## 6 Conclusions  
Optimal sensor deployment is essential for system abnormality detection in quality engineering. This paper aims to develop a BN with an SFLA-based sensor deployment methodology that can process the cause-effect relationship between physical variables, the quantitative heterogeneous properties of sensors, and multiple-objective optimization in a wet-grinding system. A BN is created to represent the causal relationships among the physical variables by employing the path model in the wet-grinding system. A prescribed detectability requirement is developed as a constraint for MOO to ensure all the physical variables whose mean shifts are detectable by the sensor set. To facilitate the identification of sensitive areas of the chamber wall under the bead-wall impact events for various disparate operating conditions, coupled CFD-DEM simulations are performed to collect the bead-wall impact event data. The sensing location of accelerometers mounted on the chamber shell is determined using proper signal processing. By introducing the SFLA, a strategy of optimal sensor placement is proposed to minimize the maximum data output deviation index among all sensor sets and minimize the sensory cost under the constraints of the mean shift detectability, thus achieving optimum sensor deployment. The performance of the proposed strategy was tested and validated for the zirconia powder production process. The results illustrate that, in comparison to a DG with greedy and saturated sensing, a BN with an SFLA-based strategy can acquire a lower maximum data output deviation index, average deviation index, and sensory costs. It can be easily integrated into monitoring architecture for parameter estimation in complex systems such as drum-grinding systems, cutting processing systems, and multistation assembly processes. Future research should focus on integrating the proposed sensor-deployment strategy into wet-grinding systems and adaptively adjusting the sensor deployment according to the data output accuracy of physical variables, creating a dynamic framework between the sensor deployment and the performance metrics of the wet-grinding system.  
Conceptualization, H.Y. and K.H.; methodology, K.H. and F.S.; software and data curation, F.S. and Q.Y.; investigation, K.H. and B.W.; draft preparation, K.H. and Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Natural Science Foundation of Anhui Province, grant number 2108085 ME172; in part by the Suzhou University key project, grant numbers 2021 yxzd02, 2022 yzd08, and 2022 yzd09; in part by corporate-funded R&D projects, grant numbers 2021 hx023, 2021 hx022, 2021 hxh024, and 2022 xhx122; in part by the Suzhou College Teacher Application Ability Development Workstation, grant number 2020 XY703; in part by the Suzhou University Professor (Ph.D) Scientific Research Foundation, grant number 2016 JB09; in part by the Natural Science Research Project of Anhui Educational Committee: Research on Particle Size Distribution of Wet Grinding Products based on optimized sensor data fusion; and in part by the Suzhou University Scientific Research Platform Project, grant number 2020 ykf14.  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study due to this study not involving humans or animals.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** This work was carried out at the High-End Micro-Nano Grinding Equipment School-Enterprise Collaborative Innovation Engineering Center, which is the School-Enterprise Joint Laboratory at Suzhou University-Anhui Root Industrial Co., Ltd. We sincerely thank [PERSON] and [PERSON] for their technical support.  
**Conflicts of Interest:** The authors declare no conflict of interest.