University (CWRU). As técnicas propostas melhoraram a capacidade de generalização dos modelos de similaridade e do classificador final, atingindo acurácias de 98.5% na MaFaulDa e 98.9% na base de dados CWRU. Esses resultados apontam que a abordagem proposta baseada na técnica SBM tem potencial para ser investigada em mais profundidade

Real-time detection of uncalibrated sensors using Neural Networks

Nowadays, sensors play a major role in several contexts like science, industry and daily life which benefit of their use. However, the retrieved information must be reliable. Anomalies in the behavior of sensors can give rise to critical consequences such as ruining a scientific project or jeopardizing the quality of the production in industrial production lines. One of the more subtle kind of anomalies are uncalibrations. An uncalibration is said to take place when the sensor is not adjusted or standardized by calibration according to a ground truth value. In this work, an online machine-learning based uncalibration detector for temperature, humidity and pressure sensors was developed. This solution integrates an Artificial Neural Network as main component which learns from the behavior of the sensors under calibrated conditions. Then, after trained and deployed, it detects uncalibrations once they take place. The obtained results show that the proposed solution is able to detect uncalibrations for deviation values of 0.25 degrees, 1% RH and 1.5 Pa, respectively. This solution can be adapted to different contexts by means of transfer learning, whose application allows for the addition of new sensors, the deployment into new environments and the retraining of the model with minimum amounts of data

Применение метода малых отклонений для диагностирования технического состояния авиационного газотурбинного двигателя на переходных режимах его работы

The article deals with issues related to the use of parametric information of the transient-state gas turbine engines (GTE) operation conditions for diagnosing their technical condition during the operation. A review of general approaches to computational algorithms for the recognition and classification of the condition applicable to aircraft GTE has been carried out. The significance of analytical models in modern algorithms for assessing the technical GTE condition is emphasized. The construction of a linearized mathematical model for the transient-state condition of the generalized-scheme aircraft GTE operation has been considered. It represents a system of equations analytically combining the relative parameter divergences measured during the engine operation with the relative divergences of unmeasured thermogasdynamic parameters and geometric gas-air flow duct parameters allowing for the technical condition of gas-air channel elements to be classified. A method for constructing mathematical and diagnostic engine models, using the transient response data, has been formulated. The capability of employing a method of insignificant divergences, used to build linear (linearized) mathematical and diagnostic GTE models for the steady-state conditions of its operation, has been demonstrated as well. It is shown that, despite the structural similarity of linear models of the steady and transient-state processes, diagnostics by means of the stated above processes is based on completely different principles – under the steady-state condition, the classification of a technical condition is determined by the variation in the value of the group of controlled responses, and under the transient-state condition, this operation is based on correlating the change in the transient-state behavior. To ensure the versatility of employing proposed methods regarding various GTE designs installed on modern civil aircraft, a generalized-design aircraft GTE model – a three-shaft bypass turbojet engine with mixing flows in a common jet nozzle, has been considered.В статье рассмотрены вопросы, связанные с использованием параметрической информации переходных режимов работы газотурбинных двигателей (ГТД) для диагностирования их технического состояния в процессе эксплуатации. Проведен обзор общих подходов к вычислительным алгоритмам распознавания и классификации состояний применительно к авиационным ГТД. Показано место аналитических моделей в современных алгоритмах оценки технического состояния авиационных ГТД. Рассмотрено построение линеаризованной математической модели переходного режима работы авиационного ГТД обобщенной схемы – системы уравнений, аналитически связывающих относительные отклонения параметров, измеряемых в процессе работы двигателя, с относительными отклонениями неизмеряемых термогазодинамических параметров и геометрических параметров газовоздушного тракта, позволяющих классифицировать техническое состояние элементов проточной части газотурбинного двигателя. Сформулирован метод построения математической и диагностической моделей двигателя с использованием характеристик переходного процесса, а также показана возможность применения метода малых отклонений, используемого для построения линейных (линеаризованных) математических и диагностических моделей ГТД для стационарных режимов его работы. Показано, что, несмотря на структурное сходство линейных моделей установившегося и переходного процессов, диагностирование с их помощью базируется на совершенно разных принципах – на установившемся режиме классификация технического состояния определяется по изменению величины группы контролируемых откликов, а на переходном режиме эта операция основывается на сопоставлении изменения характера протекания переходного процесса. Для обеспечения универсальности применения предложенных методов к различным схемам ГТД, устанавливаемых на современных самолетах гражданской авиации, рассмотрена модель обобщенной схемы авиационного газотурбинного двигателя – трехвального двухконтурного турбореактивного двигателя со смешением потоков в общем реактивном сопле

The Impact of Computational Pharmacology

Pharmacology is the study of how drugs interact within the human body. The field covers a variety of topics such as pharmaceutical capabilities and interactions. Early studies in pharmacology focused on the effects of natural substances in the body as a means of therapeutic treatment. Modern pharmacology uses computation and modeling as research tools on a cellular level. Computational models are useful in almost every scientific and engineering discipline especially when practical and ethical considerations prevent experimenting with real systems. The models are needed to design various parts of the drug discovery process. Computer programs for designing compounds is one key area of computational pharmacology. Another area of interest are digital repositories for investigating chemical interactions. Modern pharmacology now involves using a computational method. This paper provides a brief introduction to computational pharmacology