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Prognostics and health management (PHM) is a framework that offers comprehensive yet individualized solutions for managing system health. In recent years, PHM has emerged as an essential approach for achieving competitive advantages in the global market by improving reliability, maintainability, safety, and affordability. Concepts and components in PHM have been developed separately in many areas such as mechanical engineering, electrical engineering, and statistical science, under varied names. In this paper, we provide a concise review of mainstream methods in major aspects of the PHM framework, including the updated research from both statistical science and engineering, with a focus on data-driven approaches. Real world examples have been provided to illustrate the implementation of PHM in practice.

In this paper, we studied a prediction method for the remaining useful life of Lithium-ion batteries. First, a battery degradation model is obtained based on exponential degradation signal modeling with data collected from second generation 18650-size lithiumion cells from NASA. Using a Bayesian updating procedure, we then obtain the conditional cumulative distribution function (cdf) of the residual life of the battery at various time intervals. Finally, we discuss this method and draw the conclusion that the model is accurate in terms of prediction.

In order to avoid machine related catastrophes, the early detection of cracks is in urgent demand. Sensors are put into the rotating parts of machine and vibration signal data are collected to diagnose machine health. This paper proposes a comprehensive method to look into the development of damage with multinomial logit model (MLM) and cumulative link model (CLM). We first select features according to analysis of variance (ANOVA), and then compare the MLM, CLM method with weighted k-nearest neighbor method (WKNN) — a black box machine learning algorithm and we conclude that these methods have their pros and cons in the diagnosis of faults.

Prognostic methods are potentially beneficial for public health management. The blending of data-driven methods with the domain knowledge is essential to efficiently advance feature selection, anomaly detection, prognostics forecasting, data matching and clustering. This paper attempts to demonstrate how prognostic methods enable accurate Methicillin-resistant Staphylococcus Aureus (MRSA) patient life prediction. The methodology is applied to MRSA patient survival analysis. Significant linear relationship is found between log (hazard) and age (p