Applying the General Path Model to Estimation of Remaining Useful Life

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life of individual systems or components based on their use and performance. This class of prognostic algorithms is termed Effects-Based or Type III Prognostics. Traditional individual-based prognostics involve identifying an appropriate degradation measure to characterize the system's progression to failure. A functional fit of this parameter is then extrapolated to a pre-defined failure threshold to estimate the remaining useful life of the system or component. This paper proposes a specific formulation of the General Path Model with dynamic Bayesian updating as one effects-based prognostic algorithm. The method is illustrated with an application to the prognostics challenge problem posed at PHM '08

Analysis of acoustic emission data for bearings subject to unbalance

Acoustic Emission (AE) is an effective nondestructive method for investigating the behavior of materials under stress. In recent decades, AE applications in structural health monitoring have been extended to other areas such as rotating machineries and cutting tools. This research investigates the application of acoustic emission data for unbalance analysis and detection in rotary systems. The AE parameter of interest in this study is a discrete variable that covers the significance of count, duration and amplitude of AE signals. A statistical model based on Zero-Inflated Poisson (ZIP) regression is proposed to handle over-dispersion and excess zeros of the counting data. The ZIP model indicates that faulty bearings can generate more transient wave in the AE waveform. Control charts can easily detect the faulty bearing using the parameters of the ZIP model. Categorical data analysis based on generalized linear models (GLM) is also presented. The results demonstrate the significance of the couple unbalance

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Application of Fuzzy-Neural Network in Classification of Soils using Ground-penetrating Radar Imagery

Errors associated with visual inspection and interpretation of radargrams often inhibits the intensive surveying of widespread areas using ground-penetrating radar (GPR). To automate the interpretive process, this paper presents an application of a fuzzy-neural network (F-NN) classifier for unsupervised clustering and classification of soil profile using GPR imagery. The classifier clusters and classifies soil profiles strips along a traverse based on common pattern similarities that can relate to physical features of the soil (e.g., number of horizons; depth, texture and structure of the horizons; and relative arrangement of the horizons, etc). This paper illustrates this classification procedure by its application on GPR data, both simulated and actual real-world. Results show that the procedure is able to classify the profile into zones that corresponded with those obtained by visual inspection and interpretation of radargrams. Results indicate that an F-NN model can supply real-time soil profile clustering and classification during field surveys

Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors

Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steady-state data obtained from electric motor accelerated degradation testing is used for method validation. Ten three-phase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life

Improved Heat Exchanger Lifecycle Prognostic Methods for Enhanced Light Water Reactor Sustainability

As the licenses of many nuclear power plants in the US and abroad are being extended, accurate knowledge of system and component condition is becoming increasingly important. The US Department of Energy (DOE) has funded a project with the primary goal of developing lifecycle prognostic methods to generate accurate and continuous Remaining Useful Life (RUL) estimates as components transition through unique stages of the component lifecycle. Specific emphasis has been placed on creating and transitioning between three distinct stages of operational availability. These stages correspond to Beginning Of Life (BOL) where little or no operational information is available, early onset operations at various expected and observed stress levels where there is the onset of detectable degradation, and degradation towards the eventual End Of Life (EOL). This paper provides an application overview of a developed lifecycle prognostic approach and applies it to a heat exchanger fouling test bed under accelerated degradation conditions resulting in an increased understanding of system degradation. Bayesian and Bootstrap Aggregation methods are applied to show improvements in RUL predictions over traditional methods that do not utilize these methods, thereby improving the lifecycle prognostic model for the component. The analyses of results from applying these lifecycle prognostic algorithms to a heat exchanger fouling experiment are detailed

Implementing a pragmatic clinical trial to tailor opioids for acute pain on behalf of the IGNITE ADOPT PGx investigators.

Opioid prescribing for postoperative pain management is challenging because of inter-patient variability in opioid response and concern about opioid addiction. Tramadol, hydrocodone, and codeine depend on the cytochrome P450 2D6 (CYP2D6) enzyme for formation of highly potent metabolites. Individuals with reduced or absent CYP2D6 activity (i.e., intermediate metabolizers [IMs] or poor metabolizers [PMs], respectively) have lower concentrations of potent opioid metabolites and potentially inadequate pain control. The primary objective of this prospective, multicenter, randomized pragmatic trial is to determine the effect of postoperative CYP2D6-guided opioid prescribing on pain control and opioid usage. Up to 2020 participants, age ≥8 years, scheduled to undergo a surgical procedure will be enrolled and randomized to immediate pharmacogenetic testing with clinical decision support (CDS) for CYP2D6 phenotype-guided postoperative pain management (intervention arm) or delayed testing without CDS (control arm). CDS is provided through medical record alerts and/or a pharmacist consult note. For IMs and PM in the intervention arm, CDS includes recommendations to avoid hydrocodone, tramadol, and codeine. Patient-reported pain-related outcomes are collected 10 days and 1, 3, and 6 months after surgery. The primary outcome, a composite of pain intensity and opioid usage at 10 days postsurgery, will be compared in the subgroup of IMs and PMs in the intervention (n = 152) versus the control (n = 152) arm. Secondary end points include prescription pain medication misuse scores and opioid persistence at 6 months. This trial will provide data on the clinical utility of CYP2D6 phenotype-guided opioid selection for improving postoperative pain control and reducing opioid-related risks