Missing Value Imputation for Industrial IoT Sensor Data With Large Gaps

In recent years, the Internet-of-Things (IoT)-oriented smart manufacturing has become a prominent solution in realizing evolutional digital transformation. Missing data are one of the biggest problems for data preprocessing in an IoT architecture, and it is crucial that missing values are recovered to improve the reliability of monitoring applications. However, due to the high-frequency collection of sensor data, missing data in IoT bring new challenges. Several methods have been developed to recover missing IoT data by utilizing data from sensors that are geographically close to the sensor which is responsible for the missing data, or from sensors which provide data that are highly correlated to the missing data. In IoT systems, because of the transmission of a large volume of data over networks, common mode failures need to be considered where a single event can lead to the loss of data from a large number of sensors. In this situation, it would be infeasible to recover missing data from other sensors. To address this issue, in this article, we focus on missing data imputation for large gaps in univariate time-series data and propose an iterative framework using multiple segmented gap iteration called Itr-MS-STLecImp to provide the most appropriate values. The gap is first segmented into several pieces to initialize the missing value imputation process and then, we iteratively run gap reconstruction and gap concatenation to obtain the final imputation results. We validate the proposed approach using sensor data collected from real manufacturing plants in Australia and the comparison results show that the proposed Itr-MS-STLecImp outperforms the state-of-the-art methods in terms of root-mean-square error. Under different gap-length conditions, the proposed approach consistently reduces the error rate more than the baseline algorithm, and the error reduction is greater when the lengths of the gaps increase, indicating that the performance is significantly improved. These analysis results further prove the effectiveness of the multiple segmentation of missing gaps and the iteration operation.