Predictive maintenance and anomaly detection are in the forefront of the application of AI in the industrial context.
Predictive maintenance is a domain where data is collected over time to monitor the state of an asset with the goal of finding patterns to predict failures.
Machine learning (especially deep learning) is able to predict the Remaining Useful Life (RUL) of different engine components.
The purpose is to detect how much time is left before the next fault in the machinery in order that maintenance can be planned in advance.
On the factory floor the company utilizes a fleet of identical machines in production around the clock with different life durations.
The engines operate normally with different degrees of initial wear and manufacturing variation at the start of each working cycle.
Occasionally and unexpectedly develop a degradation at some point during the cycle, and the fault grows in magnitude until the engine fails.
Such unforeseeable shutdowns causes costs in terms of time and expenses, while scheduled maintenance is only partly able to prevent untypical standoff.
Fleet of engines of the same type (100 in total)
3 operational settings
21 sensor measurements in the form of multivariate time series (observations in terms of time for working life)
RUL in cycles
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