As a consequence of the recent deregulation in the electrical power production industry, there has been a shift in the traditional ownership of power plants and the way they are operated. To hedge their business risks, the many new private entrepreneurs enter into long-term service agreement (LTSA) with third parties for their operation and maintenance activities. As the major LTSA providers, original equipment manufacturers have invested huge amounts of money to develop preventive maintenance strategies to minimize the occurrence of costly unplanned outages resulting from failures of the equipments covered under LTSA contracts. As a matter of fact, a recent study by the Electric Power Research Institute estimates the cost benefit of preventing a failure of a General Electric 7FA or 9FA technology compressor at $10 to $20 million. Therefore, in this dissertation, a two-phase data analytics approach is proposed to use the existing monitoring gas path and vibration sensors data to first develop a proactive strategy that systematically detects and validates catastrophic failure precursors so as to avoid the failure; and secondly to estimate the residual time to failure of the unhealthy items. For the first part of this work, the time-frequency technique of the wavelet packet transforms is used to de-noise the noisy sensor data. Next, the time-series signal of each sensor is decomposed to perform a multi-resolution analysis to extract its features. After that, the probabilistic principal component analysis is applied as a data fusion technique to reduce the number of the potentially correlated multi-sensors measurement into a few uncorrelated principal components. The last step of the failure precursor detection methodology, the anomaly detection decision, is in itself a multi-stage process. The obtained principal components from the data fusion step are first combined into a one-dimensional reconstructed signal representing the overall health assessment of the monitored systems. Then, two damage indicators of the reconstructed signal are defined and monitored for defect using a statistical process control approach. Finally, the Bayesian evaluation method for hypothesis testing is applied to a computed threshold to test for deviations from the healthy band. To model the residual time to failure, the anomaly severity index and the anomaly duration index are defined as defects characteristics. Two modeling techniques are investigated for the prognostication of the survival time after an anomaly is detected: the deterministic regression approach, and parametric approximation of the non-parametric Kaplan-Meier plot estimator. It is established that the deterministic regression provides poor prediction estimation. The non parametric survival data analysis technique of the Kaplan-Meier estimator provides the empirical survivor function of the data set comprised of both non-censored and right censored data. Though powerful because no a-priori predefined lifetime distribution is made, the Kaplan-Meier result lacks the flexibility to be transplanted to other units of a given fleet. The parametric analysis of survival data is performed with two popular failure analysis distributions: the exponential distribution and the Weibull distribution. The conclusion from the parametric analysis of the Kaplan-Meier plot is that the larger the data set, the more accurate is the prognostication ability of the residual time to failure model.

Helmut-Schmidt-Univ., Habil.-Schr.--Hamburg, 2004. / Literaturverz. S. [324] - 351.

Water distribution systems, electrical power transmission systems, and other civil infrastructure systems are essential to the smooth and stable operation of regional economies. Since the functions of such infrastructure systems often are inter-dependent, the systems sometimes suffer unforeseen functional disruptions. For example, the widespread power outage due to the malfunction of an electric power substation, which occurred in the northeastern United States and parts of Canada in August 2003, interrupted the supply of water to several communities, leading to inconvenience and economic losses. The sequence of such failures leading to widespread outages is referred to as a cascading failure. Assessing the vulnerability of communities to natural and man-made hazards should take the possibility of such failures into account. In seismic risk assessment, the risk to a facility or a building is generally specified by one of two basic approaches: through a probabilistic seismic hazard analysis (PSHA) and a stipulated scenario earthquake (SE). A PSHA has been widely accepted as a basis for design and evaluation of individual buildings, bridges and other facilities. However, the vulnerability assessment of distributed infrastructure facilities requires a model of spatial intensity of earthquake ground motion. Since the ground motions from a PSHA represent an aggregation of earthquakes, they cannot model the spatial variation in intensity. On the other hand, when a SE-based analysis is used, the spatial correlation of seismic intensities must be properly evaluated. This study presents a new methodology for evaluating the functionality of an infrastructure system situated in a region of moderate seismicity considering functional interactions among the systems in the network, cascading failure, and spatial correlation of ground motion. The functional interactions among facilities in the systems are modeled by fault trees, and the impact of cascading failures on serviceability of a networked system is computed by a procedure from the field of operations research known as a shortest path algorithm. The upper and lower bound solutions to spatial correlation of seismic intensities over a region are obtained.