AI for Machinery Integrity

Optimizing Rotating Equipment Maintenance Scheduling

Challenge

Our Client, one of the largest downstream operators in North America, is seeking an AI driven solution that will increase reliability and decrease maintenance costs for their large pumps. The solution is required to meet the following objectives:      

  • Provide optimal windows for operational duration.·      
  • Provide novel operational insights for equipment.·      
  • Proactively identify potential failure modes and time to failure before planned maintenance windows.·      
  • Improve decision-making by increasing visibility into equipment health, including predictions of performance degradation and/or equipment failure.

Solution

To achieve the desired Client objectives for optimizing the maintenance schedule for pumps, a multi fault simultaneous Prognostics system for pumps will be created. This Prognostics framework will consist of the following: Anomaly detection capturing abnormal operation, allowing experts to define and discover more faults. Fault Classification to detect specific faults and their time to failure. Remaining Useful Life Estimators to provide time to failure counters for each type of fault.

Approach

Our approach was to create two main solutions that will be cloud hosted. 1)Maintenance Optimization Framework that allows the client to correctly forecast future failure events, 2)Full prognostics framework that allows Client SME identify new failures and detect early degradation

Maintenance Optimization

Framework for optimal operational duration

To provide optimal maintenance scheduling, we will generate time-to-failure algorithms, also known as Remaining Useful Life (RUL) estimations. The output of RUL estimations will then be overlain with the equipment run hour forecasting algorithms. By overlaying plant forecast run hours with time-to-failure counters, maintenance scheduling can be optimized. This will properly place the future time window when outages are more likely needed. In addition to building and designing prognostics algorithms. The run hours forecast algorithm uses statistical techniques including ARIMA (Auto Regressive Integrated Moving Average) 

Prognostics Framework

To provide optimal maintenance scheduling, we will generate time-to-failure algorithms, also known as Remaining Useful Life (RUL) estimations. Our goal is to leverage Remaining Useful Life estimation (RUL) models for each fault type identified within the target list of pumps. RUL estimation will serve as an “odometer” for maintenance scheduling. To estimate RUL with variable operating conditions (e.g. BHA mud

motors, Turbines, pumps, Wind Turbine blades, and gearboxes). This approach relies on a Deep Learning technique called Long Short Term Memory (LSTM) Autoencoders

In addition to estimating RUL, we would build a semi-supervised early degradation detection algorithm using Deep Learning based Autoencoders and One Class Classification algorithms. This approach will make the Prognostics system more complete. Early detection algorithm will help identify early onset of system faults and therefore harden the time to failure estimation. This early detection process will also allow identifying and adding more faults to the system in the future as the usefulness of the platform is expanded.

Conclusion

BKO Services is in the process of helping a large equipment operator in reducing their maintenance cost by applying AI techniques to their integrity program.

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