Deep learning for predictive maintenance of rolling bearings

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2020, Tutor: Jordi Vitrià i Marca[en] The monitoring of machine health has become of great importance in the industry in the recent years. Unexpected equipment failures can lead to catastrophic consequences, such as production downtime and costly equipment replacement. Rolling bearings are one of the most delicate components of rotating equipment, being a common cause of machine failures. For this reason, predictive maintenance techniques of rolling bearings are fundamental to preserve the health of a machine. In this project, we present a deep learning approach to predict bearing failures in their early development. All methodologies are data-driven, therefore they do not assume any expert knowledge on the field nor require any information about the equipment’s operating conditions. For this reason, this approach is versatile and can be used to diagnose multiple machines

Deep Image Retrieval: A Survey

In recent years a vast amount of visual content has been generated and shared from various fields, such as social media platforms, medical images, and robotics. This abundance of content creation and sharing has introduced new challenges. In particular, searching databases for similar content, i.e.content based image retrieval (CBIR), is a long-established research area, and more efficient and accurate methods are needed for real time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of intelligent search. In this survey we organize and review recent CBIR works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers. We identify and present the commonly-used benchmarks and evaluation methods used in the field. We collect common challenges and propose promising future directions. More specifically, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, aiming to promote a global view of the field of instance-based CBIR.Comment: 20 pages, 11 figure

Towards Data-centric Graph Machine Learning: Review and Outlook

Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years. In this article, we conduct an in-depth and comprehensive review, offering a forward-looking outlook on the current efforts in data-centric AI pertaining to graph data-the fundamental data structure for representing and capturing intricate dependencies among massive and diverse real-life entities. We introduce a systematic framework, Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of the graph data lifecycle, including graph data collection, exploration, improvement, exploitation, and maintenance. A thorough taxonomy of each stage is presented to answer three critical graph-centric questions: (1) how to enhance graph data availability and quality; (2) how to learn from graph data with limited-availability and low-quality; (3) how to build graph MLOps systems from the graph data-centric view. Lastly, we pinpoint the future prospects of the DC-GML domain, providing insights to navigate its advancements and applications.Comment: 42 pages, 9 figure

Representation learning on complex data

Machine learning has enabled remarkable progress in various fields of research and application in recent years. The primary objective of machine learning consists of developing algorithms that can learn and improve through observation and experience. Machine learning algorithms learn from data, which may exhibit various forms of complexity, which pose fundamental challenges. In this thesis, we address two major types of data complexity: First, data is often inherently connected and can be modeled by a single or multiple graphs. Machine learning methods could potentially exploit these connections, for instance, to find groups of similar users in a social network for targeted marketing or to predict functional properties of proteins for drug design. Secondly, data is often high-dimensional, for instance, due to a large number of recorded features or induced by a quadratic pixel grid on images. Classical machine learning methods perennially fail when exposed to high-dimensional data as several key assumptions cease to be satisfied. Therefore, a major challenge associated with machine learning on graphs and high-dimensional data is to derive meaningful representations of this data, which allow models to learn effectively. In contrast to conventional manual feature engineering methods, representation learning aims at automatically learning data representations that are particularly suitable for a specific task at hand. Driven by a rapidly increasing availability of data, these methods have celebrated tremendous success for tasks such as object detection in images and speech recognition. However, there is still a considerable amount of research work to be done to fully leverage such techniques for learning on graphs and high-dimensional data. In this thesis, we address the problem of learning meaningful representations for highly-effective machine learning on complex data, in particular, graph data and high-dimensional data. Additionally, most of our proposed methods are highly scalable, allowing them to learn from massive amounts of data. While we address a wide range of general learning problems with different modes of supervision, ranging from unsupervised problems on unlabeled data to (semi-)-supervised learning on annotated data sets, we evaluate our models on specific tasks from fields such as social network analysis, information security, and computer vision. The first part of this thesis addresses representation learning on graphs. While existing graph neural network models commonly perform synchronous message passing between nodes and thus struggle with long-range dependencies and efficiency issues, our first proposed method performs fast asynchronous message passing and, therefore, supports adaptive and efficient learning and additionally scales to large graphs. Another contribution consists of a novel graph-based approach to malware detection and classification based on network traffic. While existing methods classify individual network flows between two endpoints, our algorithm collects all traffic in a monitored network within a specific time frame and builds a communication graph, which is then classified using a novel graph neural network model. The developed model can be generally applied to further graph classification or anomaly detection tasks. Two further contributions challenge a common assumption made by graph learning methods, termed homophily, which states that nodes with similar properties are usually closely connected in the graph. To this end, we develop a method that predicts node-level properties leveraging the distribution of class labels appearing in the neighborhood of the respective node. That allows our model to learn general relations between a node and its neighbors, which are not limited to homophily. Another proposed method specifically models structural similarity between nodes to model different roles, for instance, influencers and followers in a social network. In particular, we develop an unsupervised algorithm for deriving node descriptors based on how nodes spread probability mass to their neighbors and aggregate these descriptors to represent entire graphs. The second part of this thesis addresses representation learning on high-dimensional data. Specifically, we consider the problem of clustering high-dimensional data, such as images, texts, or gene expression profiles. Classical clustering algorithms struggle with this type of data since it can usually not be assumed that data objects will be similar w.r.t. all attributes, but only within a particular subspace of the full-dimensional ambient space. Subspace clustering is an approach to clustering high-dimensional data based on this assumption. While there already exist powerful neural network-based subspace clustering methods, these methods commonly suffer from scalability issues and lack a theoretical foundation. To this end, we propose a novel metric learning approach to subspace clustering, which can provably recover linear subspaces under suitable assumptions and, at the same time, tremendously reduces the required numbear of model parameters and memory compared to existing algorithms.Maschinelles Lernen hat in den letzten Jahren bemerkenswerte Fortschritte in verschiedenen Forschungs- und Anwendungsbereichen ermöglicht. Das primäre Ziel des maschinellen Lernens besteht darin, Algorithmen zu entwickeln, die durch Beobachtung und Erfahrung lernen und sich verbessern können. Algorithmen des maschinellen Lernens lernen aus Daten, die verschiedene Formen von Komplexität aufweisen können, was grundlegende Herausforderungen mit sich bringt. Im Rahmen dieser Dissertation werden zwei Haupttypen von Datenkomplexität behandelt: Erstens weisen Daten oft inhärente Verbindungen, die durch einen einzelnen oder mehrere Graphen modelliert werden können. Methoden des maschinellen Lernens können diese Verbindungen potenziell ausnutzen, um beispielsweise Gruppen ähnlicher Nutzer in einem sozialen Netzwerk für gezieltes Marketing zu finden oder um funktionale Eigenschaften von Proteinen für das Design von Medikamenten vorherzusagen. Zweitens sind die Daten oft hochdimensional, z. B. aufgrund einer großen Anzahl von erfassten Merkmalen oder bedingt durch ein quadratisches Pixelraster auf Bildern. Klassische Methoden des maschinellen Lernens versagen immer wieder, wenn sie hochdimensionalen Daten ausgesetzt werden, da mehrere Schlüsselannahmen nicht mehr erfüllt sind. Daher besteht eine große Herausforderung beim maschinellen Lernen auf Graphen und hochdimensionalen Daten darin, sinnvolle Repräsentationen dieser Daten abzuleiten, die es den Modellen ermöglichen, effektiv zu lernen. Im Gegensatz zu konventionellen manuellen Feature-Engineering-Methoden zielt Representation Learning darauf ab, automatisch Datenrepräsentationen zu lernen, die für eine bestimmte Aufgabenstellung besonders geeignet sind. Angetrieben durch eine rasant steigende Datenverfügbarkeit haben diese Methoden bei Aufgaben wie der Objekterkennung in Bildern und der Spracherkennung enorme Erfolge gefeiert. Es besteht jedoch noch ein erheblicher Forschungsbedarf, um solche Verfahren für das Lernen auf Graphen und hochdimensionalen Daten voll auszuschöpfen. Diese Dissertation beschäftigt sich mit dem Problem des Lernens sinnvoller Repräsentationen für hocheffektives maschinelles Lernen auf komplexen Daten, insbesondere auf Graphen und hochdimensionalen Daten. Zusätzlich sind die meisten hier vorgeschlagenen Methoden hoch skalierbar, so dass sie aus großen Datenmengen lernen können. Obgleich eine breite Palette von allgemeinen Lernproblemen mit verschiedenen Arten der Überwachung adressiert wird, die von unüberwachten Problemen auf unannotierten Daten bis hin zum (semi-)überwachten Lernen auf annotierten Datensätzen reichen, werden die vorgestellten Metoden anhand spezifischen Anwendungen aus Bereichen wie der Analyse sozialer Netzwerke, der Informationssicherheit und der Computer Vision evaluiert. Der erste Teil der Dissertation befasst sich mit dem Representation Learning auf Graphen. Während existierende neuronale Netze für Graphen üblicherweise eine synchrone Nachrichtenübermittlung zwischen den Knoten durchführen und somit mit langreichweitigen Abhängigkeiten und Effizienzproblemen zu kämpfen haben, führt die erste hier vorgeschlagene Methode eine schnelle asynchrone Nachrichtenübermittlung durch und unterstützt somit adaptives und effizientes Lernen und skaliert zudem auf große Graphen. Ein weiterer Beitrag besteht in einem neuartigen graphenbasierten Ansatz zur Malware-Erkennung und -Klassifizierung auf Basis des Netzwerkverkehrs. Während bestehende Methoden einzelne Netzwerkflüsse zwischen zwei Endpunkten klassifizieren, sammelt der vorgeschlagene Algorithmus den gesamten Verkehr in einem überwachten Netzwerk innerhalb eines bestimmten Zeitraums und baut einen Kommunikationsgraphen auf, der dann mithilfe eines neuartigen neuronalen Netzes für Graphen klassifiziert wird. Das entwickelte Modell kann allgemein für weitere Graphenklassifizierungs- oder Anomalieerkennungsaufgaben eingesetzt werden. Zwei weitere Beiträge stellen eine gängige Annahme von Graphen-Lernmethoden in Frage, die so genannte Homophilie-Annahme, die besagt, dass Knoten mit ähnlichen Eigenschaften in der Regel eng im Graphen verbunden sind. Zu diesem Zweck wird eine Methode entwickelt, die Eigenschaften auf Knotenebene vorhersagt, indem sie die Verteilung der annotierten Klassen in der Nachbarschaft des jeweiligen Knotens nutzt. Das erlaubt dem vorgeschlagenen Modell, allgemeine Beziehungen zwischen einem Knoten und seinen Nachbarn zu lernen, die nicht auf Homophilie beschränkt sind. Eine weitere vorgeschlagene Methode modelliert strukturelle Ähnlichkeit zwischen Knoten, um unterschiedliche Rollen zu modellieren, zum Beispiel Influencer und Follower in einem sozialen Netzwerk. Insbesondere entwickeln wir einen unüberwachten Algorithmus zur Ableitung von Knoten-Deskriptoren, die darauf basieren, wie Knoten Wahrscheinlichkeitsmasse auf ihre Nachbarn verteilen, und aggregieren diese Deskriptoren, um ganze Graphen darzustellen. Der zweite Teil dieser Dissertation befasst sich mit dem Representation Learning auf hochdimensionalen Daten. Konkret wird das Problem des Clusterns hochdimensionaler Daten, wie z. B. Bilder, Texte oder Genexpressionsprofile, betrachtet. Klassische Clustering-Algorithmen haben mit dieser Art von Daten zu kämpfen, da in der Regel nicht davon ausgegangen werden kann, dass die Datenobjekte in Bezug auf alle Attribute ähnlich sind, sondern nur innerhalb eines bestimmten Unterraums des volldimensionalen Datenraums. Das Unterraum-Clustering ist ein Ansatz zum Clustern hochdimensionaler Daten, der auf dieser Annahme basiert. Obwohl es bereits leistungsfähige, auf neuronalen Netzen basierende Unterraum-Clustering-Methoden gibt, leiden diese Methoden im Allgemeinen unter Skalierbarkeitsproblemen und es fehlt ihnen an einer theoretischen Grundlage. Zu diesem Zweck wird ein neuartiger Metric Learning Ansatz für das Unterraum-Clustering vorgeschlagen, der unter geeigneten Annahmen nachweislich lineare Unterräume detektieren kann und gleichzeitig die erforderliche Anzahl von Modellparametern und Speicher im Vergleich zu bestehenden Algorithmen enorm reduziert

Deep image retrieval: a survey

In recent years a vast amount of visual content has been generated and shared from various fields, such as social media platforms, medical images, and robotics. This abundance of content creation and sharing has introduced new challenges. In particular, searching databases for similar content, i.e.content based image retrieval (CBIR), is a long-established research area, and more efficient and accurate methods are needed for real time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of intelligent search. In this survey we organize and review recent CBIR works that are developed based on deep learning algorithms and techniques, including insights and techniques from recent papers. We identify and present the commonly-used benchmarks and evaluation methods used in the field. We collect common challenges and propose promising future directions. More specifically, we focus on image retrieval with deep learning and organize the state of the art methods according to the types of deep network structure, deep features, feature enhancement methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, aiming to promote a global view of the field of instance-based CBIR. Computer Systems, Imagery and Medi

Improving Representation Learning for Deep Clustering and Few-shot Learning

The amounts of data in the world have increased dramatically in recent years, and it is quickly becoming infeasible for humans to label all these data. It is therefore crucial that modern machine learning systems can operate with few or no labels. The introduction of deep learning and deep neural networks has led to impressive advancements in several areas of machine learning. These advancements are largely due to the unprecedented ability of deep neural networks to learn powerful representations from a wide range of complex input signals. This ability is especially important when labeled data is limited, as the absence of a strong supervisory signal forces models to rely more on intrinsic properties of the data and its representations. This thesis focuses on two key concepts in deep learning with few or no labels. First, we aim to improve representation quality in deep clustering - both for single-view and multi-view data. Current models for deep clustering face challenges related to properly representing semantic similarities, which is crucial for the models to discover meaningful clusterings. This is especially challenging with multi-view data, since the information required for successful clustering might be scattered across many views. Second, we focus on few-shot learning, and how geometrical properties of representations influence few-shot classification performance. We find that a large number of recent methods for few-shot learning embed representations on the hypersphere. Hence, we seek to understand what makes the hypersphere a particularly suitable embedding space for few-shot learning. Our work on single-view deep clustering addresses the susceptibility of deep clustering models to find trivial solutions with non-meaningful representations. To address this issue, we present a new auxiliary objective that - when compared to the popular autoencoder-based approach - better aligns with the main clustering objective, resulting in improved clustering performance. Similarly, our work on multi-view clustering focuses on how representations can be learned from multi-view data, in order to make the representations suitable for the clustering objective. Where recent methods for deep multi-view clustering have focused on aligning view-specific representations, we find that this alignment procedure might actually be detrimental to representation quality. We investigate the effects of representation alignment, and provide novel insights on when alignment is beneficial, and when it is not. Based on our findings, we present several new methods for deep multi-view clustering - both alignment and non-alignment-based - that out-perform current state-of-the-art methods. Our first work on few-shot learning aims to tackle the hubness problem, which has been shown to have negative effects on few-shot classification performance. To this end, we present two new methods to embed representations on the hypersphere for few-shot learning. Further, we provide both theoretical and experimental evidence indicating that embedding representations as uniformly as possible on the hypersphere reduces hubness, and improves classification accuracy. Furthermore, based on our findings on hyperspherical embeddings for few-shot learning, we seek to improve the understanding of representation norms. In particular, we ask what type of information the norm carries, and why it is often beneficial to discard the norm in classification models. We answer this question by presenting a novel hypothesis on the relationship between representation norm and the number of a certain class of objects in the image. We then analyze our hypothesis both theoretically and experimentally, presenting promising results that corroborate the hypothesis

A survey of face recognition techniques under occlusion

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed