Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input

by   Neelanjana Pal, et al.

Data-driven, neural network (NN) based anomaly detection and predictive maintenance are emerging research areas. NN-based analytics of time-series data offer valuable insights into past behaviors and estimates of critical parameters like remaining useful life (RUL) of equipment and state-of-charge (SOC) of batteries. However, input time series data can be exposed to intentional or unintentional noise when passing through sensors, necessitating robust validation and verification of these NNs. This paper presents a case study of the robustness verification approach for time series regression NNs (TSRegNN) using set-based formal methods. It focuses on utilizing variable-length input data to streamline input manipulation and enhance network architecture generalizability. The method is applied to two data sets in the Prognostics and Health Management (PHM) application areas: (1) SOC estimation of a Lithium-ion battery and (2) RUL estimation of a turbine engine. The NNs' robustness is checked using star-based reachability analysis, and several performance measures evaluate the effect of bounded perturbations in the input on network outputs, i.e., future outcomes. Overall, the paper offers a comprehensive case study for validating and verifying NN-based analytics of time-series data in real-world applications, emphasizing the importance of robustness testing for accurate and reliable predictions, especially considering the impact of noise on future outcomes.


page 1

page 2

page 3

page 4


Remaining Useful Life Estimation Using Functional Data Analysis

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

Generating Reliable Process Event Streams and Time Series Data based on Neural Networks

Domains such as manufacturing and medicine crave for continuous monitori...

Anomaly Detection And Classification In Time Series With Kervolutional Neural Networks

Recently, with the development of deep learning, end-to-end neural netwo...

Robust Parameter-Free Season Length Detection in Time Series

The in-depth analysis of time series has gained a lot of research intere...

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

The aim of Predictive Maintenance, within the field of Prognostics and H...

Estimation of Sea State Parameters from Ship Motion Responses Using Attention-based Neural Networks

On-site estimation of sea state parameters is crucial for ship navigatio...

A Visual Analytics Approach to Monitor Time-Series Data with Incremental and Progressive Functional Data Analysis

Many real-world applications involve analyzing time-dependent phenomena,...

Please sign up or login with your details

Forgot password? Click here to reset