Delay test for diagnosis of power switches

Power switches are used as part of power-gating technique to reduce leakage power of a design. To the best of our knowledge, this is the first work in open-literature to show a systematic diagnosis method for accurately diagnosingpower switches. The proposed diagnosis method utilizes recently proposed DFT solution for efficient testing of power switches in the presence of PVT variation. It divides power switches into segments such that any faulty power switch is detectable thereby achieving high diagnosis accuracy. The proposed diagnosis method has been validated through SPICE simulation using a number of ISCAS benchmarks synthesized with a 90-nm gate library. Simulation results show that when considering the influence of process variation, the worst case loss of accuracy is less than 4.5%; and the worst case loss of accuracy is less than 12% when considering VT (Voltage and Temperature) variations

Flight deck engine advisor

The focus of this project is on alerting pilots to impending events in such a way as to provide the additional time required for the crew to make critical decisions concerning non-normal operations. The project addresses pilots' need for support in diagnosis and trend monitoring of faults as they affect decisions that must be made within the context of the current flight. Monitoring and diagnostic modules developed under the NASA Faultfinder program were restructured and enhanced using input data from an engine model and real engine fault data. Fault scenarios were prepared to support knowledge base development activities on the MONITAUR and DRAPhyS modules of Faultfinder. An analysis of the information requirements for fault management was included in each scenario. A conceptual framework was developed for systematic evaluation of the impact of context variables on pilot action alternatives as a function of event/fault combinations

On-Line Dependability Enhancement of Multiprocessor SoCs by Resource Management

This paper describes a new approach towards dependable design of homogeneous multi-processor SoCs in an example satellite-navigation application. First, the NoC dependability is functionally verified via embedded software. Then the Xentium processor tiles are periodically verified via on-line self-testing techniques, by using a new IIP Dependability Manager. Based on the Dependability Manager results, faulty tiles are electronically excluded and replaced by fault-free spare tiles via on-line resource management. This integrated approach enables fast electronic fault detection/diagnosis and repair, and hence a high system availability. The dependability application runs in parallel with the actual application, resulting in a very dependable system. All parts have been verified by simulation

Integration of a failure monitoring within a hybrid dynamic simulation environment

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions

Active fault-tolerance of the unmanned aerial vehicle automatic control systems

This paper presents an introductory overview of principles of the three-layer hierarchy of active fault-tolerance, providing, determination of the fault type with as many details as enough to get recoverable fault reason and failure toleration by flexible redundancy using; the conception of active fault-tolerant control in abnormal modes is described. Developed models and methods of a systematic approach to fault tolerance in the direction of the effective use of the signal, parametric and structural redundancies and selection of parrying tools. Performed experimental researches of the unmanned aerial vehicle (UAV) automatic control systems (ACS)

Early fault detection with multi-target neural networks

Wind power is seeing a strong growth around the world. At the same time, shrinking profit margins in the energy markets let wind farm managers explore options for cost reductions in the turbine operation and maintenance. Sensor-based condition monitoring facilitates remote diagnostics of turbine subsystems, enabling faster responses when unforeseen maintenance is required. Condition monitoring with data from the turbines' supervisory control and data acquisition (SCADA) systems was proposed and SCADA-based fault detection and diagnosis approaches introduced based on single-task normal operation models of turbine state variables. As the number of SCADA channels has grown strongly, thousands of independent single-target models are in place today for monitoring a single turbine. Multi-target learning was recently proposed to limit the number of models. This study applied multi-target neural networks to the task of early fault detection in drive-train components. The accuracy and delay of detecting gear bearing faults were compared to state-of-the-art single-target approaches. We found that multi-target multi-layer perceptrons (MLPs) detected faults at least as early and in many cases earlier than single-target MLPs. The multi-target MLPs could detect faults up to several days earlier than the single-target models. This can deliver a significant advantage in the planning and performance of maintenance work. At the same time, the multi-target MLPs achieved the same level of prediction stability