Using immersive audio and vibration to enhance remote diagnosis of mechanical failure in uncrewed vessels.

There is increasing interest in the maritime industry in the potential use of uncrewed vessels to improve the efficiency and safety of maritime operations. This leads to a number of questions relating to the maintenance and repair of mechanical systems, in particular, critical propulsion systems which if a failure occurs could endanger the vessel. While control data is commonly monitored remotely, engineers on board ship also employ a wide variety of sensory feedback such as sound and vibration to diagnose the condition of systems, and these are often not replicated in remote monitoring. In order to assess the potential for enhancement of remote monitoring and diagnosis, this project simulated an engine room (ER) based on a real vessel in Unreal Engine 4 for the HTC ViveTM VR headset. Audio was recorded from the vessel, with mechanical faults synthesized to create a range of simulated failures. In order to simulate operational requirements, the system was remotely fed data from an external server. The system allowed users to view normal control room data, listen to the overall sound of the space presented spatially over loudspeakers, isolate the sound of particular machinery components, and feel the vibration of machinery through a body worn vibration transducer. Users could scroll through a 10-hour time history of system performance, including audio, vibration and data for snapshots at hourly intervals. Seven experienced marine engineers were asked to assess several scenarios for potential faults in different elements of the ER. They were assessed both quantitatively regarding correct fault identification, and qualitatively in order to assess their perception of usability of the system. Users were able to diagnose simulated mechanical failures with a high degree of accuracy, mainly utilising audio and vibration stimuli, and reported specifically that the immersive audio and vibration improved realism and increased their ability to diagnose system failures from a remote location

Drude conductivity of a granular metal

We present a complete derivation of the granular analogue to Drude conductivity using diagrammatic methods. The convergence issues arising when changing the order of momentum and frequency summation are more severe than in the homogeneous case. This is because there are now two momentum sums rather than one, due to the intragrain momentum scrambling in tunnelling events. By careful analytic continuation of the frequency sum, and use of integration by parts, we prove that the system is in the normal (non-superconducting) state, and derive the formula for the granular Drude conductivity expected from Einstein's relation and Fermi's golden rule. We also show that naively performing the momentum sums first gives the correct result, provided that we interpret a divergent frequency sum by analytic continuation using the Hurwitz zeta function.Comment: 18 pages, 5 figure

Assembly Line

An assembly line is a manufacturing process in which parts are added to a product in a sequential manner using optimally planned logistics to create a finished product in the fastest possible way. It is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The present edited book is a collection of 12 chapters written by experts and well-known professionals of the field. The volume is organized in three parts according to the last research works in assembly line subject. The first part of the book is devoted to the assembly line balancing problem. It includes chapters dealing with different problems of ALBP. In the second part of the book some optimization problems in assembly line structure are considered. In many situations there are several contradictory goals that have to be satisfied simultaneously. The third part of the book deals with testing problems in assembly line. This section gives an overview on new trends, techniques and methodologies for testing the quality of a product at the end of the assembling line

Radar based deep learning technology for loudspeaker faults detection and classification

Recently, radar based micro-Doppler signature analysis has been successfully applied in various sectors including both defence and civilian applications. A joint radar micro-Doppler and deep learning technology for End-Of-Line (EOL)test of loudspeakers is proposed in this paper. This approach offers the potential benefits of characterizing the mechanical motion of a loudspeaker in a noisy environment as a production line, in order to automatically identify and classify defects. Starting from real radar signal, the proposed Bidirectional Long Short-Term Memory (BiLSTM) classifier has been tested on training, validation and test dataset. The results show that the proposed approach produces a probability of correct classification abovethe98%, outperforming the traditional k-NN classifier

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Predictive Models of an Electro-mechanical Driving System for Failure Testing of Strain Gauges

Strain gauges are bonded at high stress locations on the surface of critical structural components such as turbine blades to measure fatigue characteristics and detect early warning signs of high cycle fatigue. However, strain gauges do not always report expected measurements. The usual response by maintenance technicians to these failing signals is to investigate the component for weakness, check the placement of the gauges on the component, or examine the instrumentation for failure or damage. However, little research has been conducted to show when the failing signals are the fault of the strain gauge. Such failure modes of strain gauges include improper gauge installation, over-straining, operating outside the temperature limits, physical damage and environmental wear, and improper gauge selection. Failure Modes and Effects Analysis, FMEA, is a methodology for monitoring failure modes and their potential effects, causes, and solutions. This research consisted of the introductory steps in developing and analyzing a laboratory setup for FMEA strain gauge testing and analysis. The primary goal of this research was to develop predictive models for strain gauge responses under controlled laboratory conditions. A testing station was developed that generated a mechanical motion on a beam, subjecting strain gauges to a sinusoidally-varying strain. Predictive models of the testing station were developed and experimentally analyzed. Models were also developed for two particular failure modes, debonding and wire lead termination, and experimental analysis was conducted. In general, the models adequately describe the operation of a strain gauge operating under normal conditions and in the studied failure mode. Predicted and experimental data are presented that show the characteristic signals in terms of time domain, histogram, and frequency domain analysis