Haitao Liao

Bio: Haitao Liao is an academic researcher from University of Arkansas. The author has contributed to research in topics: Reliability (statistics) & Spare part. The author has an hindex of 28, co-authored 140 publications receiving 3321 citations. Previous affiliations of Haitao Liao include University of Tennessee & University of Arizona.

A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics

Abstract: Maximum releasable capacity and internal resistance are often used as the health indicators (HIs) of a lithium-ion battery for degradation modeling and estimation of remaining useful life (RUL). However, the maximum releasable capacity is usually difficult to estimate in online applications due to complex operating conditions in the field. Moreover, measuring the internal resistance is too expensive to be implemented on-line. In this paper, an HI extraction and optimization framework requiring only the operating parameters of lithium-ion batteries is proposed for battery degradation modeling and RUL estimation. The framework carries out raw HI extraction, transformation, correlation analysis, and verification and evaluation to achieve HI enhancement. In particular, the Box–Cox transformation is adopted to improve the correlation between the extracted HI and the battery’s actual degradation state. To estimate the battery’s RUL using the enhanced HI, an optimized relevance vector-machine algorithm is utilized, which can be performed in a flexible and agile way. Experimental studies using two different industrial testing data sets illustrate the high efficiency and adaptability of the proposed framework in lithium-ion battery degradation modeling and RUL estimation.

Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model

Abstract: Reliability of an individual unit during field use is important in many critical applications such as turbine engines, life-maintaining systems and civil engineering structures. The remaining useful life (RUL) of the unit indicates its ability of surviving the operation in the future. When the failure indication (degradation) has been detected, it is essential to estimate the RUL accurately for making a timely maintenance decision for failure avoidance. In recent years, RUL prediction in service has received increasing attention. As many powerful sensors and signal processing techniques appear, multiple degradation features can be extracted for degradation detection and quantification. These features can serve as the basis for RUL prediction. This paper presents the proportional hazards model and logistic regression model, which relates the multiple degradation features of sensor signals to the specific reliability indices of the unit, and enable us to predict its RUL. Comparisons are made for the two models regarding their effectiveness and computation effort. An example of bearing test is provided to demonstrate the proposed approach in practical use. The results show that the models are capable of providing accurate RUL prediction to support timely maintenance decisions

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON