Joe M. Howe

Sensor Failure Detection, Identification, and Accommodation Using Fully Connected Cascade Neural Network

Modern control systems rely heavily on their sensors for reliable operation. Failure of a sensor could destabilize the system, which could have serious consequences to the systems operations. Therefore, there is a need to detect and accommodate such failures, particularly if the system in question is of a safety critical application. In this paper, a sensor failure detection, identification, and accommodation (SFDIA) scheme is presented. This scheme is based on the fully connected cascade (FCC) neural network (NN) architecture. The NN is trained using the neuron by neuron learning algorithm. This NN architecture is chosen because of its efficiency in terms of the number of neurons and the number of inputs required to solve a problem. The SFDIA scheme considers failures in pitch, roll, and yaw rate gyro sensors of an aircraft. A total of 105 experiments were conducted; out of which, only one went undetected. The SFDIA scheme presented here is efficient, compact, and computationally less expensive, in comparison to schemes using, for example, the popular multilayer perceptron NN. These benefits are inherited from the FCC NN architecture.

Aircraft sensor estimation for fault tolerant flight control system using fully connected cascade neural network

Flight control systems that are tolerant to failures can increase the endurance of an aircraft in case of a failure. The two major types of failure are sensor and actuator failures. This paper focuses on the failure of the gyro sensors in an aircraft. The neuron by neuron (NBN) learning algorithm, which is an improved version of the Levenberg-Marquardt (LM) algorithm, is combined with the fully connected cascade (FCC) neural network architecture to estimate an aircrafts sensor measurements. Compared to other neural networks and learning algorithms, this combination can produce good sensor estimates with relatively few neurons. The estimators are developed and evaluated using flight data collected from the X-Plane flight simulator. The developed sensor estimators can replicate a sensors measurements with as little as 2 neurons. The results reflect the combined power of the NBN algorithm and the FCC neural network architecture.

A multi-objective planning framework for optimal integration of distributed generations

This paper presents an evolutionary algorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power generation, line losses and CO2 emissions, and maximizing the benefits from the DG over a 20 years planning horizon. The method assesses the fault current constraint imposed on the distribution network by the existing and new DG in order not to violate the short circuit capacity of existing switchgear. The analysis utilizes one of the highly regarded evolutionary algorithm, the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for multi-objective optimization and MATPOWER for solving the optimal power flow problems.

Increasing endurance of an autonomous robot using an Immune-Inspired framework

This paper describes the implementation of an online immune-inspired framework to help increase endurance of an autonomous robot. Endurance is defined as the ability of the robot to exert itself for a long period of time. The immune-inspired framework provides such capability by monitoring the behavior of the robot to ensure continuous and safe behavior. The immune-inspired framework combines innate and adaptive immune inspired algorithms. Innate uses a dendritic cell based innate immune algorithm, and adaptive uses an instance based B-cell approach. Results presented in this paper shows that when the robot is implemented with the immune-inspired framework, health and survivability of a robot is improved, therefore increasing its endurance.

Power Profiling and Inherent Lag Prediction of a Wind Power Generating System for Its Integration to an Energy Storage System

A key challenge within the power sector is to address the issue of intermittency. It is the unavailability of energy at all times in order to meet the demand requirements. Intermittency is responsible for reducing the efficiency of the national infrastructure and can compromise energy security. Increasing use of renewable energy can cause the increasing intermittency. This is an important issue that needs to be dealt with. Predictive mechanisms based on historical data have been used previously to try and address energy security with renewables. However, the effectiveness of the predictive mechanisms are low. Going forward, energy storage systems will play a key role in securing the energy supply provided by renewables. Efficient use of energy storage relies on information about the generator system that it is coupled with. This paper aims to show that despite the inherent characteristics of renewable energy generation, the nature of mechanical generation of renewable systems can be equated and modelled. The model can provide the information required for energy storage coupling. The model equates the inherent lag using the torque values of the generator, as well as the generators velocity. The model is part of a larger framework that predicts the output power profile of the renewables, using an Artificial Neural Network (ANN). The predictive information can further improve the performance of the coupled energy storage system and address intermittency.

Adaptive and Online Health Monitoring System for Autonomous Aircraft

ight, especially for an Unmanned Aerial System (UAS). Good situation awareness can be achieved by incorporating an Adaptive Health Monitoring System (AHMS) to the aircraft. The AHMS monitors the ight outcome or ight behaviours of the aircraft based on its external environmental conditions and the behaviour of its internal systems. The AHMS does this by associating a health value to the aircraft’s behaviour based on the progression of its sensory values produced by the aircraft’s modules, components and/or subsystems. The AHMS indicates erroneous ight behaviour when a deviation to this health information is produced. This will be useful for a UAS because the pilot is taken out of the control loop and is unaware of how the environment and/or faults are aecting the behaviour of the aircraft. The autonomous pilot can use this health information to help produce safer and securer ight behaviour or fault tolerance to the aircraft. This allows the aircraft to y safely in whatever the environmental conditions. This health information can also be used to help increase the endurance of the aircraft. This paper describes how the AHMS performs its capabilities.

A two-stage approach to defining an affected community based on the directly affected population and the sense of community

Studies have demonstrated the inadequacy of relying on existing administrative boundaries or simple proximity to define an affected community. The proposal and siting of hazardous facilities can have a range of impacts upon people across wide areas, with some more affected than others as a result of living with the physical impacts of construction or the fear associated with perceived risk. We term those most affected the directly affected population and propose a two-stage model for identifying an affected community which places those most affected at the centre of the definition. The second stage is to identify the relationships those most affected have with the wider elements of the sense of community to discover the existing community or communities which are affected. Illustrated by the siting of a low-level radioactive waste disposal facility at Dounrey in the north of Scotland, we show that elements of the lived community experience may have very different shapes, extents and conflicting interests which pose challenges for their incorporation into a siting process. The two-stage model presented in this paper, by placing those most directly affected at the centre and working from there out into the existing communities, identifies issues early in any siting process to improve their incorporation and amelioration.

Safer Flying Using an Immune-Inspired Adaptive Health Monitoring System

An adaptive health monitoring system or AHMS was developed to improve or add situational awareness to an aircraft, former is for the manned aircraft, and latter is for the unmanned aerial system (UAS). The AHMS provides situational awareness by characterising a notion of health to the aircraft, and uses this health value to perform error detection and error compensation. The notion of health is created by correlating sensors and controller outputs available to the AHMS during flight, and the AHMS does so using an immune-inspired framework (IIF). This creates the AHMS-IIF. This paper presents the results of implementation of the AHMS-IIF on a simple aircraft: the glider system, to see if the AHMS-IIF can provide the situational awareness, which can also increase the endurance of this simple system. The paper shows the AHMS-IIF has provided safer flight outcomes for the glider system. This is judged by the longer duration of flights and higher number of safe landings for the glider, when the glider is flown with the help of the AHMS-IIF at a suitable sampling and accommodation rate. Furthermore, the presented AHMS-IIF is able to achieve its objectives without prior training and optimization of the algorithms, differentiating this framework against other vehicle health management system.