Acceleration Measurement

This paper describes a sensor-fusion-based wireless walking-inplace (WIP) interaction technique. We devised a new humanwalking detection algorithm that is based on a sensor-fusion using both acceleration and magnetic sensors integrated within a smart phone. Our sensor-fusion approach can be useful for the cases when the detection capability of a single sensor is limited to a certain range of walking speeds, when a system power source is limited, and/or when computation power is limited. The proposed algorithm is versatile enough to handle possible data-loss and random delay in the wireless communication environment, resulting in reduced wireless communication load and computation overhead. The initial study demonstrated that the algorithm can detect dynamic speeds of human walking. The algorithm can be implemented on any mobile device equipped with magnetic and acceleration sensors.

The purpose of this paper is to validate in prototype, the results related to the description of dynamic behaviour obtained in a Kaplan turbine model. A comparative analysis between model and prototype measurement at homologous operation conditions is presented. The phenomena of interest are tip vortex cavitation development and rotor stator interaction (rsi), associated to operation at high power outputs. The selection of suitable signal processing tools allows a clear identification of the role played by the modulation of the main rsi frequencies on cavitation phenomena. The comparison between pressure acceleration measurements on model and prototype at draft tube wall are consistent, both in time and frequency domain analysis. The acceleration measurement at the draft tube wall (manhole) is suggested as a trustworthy dynamic indicator for prediction (model) and monitoring (prototype) purposes.

The purpose of this paper is to validate in prototype, the results related to the description of dynamic behaviour obtained in a Kaplan turbine model. A comparative analysis between model and prototype measurement at homologous operation conditions is presented. The phenomena of interest are tip vortex cavitation development and rotor stator interaction (rsi), associated to operation at high power outputs. The selection of suitable signal processing tools allows a clear identification of the role played by the modulation of the main rsi frequencies on cavitation phenomena. The comparison between pressure acceleration measurements on model and prototype at draft tube wall are consistent, both in time and frequency domain analysis. The acceleration measurement at the draft tube wall (manhole) is suggested as a trustworthy dynamic indicator for prediction (model) and monitoring (prototype) purposes.

Early detection of pipeline damages has been highlighted in water supply industry. Water pressure change in pipeline due to a sudden rupture causes pipe to vibrate and the pressure change propagates through the pipeline. From the measurement of pipe vibration the rupture can be detected. In this paper, the field test results and observations are provided for implementing next generation of SCADA system for pipeline rupture detection. Two field tests were performed on real buried plastic and metal pipelines for rupture detection. The rupture was simulated by introducing sudden water pressure drop caused by water blow-off and valve control. The measured acceleration data at the pipe surfaces were analyzed in both time and frequency domain. In time domain, the sudden narrow increase of acceleration amplitude was used as an indication of rupture event. For the frequency domain analysis, correlation function and the short time Fourier Transform technique were adopted to trace the dominant frequency shift. The success of rupture detection was found to be dependent on several factors. From the frequency analysis, the dominant frequency of metal water pipe was shifted by the water pressure drop, however, it was hard to identify from the plastic pipeline. Also the influence of existing facility such as airvac on pipe vibrations was observed. Finally, several critical lessons learned in the viewpoint of field measurement are discussed in this paper.

In the last decades smart tyre technology has generated growing interest towards different fields of application. The main reason lies in the opportunity to turn passive elements like tyre into active sensors. Moreover, being the tyre a crucial element for vehicle dynamics and the only vehicle part directly in contact with the road, it provides the best monitoring location of road and vehicle interaction. This paper demonstrates how smart tyres can provide information about road surface. In particular, the work investigates how a vehicle fitted with smart tyres can enhance the monitoring of road conditions through the estimation of road unevenness, which is one of the most critical aspects for vehicle ride comfort. The presented solution represents a flexible and widely usable system capable of providing an indication of the IRI index (International Roughness Index), the most worldwide used road unevenness index. The solution is based on a generic vehicle fitted with four smart tyres, each equipped with an accelerometer sensor.

In the last decades smart tyre technology has generated growing interest towards different fields of application. The main reason lies in the opportunity to turn passive elements like tyre into active sensors. Moreover, being the tyre a crucial element for vehicle dynamics and the only vehicle part directly in contact with the road, it provides the best monitoring location of road and vehicle interaction. This paper demonstrates how smart tyres can provide information about road surface. In particular, the work investigates how a vehicle fitted with smart tyres can enhance the monitoring of road conditions through the estimation of road unevenness, which is one of the most critical aspects for vehicle ride comfort. The presented solution represents a flexible and widely usable system capable of providing an indication of the IRI index (International Roughness Index), the most worldwide used road unevenness index. The solution is based on a generic vehicle fitted with four smart tyres, each equipped with an accelerometer sensor.

In the last decades smart tyre technology has generated growing interest towards different fields of application. The main reason lies in the opportunity to turn passive elements like tyre into active sensors. Moreover, being the tyre a crucial element for vehicle dynamics and the only vehicle part directly in contact with the road, it provides the best monitoring location of road and vehicle interaction. This paper demonstrates how smart tyres can provide information about road surface. In particular, the work investigates how a vehicle fitted with smart tyres can enhance the monitoring of road conditions through the estimation of road unevenness, which is one of the most critical aspects for vehicle ride comfort. The presented solution represents a flexible and widely usable system capable of providing an indication of the IRI index (International Roughness Index), the most worldwide used road unevenness index. The solution is based on a generic vehicle fitted with four smart tyres, each equipped with an accelerometer sensor.

This paper describes a sensor-fusion-based wireless walking-inplace (WIP) interaction technique. We devised a new humanwalking detection algorithm that is based on a sensor-fusion using both acceleration and magnetic sensors integrated within a smart phone. Our sensor-fusion approach can be useful for the cases when the detection capability of a single sensor is limited to a certain range of walking speeds, when a system power source is limited, and/or when computation power is limited. The proposed algorithm is versatile enough to handle possible data-loss and random delay in the wireless communication environment, resulting in reduced wireless communication load and computation overhead. The initial study demonstrated that the algorithm can detect dynamic speeds of human walking. The algorithm can be implemented on any mobile device equipped with magnetic and acceleration sensors.

This paper describes a sensor-fusion-based wireless walking-inplace (WIP) interaction technique. We devised a new humanwalking detection algorithm that is based on a sensor-fusion using both acceleration and magnetic sensors integrated within a smart phone. Our sensor-fusion approach can be useful for the cases when the detection capability of a single sensor is limited to a certain range of walking speeds, when a system power source is limited, and/or when computation power is limited. The proposed algorithm is versatile enough to handle possible data-loss and random delay in the wireless communication environment, resulting in reduced wireless communication load and computation overhead. The initial study demonstrated that the algorithm can detect dynamic speeds of human walking. The algorithm can be implemented on any mobile device equipped with magnetic and acceleration sensors.

Accurate measurements of fluid flow close to the free water-sided air-water interface are vital for a proper understanding and quantification of interfacial transport phenomena. Yet, these measurements are difficult to attain with current PIV or PTV based techniques. Moreover, the observed flow structures are highly turbulent, leading to large out-ofplane components in laser-sheet based approaches. This contribution addresses these issues by introducing a novel particle based visualization technique for three component, three dimensional velocity fields (3C3D) close to the free airwater interface. The major advantage of this technique is that depth measurements are relative to the water surface and in a Lagrangian coordinate frame. Hence, uncertainties due to locating the position of the moving interface are circumvented. Also, accelerations can be deduced directly from our approach, making it an ideal tool for the quantification of interfacial fluid dynamical transport processes.

This report documents the results of extensive vehicle-based measurements on a Nissan Infiniti Q56 Sports Utility Vehicle, for the purpose of vibration source characterization. A sophisticated sensor and data acquisition system was fitted to a Nissan Infiniti Q56 Sports Utility Vehicle, supported by simultaneous video and CAN data recording. The question studied was whether the captured signals are sufficient to form the basis of a reliable and robust rumble strip detection system. The instrumentation and data analysis were not supposed be representative of such a product-specific system, but were configured as a research tool to support the future development of such a system. Signal analysis showed that a subset of the accelerometers provided information for detection. Further analysis of the production wheel-speed sensor indicates that it should be able to provide equivalent information, and, with suitable signal processing, provide the basis of a production feasible detection system.

This paper compares three ways to operate a load haul dump (LHD) machine, manual operation, automatic operation (f leet operation) and semi-automatic operation , to find the best operating mode. In a fault tree analysis, different failures are classified and analysed, but the way to recover from certain states is not accounted for, which is something a Markov model can handle. The paper is based on the analysis of real data from an underground mine. A Markov model has been built for mining application and it is shown that a semi-automatic LHD has the highest probability of being in a productive state since it has the advantage of changing operating modes (manual and automatic) depending on the need and situation. Hence, the semi-automatic LHD is the best choice from an operational point of view. The paper fills a gap in the literature on manual vs. automatically operated LHDs by providing a new way of evaluating the operating mode of LHDs using Markov modelling, while considering the operating environment.

iPhone and iPod Touch are multi-touch handheld devices that provide new possibilities for interaction techniques. We describe iPhone/iPod Touch implementation of a navigation interaction technique originally developed for a larger multi-touch device (i.e. Lemur). The interaction technique implemented on an iPhone/iPod Touch was used for navigation tasks in a CAVE virtual environment. We performed a pilot study to measure the control accuracy and to observe how human subjects respond to the interaction technique on the iPhone and iPod Touch devices. We used the preliminary results to improve the design of the interaction technique.

This paper describes a sensor-fusion-based wireless walking-inplace (WIP) interaction technique. We devised a new humanwalking detection algorithm that is based on a sensor-fusion using both acceleration and magnetic sensors integrated within a smart phone. Our sensor-fusion approach can be useful for the cases when the detection capability of a single sensor is limited to a certain range of walking speeds, when a system power source is limited, and/or when computation power is limited. The proposed algorithm is versatile enough to handle possible data-loss and random delay in the wireless communication environment, resulting in reduced wireless communication load and computation overhead. The initial study demonstrated that the algorithm can detect dynamic speeds of human walking. The algorithm can be implemented on any mobile device equipped with magnetic and acceleration sensors.

A recent innovation in underground mining operations is the use of automated Load-Haul-Dump (LHD) vehicles. These vehicles are able to transport ore from a stope to an ore-pass and return without the direct in involvement of a human operator. An interesting, and unexpected, problem that has arisen with the introduction of these vehicles is that the haul road on which they operate can become heavily degraded due to a lack of feedback on road condition. This results in increased maintenance and downtime. On non-automated LHD vehicles, the problem is circumvented by the on-board operator who senses road condition by ride quality and reports back to his or her supervisor when the road becomes unacceptably degraded. This thesis develops a simple tool, called the Road Surface Quality (RSQ) monitor, that aims to report road condition to the automated vehicle’s supervisory control system. The monitor is intended to provide feedback on road condition analogous to that provided by an on-board operator. This feedback can in turn be used to guide decisions such as the operating speed over different sections of road and when to perform road-maintenance such as re-grading. The methodology used to determine road condition is to capture the vertical acceleration of the vehicle chassis and derive a measure of the “bounce energy” of the vehicle as it traverses the road to serve as a proxy for the “quality of ride”. The approach exploits the fact that automated LHD vehicles make repeatable passes from run-to-run. In particular, the wheel tracks and speed are consistent during each transit from the stope to the ore-pass and back again. This makes possible the direct comparison of signals measured from run-to-run. The main conclusion of the thesis is that it is possible to differentiate between roads of different condition using measurements of vertical acceleration on auto-
mated LHDs. This conclusion is supported by several case studies based on installation of the monitor on the Dynamic Automation Systems (DAS) Autotram LHD in operation at Olympic Dam Mine. Data collected from the monitor shows the
system is also capable of detecting abnormally rough operation of the vehicle, e.g. unnecessarily heavy collisions of the bucket with the environment. A detailed plan for further testing of the monitor is also proposed.

Ride characteristics of a vehicle moving on a rough ground with changing travel velocity are analyzed in this paper. The solution is difficult due to the non-stationary characteristics of the problem. Hence a new technique has been proposed to overcome this difficulty. This new technique is employed in the analysis of ride characteristics of a vehicle with changing velocity in the time/frequency domain. It is found that the proposed technique gives successful results in modelling non-stationary responses in terms of a series of stationary responses.

With the introduction of autonomous LHD vehicles in underground mining operations, feedback from operators on road conditions no longer drives road maintenance. Where autonomous LHDs are used, one experience has been that roads can degraded to an unacceptably poor state before the need for maintenance has been identified. In this paper we describe a system developed to monitor the quality of underground haul roads for autotram operations. The system exploits the fact the LHD traverses the road with a high level of repeatability both in wheel track-position and vehicle speed. This allows changes in statistics of measurements accelerometers mounted on the vehicle chassis to be used to quantify road degradation. The system has been successfully trialled on the DAS autotram system at Olympic Dam Mine. The paper will detail results from those trials.

Process mining is a process management technique to extract knowledge from the event logs recorded by an information system. We show how applying an appropriate semantic lifting to the event and workflow log may help to discover the process that is actually being executed. Moreover, we show how it is possible to extract not only knowledge about the structure of the process, but also to verify if some non-functional properties, such as security properties, hold during the process execution.