Abstract
BackgroundTo reduce disruptions of processes and the cost of maintenance, predicting the onset of failure (or a similar event) of a physical system (or components of a physical system) has become important. Prediction of onset of failure would allow appropriate corrective actions at the right time. In this paper, we present a method to predict the “onset” of failure (the start of a degradation process or similar types of events) of a physical system that minimizes data collection and personalizes it for the physical system. The method applies to situations where one monitors the operating characteristics of the physical system at regular time intervals by means of attached sensors and other measurement instruments. It creates a model of the physical system, during normal operations, using the time-series data produced by the sensors and measurement instruments. However, it does not create or use any time-series models. It simply examines the distribution of time-series data across different time periods. It uses this model of normal operations in subsequent time periods to monitor the physical system for deviations from normality.ResultsWe illustrate this method with an application to predict the “onset” of subsequent decompensated heart failures for patients already treated for a heart failure at a hospital. As part of an NIH study, these heart failure patients received two ECG patches, an accelerometer and a bio-impedance measurement device for regular monitoring for a period after their release from the hospital.ConclusionsWhen dealing with non-homogenous, disparate physical systems, personalized models can be better predictors of a phenomenon compared to generalized models based on data collected from an assortment of such physical systems. In medicine such models can be a powerful addition to the set of medical diagnostic tools. And such personalized models can be built rather quickly without waiting for extensive data collection.
Highlights
To reduce disruptions of processes and the cost of maintenance, predicting the onset of failure of a physical system has become important
We propose a method for personalized modeling of a physical system for failure prediction based on time-series data produced by sensors and other measurement instruments
We show the application of this method to predict the “onset” of subsequent decompensated heart failure of three patients from the National Institutes of Health (NIH) study
Summary
To reduce disruptions of processes and the cost of maintenance, predicting the onset of failure (or a similar event) of a physical system (or components of a physical system) has become important. The method applies to situations where one monitors the operating characteristics of the physical system at regular time intervals by means of attached sensors and other measurement instruments. It creates a model of the physical system, during normal operations, using the time-series data produced by the sensors and measurement instruments. (Creating such common profiles is the task of generalization in machine learning.) For instance, heart failure patients often have very different medical histories and, makes it difficult to build highly accurate profiles for them. Generalization and accuracy of prediction suffer with these kinds of phenomena
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