Restricted boltzmann machine based autoencoders for the classification of faults in rotational mechanical systems

Contexto O objetivo deste trabalho foi investigar a possibilidade de usar Redes Neuronais Profundas (DNN) para classificar falhas (na forma de sinais anómalos representando vibrações) em rolamentos de esferas, e comparar os resultados com os de algoritmos tradicionais. Métodos O conjunto de dados usado neste trabalho foi recolhido de sensores colocados nos rolamentos de esferas de um eixo conectado a um motor, sendo insuficiente para treinar eficientemente uma DNN. Para superar esse problema, o conjunto de dados foi aumentado, primeiro separandoo em conjuntos disjuntos para minimizar a contaminação dos conjuntos de treino e teste, e em seguida um método de janela deslizante foi aplicado a esses conjuntos para gerar novos conjuntos de treino e teste. Dois modelos de DNN baseados em Máquinas de Boltzmann Restrita (RBM) foram usados neste trabalho, com duas variantes para cada um desses modelos, usando números nítidos ou fuzzy para os parâmetros. Para quantificar os resultados, foram utilizadas as métricas Exatidão e área sob a curva (AUC). Os resultados foram analisados em conjunto com algoritmos tradicionais comuns, usados para estabelecer uma base de comparação. Resultados Os resultados obtidos com assim DNN foram promissores, com o melhor modelo atingindo uma exatidão média de 99.678%. Ainda assim, os resultados obtidos com os algoritmos tradicionais foram melhores, com o melhor modelo alcançando uma exatidão média de 99.98%. Para a outra métrica utilizada para analisar os resultados (AUC), o cenário é semelhante, com o melhor modelo de DNN a alcançar uma AUC média de 0.99995, e o melhor modelo tradicional uma pontuação perfeita de 1. Conclusão Os resultados obtidos revelam que, embora as DNN possam ser eficazes em problemas de classificação, elas não são a melhor escolha para problemas como este (conjuntos de dados constituídos por dados numéricos tabulares). Isso deve-se ao desenvolvimento, afinação e volume de dados de treino necessários em comparação com os algoritmos tradicionais, que além de obter resultados ligeiramente melhores, o fizeram com tempos de treino significativamente menores e quase sem afinação. Para outras aplicações, se houver dados de treino suficientes, as DNN frequentemente demonstram melhor desempenho, uma vez que quando os algoritmos tradicionais atingem um limite no desempenho, as DNN podem continuar a melhorar se tiverem dados de treino adicionais.Background The aim of this work is to investigate the possibility of using Deep Neural Networks (DNN) to classify faults (in the form of anomalous signals representing vibrations) in ball bearings, and to compare the results with those of traditional algorithms. Methods The dataset used in this work was collected from sensors placed on the ball bearings of a shaft connected to a motor and was too small to effectively train a DNN. To overcome this problem, the dataset was augmented by first separating it into disjoint sets to minimize train-test contamination, then a sliding window method was applied to these sets to generate new training and test sets. Two Restricted Boltzmann Machine (RBM) based DNN models were used in this work, and for each of these models two variants, using either crisp or fuzzy numbers for the parameters. To quantify the results, the metrics Accuracy and area under the curve (AUC) were used. The results were analyzed together with those of common traditional algorithms, used to establish a base for comparison. Results The results obtained with the DNN were promising, the best model achieving a mean accuracy of 99.678%. Yet, the results obtained with the traditional algorithms were even better, with the best model achieving a mean accuracy of 99.98%. For the other metric used to analyze the results (AUC), the scenario is similar, with the best DNN model achieving a mean AUC of 0.99995, and the best traditional model a perfect score of 1. Conclusion The results obtained reveal that while DNN can be effective at classification problems, they are not the best choice for problems such as this (datasets consisting of tabular numeric data). This is due to the development, tuning, and volume of training data required compared to the traditional algorithms, which not only obtained slightly better results, but did so with significantly lower training times, and almost no tuning. For other applications, if there is enough training data, DNN routinely show better performance, since when traditional algorithms hit a plateau in performance, DNN can continue to improve if they are provided with additional training data

Continuous Restricted Boltzmann Machines

Restricted Boltzmann machines are a generative neural network. They summarize their input data to build a probabilistic model that can then be used to reconstruct missing data or to classify new data. Unlike discrete Boltzmann machines, where the data are mapped to the space of integers or bitstrings, continuous Boltzmann machines directly use floating point numbers and therefore represent the data with higher fidelity. The primary limitation in using Boltzmann machines for big-data problems is the efficiency of the training algorithm. This paper describes an efficient deterministic algorithm for training continuous machines

High Performance Attack Estimation in Large-Scale Network Flows

Network based attacks are the major threat to security on the Internet. The volume of traffic and the high variability of the attacks place threat detection squarely in the domain of big data. Conventional approaches are mostly based on signatures. While these are relatively inexpensive computationally, they are inflexible and insensitive to small variations in the attack vector. Therefore we explored the use of machine learning techniques on real flow data. We found that benign traffic could be identified with high accuracy

Detection and Prediction of Distributed Denial of Service Attacks using Deep Learning

Distributed denial of service attacks threaten the security and health of the Internet. These attacks continue to grow in scale and potency. Remediation relies on up-to-date and accurate attack signatures. Signature-based detection is relatively inexpensive computationally. Yet, signatures are inflexible when small variations exist in the attack vector. Attackers exploit this rigidity by altering their attacks to bypass the signatures. The constant need to stay one step ahead of attackers using signatures demonstrates a clear need for better methods of detecting DDoS attacks. In this research, we examine the application of machine learning models to real network data for the purpose of classifying attacks. During training, the models build a representation of their input data. This eliminates any reliance on attack signatures and allows for accurate classification of attacks even when they are slightly modified to evade detection. In the course of our research, we found a significant problem when applying conventional machine learning models. Network traffic, whether benign or malicious, is temporal in nature. This results in differences in its characteristics between any significant time span. These differences cause conventional models to fail at classifying the traffic. We then turned to deep learning models. We obtained a significant improvement in performance, regardless of time span. In this research, we also introduce a new method of transforming traffic data into spectrogram images. This technique provides a way to better distinguish different types of traffic. Finally, we introduce a framework for embedding attack detection in real-world applications

DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System

A key challenge in eXplainable Artificial Intelligence is the well-known tradeoff between the transparency of an algorithm (i.e., how easily a human can directly understand the algorithm, as opposed to receiving a post-hoc explanation), and its accuracy. We report on the design of a new deep network that achieves improved transparency without sacrificing accuracy. We design a deep convolutional neuro-fuzzy inference system (DCNFIS) by hybridizing fuzzy logic and deep learning models and show that DCNFIS performs as accurately as three existing convolutional neural networks on four well-known datasets. We furthermore that DCNFIS outperforms state-of-the-art deep fuzzy systems. We then exploit the transparency of fuzzy logic by deriving explanations, in the form of saliency maps, from the fuzzy rules encoded in DCNFIS. We investigate the properties of these explanations in greater depth using the Fashion-MNIST dataset

Bioinformatics Techniques for Studying Drug Resistance In HIV and Staphylococcus Aureus

The worldwide HIV/AIDS pandemic has been partly controlled and treated by antivirals targeting HIV protease, integrase and reverse transcriptase, however, drug resistance has become a serious problem. HIV-1 drug resistance to protease inhibitors evolves by mutations in the PR gene. The resistance mutations can alter protease catalytic activity, inhibitor binding, and stability. Different machine learning algorithms (restricted boltzmann machines, clustering, etc.) have been shown to be effective machine learning tools for classification of genomic and resistance data. Application of restricted boltzmann machine produced highly accurate and robust classification of HIV protease resistance. They can also be used to compare resistance profiles of different protease inhibitors. HIV drug resistance has also been studied by enzyme kinetics and X-ray crystallography. Triple mutant HIV-1 protease with resistance mutations V32I, I47V and V82I has been used as a model for the active site of HIV-2 protease. The effects of four investigational antiviral inhibitors was measured for Triple mutant. The tested compounds had significantly worse inhibition of triple mutant with Ki values of 17-40 nM compared to 2-10 pM for wild type protease. The crystal structure of triple mutant in complex with GRL01111 was solved and showed few changes in protease interactions with inhibitor. These new inhibitors are not expected to be effective for HIV-2 protease or HIV-1 protease with changes V32I, I47V and V82I. Methicillin-resistant Staphylococcus aureus (MRSA) is an opportunistic pathogen that causes hospital and community-acquired infections. Antibiotic resistance occurs because of newly acquired low-affinity penicillin-binding protein (PBP2a). Transcriptome analysis was performed to determine how MuM (mutated PBP2 gene) responds to spermine and how Mu50 (wild type) responds to spermine and spermine–β-lactam synergy. Exogenous spermine and oxacillin were found to alter some significant gene expression patterns with major biochemical pathways (iron, sigB regulon) in MRSA with mutant PBP2 protein