Prognosis of Anterior Cruciate Ligament (ACL) Reconstruction: A Data Driven Approach

Individuals who suffer anterior cruciate ligament (ACL) injury are at higher risk of developing knee osteoarthritis (OA) and almost 50% display symptoms 10 to 20 years post injury. Anterior cruciate ligament reconstruction (ACLR) often does not protect against knee OA development. Accordingly, a multiscale formulation for Data Driven Prognosis (DDP) of post ACLR is developed. Unlike traditional predictive strategies that require controlled off-line measurements or training for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situ measurements. The proposed DDP scheme is capable of predicting onset of instabilities. Since the need for off line testing (or training) is obviated, it can be easily implemented for ACLR, where such controlled a priori testing is almost impossible to conduct. The DDP algorithm facilitates hierarchical handling of the large data set, and can assess the state of recovery in post ACLR conditions based on data collected from stair ascent and descent exercises of subjects. The DDP algorithm identifies inefficient knee varus motion and knee rotation as primary difficulties experienced by some of the post ACLR population. In such cases, levels of energy dissipation rate at the knee, and its fluctuation may be used as measures for assessing progress after ACL reconstruction.Comment: arXiv admin note: substantial text overlap with arXiv:1410.861

Sensor Systems for Prognostics and Health Management

Prognostics and health management (PHM) is an enabling 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 risk. Sensor systems are needed for PHM to monitor environmental, operational, and performance-related characteristics. The gathered data can be analyzed to assess product health and predict remaining life. In this paper, the considerations for sensor system selection for PHM applications, including the parameters to be measured, the performance needs, the electrical and physical attributes, reliability, and cost of the sensor system, are discussed. The state-of-the-art sensor systems for PHM and the emerging trends in technologies of sensor systems for PHM are presented

A COMPARISON BETWEEN DATA-DRIVEN AND PHYSICS OF FAILURE PHM APPROACHES FOR SOLDER JOINT FATIGUE

Prognostics and systems health management technology is an enabling discipline of technologies and methods with the potential of solving reliability problems that have been manifested due to complexities in design, manufacturing, environmental and operational use conditions, and maintenance. Over the past decade, research has been conducted in PHM to provide benefits such as advance warning of failures, enable forecasted maintenance, improve system qualification, extend system life, and diagnose intermittent failures that can lead to field failure returns exhibiting no-fault-found symptoms. While there are various methods to perform prognostics, including model-based and data-driven methods, these methods have some key disadvantages. This thesis presents a fusion prognostics approach, which combines or ―fuses together‖ the model based and data-driven approaches, to enable increasingly better estimates of remaining useful life. A case study using an electronics system to illustrate a step by step implementation of the fusion approach is also presented. The various benefits of the fusion approach and suggestions for future work are included

Estimación del tiempo de vida útil de las baterías de litio-ion, mediante la optimización de los híper-parámetros del Kernel en un proceso Gaussiano con un algoritmo genético de valor real

La estimación del tiempo de vida útil (RUL) debido a sus siglas en inglés "Remaining Useful Life", está definida como el espacio de tiempo en el cual un determinado elemento o componente seguirá cumpliendo de manera adecuada la labor para la cual fue diseñado, o contribuyendo de forma apropiada al sistema en el que se encuentra. Hoy en día, la estimación del RUL es una herramienta esencial en distintas áreas de la ciencia tanto teóricas como aplicadas, por ende ha incursionado en ramas como: bioestadística, econometría, mecánica, y en general, cualquier tarea en la que interviene la ingeniería. Esto último, se debe al hecho de que un adecuado cálculo el RUL permite tomar decisiones correctas en variables como confiabilidad, rendimiento y mantenimiento. En la literatura, se han realizado diferentes enfoques para estimar el RUL de un dispositivo especifico que se encuentra en estudio. Algunas de las metodologías que se han aplicado para resolver este tipo de problemas son: máquinas de soporte vectorial, modelos de riesgo, redes neuronales artificiales, y algunos otros modelos de tipo estocástico. Todos ellos como generalidad, usan metodologías basadas en gradiente para resolver el problema de estimación, lo cual implica que corren el riesgo de encontrar soluciones encontradas como mínimos locales en el espacio de soluciones, y no un mínimo global como sería lo esperado. En el presente proyecto, se estima el tiempo de vida útil restante de las baterías de Litio ion que se encuentran en el repositorio de base de datos de la NASA. Para cumplir tal fin, se tiene en cuenta un proceso Gaussiano como metodología de regresión. Para que esta técnica opere de una manera correcta, es necesario definir una función de distribución previa o prior que asigne un valor promedio y una función de covarianza a los datos de entrada. Esta función de covarianza o Kernel se recomienda sea asumida de acuerdo al comportamiento que se espera tengan los datos de entrada (datos periódicos, datos constantes, etc). En este documento, se selecciona una función exponencial cuadrática para definir el Kernel, la cual se caracteriza por ser una función suave, que no genera cambios tan drásticos entre los datos de entrada (dinámica esperada por las baterías), y que fundamentalmente posee tres hiperparámetros ( que deben ser optimizados para el excelente rendimiento de la función en la estimación. Se llaman hiperparámetros ya que son parámetros definidos para la función de distribución prior o previa

Estimación del tiempo de vida útil de las baterías de litio-ion, mediante la optimización de los híper-parámetros del Kernel en un proceso Gaussiano con un algoritmo genético de valor real

La estimación del tiempo de vida útil (RUL) debido a sus siglas en inglés "Remaining Useful Life", está definida como el espacio de tiempo en el cual un determinado elemento o componente seguirá cumpliendo de manera adecuada la labor para la cual fue diseñado, o contribuyendo de forma apropiada al sistema en el que se encuentra. Hoy en día, la estimación del RUL es una herramienta esencial en distintas áreas de la ciencia tanto teóricas como aplicadas, por ende ha incursionado en ramas como: bioestadística, econometría, mecánica, y en general, cualquier tarea en la que interviene la ingeniería. Esto último, se debe al hecho de que un adecuado cálculo el RUL permite tomar decisiones correctas en variables como confiabilidad, rendimiento y mantenimiento. En la literatura, se han realizado diferentes enfoques para estimar el RUL de un dispositivo especifico que se encuentra en estudio. Algunas de las metodologías que se han aplicado para resolver este tipo de problemas son: máquinas de soporte vectorial, modelos de riesgo, redes neuronales artificiales, y algunos otros modelos de tipo estocástico. Todos ellos como generalidad, usan metodologías basadas en gradiente para resolver el problema de estimación, lo cual implica que corren el riesgo de encontrar soluciones encontradas como mínimos locales en el espacio de soluciones, y no un mínimo global como sería lo esperado. En el presente proyecto, se estima el tiempo de vida útil restante de las baterías de Litio ion que se encuentran en el repositorio de base de datos de la NASA. Para cumplir tal fin, se tiene en cuenta un proceso Gaussiano como metodología de regresión. Para que esta técnica opere de una manera correcta, es necesario definir una función de distribución previa o prior que asigne un valor promedio y una función de covarianza a los datos de entrada. Esta función de covarianza o Kernel se recomienda sea asumida de acuerdo al comportamiento que se espera tengan los datos de entrada (datos periódicos, datos constantes, etc). En este documento, se selecciona una función exponencial cuadrática para definir el Kernel, la cual se caracteriza por ser una función suave, que no genera cambios tan drásticos entre los datos de entrada (dinámica esperada por las baterías), y que fundamentalmente posee tres hiperparámetros ( que deben ser optimizados para el excelente rendimiento de la función en la estimación. Se llaman hiperparámetros ya que son parámetros definidos para la función de distribución prior o previa

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: (1) component/system/structure selection, (2) prognostic algorithms, and (3) prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web enabled tools; scalability; and standards compatibility. Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: (1) research tools, (2) PHM system development tools, (3) deployable architectures, and (4) peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A. Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: (1) component/system/structure selection, (2) prognostic algorithms, and (3) prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web enabled tools; scalability; and standards compatibility. Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: (1) research tools, (2) PHM system development tools, (3) deployable architectures, and (4) peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A. Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs

DEVELOPMENT OF DIAGNOSTIC AND PROGNOSTIC METHODOLOGIES FOR ELECTRONIC SYSTEMS BASED ON MAHALANOBIS DISTANCE

Diagnostic and prognostic capabilities are one aspect of the many interrelated and complementary functions in the field of Prognostic and Health Management (PHM). These capabilities are sought after by industries in order to provide maximum operational availability of their products, maximum usage life, minimum periodic maintenance inspections, lower inventory cost, accurate tracking of part life, and no false alarms. Several challenges associated with the development and implementation of these capabilities are the consideration of a system's dynamic behavior under various operating environments; complex system architecture where the components that form the overall system have complex interactions with each other with feed-forward and feedback loops of instructions; the unavailability of failure precursors; unseen events; and the absence of unique mathematical techniques that can address fault and failure events in various multivariate systems. The Mahalanobis distance methodology distinguishes multivariable data groups in a multivariate system by a univariate distance measure calculated from the normalized value of performance parameters and their correlation coefficients. The Mahalanobis distance measure does not suffer from the scaling effect--a situation where the variability of one parameter masks the variability of another parameter, which happens when the measurement ranges or scales of two parameters are different. A literature review showed that the Mahalanobis distance has been used for classification purposes. In this thesis, the Mahalanobis distance measure is utilized for fault detection, fault isolation, degradation identification, and prognostics. For fault detection, a probabilistic approach is developed to establish threshold Mahalanobis distance, such that presence of a fault in a product can be identified and the product can be classified as healthy or unhealthy. A technique is presented to construct a control chart for Mahalanobis distance for detecting trends and biasness in system health or performance. An error function is defined to establish fault-specific threshold Mahalanobis distance. A fault isolation approach is developed to isolate faults by identifying parameters that are associated with that fault. This approach utilizes the design-of-experiment concept for calculating residual Mahalanobis distance for each parameter (i.e., the contribution of each parameter to a system's health determination). An expected contribution range for each parameter estimated from the distribution of residual Mahalanobis distance is used to isolate the parameters that are responsible for a system's anomalous behavior. A methodology to detect degradation in a system's health using a health indicator is developed. The health indicator is defined as the weighted sum of a histogram bin's fractional contribution. The histogram's optimal bin width is determined from the number of data points in a moving window. This moving window approach is utilized for progressive estimation of the health indicator over time. The health indicator is compared with a threshold value defined from the system's healthy data to indicate the system's health or performance degradation. A symbolic time series-based health assessment approach is developed. Prognostic measures are defined for detecting anomalies in a product and predicting a product's time and probability of approaching a faulty condition. These measures are computed from a hidden Markov model developed from the symbolic representation of product dynamics. The symbolic representation of a product's dynamics is obtained by representing a Mahalanobis distance time series in symbolic form. Case studies were performed to demonstrate the capability of the proposed methodology for real time health monitoring. Notebook computers were exposed to a set of environmental conditions representative of the extremes of their life cycle profiles. The performance parameters were monitored in situ during the experiments, and the resulting data were used as a training dataset. The dataset was also used to identify specific parameter behavior, estimate correlation among parameters, and extract features for defining a healthy baseline. Field-returned computer data and data corresponding to artificially injected faults in computers were used as test data

Merging Data Sources to Predict Remaining Useful Life – An Automated Method to Identify Prognostic Parameters

The ultimate goal of most prognostic systems is accurate prediction of the remaining useful life (RUL) of individual systems or components based on their use and performance. This class of prognostic algorithms is termed Degradation-Based, or Type III Prognostics. As equipment degrades, measured parameters of the system tend to change; these sensed measurements, or appropriate transformations thereof, may be used to characterize degradation. Traditionally, individual-based prognostic methods use a measure of degradation to make RUL estimates. Degradation measures may include sensed measurements, such as temperature or vibration level, or inferred measurements, such as model residuals or physics-based model predictions. Often, it is beneficial to combine several measures of degradation into a single parameter. Selection of an appropriate parameter is key for making useful individual-based RUL estimates, but methods to aid in this selection are absent in the literature. This dissertation introduces a set of metrics which characterize the suitability of a prognostic parameter. Parameter features such as trendability, monotonicity, and prognosability can be used to compare candidate prognostic parameters to determine which is most useful for individual-based prognosis. Trendability indicates the degree to which the parameters of a population of systems have the same underlying shape. Monotonicity characterizes the underlying positive or negative trend of the parameter. Finally, prognosability gives a measure of the variance in the critical failure value of a population of systems. By quantifying these features for a given parameter, the metrics can be used with any traditional optimization technique, such as Genetic Algorithms, to identify the optimal parameter for a given system. An appropriate parameter may be used with a General Path Model (GPM) approach to make RUL estimates for specific systems or components. A dynamic Bayesian updating methodology is introduced to incorporate prior information in the GPM methodology. The proposed methods are illustrated with two applications: first, to the simulated turbofan engine data provided in the 2008 Prognostics and Health Management Conference Prognostics Challenge and, second, to data collected in a laboratory milling equipment wear experiment. The automated system was shown to identify appropriate parameters in both situations and facilitate Type III prognostic model development