Efficient deep CNNs for cross-modal automated computer vision under time and space constraints

We present an automated computer vision architecture to handle video and image data using the same backbone networks. We show empirical results that lead us to adopt MOBILENETV2 as this backbone architecture. The paper demonstrates that neural architectures are transferable from images to videos through suitable preprocessing and temporal information fusion

Learning Informative Health Indicators Through Unsupervised Contrastive Learning

Condition monitoring is essential to operate industrial assets safely and efficiently. To achieve this goal, the development of robust health indicators has recently attracted significant attention. These indicators, which provide quantitative real-time insights into the health status of industrial assets over time, serve as valuable tools for fault detection and prognostics. In this study, we propose a novel and universal approach to learn health indicators based on unsupervised contrastive learning. Operational time acts as a proxy for the asset's degradation state, enabling the learning of a contrastive feature space that facilitates the construction of a health indicator by measuring the distance to the healthy condition. To highlight the universality of the proposed approach, we assess the proposed contrastive learning framework in two distinct tasks - wear assessment and fault detection - across two different case studies: a milling machines case study and a real condition monitoring case study of railway wheels from operating trains. First, we evaluate if the health indicator is able to learn the real health condition on a milling machine case study where the ground truth wear condition is continuously measured. Second, we apply the proposed method on a real case study of railway wheels where the ground truth health condition is not known. Here, we evaluate the suitability of the learned health indicator for fault detection of railway wheel defects. Our results demonstrate that the proposed approach is able to learn the ground truth health evolution of milling machines and the learned health indicator is suited for fault detection of railway wheels operated under various operating conditions by outperforming state-of-the-art methods. Further, we demonstrate that our proposed approach is universally applicable to different systems and different health conditions

Automated machine learning in practice : state of the art and recent results

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically – AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results of the most important AutoML algorithms

Lightweight Multi-Material-Design of a Liquid Cooled 18650 Cell Holder

Due to ever stricter climate targets, the focus across all industries increasingly shifts to the reduction of CO2 emissions, which, for mobile systems, can be achieved by saving mass through lightweight design. One promising approach to increase the lightweight design potential lies in multi-material design (MMD). This approach uses the material that is best suited to fulfill the function at the respective locations in the system. However, this approach poses the challenge of initially gaining the necessary knowledge of the system in order to select the best material for the job. This work is dedicated to this challenge as well as the implementation of an MMD for a liquid cooled battery module. For this purpose, the approaches of systemic lightweight design were used, for instance, to identify components with excessive weight and to analyze their lightweight design potential. By coupling CFD, thermal and structural simulations as well as accompanying experiments, it was possible to verify and validate very early during development as well as to incorporate the interactions between product and production directly in the design phase. This made it possible to develop an MMD module design that saves over 60 % of mass compared to the reference system made out of a single material. In addition, it showed that systemic lightweight design and MMD not only reduce mass while guaranteeing the same functionality, but can also decrease costs through smart choices of materials

Supervised Learning and Reinforcement Learning of Feedback Models for Reactive Behaviors: Tactile Feedback Testbed

Robots need to be able to adapt to unexpected changes in the environment such that they can autonomously succeed in their tasks. However, hand-designing feedback models for adaptation is tedious, if at all possible, making data-driven methods a promising alternative. In this paper we introduce a full framework for learning feedback models for reactive motion planning. Our pipeline starts by segmenting demonstrations of a complete task into motion primitives via a semi-automated segmentation algorithm. Then, given additional demonstrations of successful adaptation behaviors, we learn initial feedback models through learning from demonstrations. In the final phase, a sample-efficient reinforcement learning algorithm fine-tunes these feedback models for novel task settings through few real system interactions. We evaluate our approach on a real anthropomorphic robot in learning a tactile feedback task.Comment: Submitted to the International Journal of Robotics Research. Paper length is 21 pages (including references) with 12 figures. A video overview of the reinforcement learning experiment on the real robot can be seen at https://www.youtube.com/watch?v=WDq1rcupVM0. arXiv admin note: text overlap with arXiv:1710.0855

Deep learning in the wild

Invited paperDeep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice

Improving Generalization of Deep Fault Detection Models in the Presence of Mislabeled Data

Mislabeled samples are ubiquitous in real-world datasets as rule-based or expert labeling is usually based on incorrect assumptions or subject to biased opinions. Neural networks can "memorize" these mislabeled samples and, as a result, exhibit poor generalization. This poses a critical issue in fault detection applications, where not only the training but also the validation datasets are prone to contain mislabeled samples. In this work, we propose a novel two-step framework for robust training with label noise. In the first step, we identify outliers (including the mislabeled samples) based on the update in the hypothesis space. In the second step, we propose different approaches to modifying the training data based on the identified outliers and a data augmentation technique. Contrary to previous approaches, we aim at finding a robust solution that is suitable for real-world applications, such as fault detection, where no clean, "noise-free" validation dataset is available. Under an approximate assumption about the upper limit of the label noise, we significantly improve the generalization ability of the model trained under massive label noise.Comment: 12 pages, 3 figures, 5 table

Controlled generation of unseen faults for Partial and Open-Partial domain adaptation

New operating conditions can result in a significant performance drop of fault diagnostics models due to the domain shift between the training and the testing data distributions. While several domain adaptation approaches have been proposed to overcome such domain shifts, their application is limited if the fault classes represented in the two domains are not the same. To enable a better transferability between two different domains, particularly in setups where only the healthy data class is shared between the two domains, we propose a new framework for Partial and Open-Partial domain adaptation based on generating distinct fault signatures with a Wasserstein GAN. The main contribution of the proposed framework is the controlled data generation with two characteristics. Firstly, previously unobserved target faults can be generated by having only access to healthy target and faulty source samples. Secondly, distinct fault types and severity levels can be generated precisely. The proposed method is especially suited for extreme domain adaption settings that are particularly relevant in the context of complex and safety-critical systems, where only one class is shared between the two domains. We evaluate the proposed framework on Partial as well as Open-Partial domain adaptation tasks on two bearing fault diagnostics case studies. In the evaluated case studies the proposed methodology demonstrated superior results compared to other methods, particularly in the presence of large domain gaps. The experiments conducted in different label space settings (Partial and Open-Partial) showcase the versatility of the proposed framework.ISSN:0951-832