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Internet of Things systems generate a large amount of sensor data that needs to be analyzed for extracting useful insights on the health status of the machine under consideration. Sensor data of all possible states of a system are used for building machine learning models. These models are further used to predict the possible downtime for proactive action on the system condition. Aircraft engine data from run to failure is used in the current study. The run to failure data includes states like new installation, stable operation, first reported issue, erroneous operation, and final failure. In the present work, the non-linear multivariate sensor data is used to understand the health status and anomalous behavior. The methodology is based on different sampling sizes to obtain optimum results with great accuracy. The time series of each sensor is converted to a 2D image with a specific time window. Converted Images would represent the health of a system in higher-dimensional space. The created images were fed to Convolutional Neural Network, which includes both time variation and space variation of each sensed parameter. Using these created images, a model for estimating the remaining life of the aircraft is developed. Further, the proposed net is also used for predicting the number of engines that would fail in the given time window. The current methodology is useful in avoiding the health index generation for predicting the remaining useful life of the industrial components. Better accuracy in the classification of components is achieved using the TimeImagenet-based approach.

A TimeImageNet Sequence Learning for Remaining Useful Life Estimation of Turbofan Engine in Aircraft Systems