The Sentinel Actuator product is used to detect anomalies in the operation of electromechanical actuators (EMAs). The detected anomalies can stem from electrical drive stages or mechanical faults in gearboxes. After detection, state-of-health (SoH) and remaining useful life (RUL) are calculated using advanced algorithms and presented on the Graphical User Interface (GUI).
- Application: Actuator
- Sensor: Sentinel Probe
- Data Type: Ripple Voltage
- Reasoner: DigIO
- GUI: Sentinel Dashboard
- Acquire and characterize the pertinent multivariate servo drive data associated with each fault condition (both electrical and mechanical) and the resulting stress effect on other components in the system.
- Develop the fault-to-failure progression (FFP) signatures of the acquired multivariate data to populate fault dictionary.
- Results: Detection of precursor events that mark impending failure of the servo drive subsystem or damage to its individual components.
Features & Benefits
- Electro-hydrostatic actuator (EHA) prognostics
- Degradation signature extraction for State of health (SoH)
- Algorithmic processing of SoH for Remaining Useful Life (RUL)
- Supports the Autonomic Logistics Information System (ALIS)
Adaptive Remaining Useful Life Reasoner for Prognostics and Health Management (PHM) Applications
ARULE Product Brief
Ridgetop’s Adaptive Remaining Useful Life Estimator (ARULE) is a powerful reasoner to determine the remaining useful life (RUL) and state of health (SoH) of complex systems. Working from acquired sensor data, ARULE employs an advanced prediction method related to extended Kalman filtering (EKF) to produce new RUL and SoH estimates for each new sensor data point.
ARULE is versatile and can be used for determining electronic and mechanical fatigue damage. The reasoner calculates fault-to-failure progression (FFP) signatures, accurate RUL (time-to-failure) estimates, and SoH estimates, which provide an early warning indicator for system maintenance personnel to schedule service to the system prior to catastrophic failure.
ARULE relies on diagnostic sensor data and a predefined model to produce an RUL estimate. It requires a sensor to "sense" data that are above a predefined "good-as-new" floor and below a "failed" ceiling. A new RUL estimate is produced based on changes to the model space; additionally, the new RUL estimate is used to produce a new SoH estimate.
- Power systems
- Battery management systems
- Actuator control systems
- Cable connection integrity