David Baglee

The effect of driving style on electric vehicle performance, economy and perception

It has long been known that driving style has a major impact on the efficiency of conventional combustion engine powered vehicles. Particular aspects of conventional driving such as harsh acceleration and deceleration and poor anticipation have been demonstrated to be unfavourable for clear technical reasons relating to the efficiency of the internal combustion engine at particular speeds and loads. Furthermore, definite trends have been identified in terms of the relationship between age and driving style for conventional vehicles. Little work has been done in this area using electric vehicles. This paper addresses this by presenting a detailed study of the performance of a number of drivers around a standard route in an electric vehicle. In addition to highlighting how particular aspects of driving style influence power consumption and regeneration. We also look at how the drivers perceived the electric vehicle compared to conventional vehicles of the same class.

Achieving Autonomous Compressive Spectrum Sensing for Cognitive Radios

Compressive sensing (CS) technologies present many advantages over other existing approaches for implementing wideband spectrum sensing in cognitive radios (CRs), such as reduced sampling rate and computational complexity. However, there are two significant challenges: 1) choosing an appropriate number of sub-Nyquist measurements and 2) deciding when to terminate the greedy recovery algorithm that reconstructs wideband spectrum. In this paper, an autonomous compressive spectrum sensing (ACSS) framework is presented that enables a CR to automatically choose the number of measurements while guaranteeing the wideband spectrum recovery with a small predictable recovery error. This is realized by the proposed measurement infrastructure and the validation technique. The proposed ACSS can find a good spectral estimate with high confidence by using only a small testing subset in both noiseless and noisy environments. Furthermore, a sparsity-aware spectral recovery (SASR) algorithm is proposed to recover the wideband spectrum without requiring knowledge of the instantaneous spectral sparsity level. Such an algorithm bridges the gap between CS theory and practical spectrum sensing. Simulation results show that ACSS not only can recover the spectrum using an appropriate number of measurements but can considerably improve the spectral recovery performance as well, compared with existing CS approaches. The proposed recovery algorithm can autonomously adopt a proper number of iterations, therefore solving the problems of underfitting or overfitting, which commonly exist in most greedy recovery algorithms.

Reinforcement Learning for Scheduling of Maintenance

Improving maintenance scheduling has become an area of crucial importance in recent years. Condition-based maintenance (CBM) has started to move away from scheduled maintenance by providing an indication of the likelihood of failure. Improving the timing of maintenance based on this information to maintain high reliability without resorting to over-maintenance remains, however, a problem. In this paper we propose Reinforcement Learning (RL), to improve long term reward for a multistage decision based on feedback given either during or at the end of a sequence of actions, as a potential solution to this problem. Several indicative scenarios are presented and simulated experiments illustrate the performance of RL in this application.

Maintenance Strategy Development in the UK Food and Drink Industry

The food and drink industry is a key industrial sector employing many thousands of people making a substantial contribution to the global economy. Small to medium enterprises (SMEs) account for a considerable percentage of the sector’s activity. While many manufacturing sectors have embraced and contributed to the development of modern maintenance practices, the food and drink industry is perceived to be falling behind, a trend which is having a negative effect on the productivity of this industry. This paper investigates the barriers, both real and perceived, to the implementation of modern maintenance practices and the opportunities to apply modern technology to support improved efficiency.

The measurement of maintenance function efficiency through financial KPIs

The measurement of the performance in the maintenance function has produced large sets of indicators that due to their nature and disparity in criteria and objectives have been grouped in different subsets lately, emphasizing the set of financial indicators. The generation of these indicators demands data collection of high reliability that is only made possible through a model of costs adapted to the special casuistry of the maintenance function, characterized by the occultism of these costs.

Maintenance Strategy Development Within SME’s: The Development of an Integrated Approach

The importance of maintenance has increased, as high productivity and quality can be achieved by means of well-developed and organised maintenance strategies. However, this assumes that maintenance is controlled in such a way that equipment is stopped for maintenance via a systematic schedule. With the recent advances in technology many methodologies, tools, techniques and strategies have been developed and tested. Unfortunately, the majority of organisations are constrained by certain barriers with the resulting loss of major benefits. These are usually classified as Small and Medium Sized Enterprises (SMEs). Based upon our data analysis a new maintenance methodology, the Advanced Integrated Maintenance Management System (AIMMS) is developed. To enable the implementation, monitoring and evaluation of AIMMS a computerised system — Maintenance Management (MainMan) — was developed and implemented within several case study companies. This paper examines the implementation process within one of these companies. The results indicate that AIMMS supports strategic maintenance decisions, and helps to increase equipment effectiveness through prioritising equipment criticality and focusing on specific resources that will maximise gains based upon a return on investment.Copyright

Self-maintenance techniques: a smart approach towards self-maintenance system

The modern systems operating at varying conditions brought a new paradigm shift to in-machine renovation and repair. These systems often encounter an infinite collection of clumsy diagnostic tools and applications that decrease agility, increase time-to-repair, and increase management overheads. One approach is to remove the human and potential costly and time consuming human errors, from the diagnosis of faults and implementation of a maintenance strategy. In order to achieve this it is necessary to develop systems that support advanced intelligent maintenance systems or smart maintenance technologies. Self-maintenance machines can be a better option with the capabilities of condition monitoring, diagnosing, repair planning and executing in order to extend the life and performance of equipment. The objective of this paper is to discuss the concept of self-maintenance, need of self-maintenance, potential scenarios where self-maintenance can be successfully implemented and issues related to self-maintenance machines. It has been concluded that the aim is to have self-maintenance system in order to make a machine capable of reconfiguration, compensation, and self-maintenance.

Converter simulation using SimPowerSystems a comparison of drive cycles and control strategies

This paper describes the setup of a direct current to direct current (DC-DC) converter simulation using Matlab / Simulinks SimPowerSystems (SPS). The simulation is set up to focus on full drive cycle simulation to be able to visualize the effects of a control strategy on a drive cycle over time. The aim of these simulations is to compare the effects of different control strategies and to introduce a modular control strategy. A modular control strategy is proposed and tested and the effects of the control strategies are described and discussed. The load factor of the battery current is calculated to look for improvements in smoothness.

Condition Management of Marine Lube Oil and the Role of Intelligent Sensor Systems in Diagnostics

Failures in marine diesel engines can be costly and can cause extreme inconvenience when they result in ships becoming stranded. Lubricating oil is a crucial component in maintaining engine reliability and so monitoring its condition is essential. Furthermore the lubricating oil offers early indication of various other engine faults. Current approaches to oil-based condition monitoring involve samples being sent for land based testing which involves considerable delay during which the situation could deteriorate further. Furthermore there is a substantial risk of contamination. The POSSEIDON project aimed to address this by developing a system involving real-time condition monitoring sensors observing the properties of the lubricating oil. Novel sensors were developed which address the specific issues associated with the marine environment. Furthermore, to complement the sensor system outputs, specific monitoring and diagnosis software has been developed to support the operation of onboard personnel with specific advice. On-line management of engine and lubricant condition aboard the ship may thus be achieved. In this paper we will describe the progress achieved in this area by the recently completed POSSEIDON project, outline the opportunities for ongoing development in this area and describe the roadmap for future development. The Reliability Centered Maintenance (RCM) paradigm will be applied to identify critical aspects of oil condition and prioritize parameters for measurement. The critical issues for development of the prototype unit into a viable commercial unit will be discussed including hardware design constraints, sensor miniaturization and display optimization. Issues such onboard connectivity, ship to shore communications will also be addressed.

The Development of a Mobile e-maintenance system utilizing RFID and PDA Technologies

Technological developments in e-maintenance systems, radio-frequency identification (RFID) and personal digital assistant (PDA) have proven to satisfy the increasing demand for improved machinery reliability, efficiency and safety. RFID technology is used to store and remotely retrieve electronic maintenance data in order to provide instant access to up-to-date, accurate and detailed information. PDA technology supports the transfer of data between user and central maintenance database systems. The DYNAMITE (Dynamic Decisions in Maintenance) project intends to support maintenance decisions by developing and applying a blend of leading-edge communications and sensor technology including RFID and PDA to enhance diagnostic and prognostic capabilities. The paper will present the development and implementation of an innovative system using newly developed RFID and PDA technology which is capable of storing and analyzing pertinent maintenance data which can be accessed by both mobile and static computing devices.