AI-First Company for Digital, Cognitive & Industry 4.0 Era

Data Pipeline

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.

Training Pipeline

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.

Inference Pipeline

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.

Monitoring And Logging

Wavelabs Insight also provides for dashboards to track efficiency, create predictive maintenance plans, and remotely monitor the real-time status of all systems.

Remaining useful life

Achieve Higher Operational Effectiveness

Asset & Inventory Optimization

Data Pipeline

Training Pipeline

Inference Pipeline

Monitoring And Logging