WrapperRL: Reinforcement Learning Agent for Feature Selection in High-Dimensional Industrial Data

Abstract

Finding the set of discriminatory features in a classification task is imperative for the interpretability of the “black box” deep learning (DL) models, especially in high-stakes industrial applications such as predictive maintenance and industrial noise classification. In cases with time-series Time-Frequency (TF) domain data, the interpretability of DL models is challenged by the data’s high dimensionality and the need to maintain the characteristics of the original signal when interpreting classification results. This paper devises a three-stage process that supports the interpretability of a DL model identifying industrial noise through a forward feature selection procedure. The first stage transforms the original TF data into an image representation. The second stage proposes a 2D Convolutional Neural Network (CNN) with a self-attention mechanism (SA-CNN) that classifies the data into instances with and without industrial noise. The final stage, termed WrapperRL, utilizes a Reinforcement Learning (RL) agent, to find the set of discriminatory frequency bands contributing to classification results. SA-CNN and WrapperRL both outperform the state-of-the-art implementations, each in their own specialty. The insights provided by WrapperRL suggest the contribution of around 20% of frequency bands to the existence of industrial noise, mainly residing in the low-frequency domain. Together, both of these approaches serve as a promising starting point for enhancing the interpretability of DL models and explaining the classification results of industrial TF data.

Keywords

• Deep learning
• CNN model interpretability
• anomaly detection
• manufacturing noise