AN OPTIONS APPROACH TO QUANTIFY THE VALUE OF DECISIONS AFTER PROGNOSTIC INDICATION

Safety, mission and infrastructure critical systems have started adopting prognostics and health management, a discipline consisting of technologies and methods to assess the reliability of a product in its actual life-cycle conditions to determine the advent of failure and mitigate system risks. The output from a prognostic system is the remaining useful life of the host system; it gives the decision-maker lead-time and flexibility in maintenance. Examples of flexibility include delaying maintenance actions to use up the remaining useful life and halting the operation of the system to avoid critical failure. Quantifying the value of flexibility enables decision support at the system level, and provides a solution to the fundamental tradeoff in maintenance of systems with prognostics: minimize the remaining useful life thrown while concurrently minimizing the risk of failure. While there are cost-benefit models to quantify the value of implementing prognostics, they are applicable to the fleet level, they do not incorporate the value of decisions after prognostic indication (value of flexibility or contingency actions), and do not use PHM information for dynamic maintenance scheduling. This dissertation develops a decision support model based on `options' theory- a financial derivative tool extended to real assets - to quantify maintenance decisions after a remaining useful life prediction. A hybrid methodology based on Monte Carlo simulations and decision trees is developed. The methodology incorporates the value of contingency actions when assessing the benefits of PHM. The model is extended and combined with least squares Monte Carlo methods to quantify the option to wait to perform maintenance; it represents the value obtained from PHM at the system level. The methodology also allows quantifying the benefits of PHM for individualized maintenance policies for systems in real-time, and to set a dynamic maintenance threshold based on PHM information. This work is the first known to quantify the flexibility enabled by PHM and to address the cost-benefit-risk ramifications after prognostic indication at the system level. The contributions of the dissertation are demonstrated on data for wind farms

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity

A "DESIGN FOR AVAILABILITY" METHODOLOGY FOR SYSTEMS DESIGN AND SUPPORT

Prognostics and Health Management (PHM) methods are incorporated into systems for the purpose of avoiding unanticipated failures that can impact system safety, result in additional life cycle cost, and/or adversely affect the availability of a system. Availability is the probability that a system will be able to function when called upon to do so. Availability depends on the system's reliability (how often it fails) and its maintainability (how efficiently and frequently it is pro-actively maintained, and how quickly it can be repaired and restored to operation when it does fail). Availability is directly impacted by the success of PHM. Increasingly, customers of critical systems are entering into "availability contracts" in which the customer either buys the availability of the system (rather than actually purchasing the system itself) or the amount that the system developer/manufacturer is paid is a function of the availability achieved by the customer. Predicting availability based on known or predicted system reliability, operational parameters, logistics, etc., is relatively straightforward and can be accomplished using several methods and many existing tools. Unfortunately in these approaches availability is an output of the analysis. The prediction of system's parameters (i.e., reliability, operational parameters, and/or logistics management) to meet an availability requirement is difficult and cannot be generally done using today's existing methods. While determining the availability that results from a set of events is straightforward, determining the events that result in a desired availability is not. This dissertation presents a "design for availability" methodology that starts with an availability requirement and uses it to predict the required design, logistics and operations parameters. The method is general and can be applied when the inputs to the problem are uncertain (even the availability requirement can be represented as a probability distribution). The method has been demonstrated on several examples with and without PHM

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

Objective assessment of functional and motor-cognitive outcomes among asymptomatic primary hyperparathyroidism patients undergoing parathyroidectomy using wearable technologies: a pilot study towards better informed clinical decision-making

For the past 40 years, most patients with Primary Hyperparathyroidism (PHPT) have presented with the asymptomatic form of PHPT. Despite the dominance of the asymptomatic PHPT phenotype, current National Institutes of Health (NIH) indications for parathyroidectomy fail to identify as many as 80% of patients afflicted with asymptomatic PHPT. To date, studies of the therapeutic benefits of parathyroidectomy among asymptomatic PHPT patients have relied on general health questionnaires and patient reports of their satisfaction with the surgery. The purpose of the present study was to implement objective, quantifiable metrics in assessing whether or not asymptomatic PHPT patients experience improvements in domains salient to them such as mobility and cognitive function following parathyroidectomy. This information may help set the foundation for more accurately identifying patients who would benefit from parathyroidectomy. We hypothesized that asymptomatic patients would exhibit improvement in motor-cognitive outcomes following successful parathyroidectomy. We performed a single-center prospective assessment of gait, frailty, and motor-cognitive function among patients diagnosed with PHPT. Demographics, medical history, and perioperative labs were recorded. Pre- and post-surgical measures included the Fried frailty criteria, the PROMIS 10 Global Health Scale, and gait analysis under habitual (ST), walking while performing working memory test (dual-task: DT), and fast-walking conditions, an upper extremity frailty (UEF) test, and an interactive trail-making task (iTMT) . Descriptive statistics, Chi-squared, 2-sample t tests, and repeated measures analysis of variance were applied where appropriate. 22 parathyroidectomy patients (male 7; 31.8%); median age of 54.9 (standard deviation=15.5) years participated. The prevalence of frailty/pre-frailty was 60% at baseline and reduced to 33% at 3 weeks post-op. PROMIS 10 physical health improved significantly by 3 months post-op (d=0.93, p=0.010). DT and fast walk velocities were significantly increased by 3 weeks post-op (p<0.050) with highest effect size observed during DT conditions (24%, Cohen's effect size d=1.30 , p=0.017). ST velocity increased but not significantly (17.5%, d=0.46, p=0.422). Results from UEF tests and iTMT did not achieve statistical significance at any visit date. Asymptomatic PHPT patients experience significant resolution of motor-cognitive symptoms as measured by DT gait and PROMIS 10 Global Health Scale following parathyroidectomy performed by a skilled surgeon

Molecular imaging using positron emission tomography in gastrointestinal malignancy

Positron Emission Tomography (PET) with 18F-FDG has emerged as a powerful tool in oncology. Furthermore, recent advent of PET/CT and novel tracers are continually expanding its role. This thesis investigates its application in two solid cancer models. In the diagnosing of primary pancreatic cancer, 18F-FDG PET/CT was shown to be more accurate than conventional CT. It did not add information to locoregional staging of disease but impacted management of patients with potentially operable tumours, by accurately confirming the presence / absence of metastases. In the pre-operative staging of patients with colorectal liver metastases (CLM), 18F- FDG PET/CT was also superior to CT in assessing extrahepatic disease, where it again impacted management. The accuracy of detecting hepatic disease was similar for both. Compared to PET alone, PET/CT improved the accuracy of lesions localization and interpretation. Next, the feasibility of imaging with the novel thymidine analogue tracer 18F-FLT was investigated. Overall, 18F-FLT PET was less accurate than 18F-FDG in detecting lesions in both cancer types, thus suggesting it to be an unsuitable tracer for routine diagnosis and staging. In the cohort of pancreatic cancer patients, 18F-FLT uptake (SUVs) were found to strongly correlate with the immunohistochemical proliferation marker, Ki-67 antigen. This supported 18F-FLT‟s potential role as a surrogate marker of proliferation. The prognostic implications of these require further investigation. Finally, an in vitro model was use to examine early changes in 18F-FLT uptake in response to treatment with cytotoxics. At 2 hours following pulse treatment with 5-fluorouracil, (and before changes in cell numbers and cell cycle phase were seen), a dose dependent increase in 18F-FLT uptake was seen. No change was observed with 18F-FDG nor following Cisplatin treatment. This adaptive response may have a role as an early predictor of response to 5-FU (and potentially other antimetabolites), which requires further investigation

Prognostic Approaches Using Transient Monitoring Methods

The utilization of steady state monitoring techniques has become an established means of providing diagnostic and prognostic information regarding both systems and equipment. However, steady state data is not the only, or in some cases, even the best source of information regarding the health and state of a system. Transient data has largely been overlooked as a source of system information due to the additional complexity in analyzing these types of signals. The development for algorithms and techniques to quickly, and intuitively develop generic quantification of deviations a transient signal towards the goal of prognostic predictions has until now, largely been overlooked. By quantifying and trending these shifts, an accurate measure of system heath can be established and utilized by prognostic algorithms. In fact, for some systems the elevated stress levels during transients can provide better, more clear indications of system health than those derived from steady state monitoring. This research is based on the hypothesis that equipment health signals for some failure modes are stronger during transient conditions than during steady-state because transient conditions (e.g. start-up) place greater stress on the equipment for these failure modes. From this it follows that these signals related to the system or equipment health would display more prominent indications of abnormality if one were to know the proper means to identify them. This project seeks to develop methods and conceptual models to monitor transient signals for equipment health. The purpose of this research is to assess if monitoring of transient signals could provide alternate or better indicators of incipient equipment failure prior to steady state signals. The project is focused on identifying methods, both traditional and novel, suitable to implement and test transient model monitoring in both an useful and intuitive way. By means of these techniques, it is shown that the addition information gathered during transient portions of life can be used to either to augment existing steady-state information, or in cases where such information is unavailable, be used as a primary means of developing prognostic models