Ashraf Tantawy

Aircraft AC generators: Hybrid system modeling and simulation

Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation is a necessary component for safe and reliable operation of the IDG and the aircraft. IDGs are complex systems, and a majority of the existing fault detection and isolation techniques are based on signal analysis and heuristic methods derived from experience. Model-based fault diagnosis techniques are hypothesized to be more general and powerful in designing detection and isolation schemes, but building sufficiently accurate models of complex IDGs is a difficult task. dq0 models have been developed for design and control of generators, but these models are not suitable for fault situations, where the generator may become unbalanced. In this paper, we present a hybrid phase-domain model for the aircraft generator that accurately represents both nominal and parametric faulty behaviors. We present the details of the hybrid modeling approach and simulation results of nominal operation and fault behaviors associated with parametric faults in the aircraft generator. The simulation results show that the developed model is capable of accurately capturing the generator dynamics under a variety of normal and faulty configurations.

Aircraft Power Generators: Hybrid Modeling and Simulation for Fault Detection

Integrated drive generators (IDGs) are the main source of electrical power for a number of critical systems in aircraft. Fast and accurate fault detection and isolation (FDI) are necessary components for safe and reliable operation of the IDG and the aircraft. IDGs are complex systems, and a majority of the existing FDI techniques for the electrical subsystem (brushless generator) are based on signal analysis and heuristic methods derived from experience. Model-based fault diagnosis techniques are hypothesized to be more general and powerful in designing detection and isolation schemes. However, building sufficiently accurate models of brushless generators is a difficult task. dq models have been developed for single generators, but these models are not suitable to represent the complete brushless generator either in normal or fault situations, where the generator may become unbalanced. In this paper, we develop a novel hybrid dynamical model for the complete brushless ac generator. We exploit the hybrid modeling capability to accurately model different rectifier diode faults and rotor winding faults, reported as the most severe brushless generator faults. We simulate the hybrid model for nominal and different faulty conditions, and develop fault signatures for different machine faults.

Detection using intermittent observations for passive wireless sensors

Passive wireless sensors have emerged as a new technology to measure multiple phenomena in our daily life. Passive sensors require no power source, and therefore their application domains are numerous, including health care, infrastructure protection, and national security. The deployment of passive wireless sensors and their readers has changed how detection needs to be performed. Passive sensors cannot pre-process the measurements as they have limited computational power. Therefore, no local decision is taken. Also, the reader polls the information from multiple sensors at the same time, and this causes collisions and hence packet drops and delays. Detectors designed without considering the properties of the communication channel have degraded performance. Therefore, analysis is required to quantify the degradation and take the necessary remedy action. In this paper, we study the effect of sensor-reader channel imperfection on the local detection performance of the reader, assuming no data pre-processing at the passive sensor.We consider the case of a single sensor-reader communication over a Bernoulli communication channel. We formulate the detector performance and compare with the ideal case. We present the problem of DC level detection in White Gaussian Noise, as a case study. Finally, we propose a heuristic approach to restore the original detector performance working with non-ideal channel, with the cost of increasing the delay for detection.

Maximum likelihood detection with intermittent observations

The decentralized detection performance, using wireless passive sensor networks, is analyzed according to the minimum probability of error criterion. Passive sensors communicate their measurements to the reader using data network packets, and therefore, the two main phenomena affecting the detection performance are packet loss and packet delay. In this paper, we formulate the decentralized detection problem with passive sensors and show that the optimal decision rule with packet loss is the likelihood ratio test. We present a comparative analysis study between detection with ideal and non-ideal channels, for the problem of DC level detection in White Gaussian Noise. We validate the analytical results using Monte Carlo Simulation study. Finally, we present a simple scheme for adaptive detector design, to restore the original detection performance, with the cost of increasing the delay for detection.

Transmission Control Policy design for decentralized detection in sensor networks

A Wireless Sensor Network (WSN) deployed for detection applications has the distinguishing feature that sensors cooperate to perform the detection task. Therefore, the decoupled design approach typically used to design communication networks, where each network layer is designed independently, does not lead to the desired optimal detection performance. Recent work on decentralized detection has addressed the design of MAC and routing protocols for detection applications by considering independently the Quality of Information (QoI), Channel State Information (CSI), and Residual Energy Information (REI) for each sensor. However, little attention has been given to integrate the three quality measures (QoI, CSI, REI) in the complete system design. In this work, we pursue a cross-layer approach to design a QoI, CSI, and REI-aware Transmission Control Policy (TCP) that coordinates communication between local sensors and the fusion center, in order to maximize the detection performance. We formulate and solve a constrained nonlinear optimization problem to find the optimal TCP design variables. We compare our design with the decoupled approach, where each layer is designed separately, in terms of the delay for detection and WSN lifetime.

Model-Based Design of Tree WSNs for Decentralized Detection.

The classical decentralized detection problem of finding the optimal decision rules at the sensor and fusion center, as well as variants that introduce physical channel impairments have been studied extensively in the literature. The deployment of WSNs in decentralized detection applications brings new challenges to the field. Protocols for different communication layers have to be co-designed to optimize the detection performance. In this paper, we consider the communication network design problem for a tree WSN. We pursue a system-level approach where a complete model for the system is developed that captures the interactions between different layers, as well as different sensor quality measures. For network optimization, we propose a hierarchical optimization algorithm that lends itself to the tree structure, requiring only local network information. The proposed design approach shows superior performance over several contentionless and contention-based network design approaches.

A Cross-Layer Design for Decentralized Detection in Tree Sensor Networks

The design of wireless sensor networks for detection applications is a challenging task. On one hand, classical work on decentralized detection does not consider practical wireless sensor networks. On the other hand, practical sensor network design approaches that treat the signal processing and communication aspects of the sensor network separately result in sub optimal detection performance because network resources are not allocated efficiently. In this work, we attempt to cross the gap between theoretical decentralized detection work and practical sensor network implementations. We consider a cross-layer approach, where the quality of information, channel state information, and residual energy information are included in the design process of tree-topology sensor networks. The design objective is to specify which sensors should contribute to a given detection task, and to calculate the relevant communication parameters. We compare two design schemes: (1) direct transmission, where raw data are transmitted to the fusion center without compression, and (2) in-network processing, where data is quantized before transmission. For both schemes, we design the optimal transmission control policy that coordinates the communication between sensor nodes and the fusion center. We show the performance improvement for the proposed design schemes over the classical decoupled and maximum throughput design approaches.

Fault Isolation for Spacecraft Systems: An Application to a Power Distribution Testbed

Abstract Modern electrical power disribution systems play a critical role in system operations. Therefore, early fault detection and isolation is essential to maintaining system safety and avoiding catastrophic failures. This paper discusses a fault isolation scheme based on a qualitative fault signature-based isolation mechanism that applies to abrupt, incipient and intermittent faults in the system. We discuss the isolation algorithms for a combination of these faults, and demonstrate their performance on a set of test cases generated from a NASA Ames spacecraft power distribution testbed. Our results show good isolation accuracy with 103 out of 134 faulty scenarios isolated correctly. Most of the isolation errors can be attributed to errors in the detection scheme.

Detection and Estimation of Multiple Fault Profiles Using Generalized Likelihood Ratio Tests: A Case Study

Abstract Aircraft and spacecraft electrical power distribution systems are critical to overall system operation, but these systems may experience faults. Early fault detection makes it easier for system operators to respond and avoid catastrophic failures. This paper discusses a fault detection scheme based on a tunable generalized likelihood algorithm. We discuss the detector algorithm, and then demonstrate its performance on test data generated from a spacecraft power distribution testbed at NASA Ames. Our results show high detection accuracy and low false alarm rates.

Optimal performance for detection systems in wireless passive sensor networks

Passive wireless sensors have emerged as a new technology to measure a vast majority of phenomena in our daily life. Passive sensors require no power source, and therefore their application domains are numerous, including health care, infrastructure protection, and national security, among many others. The deployment of wireless passive sensors and their readers has changed how detection needs to be performed. Passive sensors cannot pre-process the measurements as they have limited computational power. Therefore, no local decision is taken. Also, the reader polls the information from multiple sensors at the same time, and this causes collisions and hence packet drops and delays. In this paper, we formulate the detection performance, with non-ideal channels, in a probabilistic way, and compare with classical detection performance. We design an optimal adaptive Neyman-Pearson detector, given the channel probabilistic model, by formulating and solving a constrained optimization problem.