Time series data preparation for failure prediction in smart water taps (SWT)

Abstract

Smart water tap (SWT) time series model development for failure prediction requires acquiring data on the variables of interest to researchers, planners, engineers and decision makers. Thus, the data are expected to be ‘noiseless’ (i.e., without discrepancies such as missing data, data redundancy and data duplication) raw inputs for modelling and forecasting tasks. However, historical datasets acquired from the SWTs contain data discrepancies that require preparation before applying the dataset to develop a failure prediction model. This paper presents a combination of the generative adversarial network (GAN) and the bidirectional gated recurrent unit (BiGRU) techniques for missing data imputation. The GAN aids in training the SWT data trend and distribution, enabling the imputed data to be closely similar to the historical dataset. On the other hand, the BiGRU was adopted to save computational time by combining the model’s cell state and hidden state during data imputation. After data imputation there were outliers, and the exponential smoothing method was used to balance the data. The result shows that this method can be applied in time series systems to correct missing values in a dataset, thereby mitigating data noise that can lead to a biased failure prediction model. Furthermore, when evaluated using different sets of historical SWT data, the method proved reliable for missing data imputation and achieved better training time than the traditional data imputation method.