James Hare

Optimal Sensor Selection and Fusion for Heat Exchanger Fouling Diagnosis in Aerospace Systems

Heat exchangers are critical components of the environmental control system (ECS) of an aircraft. The ECS regulates temperature, pressure, and humidity of the cabin air. Fouling of the heat exchangers in an ECS may occur due to the deposition of external substances (e.g., debris) on the fins that obstruct the air flow, which increases the pressure drop across the heat exchanger and degrades its efficiency. Fouling is a critical issue, because it necessitates time consuming, periodic, and expensive maintenance. In this regard, this paper presents a two step process for fouling diagnosis of the heat exchanger: optimal sensor set selection that contains the most relevant information for fault classification and robust data analysis and sensor fusion in the presence of various uncertainties for the inference of fouling severity via different machine learning tools. This process of heat exchanger fouling diagnosis is implemented and tested on the data generated from an experimentally validated high-fidelity Simulink model of the ECS provided by an industry partner.

Decentralized smart sensor scheduling for multiple target tracking for border surveillance

Border surveillance requires regular patrolling to prevent intruders from crossing across, emphasizing the need for an automated network of sensing devices that is capable of detecting and estimating multiple moving targets. This paper proposes a fusion-driven decentralized sensor scheduling scheme that enables dynamic space-time clustering around multiple moving targets for energy-efficient track estimation. Each sensor node runs a Probabilistic Finite State Automata (PFSA) that controls the sensing and communication devices in an energy-efficient manner. This decentralized scheduling scheme is validated and compared with traditional scheduling schemes. The results show that the proposed scheme conserves energy while maintaining accurate track estimation.

Distributed smart sensor scheduling for underwater target tracking

Underwater Sensor Networks include multiple sensor nodes that possess the ability to sense and communicate the environmental information where they are deployed. It is desired that these networks are intelligent in the sense that they allow for rapid deployment, self-organization, energy conservation, and fault tolerance via implementation of rapid multi-objective optimization algorithms. This paper proposes a decentralized sensor scheduling approach that enables dynamic space-time clustering for an energy-efficient target-tracking sensor network. Each sensor node is modeled as a Probabilistic Finite State Automata (PFSA) that governs its energy consumption, sensing, and communication activities. This PFSA allows the sensor nodes to dynamically change their states to conserve energy when a target is absent and turn on their high power sensing devices when the target is present. The algorithm proposed is compared with traditional scheduling schemes and the results show that the proposed method conserves energy while maintaining an accurate track estimation in a decentralized manor.

A review of faults and fault diagnosis in micro-grids electrical energy infrastructure

With the advancements of sensing, communication, and control technologies, the existing power systems have evolved with the development of Smart Micro-grids. Smart micro-grids integrate information technology, communication technology and power generation systems into one unified micro power system for robust and reliable power. Current and future microgrids are expected to have significant clean energy penetration with rising environmental concerns. A critical problem in power systems is the cascading effect of faults leading to severe failures and blackouts unless timely protective actions are taken. As a recovery mechanism, smart micro-grids are envisioned to detect these critical changes and switch into island mode for continual power generation and system stability. However, their electrical energy infrastructure is also prone to faults and instabilities emphasizing the need to develop real-time algorithms for self diagnosis that can capture failure characteristics in the early phase of their evolution using data collected from monitoring units. This paper provides a comprehensive review that focuses on faults and fault diagnosis methods in smart micro-grids with clean and conventional generation systems as well as their interconnections.

Human activity recognition using LZW-Coded Probabilistic Finite State Automata

Human activity recognition has become an increasingly important field of research with many practical applications related to health care and leisure activities. The accessibility of inexpensive portable sensors, such as accelerometers, allows for a widespread use of this technology for both commercial and personal activity recognition. This paper develops a novel feature extraction approach to human activity recognition through the development of the Lempel-Ziv-Welch Coded Probabilistic Finite State Automata (LZW-Coded PFSA) to classify activities such as walking, jumping, running, waist rotations, and shoulder rotations. The PFSA reveal the underlying architecture of a given activity and classify it without making any a priori assumptions by inferring patterns from the sensor measurements. LZW-Coded PFSA select the optimal variable length state from the time-series data and compress it into class-separable state transition matrices π. This algorithm is robust to subject biases and is shown to be effective with a correct classification rate of 95.63%.

Real-time integrated model of a micro-grid with distributed clean energy generators and their power electronics

This paper presents a real-time integrated model of a micro-grid to simulate its electrical energy infrastructure. This infrastructure includes two PV arrays, a fuel cell, and a diesel generator that support building loads when islanded from the utility grid. The paper reviews existing models, which are usually available either as 1) low-level dynamic models, along with power electronics for specific components, e.g. PV system or fuel cell, or 2) high-level such as with conventional power systems where power electronics are ignored due to their faster dynamics. The proposed modeling strategy for sustainable power generation is emphasized for both grid-connected and islanding modes and combines slow and fast dynamics where both a micro-grid and related power electronics dynamics are simulated to show how a high-fidelity model can be used in a dynamic micro-grid environment. A synchronized regulator for islanded mode is presented. The PV arrays and fuel cell are assumed to be always available and variable irradiance conditions (e.g. nighttime) and the change of hydrogen and oxygen densities of fuel cell are shown in the paper. The diesel generator is used for black start or when the utility grid voltage or frequency drop below a threshold during which potential grid collapse could occur and the micro-grid goes into the island mode. The micro-grid model is simulated using a real-time simulator so that longer case studies and scenarios can be studied without ignoring fast dynamics. The main contribution of this paper is that both these time scales (slow and fast) are integrated in this real-time simulation platform for more realistic performance analysis. The results show the ability of the integrated micro-grid simulation to function in both grid-tied and islanded modes.

SLAM based adaptive navigation of AUVs for oil spill cleaning

Oil spill pollution causes serious contamination of the ocean, harms marine life, and results in severe impact to the economy and ecological balance. Several efforts have been dedicated for detection, monitoring and localization of oil spills; however, the complete cleaning of oil spills remains a challenging problem. Due to dynamic ocean currents on the surface and limitations in accuracies of the localization systems, the exact locations of oil spills are difficult to estimate. Thus, the estimated oil spill area may not match the actual shape of the spill. Furthermore, the access to GPS may not be always available and obstacles may exist as well. Therefore, it requires an adaptive oil cleaning method that detects oil in situ and allows the autonomous vehicle to adapt its cleaning trajectories to the oil shape while building the map and avoiding obstacles. This paper presents a SLAM based adaptive oil cleaning system using an autonomous vehicle in GPS-denied environments. The exact a-prior knowledge of the oil spill area is assumed to be unknown or merely partially known. The autonomous vehicle can dynamically detect oil shape and obstacles, and adapt its trajectories for complete oil cleaning. The proposed system introduces a discrete event supervisory controller that develops a concept of multi-resolution navigation to prevent the autonomous vehicle from getting trapped into a local minimum which is encountered generally in potential field based methods. The SLAM algorithms provide online estimate of the vehicle location. The efficiency of the proposed method is validated on the widely used high-fidelity Player/Stage simulator.

POSE: Prediction-Based Opportunistic Sensing for Energy Efficiency in Sensor Networks Using Distributed Supervisors

This paper presents a distributed supervisory control algorithm that enables opportunistic sensing for energy-efficient target tracking in a sensor network. The algorithm called Prediction-based Opportunistic Sensing (POSE), is a distributed node-level energy management approach for minimizing energy usage. Distributed sensor nodes in the POSE network self-adapt to target trajectories by enabling high power consuming devices when they predict that a target is arriving in their coverage area, while enabling low power consuming devices when the target is absent. Each node has a Probabilistic Finite State Automaton which acts as a supervisor to dynamically control its various sensing and communication devices based on target’s predicted position. The POSE algorithm is validated by extensive Monte Carlo simulations and compared with random scheduling schemes. The results show that the POSE algorithm provides significant energy savings while also improving track estimation via fusion-driven state initialization.

Game-theoretic cooperative coverage using autonomous vehicles

This paper presents a game-theoretic method for cooperative coverage of a priori unknown environments using a team of autonomous vehicles. These autonomous vehicles are required to cooperatively scan the search area without human supervision as autonomous entities. However, due to the lack of a priori knowledge of the exact obstacle locations, the trajectories of autonomous vehicles cannot be computed offline and need to be adapted as the environment is discovered in situ. In this regard, the cooperative coverage method is based upon the concept of multi-resolution navigation that consists of local navigation and global navigation. The main advantages of this algorithm are: i) the local navigation enables real-time locally optimal decisions with a reduced computational complexity by avoiding unnecessary global computations, and ii) the global navigation offers a wider view of the area seeking for unexplored regions. This algorithm prevents the autonomous vehicles from getting trapped into local minima, which is commonly encountered in potential field based algorithms. The neighboring agents among the team of autonomous vehicles exchange the most up-to-date environment information for collaborations. Given sufficient operation time, the team of autonomous vehicles are capable of achieving complete coverage in their own regions. However, in order to further improve cleaning efficiency and reduce operation time, the vehicles that finish early should participate in assisting others that are in need of help. In this sense, a cooperative game is designed to be played among involved agents for optimal task reallocation. This paper considers the cooperative oil spill cleaning application; however the concepts can be applied to general class of coverage problems. The efficacy of the algorithm is validated using autonomous vehicles equipped with lasers in an obstacle-rich environment on the high-fidelity Player/Stage simulator.