Machine Learning-based Predictive Maintenance for Optical Networks

Optical networks provide the backbone of modern telecommunications by connecting the world faster than ever before. However, such networks are susceptible to several failures (e.g., optical fiber cuts, malfunctioning optical devices), which might result in degradation in the network operation, massive data loss, and network disruption. It is challenging to accurately and quickly detect and localize such failures due to the complexity of such networks, the time required to identify the fault and pinpoint it using conventional approaches, and the lack of proactive efficient fault management mechanisms. Therefore, it is highly beneficial to perform fault management in optical communication systems in order to reduce the mean time to repair, to meet service level agreements more easily, and to enhance the network reliability. In this thesis, the aforementioned challenges and needs are tackled by investigating the use of machine learning (ML) techniques for implementing efficient proactive fault detection, diagnosis, and localization schemes for optical communication systems. In particular, the adoption of ML methods for solving the following problems is explored: - Degradation prediction of semiconductor lasers, - Lifetime (mean time to failure) prediction of semiconductor lasers, - Remaining useful life (the length of time a machine is likely to operate before it requires repair or replacement) prediction of semiconductor lasers, - Optical fiber fault detection, localization, characterization, and identification for different optical network architectures, - Anomaly detection in optical fiber monitoring. Such ML approaches outperform the conventionally employed methods for all the investigated use cases by achieving better prediction accuracy and earlier prediction or detection capability

Next generation >200 Gb/s multicore fiber short-reach networks employing machine learning

This work proposes and evaluates the use of machine learning (ML) techniques on >200 Gb/s short-reach systems employing weakly coupled multicore fiber (MCF) and Kramers-Kronig (KK) receivers. The short-reach systems commonly found in intra data centers (DC) connections demand low cost-efficient direct detection receivers (DD). The KK receivers allow the combination of higher modulation order, such as 16-QAM used in coherent systems, with the low complexity and low cost of DD. Thus, the use of KK receivers allows to increase the bit rate and spectral efficiency while maintaining the cost of DD systems as this is an important requirement in DC. The use of MCF allows to increase the system capacity as well as the system cable density, although the use of MCF induces additional distortion, known as inter-core crosstalk (ICXT), to the system. Thus, low complexity ML techniques such as k-means clustering, k nearest neighbor (KNN) and artificial neural network (ANN) (estimation feedforward neural network (FNN) and classification feedforward neural network) are proposed to mitigate the effects of random ICXT. The performance improvement provided by the k-means clustering, KNN and the two types of FNN techniques is assessed and compared with the system performance obtained without the use of ML. The use of estimation and classification FNN prove to significantly improve the system performance by mitigating the impact of the ICXT on the received signal. This is achieved by employing only 10 neurons in the hidden layer and four input features. It has been shown that k-means or KNN techniques do not provide performance improvement compared to the system without using ML. These conclusions are valid for direct detection MCF-based short-reach systems with the product between the skew (relative time delay between cores) and the symbol rate much lower than one (skew x symbol rate « 1). By employing the proposed ANNs, the system shows an improvement of approximately 12 dB on the ICXT level, for the same outage probability when comparing with the system without the use of ML. For the BER threshold of 10−1.8 and compared with the standard system operating without employing ML techniques, the system operating with the proposed ANNs show a received optical power improvement of almost 3 dB and a ICXT level improvement of approximately 9 dB when the mean BER is analized.Este trabalho propõe e avalia o uso de técnicas de machine learning (ML) em sistemas de curto alcance com ritmo binário superior a 200 Gb/s utilizando receptores Kramers-Kronig (KK) e fibras multinúcleo (MCF). Os sistemas de curto alcance usualmente encontrados em conexões intra-data centers (DC) exigem receptores de deteção direta (DD) de baixo custo. Os receptores KK permitem a combinação de sistemas de modulação de maior ordem, tais como o 16-QAM, usado em sistemas coerentes, com o baixo custo dos receptores DD. Portanto, o uso de receptores KK permite melhorar o ritmo binário e eficiência espectral e manter a eficiência de custo dos sistemas DD, o que é importante em DC. O uso de fibras multinúcleo permite o aumento da capacidade do sistema, bem como a densidade de cabos. No entanto, o uso de MCF introduz uma distorção adicional no sistema conhecida como inter-core crosstalk (ICXT). Para mitigar os efeitos do ICXT aleatório, são propostas e avaliadas técnicas de ML de baixa complexidade como k-means clustering, k nearest neighbor (KNN) e rede neuronais artificiais (ANN). O desempenho associado à utilização de algoritmos de ML (k-means, KNN e duas redes neuronais do tipo feedforward (FNN): uma para estimação e outra para classificação), é avaliado e comparado com o desempenho do sistema obtido sem o uso de ML. A utilização de FNN para estimação e classificação conduziram a uma melhoria significativa no desempenho do sistema, mitigando o impacto do ICXT no sinal recebido. Isso é alcançado com o uso de uma rede neuronal com uma arquitetura muito simples contendo quatro entradas e 10 neurónios na camada escondida. Foi demonstrado que os algoritmos k-means e KNN não proporcionam melhoria de desempenho em comparação com o sistema sem o uso de ML. Essas conclusões são válidas para sistemas DD de curto alcance baseados em MCF com o produto entre o skew (atraso relativo entre os núcleos) e o ritmo de símbolos muito menor que um (skew x symbol rate « 1). Com o uso das ANNs, o sistema apresenta uma melhoria de aproximadamente 12 dB na probabilidade de indisponibilidade quando comparado com o sistema sem o uso de ML. Para o limite de BER de 10−1.8 , e comparado com o sistema padrão sem o uso de técnicas de ML, o sistema com o uso de ANN mostra uma melhoria na potência óptica recebida de quase 3 dB e uma melhoria no nível de ICXT de aproximadamente 9 dB em relação ao BER médio

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs

Optical fibre communication over a noisy partially coherent channel

As global IP traffic grows unceasingly, optical networks demand for technology upgrades in order to keep the feared “capacity crunch” away. The most celebrated technologies of coherent detection and wavelength-division multiplexing (WDM), widely deployed in long-haul links, are gaining ground in access networks, which is particularly challenging due to the shared-cost requirements, leading to denser channel spacings and the use of cheaper devices that tend to be noisier. In order to make the most of this technology combination, it is crucial to have a model of the channel that accurately describes all the present sources of noise. Traditionally, the most used model has been the additive white Gaussian noise (AWGN) channel, which, although only accounting for a linear contribution of complex noise and being insensitive to rotational phenomena, has shown its validity in numerous studies, as well as in commercial equipment. In this thesis, however, it is observed that the adoption of coherent detection and WDM, with lower-grade semiconductor lasers showing a moderate linewidth, yields scenarios where a phase-sensitive model becomes a must. The partially coherent AWGN (PCAWGN) channel is a popular choice that fulfils this need, but its high complexity due to non-trivial functions involved, deprives it from being suitable in high-speed digital circuits. The main goal of this thesis is to describe a reduced-complexity approximation in polar coordinates, accurate enough to find its applicability in modern systems. Furthermore, this works explores some possible end-to-end applications, like channel capacity estimation or symbol detection, assessing its performance by means of extensive simulations. Lastly, the emerging field of complex modulation of directly modulated lasers is revisited, with a special interest in how the proposed approximation can help to improve the performance of previously reported techniques, as well as proposing a new way to design spiral-shaped constellations aimed to maximise the channel capacity

Aerospace Medicine and biology: A continuing bibliography with indexes, supplement 123, January 1974

This special bibliography lists 226 reports, articles, and other documents introduced into the NASA scientific and technical information system in Dec. 1973

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

NASA Tech Briefs, November 1994

Topics: Advanced Manufacturing; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports