DeepAI AI Chat
Log In Sign Up

Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models

03/23/2019
by   Vishnu TV, et al.
Tata Consultancy Services
0

Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) future operational conditions are unobserved and (iii) inherent noise is present in the sensor readings. All three scenarios mentioned are unavoidable sources of uncertainty in the RUL estimation process often resulting in unreliable RUL estimates. To address (i), we formulate RUL estimation as an Ordinal Regression (OR) problem, and propose LSTM-OR: deep Long Short Term Memory (LSTM) network based approach to learn the OR function. We show that LSTM-OR naturally allows for incorporation of censored operational instances in training along with the failed instances, leading to more robust learning. To address (ii), we propose a simple yet effective approach to quantify predictive uncertainty in the RUL estimation models by training an ensemble of LSTM-OR models. Through empirical evaluation on C-MAPSS turbofan engine benchmark datasets, we demonstrate that LSTM-OR is significantly better than the commonly used deep metric regression based approaches for RUL estimation, especially when failed training instances are scarce. Further, our uncertainty quantification approach yields high quality predictive uncertainty estimates while also leading to improved RUL estimates compared to single best LSTM-OR models.

READ FULL TEXT

page 1

page 2

page 3

page 4

04/12/2019

Remaining Useful Life Estimation Using Functional Data Analysis

Remaining Useful Life (RUL) of an equipment or one of its components is ...
12/12/2022

Multi-Dimensional Self Attention based Approach for Remaining Useful Life Estimation

Remaining Useful Life (RUL) estimation plays a critical role in Prognost...
03/09/2023

On the Soundness of XAI in Prognostics and Health Management (PHM)

The aim of Predictive Maintenance, within the field of Prognostics and H...
12/04/2019

Regression with Uncertainty Quantification in Large Scale Complex Data

While several methods for predicting uncertainty on deep networks have b...
09/12/2022

TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification

In this paper, we propose TEDL, a two-stage learning approach to quantif...