Studies in condition based maintenance using proportional hazards models with imperfect observations

Introduction and literature review -- Preliminary notations -- problem statement -- Optimal inspection period and replacement policy for CBM with imperfect information using PHM -- Problem formulation -- Formulation of the POMDP -- Long-run average cost and total long-run average cost -- Optimal inspection period -- Numerical example -- Evaluating the remaining life for equipment with unobservable states -- Practical implications -- Model assumptions -- Development of parameter estimation methods for a condition based maintenance with indirect observations -- Proposed model -- Parameters' estimation -- Optimal inspection interval and optimal replacement policy -- Reliability function and mean residual life -- Estimation of the model's parameter

Short and long-term wind turbine power output prediction

In the wind energy industry, it is of great importance to develop models that accurately forecast the power output of a wind turbine, as such predictions are used for wind farm location assessment or power pricing and bidding, monitoring, and preventive maintenance. As a first step, and following the guidelines of the existing literature, we use the supervisory control and data acquisition (SCADA) data to model the wind turbine power curve (WTPC). We explore various parametric and non-parametric approaches for the modeling of the WTPC, such as parametric logistic functions, and non-parametric piecewise linear, polynomial, or cubic spline interpolation functions. We demonstrate that all aforementioned classes of models are rich enough (with respect to their relative complexity) to accurately model the WTPC, as their mean squared error (MSE) is close to the MSE lower bound calculated from the historical data. We further enhance the accuracy of our proposed model, by incorporating additional environmental factors that affect the power output, such as the ambient temperature, and the wind direction. However, all aforementioned models, when it comes to forecasting, seem to have an intrinsic limitation, due to their inability to capture the inherent auto-correlation of the data. To avoid this conundrum, we show that adding a properly scaled ARMA modeling layer increases short-term prediction performance, while keeping the long-term prediction capability of the model

Multivariate calibration of a water and energy balance model in the spectral domain

The objective of this paper is to explore the possibility of using multiple variables in the calibration of hydrologic models in the spectral domain. A simple water and energy balance model was used, combined with observations of the energy balance and the soil moisture profile. The correlation functions of the model outputs and the observations for the different variables have been calculated after the removal of the diurnal cycle of the energy balance variables. These were transformed to the frequency domain to obtain spectral density functions, which were combined in the calibration algorithm. It has been found that it is best to use the square root of the spectral densities in the parameter estimation. Under these conditions, spectral calibration performs almost equally as well as time domain calibration using least squares differences between observed and simulated time series. Incorporation of the spectral coefficients of the cross-correlation functions did not improve the results of the calibration. Calibration on the correlation functions in the time domain led to worse model performance. When the meteorological forcing and model calibration data are not overlapping in time, spectral calibration has been shown to lead to an acceptable model performance. Overall, the results in this paper suggest that, in case of data scarcity, multivariate spectral calibration can be an attractive tool to estimate model parameters

New Methods for Estimating Labor Supply Functions: A Survey

This paper surveys new methods for estimatifg labor supply functions. A unified framework of analysis is presented. All recent models of labor supply are special cases of a general index function model developed for the analysis o dummy endogenous variables.

Competition Among Spatially Differentiated Firms: An Empirical Model with an Application to Cement

The theoretical literature of industrial organization shows that the distances between consumers and firms have first-order implications for competitive outcomes whenever transportation costs are large. To assess these effects empirically, we develop a structural model of competition among spatially differentiated firms and introduce a GMM estimator that recovers the structural parameters with only regional-level data. We apply the model and estimator to the portland cement industry. The estimation fits, both in-sample and out-of-sample, demonstrate that the framework explains well the salient features of competition. We estimate transportation costs to be $0.30 per tonne-mile, given diesel prices at the 2000 level, and show that these costs constrain shipping distances and provide firms with localized market power. To demonstrate policy-relevance, we conduct counter-factual simulations that quantify competitive harm from a hypothetical merger. We are able to map the distribution of harm over geographic space and identify the divestiture that best mitigates harm.

TEXAS FIELD CROPS: ESTIMATION WITH CURVATURE

Some implications of theory are easily maintained in econometric estimation, but computational costs of maintaining curvature properties (sufficient for existence of an optimal solution) have often proved prohibitive. They also have been violated frequently by unrestricted econometric estimates. A computationally manageable procedure for maintaining and testing curvature is used here to obtain estimates of product supplies and input demands for Texas field crops consistent with the theory of the competitive industry. The curvature properties are tested along with several technology restrictions.Crop Production/Industries,

Expert Elicitation for Reliable System Design

This paper reviews the role of expert judgement to support reliability assessments within the systems engineering design process. Generic design processes are described to give the context and a discussion is given about the nature of the reliability assessments required in the different systems engineering phases. It is argued that, as far as meeting reliability requirements is concerned, the whole design process is more akin to a statistical control process than to a straightforward statistical problem of assessing an unknown distribution. This leads to features of the expert judgement problem in the design context which are substantially different from those seen, for example, in risk assessment. In particular, the role of experts in problem structuring and in developing failure mitigation options is much more prominent, and there is a need to take into account the reliability potential for future mitigation measures downstream in the system life cycle. An overview is given of the stakeholders typically involved in large scale systems engineering design projects, and this is used to argue the need for methods that expose potential judgemental biases in order to generate analyses that can be said to provide rational consensus about uncertainties. Finally, a number of key points are developed with the aim of moving toward a framework that provides a holistic method for tracking reliability assessment through the design process.Comment: This paper commented in: [arXiv:0708.0285], [arXiv:0708.0287], [arXiv:0708.0288]. Rejoinder in [arXiv:0708.0293]. Published at http://dx.doi.org/10.1214/088342306000000510 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Failure mode prediction and energy forecasting of PV plants to assist dynamic maintenance tasks by ANN based models

In the field of renewable energy, reliability analysis techniques combining the operating time of the system with the observation of operational and environmental conditions, are gaining importance over time. In this paper, reliability models are adapted to incorporate monitoring data on operating assets, as well as information on their environmental conditions, in their calculations. To that end, a logical decision tool based on two artificial neural networks models is presented. This tool allows updating assets reliability analysis according to changes in operational and/or environmental conditions. The proposed tool could easily be automated within a supervisory control and data acquisition system, where reference values and corresponding warnings and alarms could be now dynamically generated using the tool. Thanks to this capability, on-line diagnosis and/or potential asset degradation prediction can be certainly improved. Reliability models in the tool presented are developed according to the available amount of failure data and are used for early detection of degradation in energy production due to power inverter and solar trackers functional failures. Another capability of the tool presented in the paper is to assess the economic risk associated with the system under existing conditions and for a certain period of time. This information can then also be used to trigger preventive maintenance activities