The application of time encoded signals to automated machine condition classification using neural networks

This thesis considers the classification of physical states in a simplified gearbox using acoustical data and simple time domain signal shape characterisation techniques allied to a basic feedforward multi-layer perceptron neural network. A novel extension to the signal coding scheme (TES), involving the application of energy based shape descriptors, was developed. This sought specifically to improve the techniques suitability to the identification of mechanical states and was evaluated against the more traditional minima based TES descriptors. The application of learning based identification techniques offers potential advantages over more traditional programmed techniques both in terms of greater noise immunity and in the reduced requirement for highly skilled operators. The practical advantages accrued by using these networks are studied together with some of the problems associated in their use within safety critical monitoring systems.Practical trials were used as a means of developing the TES conversion mechanism and were used to evaluate the requirements of the neural networks being used to classify the data. These assessed the effects upon performance of the acquisition and digital signal processing phases as well as the subsequent training requirements of networks used for accurate condition classification. Both random data selection and more operator intensive performance based selection processes were evaluated for training. Some rudimentary studies were performed on the internal architectural configuration of the neural networks in order to quantify its influence on the classification process, specifically its effect upon fault resolution enhancement.The techniques have proved to be successful in separating several unique physical states without the necessity for complex state definitions to be identified in advance. Both the computational demands and the practical constraints arising from the use of these techniques fall within the bounds of a realisable system

Development of a classification algorithm for vehicle impacts: an anomaly detection approach

Dissertação de mestrado em Engenharia InformáticaIn the past decade, Machine Learning has been heavily applied to automobile industry solutions, the most promising being development of autonomous vehicles. New mobility services are available today as alternatives to owning a car, like ride hailing and carsharing. High costs associated with the maintenance of the vehicle and the reduced rate of vehicle use throughout the day are some of the factors for the popularity of these services. Car-sharing is self-service mode of transport that provides its members with access to a fleet of vehicles parked in various locations throughout a city. Damages are expected to happen when vehicles are used and the required repair implies costs to fleet operators. Systems able to detect these damages will promote a better use of these vehicles by vehicle users. Vehicle damages result from impacts with other objects, for instance, other vehicles or structures of any kind and these impacts inflict deformations to the vehicle exterior structure. Most of these impacts can be perceived or detected by the forces involved as result of the impact. Anomaly Detection is a technique applicable in a variety of domains, such as intrusion detection, fraud detection, event detection in sensor network or detection ecosystem disturbances. The objective of this thesis is the study and development of a semi-supervised intelligent system for detection and classification of vehicle impacts with an Anomaly Detection approach, using the accelerometer data, and following a strategy that would allow exploring a Machine Learning cycle. This thesis was developed under an internship in the company Bosch Car Multimedia S.A, located in Braga.Na última década, Machine Learning tem sido extensamente utilizado em soluções na indústria automóvel, o mais promissor sendo o desenvolvimento de veículos com condução autônoma. Novos serviços de mobilidade estão disponíveis hoje como alternativas à posse de um carro, como ride hailing ou car-sharing. Os elevados custos associados à manutenção do veículo e a sua reduzida taxa de utilização ao longo do dia são alguns dos fatores que contribuem para a popularidade destes serviços. Car-sharing é um modo de transporte self-service que fornece aos seus membros acesso a uma frota de veículos estacionados em vários locais duma cidade. Danos são espectáveis de ocorrer quando os veículos são usados e a reparação necessária implica custos para os operadores da frota. Sistemas capazes de detectar esses danos irão promover um melhor aproveitamento desses veículos pelos utilizadores dos veículos. Os danos de veículos resultam de impactos com outros objetos como, por exemplo, outros veículos ou estruturas e esses impactos provocam deformações na estrutura externa do veículo. A maioria desses impactos podem ser compreendidos ou detetados pelas forças envolvidas do resultado do impacto. Anomaly Detection é uma técnica aplicável em uma variedade de domínios, como deteção de intrusões, deteção de fraude, deteção de eventos numa rede de sensores ou deteção de distúrbios no ecossistema. O objetivo desta dissertação foi o estudo e desenvolvimento de um sistema inteligente semi-supervisionado para detecção e classificação de impactos de veículos a partir de uma abordagem de Anomaly Detection, utilizando os dados de acelerómetro, e seguindo uma estratégia que permitisse explorar um ciclo de Machine Learning. Esta dissertação foi desenvolvida no âmbito de um estágio na empresa Bosch Car Multimedia S.A, situada em Braga

The Soundscape Indices (SSID) Protocol: A Method for Urban Soundscape Surveys—Questionnaires with Acoustical and Contextual Information

A protocol for characterizing urban soundscapes for use in the design of Soundscape Indices (SSID) and general urban research as implemented under the European Research Council (ERC)-funded SSID project is described in detail. The protocol consists of two stages: (1) a Recording Stage to collect audio-visual recordings for further analysis and for use in laboratory experiments, and (2) a Questionnaire Stage to collect in situ soundscape assessments via a questionnaire method paired with acoustic data collection. Key adjustments and improvements to previous methodologies for soundscape characterization have been made to enable the collation of data gathered from research groups around the world. The data collected under this protocol will form a large-scale, international soundscape database

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes