Hierarchical spline for time series forecasting: An application to Naval ship engine failure rate

12/02/2020
by   Hyunji Moon, et al.
0

Predicting equipment failure is important because it could improve availability and cut down the operating budget. Previous literature has attempted to model failure rate with bathtub-formed function, Weibull distribution, Bayesian network, or AHP. But these models perform well with a sufficient amount of data and could not incorporate the two salient characteristics; imbalanced category and sharing structure. Hierarchical model has the advantage of partial pooling. The proposed model is based on Bayesian hierarchical B-spline. Time series of the failure rate of 99 Republic of Korea Naval ships are modeled hierarchically, where each layer corresponds to ship engine, engine type, and engine archetype. As a result of the analysis, the suggested model predicted the failure rate of an entire lifetime accurately in multiple situational conditions, such as prior knowledge of the engine.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 11

09/17/2021

TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

Time series forecasting is a demanding task ranging from weather to fail...
01/10/2021

Bayesian estimation of a competing risk model based on Weibull and exponential distributions under right censored data

In this paper we investigate the estimation of the unknown parameters of...
05/15/2014

Effective Bayesian Modeling of Groups of Related Count Time Series

Time series of counts arise in a variety of forecasting applications, fo...
09/20/2021

SFFDD: Deep Neural Network with Enriched Features for Failure Prediction with Its Application to Computer Disk Driver

A classification technique incorporating a novel feature derivation meth...
08/14/2019

Mixed pooling of seasonality in time series pallet forecasting

Multiple seasonal patterns play a key role in time series forecasting, e...
07/22/2021

A Framework for Imbalanced Time-series Forecasting

Time-series forecasting plays an important role in many domains. Boosted...
01/22/2021

Bayesian hierarchical stacking

Stacking is a widely used model averaging technique that yields asymptot...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.