Merging Parameter Estimation and Classification Using LASSO

TL;DR

This paper introduces a systematic method for constructing soft sensors that simultaneously optimize model fit, sparsity, and sensor relevance, adaptable to varying conditions and sensor usefulness.

Contribution

It proposes a unified estimation criterion combining model fit and sparsity to improve soft sensor construction under diverse operating conditions.

Findings

Effective on real-world vehicle data

Reduces number of sensors needed

Adapts to different operating conditions

Abstract

Soft sensing is a way to indirectly obtain information of signals for which direct sensing is difficult or prohibitively expensive. It may not \textit{a priori} be evident which sensors provide useful information about the target signal, and various operating conditions often necessitate different models. In this paper, we provide a systematic method to construct a soft sensor that can deal with these issues. We propose a single estimation criterion, where the objectives are encoded in terms of model fit, model sparsity (reducing the number of different models), and model parameter coefficient sparsity (to exclude irrelevant sensors). The proposed method is tested on real-world scenarios involving prototype vehicles, demonstrating its effectiveness.