Building robust prediction models for defective sensor data using Artificial Neural Networks

TL;DR

This paper presents an ANN-based framework for predicting component health in complex systems, effectively handling sensor failures and noise through data augmentation, validated on real industrial data.

Contribution

It introduces a novel ANN framework that leverages all sensor data and employs data augmentation to improve robustness against sensor failures and noise.

Findings

Effective prediction accuracy despite sensor noise

Robustness to sensor failure demonstrated on industrial data

Data augmentation enhances model reliability

Abstract

Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approach involves selecting a smaller set of relevant sensor signals using feature selection and using them to train a machine learning algorithm. However, this fails to address two prominent problems: (1) sensors are susceptible to failure when exposed to extreme conditions over a long periods of time; (2) sensors are electrical devices that can be affected by noise or electrical interference. Using the failed and noisy sensor signals as inputs largely reduce the prediction accuracy. To tackle this problem, it is advantageous to use the information from all sensor signals, so that the failure of one sensor can be compensated by another. In this work, we propose an Artificial Neural Network (ANN) based framework to exploit the information from a large number of signals. Secondly, our framework introduces a data augmentation approach to perform accurate predictions in spite of noisy signals. The plausibility of our framework is validated on real life industrial application from Robert Bosch GmbH.