Predictive Maintenance (PMx) methods leverage available heritage data, maintenance records, and vehicle information to forecast rotorcraft failure modes before they become a problem. The goal of PMx is to decrease cost and vehicle downtime while increasing availability. When the required data are incomplete or corrupted, the worst case (grossly conservative) scenario must be assumed and unnecessary costs are incurred. In this manuscript we propose data-science methods to identify and characterize regions of data corruption, and machine-learning (ML) techniques to address the problem of missing and corrupted tri-axial H-60 4G accelerometer data for PMx for a H-60 rotorcraft. Accurate 4G sensor readings are a critical component of helicopter flight regime recognition and flight damage assessment. In contrast to the traditional time-series prediction approach, which commonly use recurrent or long short-term memory (LSTM) networks, our proposed methods use a simpler deep neural network (DNN) to reconstruct the 4G accelerometer signal independently at every time instant. We demonstrate that the DNN approach is a viable option for sensor reconstruction independent of the length of period the sensor malfunctions.