Dimensionality Reduction and Anomaly Detection for CPPS Data using Autoencoder

TL;DR

This paper explores the use of autoencoders for dimensionality reduction and anomaly detection in CPPS data, demonstrating that reconstruction error can effectively identify anomalies and outperform existing methods.

Contribution

The study introduces a novel approach combining autoencoder-based dimensionality reduction with anomaly detection, validated on real-world datasets and outperforming state-of-the-art techniques.

Findings

Autoencoder reconstruction error effectively detects anomalies in CPPS data.

Latent space analysis enhances anomaly detection accuracy.

Proposed method outperforms existing techniques on multiple datasets.

Abstract

Unsupervised anomaly detection (AD) is a major topic in the field of Cyber-Physical Production Systems (CPPSs). A closely related concern is dimensionality reduction (DR) which is: 1) often used as a preprocessing step in an AD solution, 2) a sort of AD, if a measure of observation conformity to the learned data manifold is provided. We argue that the two aspects can be complementary in a CPPS anomaly detection solution. In this work, we focus on the nonlinear autoencoder (AE) as a DR/AD approach. The contribution of this work is: 1) we examine the suitability of AE reconstruction error as an AD decision criterion in CPPS data. 2) we analyze its relation to a potential second-phase AD approach in the AE latent space 3) we evaluate the performance of the approach on three real-world datasets. Moreover, the approach outperforms state-of-the-art techniques, alongside a relatively simple and straightforward application.