Built on the strengths of artificial intelligence and predictive analytics, Wavelabs Insight can create statistically valid models of equipment life based on operational data and other factors. These models enable you to focus on critical risks that affect operational reliability and availability.
Wavelabs Insight harnesses the data from all assets and further divides them into training and test subsets. Each asset is assumed to start with some degree of initial wear and tear, and manufacturing variation. These factors are noted as normal, and not a fault condition.
The machine operates normally at the start of each time series and develops a fault at some point during the series. ML models are trained on sets until the fault grows in magnitude and the asset fails. In the test set, the time series ends at some time prior to the system failure, thereby, predicting the number of remaining operational cycles left before the machine completely fails.
We validated this framework using a piece-wise linear function. We assumed that the first few cycles have the maximum health of the machine before it starts to decrease linearly. Creating a virtual environment, we build predictive models that can estimate the remaining operational cycles that the machine would run for, thereby, predicting equipment performance and lifetime.
Wavelabs Insight also provides for dashboards to track efficiency, create predictive maintenance plans, and remotely monitor the real-time status of all systems.
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