Low-cost sensor outlier detection framework for on-line monitoring of particle pollutants in multiple scenarios.

Monitoring the concentration of particle pollutants is very important for industrial production control and workers' health protection. Low-cost sensors are widely used to reduce deployment costs. The outliers in the observed data of pollutant concentration can be eliminated by outlier detection algorithms. However, it is difficult to meet the actual needs of changing working conditions or scene migration in factories by building a single algorithm for specific scenarios. It is a feasible scheme to identify the changing characteristics of data and adaptively adjust the outlier detection algorithm. From the point of view of data characteristics, we creatively match typical data types with high-performance algorithms. The framework proposed in this paper provides a general process including five basic tasks and uses a modular structure to complete the outlier detection target. The actual pollutant data of the workshops are used to evaluate the performance of our framework. At last, we compare eight different strategies under this framework and analyze the contribution of each step to outlier detection from the perspective of algorithm principle. The results show that low-cost sensors following the framework can meet the outlier detection requirements in the field of pollutant monitoring, thus greatly reducing the cost of algorithm selection and data adaptation.