InfoPos: A Design Support Framework for ML-Assisted Fault Detection and Identification in Industrial Cyber-Physical Systems

TL;DR

This paper introduces the InfoPos framework to assist in selecting effective ML-based fault detection components for industrial cyber-physical systems based on available data and knowledge levels, streamlining design.

Contribution

The paper presents the first version of InfoPos, a framework that guides the placement of fault detection use-cases according to data and knowledge availability, improving solution design efficiency.

Findings

Different building blocks impact ML model performance.

The framework helps identify effective configurations.

Public datasets support reproducibility.

Abstract

The variety of building blocks and algorithms incorporated in data-centric and ML-assisted fault detection and identification solutions is high, contributing to two challenges: selection of the most effective set and order of building blocks, as well as achieving such a selection with minimum cost. Considering that ML-assisted solution design is influenced by the extent of available data and the extent of available knowledge of the target system, it is advantageous to be able to select effective and matching building blocks. We introduce the first iteration of our InfoPos framework, allowing the placement of fault detection/identification use-cases based on the available levels (positions), i.e., from poor to rich, of knowledge and data dimensions. With that input, designers and developers can reveal the most effective corresponding choice(s), streamlining the solution design process. The results from a demonstrator, a fault identification use-case for industrial Cyber-Physical Systems, reflects achieved effects when different building blocks are used throughout knowledge and data positions. The achieved ML model performance is considered as the indicator for a better solution. The data processing code and composed datasets are publicly available.