Machine health diagnostics using acoustic imaging and algorithms for machine learning

Nowadays monitoring health conditions of machines is necessary to reduce costs and repairing time and to secure the quality of the products. Therefore, the potential of acoustic measurements in combination with machine learning techniques for non-invasive diagnostics of machine performance has been investigated. The idea is to establish relations between the acoustic images produced by a sound camera and the machine conditions and then create a strategy for processing the images using Convolutional Neural Networks. Several working conditions of the machine have been considered and experiments have been performed both under nominal and abnormal conditions of the machine, obtained by mimicking the presence of a disturbance. The use of the algorithms for image classification allows isolation of the faults in the machine behaviour by the definition of the primary sound sources. The procedure shows promising results with a short computational time, easy application and high accuracy

Digital reality: a model-based approach to supervised learning from synthetic data

Hierarchical neural networks with large numbers of layers are the state of the art for most computer vision problems including image classification, multi-object detection and semantic segmentation. While the computational demands of training such deep networks can be addressed using specialized hardware, the availability of training data in sufficient quantity and quality remains a limiting factor. Main reasons are that measurement or manual labelling are prohibitively expensive, ethical considerations can limit generating data, or a phenomenon in questions has been predicted, but not yet observed. In this position paper, we present the Digital Reality concept are a structured approach to generate training data synthetically. The central idea is to simulate measurements based on scenes that are generated by parametric models of the real world. By investigating the parameter space defined of such models, training data can be generated in a controlled way compared to data that was captured from real world situations. We propose the Digital Reality concept and demonstrate its potential in different application domains, including industrial inspection, autonomous driving, smart grid, and microscopy research in material science and engineering

Automatic Registration of 3D Datasets using Gaussian Fields

Abstract—In this paper we introduce a new 3D automatic registration method based on Gaussian fields and energy minimization. The method defines a simple C energy function, which is convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. We show that the size of the region of convergence can be significantly extended reducing the need for close initialization and overcoming local convergence problems of the standard Iterative Closest Point (ICP) algorithms. Furthermore, the Gaussian criterion can be evaluated with linear computational complexity using Fast Gauss Transform methods, allowing for an efficient implementation of the registration algorithm. Experimental analysis of the technique using real world datasets shows the usefulness as well as the limits of the approach

36 Registering Multi-sensor Datasets from a Robotic Inspection Platform

In this paper we present a new method for the registration of multiple sensors applied to a mobile robotic inspection platform. Our main technical challenge is automating the integration process for various multimodal inputs, such as depth maps, and multi-spectral images. This task is approached through a unified framework based on a new registration criterion that can be employed for both 3D and 2D datasets. The system embedding this technology reconstructs 3D models of scenes and objects that are inspected by an autonomous platform in high security areas. The models are processed and rendered with corresponding multi-spectral textures, which greatly enhances both human and machine identification of threat objects