Student Branch
Software
Assessing the engine’s remaining useful life (RUL) is crucial. During their lifetime, aircraft components are prone to degradation, affecting their reliability and performance. Among the various types of aircraft equipment, the performance of turbo engines, which consist of subunits such as fans and compressors, is particularly important, and the ability to accurately predict RUL is said to be the key to achieving advanced maintenance. It is a challenging task to predict RUL based on the entire life cycle data in order to provide the necessary maintenance behavior. Use the given dataset and provide a framework to predict the RUL of the turbofan engine considering HPC failure. The dataset FD001 contains time series of 21 sensors and 3 operational settings of 100 units (turbofan engine). These 3 operational settings have a substantial effect on engine performance. Note that the data is contaminated with sensor noise. Each engine works normally at the beginning of each time series and fails at the end of the time series. Each row is a snapshot of the data taken during a single operation cycle. Data Set : FD001 Train trajectories: 100 Test trajectories: 100 Conditions: ONE (Sea Level) Fault Modes: ONE (HPC Degradation) The data are provided as a zip-compressed text file with 26 columns of numbers, separated by spaces. Each row is a snapshot of data taken during a single operational cycle; each column is a different variable. The columns correspond to the following: 1) unit number 2) time, in cycles 3) operational setting 1 4) operational setting 2 5) operational setting 3 6) sensor measurement 1 7) sensor measurement 2 ... 26) sensor measurement 26 Link to Dataset: https://drive.google.com/file/d/1fklUPvfe7MXl1Q9Rk8LQaJzLAlBNeHi2/view?usp=sharing