Carl S. Byington

A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection

This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which employs features or condition indicators derived from sensor data in combination with simple models of the systems degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.

Data-driven neural network methodology to remaining life predictions for aircraft actuator components

Actuators are complex electro-hydraulic or mechanical mechanisms utilized in aircraft to drive flight control surfaces, landing gear, cargo doors, and weapon systems. Impact has developed a prognostic and health management (PHM) methodology for these critical systems that includes signal processing and neural network tracking techniques, along with automated reasoning, classification, knowledge fusion, and probabilistic failure mode progression algorithms. The processing utilizes the command/response signal and hydraulic pressure data from the actuators and provides a real-time assessment of the current/future actuator health state. This methodology was applied to F/A-18 stabilator electro-hydraulic servo valves (EHSVs) using test stand data provided by Boeing Phantom works. The automated module demonstrated excellent health state classification results. The prognosis was also successfully performed however no data was available to validate the prediction. These algorithms were developed with consideration to sensor/processing limitations for potential onboard implementation. Many of the PHM elements presented here could also be adapted for other actuator types and applications.

DYNAMIC MODELING AND WEAR-BASED REMAINING USEFUL LIFE PREDICTION OF HIGH POWER CLUTCH SYSTEMS

A model-based technique is presented for remaining useful life (RUL) prediction of highly dynamic, high-power dry clutch systems by combining physicsbased simulation and wear-prediction models. Primary load and engagement shear drivers (i.e., torque, speed, and clutch surface temperature) are modeled using a first principles approach. An extension of Archard’s law is presented in which life usage is predicted by using multiple stochastic models to determine a wear coefficient for each applicable wear mechanism. These models consider the physical wear process, including debris-particle and protective-layer formation, using parameters such as surface roughness, particle size, and surface temperature. These stochastic variables are evaluated in a probabilistic framework, using statistical methods such as Monte Carlo and importance sampling, which consider both measurement and modeling uncertainty. Confidence interval prognostic results are provided to predict the RUL of the clutch throughout its limited life in near-real time.

Electronic prognostics ¿ A case study using global positioning system (GPS).

Prognostic health management (PHM) of electronic systems presents challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. Recent changes in weapons platform acquisition and support requirements has spurred renewed interest in electronics PHM, revealing possible applications, accessible data sources, and previously unexplored predictive techniques. The approach, development, and validation of electronic prognostics for a radiofrequency (RF) system are discussed in this paper. Conventional PHM concepts are refined to develop a three-tier failure mode and effects analysis (FMEA). The proposed method identifies prognostic features by performing device, circuit, and system-level modeling. Accelerated failure testing validates the identified diagnostic features. The results of the accelerated failure tests accurately predict the remaining useful life of a COTS GPS receiver to within plusmn5 thermal cycles. The solution has applicability to a broad class of mixed digital/analog circuitry, including radar and software defined radio

An Overview of Selected Prognostic Technologies With Application to Engine Health Management

The DoD has various vehicle platforms powered by high performance gas turbine engines that would benefit greatly from predictive health management technologies that can detect, isolate and assess remaining useful life of critical line replaceable units (LRUs) or subsystems. In order to meet these needs for next generation engines, dedicated prognostic algorithms must be developed that are capable of operating in an autonomous and real-time engine health management system software architecture that is distributed in nature. This envisioned prognostic and health management system should allow engine-level reasoners to have visibility and insight into the results of local diagnostic and prognostic technologies implemented down at the LRU and subsystem levels. To accomplish this effectively requires an integrated suite of prognostic technologies that can be applied to critical engine systems and can capture fault/failure mode propagation and interactions that occur in these systems, all the way up through the engine and eventually vehicle level. In the paper, the authors will present a generic set of selected prognostic algorithm approaches that can be applied to gas turbine engines, as well as provide an overview of the required reasoning architecture needed to integrate the prognostic information across the engine.

Embedded diagnostic/prognostic reasoning and information continuity for improved avionics maintenance

The authors are developing enhanced onboard and at-wing diagnostic technologies applicable to both legacy and new avionics. The paper identifies onboard information sources and automated reasoning techniques that build upon existing built-in-test (BIT) results to improve fault isolation accuracy. Modular software and data elements that combine BIT with contextual information, component usage models, and novel reasoning techniques are described. In addition, the authors identify candidate avionics component applications to implement prognostics (prediction of impending problem) using forecasting techniques. A demonstration of diagnostic/prognostic prototype reasoners and information continuity using an open architecture framework within the streamlined maintenance concept is offered.

Advanced diagnostic/prognostic reasoning and evidence transformation techniques for improved avionics maintenance

Techniques used in the design and implementation of modern avionics suggest an opportunity to re-examine the maintenance and repair process for current and future systems. The authors have developed a framework with automated evidence collection, data representation and storage, and, advanced automated reasoning techniques to implement within an avionics health management system. This paradigm shift approach utilizes advanced capture and representation of environmental, operational, and component inter-relationship evidence at a systems level to reduce diagnostic ambiguity, guide at-wing testing, and provide prognostics. Modular and reusable software and data elements that combine built-in-test (BIT) with contextual information, component usage models, and evidentiary reasoning techniques are described. A use case scenario is presented that illustrates evidence collection and XML transformation, automated database storage and retrieval, and automated advanced reasoning for diagnostics and prognostics, and finally, an example of at wing ambiguity reduction using an advanced evidence-based reasoner is presented.

In-line health monitoring system for hydraulic pumps and motors

Abshact Aircraft hydraulic systems are used to actuate flight control surfaces (flaps, spoilers, ailerons, elevators, and rudder), thrust vectoring and reversing mechanisms, landing gear, cargo doors, and in some cases, weapon systems. Within these flight and mission critical hydraulic systems, the hydraulic pump is widely recognized to be the most critical component. The current paper discusses methods to fulfill the need for on-line pump diagnostics through the development of a prototype in-line, intelligent pump monitor for critical hydraulic pumps and motors. The approach includes performance modeling, signal processing and feature extraction, feature level fusion, automated classification, and knowledge fusion for estimating degradation through the collection of inline pump sensor data and onboard processing. The methods employed and an initial hardware and software realization are reviewed. The effects and tracking of pump wear and fatigue damage as witnessed from a series of endurance tests conducted at the Air Force Research Laboratory (AFRL) will be shown from the successful results of stimulating the in-line pump with digitized test data.

Electronic prognostics - a case study using Global Positioning System (GPS)

Prognostic health management (PHM) of electronic systems presents challenges traditionally viewed as either insurmountable or otherwise not worth the cost of pursuit. Recent changes in weapons platform acquisition and support requirements has spurred renewed interest in electronics PHM, revealing possible applications, accessible data sources, and previously unexplored predictive techniques. The approach, development, and validation of electronic prognostics for a radiofrequency (RF) system are discussed in this paper. Conventional PHM concepts are refined to develop a three-tier failure mode and effects analysis (FMEA). The proposed method identifies prognostic features by performing device, circuit, and system-level modeling. Accelerated failure testing validates the identified diagnostic features. The results of the accelerated failure tests accurately predict the remaining useful life of a COTS GPS receiver to within plusmn5 thermal cycles. The solution has applicability to a broad class of mixed digital/analog circuitry, including radar and software defined radio

Prognostic health management for avionic systems

Maintenance of aircraft electronic systems has traditionally been performed in reaction to reported failures or through periodic system replacements. Recent changes in weapons platform acquisition and support requirements have spurred interest in application of prognostic health management (PHM) concepts developed for mechanical systems to electronic systems. The approach, development, and validation of prognostics for two types of electronic equipment are discussed in this paper. The two applications, a switch-mode power supply and a GPS receiver were selected based on their relatively high failure rates and relevance to many commonly used avionics systems. The method identifies prognostic features by performing device, circuit, and system-level modeling. Device modeling with equivalent circuit and mathematical physics of failure models describe parameter degradation resulting from damage accumulation for each device. Prognostic features extracted from a small array of sensors on the power supply, and from the GPS operational communication data stream are used to update life usage and failure progression models to provide an indication of the health state. The results of accelerated failure tests on both systems are used to illustrate the approach and demonstrate its effectiveness in predicting the useful life remaining. The solutions have applicability to power supplies in many avionic systems, and to a broad class of mixed digital/analog circuitry including radar and software defined radio