A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance

Prognostic Health Management aims to predict the Remaining Useful Life (RUL) of degrading components/systems utilizing monitoring data. These RUL predictions form the basis for optimizing maintenance planning in a Predictive Maintenance (PdM) paradigm. We here propose a metric for assessing data-driven prognostic algorithms based on their impact on downstream PdM decisions. The metric is defined in association with a decision setting and a corresponding PdM policy. We consider two typical PdM decision settings, namely component ordering and/or replacement planning, for which we investigate and improve PdM policies that are commonly utilized in the literature. All policies are evaluated via the data-driven estimation of the long-run expected maintenance cost per unit time, relying on available monitoring data from run-to-failure experiments. The policy evaluation enables the estimation of the proposed metric. The latter can further serve as an objective function for optimizing heuristic PdM policies or algorithms' hyperparameters. The effect of different PdM policies on the metric is initially investigated through a theoretical numerical example. Subsequently, we employ four data-driven prognostic algorithms on a simulated turbofan engine degradation problem, and investigate the joint effect of prognostic algorithm and PdM policy on the metric, resulting in a decision-oriented performance assessment of these algorithms

Integrating discrete stochastic models with single-cell and single-molecule experiments

2019 Summer.Includes bibliographical references.Modern biological experiments can capture the behaviors of single biomolecules within single cells. Much like Robert Brown looking at pollen grains in water, experimentalists have noticed that individual cells that are genetically identical behave seemingly randomly in the way they carry out their most basic functions. The field of stochastic single-cell biology has been focused developing mathematical and computational tools to understand how cells try to buffer or even make use of such fluctuations, and the technologies to measure such fluctuations has vastly improved in recent years. This dissertation is focused on developing new methods to analyze modern single-cell and single-molecule biological data with discrete stochastic models of the underlying processes, such as stochastic gene expression and single-mRNA translation. The methods developed here emphasize a strong link between model and experiment to help understand, design, and eventually control biological systems at the single-cell level

Data Analysis and Experimental Design for Accelerated Life Testing with Heterogeneous Group Effects

abstract: In accelerated life tests (ALTs), complete randomization is hardly achievable because of economic and engineering constraints. Typical experimental protocols such as subsampling or random blocks in ALTs result in a grouped structure, which leads to correlated lifetime observations. In this dissertation, generalized linear mixed model (GLMM) approach is proposed to analyze ALT data and find the optimal ALT design with the consideration of heterogeneous group effects. Two types of ALTs are demonstrated for data analysis. First, constant-stress ALT (CSALT) data with Weibull failure time distribution is modeled by GLMM. The marginal likelihood of observations is approximated by the quadrature rule; and the maximum likelihood (ML) estimation method is applied in iterative fashion to estimate unknown parameters including the variance component of random effect. Secondly, step-stress ALT (SSALT) data with random group effects is analyzed in similar manner but with an assumption of exponentially distributed failure time in each stress step. Two parameter estimation methods, from the frequentist’s and Bayesian points of view, are applied; and they are compared with other traditional models through simulation study and real example of the heterogeneous SSALT data. The proposed random effect model shows superiority in terms of reducing bias and variance in the estimation of life-stress relationship. The GLMM approach is particularly useful for the optimal experimental design of ALT while taking the random group effects into account. In specific, planning ALTs under nested design structure with random test chamber effects are studied. A greedy two-phased approach shows that different test chamber assignments to stress conditions substantially impact on the estimation of unknown parameters. Then, the D-optimal test plan with two test chambers is constructed by applying the quasi-likelihood approach. Lastly, the optimal ALT planning is expanded for the case of multiple sources of random effects so that the crossed design structure is also considered, along with the nested structure.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Computer Aided Product Design and Development for Peroxide Based Disinfectants

Disinfectants are antimicrobial chemicals that are commonly used in health care facilities to prevent or reduce the spread of pathogenic microorganisms. These products are under national regulations for the claims they make and have to be tested for their microbial activity against different microorganisms. They have to also be tested for product stability, corrosion and toxicity. These tests, especially the microbial efficacy tests, are very expensive and take a long time to perform (anywhere from two days to four months). Disinfectant formulations have to have a balance between their microbial activity, corrosivity, and safety. The more active ingredients in the formulation, the stronger the product, but the higher the corrosivity and toxicity. Therefore, it is desirable to use as low concentrations of ingredients as possible in the formulation to achieve the acceptable antimicrobial activity. The final product has to also be chemically and physically stable for at least one year. Consequently, the product development process takes at least six months and sometimes even up to two years. The cost might also reach hundreds of thousands of dollars. The objective of this project was to design a systematic way to take advantage of the historical data, augment them with some experimental trials, perform a regression analysis using the best possible methods available such as least squares or neural networks, invert the models, and finally use optimization techniques to develop the new products in the shortest period of time. The formulation predicted by this model will be much closer to the final formulation resulting in significant reductions in time and cost of the product development process. Furthermore, the model can be updated with the newly generated data to improve its predictive capability. Lastly, the disinfectant formulation can be viewed as a case study for a broader problem, formulation product design, and can be implemented in similar cases where the formulation of a new product should pass certain interfering criteria, such as adhesives, pharmaceutical drugs, agriculture pesticides, detergents, etc

Planning and inference of sequential accelerated life tests

Ph.DDOCTOR OF PHILOSOPH