A Cloud AI Platform for Continuous Process Improvement

  • Guided decision making for every machine, system and plant across your fleet. Gain new competitive advantage through your data.

    Genesis is a decision support system that flows your existing operational and maintenance data into next generation AI/Machine Learning engines data that increase uptime while lowering maintenance costs and extend equipment life.

    Data Unification


    Poor data quality adversely impacts the accuracy of any analytics solutions often generating significant false alarms. Data dropouts due to harsh industrial environments and frequent outliers due to varying data acquisition speeds are just a few examples of poor data quality.. Genesis Data Quality algorithms contain knowledge-based data curation libraries and imputation algorithms that allow users to recover missing observations and enhance data quality.

    Feature Engineering


    Industrial data often has diminished value in its raw form. Advanced data transformations using statistical machine learning and signal processing methods enable the extraction of high-fidelity degradation and performance based features that can be utilized to improve fault diagnostics and prognostics.

    AI/Machine Learning


    Fault detection algorithms are often plagued with false alarms, which typically increase exponentially with the number of sensors being monitored. Advanced data fusion and variable selection algorithms are used to minimize and control false alarm rates while harnessing high density sensor observations to improve detection sensitivity.

    Fault Diagnostics


    Effective fault diagnostics are essential for engineering and service personnel. The effectiveness of the diagnostic algorithm is a direct function of the accuracy of the governing fault detection algorithm. Genesis utilizes proprietary machine learning methods and powerful feature extraction capabilities to ensure high-fidelity diagnostic capability.




    Existing prediction algorithms take a simplistic approach and assume a single failure mode. In reality, capital-intensive equipment are complex and can fail in different ways (failure modes). They are often monitored by different types of sensors and operate under varying operating conditions. Genesis prognostics technology utilizes advanced data fusion algorithms combined data from different sensors enabling that allow for anticipating the likelihood of a given failure mode and predicting the associated RUL.

    Remaining Useful Life



    Most existing predictive technologies are based on adhoc methods that only generate short-term predictions, which are often inaccurate. These typically include simple trend analysis or physics-based first principles approaches. Genesis proprietary prognostics algorithms leverage uncertainty quantification methods to generate long-term statistical predictions of remaining useful life (RUL) with probabilistic risk profiles and confidence intervals, which describe an asset’s health as soon as it is put into operation.

    Condition Based


    Most industrial maintenance policies are based on periodic schedules. Enterprises that have utilized condition based maintenance (CBM) only utilize fault detection methods to trigger maintenance. In contrast, Genesis' CBM algorithms integrate long-term RUL predictions with economic models to obtain cost-optimal maintenance schedules for different loads and settings including opportunistic maintenance policies and fleet-management.


    Operational Decisions


    Plant-wide operational decisions such as loading profiles, throughput rates, production schedules, etc. have a significant impact on equipment degradation rates, which is typically overlooked in most existing software solutions. Genesis’ algorithms integrate RUL predictions with operational and planning decision support systems to allow operators to evaluate the impact of a particular loading profile on the asset’s RUL.


    Service Logistics


    Logistic and supply chain software typically focus only on raw materials and finished goods where demand functions are well-understood and can be estimated using market data. In contrast, service logistics or spare parts supply chain is still in its infancy due to a lack of understanding of how equipment reliability interacts with spare part demand. Genesis Adaptive Service Logistics utilizes sensor-based predictive analytics to estimate demand of spare parts and develop cost-optimal spare part ordering policies and optimized inventory management to create a more efficient service organization.