Reliable integration and solid configuration of monitoring systems constitute a fundamental prerequisites for achieving high efficiency and productivity in contemporary manufacturing environments. Design decisions on sensor type and system architecture have to be made at an early stage and under comparably high uncertainty. This work investigates a research direction that deviates from the traditional monitoring-system development process by shifting the attention from algorithm design to a deeper analysis of the inspection problem. In contrast to traditional design cycles, this paper proposes to gradually collect knowledge and store it in an abstract system model. This enables the retrieval of similar solutions for future use cases, preventing the need for expensive model training from scratch and allowing instead for the incremental refinement of existing base configurations. Reuse of previously generated pipelines reduces the risk of late and costly revisions. As there is little knowledge on cross-domain transferability of filter pipelines, this study analyzes the potential of retrieving filter pipelines to transfer them to different but similar segmentation problems. Finally, we statistically analyze the benefits of this `transfer learning' variant which is predominantly applied to image segmentation problems. In addition, we discuss how simple models help balancing the trade-off between complexity, technical requirements, and reliability in the design process.

翻译：监控系统的可靠集成与稳健配置是实现现代制造业高效率和高效能的基本前提。传感器类型和系统架构的设计决策必须在早期阶段且面对较高不确定性时做出。本研究探索了一条有别于传统监控系统开发流程的研究路径，将关注点从算法设计转向对检测问题的深入分析。与传统设计周期不同，本文提出逐步积累知识并将其存储于抽象系统模型中。这使得能够针对未来用例检索相似解，从而避免昂贵的模型从头训练，转而支持对现有基础配置进行增量式优化。重复使用先前生成的流水线可降低后期高成本修改的风险。鉴于滤波流水线跨领域可迁移性的认知尚不充分，本研究分析了检索滤波流水线并将其迁移至不同但相似的分割问题的潜力。最后，我们统计分析了这种主要应用于图像分割问题的“迁移学习”变体的优势。此外，我们还讨论了简单模型如何帮助在设计过程中平衡复杂度、技术需求与可靠性之间的权衡。