Self-learning of fault diagnosis identification

A good and early fault detection and isolation system along with efficient alarm management and fine sensor validation systems are very important in today¿s complex process plants, specially in terms of safety enhancement and costs reduction. This paper presents a methodology for fault characterization. This is a self-learning approach developed in two phases. An initial, learning phase, where the simulation of process units, without and with different faults, will let the system (in an automated way) to detect the key variables that characterize the faults. This will be used in a second (on line) phase, where these key variables will be monitored in order to diagnose possible faults. Using this scheme the faults will be diagnosed and isolated in an early stage where the fault still has not turned into a failure.

Distributed Fault Diagnosis Using Bayesian Reasoning in MAGNETO

Many of the emerging telecom services make use of Outer Edge Networks, in particular Home Area Networks. The configuration and maintenance of such services may not be under full control of the telecom operator which still needs to guarantee the service quality experienced by the consumer. Diagnosing service faults in these scenarios becomes especially difficult since there may be not full visibility between different domains. This paper describes the fault diagnosis solution developed in the MAGNETO project, based on the application of Bayesian Inference to deal with the uncertainty. It also takes advantage of a distributed framework to deploy diagnosis components in the different domains and network elements involved, spanning both the telecom operator and the Outer Edge networks. In addition, MAGNETO features self-learning capabilities to automatically improve diagnosis knowledge over time and a partition mechanism that allows breaking down the overall diagnosis knowledge into smaller subsets. The MAGNETO solution has been prototyped and adapted to a particular outer edge scenario, and has been further validated on a real testbed. Evaluation of the results shows the potential of our approach to deal with fault management of outer edge networks.

Study of the evolution of the seismic cycle of stress and strain associated to the El Salvador Fault Zone

• Central America: – Regional studies in Central America (Seismic Hazard). – El Salvador Fault Zone (ESFZ). – Aguacaliente‐Navarro Fault Zone (ANFZ), Central Valley of Costa Rica. – Haiti (seismic hazard) • Spain: – Regional‐Nacional studies of seismic hazards (applications to building codes, eurocode, emergency plans, etc.) – Betic range zone, south of Spain. – Ibero‐Maghrebi region (collision zone)

Fault Tolerance Analysis and Self-Healing Strategy of Autonomous, Evolvable Hardware Systems

This paper presents an analysis of the fault tolerance achieved by an autonomous, fully embedded evolvable hardware system, which uses a combination of partial dynamic reconfiguration and an evolutionary algorithm (EA). It demonstrates that the system may self-recover from both transient and cumulative permanent faults. This self-adaptive system, based on a 2D array of 16 (4×4) Processing Elements (PEs), is tested with an image filtering application. Results show that it may properly recover from faults in up to 3 PEs, that is, more than 18% cumulative permanent faults. Two fault models are used for testing purposes, at PE and CLB levels. Two self-healing strategies are also introduced, depending on whether fault diagnosis is available or not. They are based on scrubbing, fitness evaluation, dynamic partial reconfiguration and in-system evolutionary adaptation. Since most of these adaptability features are already available on the system for its normal operation, resource cost for self-healing is very low (only some code additions in the internal microprocessor core)

A mathematical framework for new fault detection schemes in nonlinear stochastic continuous-time dynamical systems

n this work, a mathematical unifying framework for designing new fault detection schemes in nonlinear stochastic continuous-time dynamical systems is developed. These schemes are based on a stochastic process, called the residual, which reflects the system behavior and whose changes are to be detected. A quickest detection scheme for the residual is proposed, which is based on the computed likelihood ratios for time-varying statistical changes in the Ornstein–Uhlenbeck process. Several expressions are provided, depending on a priori knowledge of the fault, which can be employed in a proposed CUSUM-type approximated scheme. This general setting gathers different existing fault detection schemes within a unifying framework, and allows for the definition of new ones. A comparative simulation example illustrates the behavior of the proposed schemes.

A multi-agent system with distributed bayesian reasoning for network fault diagnosis

In this paper, an innovative approach to perform distributed Bayesian inference using a multi-agent architecture is presented. The final goal is dealing with uncertainty in network diagnosis, but the solution can be of applied in other fields. The validation testbed has been a P2P streaming video service. An assessment of the work is presented, in order to show its advantages when it is compared with traditional manual processes and other previous systems.

Damage assessment on building structures subjected to the recent near-fault earthquake in Lorca (Spain)

The city of Lorca (Spain) was hit on May 11th 2011 by two consecutive earthquakes with 4.6 and 5.2 Mw respectively, causing casualties and important damage in buildings. Lorca is located in the south-east region of Spain and settled on the trace of the Murcia-Totana-Lorca fault. Although the magnitudes of these ground motions were not severe, the damage observed was considerable over a great amount of buildings. More than 300 of them have been demolished and many others are being retrofitted. This paper reports a field study on the damage caused by these earthquakes. The observed damage is related with the structural typology. Further, prototypes of the damaged buildings are idealized with nonlinear numerical models and their seismic behavior and proneness to damage concentration is further investigated through dynamic response analyses.

Fault detection and isolation on a noisy nonlinear circuit

In this paper fault detection and isolation (FDI) schemes are applied in the context of the surveillance of emerging faults in an electrical circuit. The FDI problem is studied on a noisy nonlinear circuit, where both abrupt and incipient faults in the voltage source are considered. A rigorous analysis of fault detectability precedes the application of the fault detection (FD) scheme; then, the fault isolation (FI) phase is accomplished with two alternative FI approaches, proposed as new extensions of that FD approach. Numerical simulations illustrate the applicability of the mentioned schemes.

Fault isolation schemes for a class of continuous-time stochastic dynamical systems

In this paper a new method for fault isolation in a class of continuous-time stochastic dynamical systems is proposed. The method is framed in the context of model-based analytical redundancy, consisting in the generation of a residual signal by means of a diagnostic observer, for its posterior analysis. Once a fault has been detected, and assuming some basic a priori knowledge about the set of possible failures in the plant, the isolation task is then formulated as a type of on-line statistical classification problem. The proposed isolation scheme employs in parallel different hypotheses tests on a statistic of the residual signal, one test for each possible fault. This isolation method is characterized by deriving for the unidimensional case, a sufficient isolability condition as well as an upperbound of the probability of missed isolation. Simulation examples illustrate the applicability of the proposed scheme.

Positional Accuracy in Smart-Phones and Its Effect on LBS Applications.

Location-based services (LBS) highly rely on the location of the mobile user in order to provide the service tailored to that location. This location is calculated differently depending on the technology available in the used mobile device. No matter which technology is used, the location will never be calculated 100% correctly; instead there will always be a margin of error generated during the calculation, which is referred to as positional accuracy. This research has reviewed the eight most common positioning technologies available in the major current smart-phones and assessed their positional accuracy with respect to its usage by LBS applications. Given the vast majority of these applications, this research classified them into thirteen categories, and these categories were also classified depending on their level criticality as low, medium, or high critical, and whether they function indoor or outdoor. The accuracies of different positioning technologies are compared to these two criteria. Low critical outdoor and high critical indoor applications were found technologically covered; high and medium critical outdoor ones weren?t fully resolved. Finally three potential solutions are suggested to be implemented in future smartphones to resolve this technological gap: Real-Time Kinematics Global Positioning System (RTK GPS), terrestrial transmitters, and combination of Wireless Sensors Network and Radio Frequency Identification (WSN-RFID).

Evaluation of the applicability of FRA for inter-turn fault detection in stator windings

In this paper, the applicability of the FRA technique is discussed as a method for detecting inter-turn faults in stator windings. Firstly, this method is tested in an individual medium-voltage stator coil with satisfactory results. Secondly, the tests are extended to a medium-voltage induction motor stator winding, in which inter-turn faults are performed in every coil end of one phase. Results of the frequency response in case of inter-turn faults are evaluated in both cases for different fault resistance values. The experimental setup is also described for each experiment. The results of the application of this technique to the detection of inter-turn faults justify further research in optimizing this technique for preventive maintenance.

Evaluation of the Grounding Circuit Measurements for Stator Ground-Fault Location of Synchronous Generators

Locating stator-winding ground faults accurately is a very difficult task. In this paper the grounding circuit measurements are evaluated in order to obtain information about the stator ground-fault location in synchronous generators. In power generators grounded through a high impedance, the relation between the neutral voltage and the phase voltage provide a first estimation of the fault location. The location error by using this ratio depends on the fault resistance and the value of the capacitance to ground of the stator winding. However, the error added by ignoring the value of the fault resistance is the most relevant term. This location estimation and the location error have been evaluated through the data of a real synchronous machine.

New On-Line Excitation-System Ground Fault Location Method Tested in a 106 MVA Synchronous Generator

In this paper, a novel excitation-system ground-fault location method is described and tested in a 106 MVA synchronous machine. In this unit, numerous rotor ground-fault trips took place always about an hour after the synchronization to the network. However, when the field winding insulation was checked after the trips, there was no failure. The data indicated that the faults in the rotor were caused by centrifugal forces and temperature. Unexpectedly, by applying this new method, the failure was located in a cable between the excitation transformer and the automatic voltage regulator. In addition, several intentional ground faults were performed along the field winding with different fault resistance values, in order to test the accuracy of this method to locate defects in rotor windings of large generators. Therefore, this new on-line rotor ground-fault detection algorithm is tested in high-power synchronous generators with satisfactory results.

Automatic fault detection on BIPV systems without solar irradiation data

BIPV systems are small PV generation units spread out over the territory, and whose characteristics are very diverse. This makes difficult a cost-effective procedure for monitoring, fault detection, performance analyses, operation and maintenance. As a result, many problems affecting BIPV systems go undetected. In order to carry out effective automatic fault detection procedures, we need a performance indicator that is reliable and that can be applied on many PV systems at a very low cost. The existing approaches for analyzing the performance of PV systems are often based on the Performance Ratio (PR), whose accuracy depends on good solar irradiation data, which in turn can be very difficult to obtain or cost-prohibitive for the BIPV owner. We present an alternative fault detection procedure based on a performance indicator that can be constructed on the sole basis of the energy production data measured at the BIPV systems. This procedure does not require the input of operating conditions data, such as solar irradiation, air temperature, or wind speed. The performance indicator, called Performance to Peers (P2P), is constructed from spatial and temporal correlations between the energy output of neighboring and similar PV systems. This method was developed from the analysis of the energy production data of approximately 10,000 BIPV systems located in Europe. The results of our procedure are illustrated on the hourly, daily and monthly data monitored during one year at one BIPV system located in the South of Belgium. Our results confirm that it is possible to carry out automatic fault detection procedures without solar irradiation data. P2P proves to be more stable than PR most of the time, and thus constitutes a more reliable performance indicator for fault detection procedures. We also discuss the main limitations of this novel methodology, and we suggest several future lines of research that seem promising to improve on these procedures.