InfoPos框架帮助工业系统快速选对故障检测的AI组件组合。

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.