A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications

The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Data driven methods for updating fault detection and diagnosis system in chemical processes

Modern industrial processes are becoming more complex, and consequently monitoring them has become a challenging task. Fault Detection and Diagnosis (FDD) as a key element of process monitoring, needs to be investigated because of its essential role in decision making processes. Among available FDD methods, data driven approaches are currently receiving increasing attention because of their relative simplicity in implementation. Regardless of FDD types, one of the main traits of reliable FDD systems is their ability of being updated while new conditions that were not considered at their initial training appear in the process. These new conditions would emerge either gradually or abruptly, but they have the same level of importance as in both cases they lead to FDD poor performance. For addressing updating tasks, some methods have been proposed, but mainly not in research area of chemical engineering. They could be categorized to those that are dedicated to managing Concept Drift (CD) (that appear gradually), and those that deal with novel classes (that appear abruptly). The available methods, mainly, in addition to the lack of clear strategies for updating, suffer from performance weaknesses and inefficient required time of training, as reported. Accordingly, this thesis is mainly dedicated to data driven FDD updating in chemical processes. The proposed schemes for handling novel classes of faults are based on unsupervised methods, while for coping with CD both supervised and unsupervised updating frameworks have been investigated. Furthermore, for enhancing the functionality of FDD systems, some major methods of data processing, including imputation of missing values, feature selection, and feature extension have been investigated. The suggested algorithms and frameworks for FDD updating have been evaluated through different benchmarks and scenarios. As a part of the results, the suggested algorithms for supervised handling CD surpass the performance of the traditional incremental learning in regard to MGM score (defined dimensionless score based on weighted F1 score and training time) even up to 50% improvement. This improvement is achieved by proposed algorithms that detect and forget redundant information as well as properly adjusting the data window for timely updating and retraining the fault detection system. Moreover, the proposed unsupervised FDD updating framework for dealing with novel faults in static and dynamic process conditions achieves up to 90% in terms of the NPP score (defined dimensionless score based on number of the correct predicted class of samples). This result relies on an innovative framework that is able to assign samples either to new classes or to available classes by exploiting one class classification techniques and clustering approaches.Los procesos industriales modernos son cada vez más complejos y, en consecuencia, su control se ha convertido en una tarea desafiante. La detección y el diagnóstico de fallos (FDD), como un elemento clave de la supervisión del proceso, deben ser investigados debido a su papel esencial en los procesos de toma de decisiones. Entre los métodos disponibles de FDD, los enfoques basados en datos están recibiendo una atención creciente debido a su relativa simplicidad en la implementación. Independientemente de los tipos de FDD, una de las principales características de los sistemas FDD confiables es su capacidad de actualización, mientras que las nuevas condiciones que no fueron consideradas en su entrenamiento inicial, ahora aparecen en el proceso. Estas nuevas condiciones pueden surgir de forma gradual o abrupta, pero tienen el mismo nivel de importancia ya que en ambos casos conducen al bajo rendimiento de FDD. Para abordar las tareas de actualización, se han propuesto algunos métodos, pero no mayoritariamente en el área de investigación de la ingeniería química. Podrían ser categorizados en los que están dedicados a manejar Concept Drift (CD) (que aparecen gradualmente), y a los que tratan con clases nuevas (que aparecen abruptamente). Los métodos disponibles, además de la falta de estrategias claras para la actualización, sufren debilidades en su funcionamiento y de un tiempo de capacitación ineficiente, como se ha referenciado. En consecuencia, esta tesis está dedicada principalmente a la actualización de FDD impulsada por datos en procesos químicos. Los esquemas propuestos para manejar nuevas clases de fallos se basan en métodos no supervisados, mientras que para hacer frente a la CD se han investigado los marcos de actualización supervisados y no supervisados. Además, para mejorar la funcionalidad de los sistemas FDD, se han investigado algunos de los principales métodos de procesamiento de datos, incluida la imputación de valores perdidos, la selección de características y la extensión de características. Los algoritmos y marcos sugeridos para la actualización de FDD han sido evaluados a través de diferentes puntos de referencia y escenarios. Como parte de los resultados, los algoritmos sugeridos para el CD de manejo supervisado superan el rendimiento del aprendizaje incremental tradicional con respecto al puntaje MGM (puntuación adimensional definida basada en el puntaje F1 ponderado y el tiempo de entrenamiento) hasta en un 50% de mejora. Esta mejora se logra mediante los algoritmos propuestos que detectan y olvidan la información redundante, así como ajustan correctamente la ventana de datos para la actualización oportuna y el reciclaje del sistema de detección de fallas. Además, el marco de actualización FDD no supervisado propuesto para tratar fallas nuevas en condiciones de proceso estáticas y dinámicas logra hasta 90% en términos de la puntuación de NPP (puntuación adimensional definida basada en el número de la clase de muestras correcta predicha). Este resultado se basa en un marco innovador que puede asignar muestras a clases nuevas o a clases disponibles explotando una clase de técnicas de clasificación y enfoques de agrupamientoPostprint (published version