A key activity of the MANTIS WP4 work on analysis and decision making functionalities has focus on identification and development of methods for forecasting the remaining useful life of an asset by modelling it’s deterioration and by extrapolating this into the future. When the extrapolated deterioration reaches a certain threshold that marks the end-of-life of the asset, and that point in time can be used as the forecasted remaining useful life (RUL). Simultaneously, the extrapolated deterioration gives an indication of the expected wear and tear, which can be compared with the observed wearing out to monitor the behavior of an asset.
Based on the predicted remaining lifetime, suitable maintenance tasks can be planned, in order to prevent unscheduled system down time.
Overall working strategy
The approach followed by MANTIS to address the estimation challenge is as follows:
- Develop algorithms and train models to estimate the RUL or predict the wear and tear of an asset.
- Monitored data (from embedded sensors or from quality assurance checks) is streamed to the cloud based storage system, where it will be investigated using time-series analysis or temporal data mining approaches.
- These methods will be used to discover revealing relationships between parameters to identify significant patterns, trends and anomalies.
- Based on this analysis of the historical data, a diagnostic model will be made self-learning, comparing predicted and actual data.
- Flexible modelling techniques will be used on diverse information, such as condition parameters, data patterns, sparse data and/or existing expertise to learn more complex relationships between the different parameters or train models which allow prediction of future performance in terms of expected failures.
Current status of work
An overview of relevant deterioration models and methods to estimate the remaining useful life (RUL) has been made. Also, for each use case, the present status with respect to available data, deterioration models and methods for estimation of RUL has been collected. In particular, for each of the use cases, the following information is relevant:
- Description of data typically available
- Description of relevant deterioration processes
- Description of models used for estimation of RUL
During the next 6 months, the general models and methods will be analysed and compared with the present status for the individual use case (available data, deterioration process, RUL estimation models). Following this analysis, specific models and methods will be selected and unified as much as possible, and thereafter applied and validated on the use cases during the second half of the MANTIS project.
This will be achieved by using a bottom-top-bottom approach:
- Collecting input for individual use cases
- Seeking for commonalities and unification into a generic approach
- Applying the generic approach on the individual use cases