3 research outputs found

    Non-invasive dynamic condition assessment techniques for railway pantographs

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    The railway industry desires to improve the dependability and longevity of railway pantographs by providing more effective maintenance. The problem addressed in this thesis is the development of an effective condition-based fault detection and diagnosis procedure capable of supporting improved on–condition maintenance actions. A laboratory-based pantograph test rig established during the course of the project at the University of Birmingham has been enhanced with additional sensors and used to develop and carry out dynamic tests that provide indicators that support practical pantograph fault detection and diagnosis. A 3D multibody simulation of a Pendolino pantograph has also been developed. Three distinct dynamic tests have been identified as useful for fault detection and diagnosis: (i) a hysteresis test; (ii) a frequency-response test; and (iii) a novel changing-gradient test. These tests were carried out on a new Pendolino pantograph, a used pantograph about to go for an overhaul, the new pantograph with individual parts replaced by old components, and on the new pantograph with various changes made to, for example, the greasing or chain tightness. Through a comparison of absolute measurements and features acquired from the three dynamic tests, it was possible to extract features associated with different failure modes. Finally, with a focus on the practical constraints of depot operations, a condition-based pantograph fault detection and diagnosis routine is proposed that draws on decision tree analysis. This novel testing procedure integrates the three dynamic tests and is able to identify and locate common failure modes on pantographs. The approach is considered to be appropriate for an application using an adapted version of the test rig in a depot setting

    Optimization of AI models as the Main Component in Prospective Edge Intelligence Applications

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    Artificial Intelligence (AI) is a successful paradigm with application in many fields; however, there can be some challenging scenarios like the deployment of AI models in remote locations or with limited connectivity, possibly needing to perform inference closer to where data is collected. A potential solution is the study of ways to optimize AI models, for deployment of intelligent algorithms closer to the edge. This thesis focuses on applications of AI that need to have characteristics that make them suitable for implementation on portable devices (e.g., aeroponics container, drone, mobile robot); thus, the development and implementation of custom models, and their optimization (i.e., reduction in size and inference time) is of upmost importance and the main goal of this dissertation. For this task, a number of options have been explored, including developing techniques to select relevant features from the samples that the model analyzes, and pruning and quantization. Therefore, this thesis proposes a scheme for the development, implementation, and optimization of custom AI models used mainly in agriculture, so that they have the desired characteristics that are needed for their deployment in edge devices. This main goal is fulfilled by implementing a number of sequential steps that include the validation of the hypothesis that there is at least an AI model capable of generating useful predictions for the applications being studied, the exploration and implementation of an approach for their optimization, and their final implementation in hardware of limited resources. The main contributions of this thesis are on the general workflow for optimization of custom models, as well as in the proposed scheme for feature selection based on model interpretability approaches. This carries most of the novelty of the thesis, since we have not found previous implementations of these ideas, at least in the field under study. This optimization is mainly based on a feature selection approach, but finally complemented with pruning and quantization. The implementation of some of these models on an edge-like device, demonstrates the feasibility of this approach. Finally, although this thesis tries to be a self-contained work, encompassing all the aspects required to go from AI model design to implementation on an edge device, there are some aspects that could be further studied, analyzed, and improved. Furthermore, it is almost impossible to keep the pace with all the new developments in the fields of AI, edge computing, hardware and software tools, etc. which opens the field for new discussions and proposals. This work tries to fill some gaps and to propose some ideas that hopefully will be useful for future researchers in the development of new technologies and solutions

    Stacked Sparse Auto-Encoders (SSAE) Based Electronic Nose for Chinese Liquors Classification

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    This paper presents a stacked sparse auto-encoder (SSAE) based deep learning method for an electronic nose (e-nose) system to classify different brands of Chinese liquors. It is well known that preprocessing; feature extraction (generation and reduction) are necessary steps in traditional data-processing methods for e-noses. However, these steps are complicated and empirical because there is no uniform rule for choosing appropriate methods from many different options. The main advantage of SSAE is that it can automatically learn features from the original sensor data without the steps of preprocessing and feature extraction; which can greatly simplify data processing procedures for e-noses. To identify different brands of Chinese liquors; an SSAE based multi-layer back propagation neural network (BPNN) is constructed. Seven kinds of strong-flavor Chinese liquors were selected for a self-designed e-nose to test the performance of the proposed method. Experimental results show that the proposed method outperforms the traditional methods
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