The Analytics Engine for Performance Digital Twin (PDT) provides unique functionality for multi-level analysis of the sensor data. It analyses large sets of data images recorded across the asset fleet over many time periods; each image contains multivariable time-series data for a time period. The time period could be a single aircraft flight, car trip, one day of industrial plant operation, etc.

Asset Fleet Performance

Our Performance Digital Twin software analyzes sensor time series data, such as aircraft flight recorder data, collected during normal operation of the IIoT assets. Digital Twin is named one of top ten strategic technology trends by Gartner for several years in row. There are several technologies being developed under this name. Mitek Analytics provides Operational Digital Twins that include building asset models from historical data. These models characterize variation across the asset fleet and trends along the time.

In the example illustrated in the figure, the Performance Digital Twin analytics is applied to operational data collected in the jet engine fleet. The Predictive Analytics train the models on big data sets. The Prescriptive Analytics realize business value using these predictive models. The jet engine fleet monitoring application is one of the most demanding in the Industrial IoT because turbomachines must be maintained every few thousands hours, are mission critical, and must operate with high performance. Performance Digital Twin can be deployed for a variety of assets. Our Digital Twin analytics are driven by IIoT data collected in operation for fleet of assets; these are Operational Digital Twins. Historical data is used to build predictive models. These models are then used to prescribe actions, provide decision support for current data. 

Asset Fleet Analytics

PDT technology was used for multi-level analysis of the performance data for fleets of IIoT assets. The analysis includes variability across the population and trends along the time. In one project, a multi-level model with 10 million regressors has been estimated from 1 trillion data points (flight data for 300-aircraft fleet over 3 years); these computations take 12 hours on a 20-core cluster. PDT analytics includes data integrity check and data cleansing, which must be done before training the model. Up to 10% of all data records might have bad data that could distort the model training.

The models for asset fleets can be build based on historical operational data. This allows to deploy asset fleet analytics even when the asset original manufacturer does not provide suitable models.

The PDT Business Case is that added value is enabled by fleet-wide energy efficient performance, condition-based maintenance, availability, safety, and other desirable outcomes.