Mechanical Engineering

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Subject: search.filters.subject.Prognostics and health management

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    Conference Proceedings Report: ASME-SERAD and UMD-CRR Interactive seminar & pre-workshop on the intersection of PRA and PHM
    (2020-10) Groth, Katrina; Pourgol-Mohammad, Mohammad; Modarres, Mohammad
    This event is the first in a two-part series exploring the intersection of PRA and PHM in the context of complex engineering systems. Initially the workshop was planned as a fully in-person workshop to be held in April, 2020, but as with many events in 2020, it was postponed due to the travel restrictions resulting from COVID-19 pandemic. The organizers recognized that the online format isn't amenable to the deep discussions which were intended to be at the heart of the in person workshop, but we decided to try an experiment: to see if we could make a “pre-workshop” as interactive possible in an era of webinar fatigue. Thus the workshop was reimagined as an online, interactive pre-workshop in 2020, to be followed with the in person, discussion-heavy workshop to be held when we are able to travel again in 2021.
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    DEVELOPING HYBRID PHM MODELS FOR PIPELINE PITTING CORROSION, CONSIDERING DIFFERENT TYPES OF UNCERTAINTY AND CHANGES IN OPERATIONAL CONDITIONS
    (2019) Heidarydashtarjandi, Roohollah; Groth, Katrina M; Reliability Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Pipelines are the most efficient and reliable way to transfer oil and gas in large quantities. Pipeline infrastructures represent a high capital investment and, if they fail, a source of environmental hazards and a potential threat to life. Among different pipeline failure mechanisms, pitting corrosion is of most concern because of the high growth rate of pits. In this dissertation two hybrid prognostics and health management (PHM) models are developed to evaluate degradation level of piggable pipelines, due to internal pitting corrosion. These models are able to incorporate multiple sensors data and physics of failure (POF) knowledge of internal pitting corrosion process. This dissertation covers both cases when in some pipeline's segments the pit density is low and in some segments it is high. In addition, it takes into account four types of uncertainty, including epistemic uncertainty, variability in the temporal aspects, spatial heterogeneity, and inspection errors. For a pipeline segment with a low pit density, a hybrid defect-based algorithm is developed to estimate probability distribution of maximum depth of each individual pit on that segment. This algorithm considers change in operational condition in internal pitting corrosion degradation modeling for the first time. In this way a two-phase similarity-based data fusion algorithm is developed to fuse POF knowledge, in-line inspection (ILI) and online inspection (OLI) data. In the first phase, a hierarchical Bayesian method based on a non-homogeneous gamma process is used to fuse POF knowledge and in-line inspection (ILI) data on multiple pits, and augmented particle filtering is used to fuse POF knowledge and online inspection (OLI) data of an active reference pit. The results are used to define a similarity index between each ILI pit and the OLI pit. In the second phase, this similarity index is used to generate dummy observations of depth for each ILI pit, based on the inspection data of the OLI pit. Those dummy observations are used in augmented particle filtering to estimate the remaining useful life (RUL) of that segment after the change in operational conditions when there is no new ILI data. For a pipeline segment with a high pit density, a hybrid population-based algorithm is developed to estimate the probability density function of maximum depth of the pit population on that segment. This algorithm eliminates the need of matching procedure that is computationally expensive and prone to error when the pit density is high. In this algorithm three types of measurement uncertainty including sizing error, probability of detection (POD), and probability of false call (POFC) are taken into account. In addition, initiation of new pits between the last ILI and a prediction time is modeled by using a homogeneous Poisson process. The non-linearity of the pitting corrosion process and the POF knowledge of this process is modeled by using a non-homogeneous gamma process. The estimation of these two algorithms are used in a series system to estimate the reliability of a long pipeline with multiple segments, when in some segments the pit density is low and in some segments it is high. The output of this research can be used to find the optimal maintenance action and time for each segment and the optimal next ILI time for the whole pipeline that eventually decreases the cost of unpredicted failures and unnecessary maintenance activities.
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    PHM-BASED PREDICTIVE MAINTENANCE SCHEDULING FOR WIND FARMS MANAGED USING OUTCOME-BASED CONTRACTS
    (2018) LEI, XIN; Sandborn, Peter; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Prognostics and Health Management (PHM) technologies have been introduced into wind turbines to forecast the Remaining Useful Life (RUL), and enable predictive maintenance opportunities prior to failure thus avoiding corrective maintenance that may be expensive and cause long downtimes. For a wind turbine, when an RUL is predicted, a predictive maintenance option is triggered that the maintenance decision-maker has the managerial flexibility to decide if and when to exercise before the turbine fails. By implementing the predictive maintenance, the high cost of corrective maintenance can be avoided; however a portion of the RUL will be thrown away that can be translated into cumulative revenue loss. In this dissertation, a simulation-based European-style Real Options Analysis (ROA) approach is used to schedule the predictive maintenance for a single wind turbine with an RUL prediction managed using an as-delivered payment model. When an RUL is predicted for the wind turbine, the predictive maintenance value paths are simulated by considering the uncertainties in the RUL prediction and wind speeds. By valuating the European-style predictive maintenance option at all possible predictive maintenance opportunities, a series of predictive maintenance option values can be obtained, and the predictive maintenance opportunity with the highest expected predictive maintenance option value can be selected. By extending the approach for a single wind turbine, a wind farm managed using an outcome-based contract, specifically a Power Purchase Agreement (PPA), with multiple turbines indicating RULs concurrently can be analyzed. The predictive maintenance value for each wind turbine with an RUL indication depends on the operational state of all the other turbines, the amount of energy delivered, and the energy delivery target, prices and penalization mechanism for under-delivery defined in the PPA. A case study is provided demonstrating that the selected predictive maintenance opportunity for a PPA-managed wind farm is different from the same wind farm managed using an as-delivered payment model, and also differs from the selected predictive maintenance opportunities for the individual turbines with RULs managed in isolation. Finally, the magnitude of the life-cycle benefit that the developed approach can bring to the wind farm owner is estimated through a simple case study. Using the European-style ROA approach to determine the wind farm maintenance policy, the improvement to the wind farm expected life-cycle net revenue is significant compared with the state-of-art wind farm maintenance policies, i.e., up to 25% higher than the corrective maintenance policy, and up to 83% higher than the predictive maintenance at the earliest opportunity policy.