Matthew J. Watson

Model-based predictive diagnostics for primary and secondary batteries

The model-based effort described here is focused on predictive diagnostics for primary and secondary batteries. However, this novel approach can also be applied to other electrochemical energy sources such as fuel cells. This method is based on accurate parametric modeling of the transport mechanisms within the battery. This system knowledge was used for the careful development of electrochemical and thermal models. These models have been used to extract new features to be used in conjunction with several traditional measured parameters to assess the condition of the battery. The resulting output and any usable information available about the battery is then evaluated using hybrid automated reasoning schemes consisting of neural network and decision theoretic methods. The focus of this paper is on the model identification and data fusion of the monitored and virtual sensor data. The methodology and analysis presented in this paper is applicable to mechanical systems where multiple sensor types are used for diagnostic assessment.

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.

Model-based predictive diagnostics for electrochemical energy sources

The model-based effort described here is focused on predictive diagnostics for primary and secondary batteries. However, this novel approach can also be applied to other electrochemical energy sources such as fuel cells. This method is based on accurate parametric modeling of the transport mechanisms within the battery. This system knowledge was used for the careful development of electrochemical and thermal models. These models have been used to extract new features to be used in conjunction with several traditional measured parameters to assess the condition of the battery. The resulting output and any usable information available about the battery is then evaluated using hybrid automated reasoning schemes consisting of neural network and decision theoretic methods. The focus of this paper is on the model identification and data fusion of the monitored and virtual sensor data. The methodology and analysis presented is applicable to mechanical systems where multiple sensor types are used for diagnostic assessment.

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.

A Comprehensive High Frequency Vibration Monitoring System for Incipient Fault Detection and Isolation of Gears, Bearings and Shafts/Couplings in Turbine Engines and Accessories

The authors have developed a comprehensive, high frequency (1–100 kHz) vibration monitoring system for incipient fault detection of critical rotating components within engines, drive trains, and generators. The high frequency system collects and analyzes vibration data to estimate the current condition of rotary components; detects and isolates anomalous behavior to a particular bearing, gear, shaft or coupling; and assesses the severity of the fault in the isolated faulty component. The system uses either single/multiple accelerometers, mounted on externally accessible locations, or non-contact vibration monitoring sensors to collect data. While there are published instances of vibration monitoring algorithms for bearing or gear fault detection, there are no comprehensive techniques that provide incipient fault detection and isolation in complex machinery with multiple rotary and drive train components. The author’s techniques provide an algorithm-driven system that fulfills this need. The concept at the core of high frequency vibration monitoring for incipient fault detection is the ability of high frequency regions of the signal to transmit information related to component failures during the fault inception stage. Unlike high frequency regions, the lower frequency regions of vibration data have a high machinery noise floor that often masks the incipient fault signature. The low frequency signal reacts to the fault only when fault levels are high enough for the signal to rise over the machinery noise floor. The developed vibration monitoring system therefore utilizes high frequency vibration data to provide a quantitative assessment of the current health of each component. The system sequentially ascertains sensor validity, extracts multiple statistical, time, and frequency domain features from broadband data, fuses these features, and acts upon this information to isolate faults in a particular gear, bearing, or shaft. The techniques are based on concepts like mechanical transmissibility of structures and sensors, statistical signal processing, demodulation, time synchronous averaging, artificial intelligence, failure modes, and faulty vs. healthy vibration behavior for rotating components. The system exploits common aspects of vibration monitoring algorithms, as applicable to all of the monitored components, to reduce algorithm complexity and computational cost. To isolate anomalous behavior to a particular gear, bearing, shaft, or coupling, the system uses design information and knowledge of the degradation process in these components. This system can function with Commercial Off-The-Shelf (COTS) data acquisition and processing systems or can be adapted to aircraft on-board hardware. The authors have successfully tested this system on a wide variety of test stands and aircraft engine test cells through seeded fault and fault progression tests, as described herein. Verification and Validation (V&V) of the algorithms is also addressed.Copyright

Experimental and analytical development of health management for Electro-Mechanical Actuators

Expanded deployment of Electro-Mechanical Actuators (EMAs) in critical applications has created much interest in EMA Prognostic Health Management (PHM), a key enabling technology of Condition Based Maintenance (CBM). As such, Impact Technologies, LLC is collaborating with the NASA Ames Research Center to perform a number of research efforts in support of NASAs Integrated Vehicle Health Management (IVHM) initiatives. These efforts have combined experimental test stand development, laboratory seeded fault testing, and physical model-based health monitoring in a comprehensive PHM system development strategy. This paper discusses two closely related EMA research programs being conducted by Impact and NASA Ames. The first of these efforts resulted in the creation of an electro-mechanical actuator test stand for the Prognostics Center of Excellence at the NASA Ames Research Center. The second effort is ongoing and is utilizing physics-based modeling techniques to develop an algorithm and software package toolset for PHM of aircraft EMA systems using a hybrid (virtual sensor) approach.

Prognostic Enhancements to Diagnostic Systems (PEDS) Applied to Shipboard Power Generation Systems

Numerous advancements have been made in gas turbine health monitoring technologies focused on detection, classification, and prediction of developing machinery faults and performance degradation. Existing monitoring systems such as ICAS (Integrated Condition Assessment System), which is the Navy’s program of record and is deployed on many US Navy ships, employ alarm thresholds and event detection using rulebased algorithms. Adding the capability to predict the future condition (prognostics) of a machine would add significant benefit to the Navy practice. The current paper describes a framework and development process that allows more “plug ‘n play” integration of new diagnostic and prognostic technologies using evolving Open System Architecture (OSA) standards. Although many modules were developed in the PEDS framework, specific gas turbine modules that focus on compressor and nozzle degradation algorithms are discussed. The modules use statistical prediction algorithms and were developed using seeded fault data generated by the Navy engineering station. The modules are fully automated, interact with the existing monitoring system, process real-time data, and utilize advanced forecasting techniques. Such an advanced prognostic capability can enable a higher level of conditionbased maintenance for optimally managing total Life Cycle Costs (LCC) and readiness of assets.

Very High Frequency Monitoring System for Engine Gearbox and Generator Health Management

In cooperation with the major propulsion engine manufacturers, the authors are developing and demonstrating a unique very high frequency (VHF) vibration monitoring system that integrates various vibroacoustic data with intelligent feature extraction and fault isolation algorithms to effectively assess engine gearbox and generator health. The system is capable of reporting on the early detection and progression of faults by utilizing piezoelectric, optical, and acoustic frequency measurements for improved, incipient anomaly detection. These gas turbine engine vibration monitoring technologies will address existing operation and maintenance goals for current military system and prognostics health management algorithms for advanced engines. These system features will be integrated in a state-of-the-art vibration monitoring system that will not only identify faults more confidently and at an earlier stage, but also enable the prediction of the time-to-failure or a degraded condition worthy of maintenance action. The authors have made significant progress toward identifying, computing, and comparing the high frequency feature sets generated with various vibroacoustic measurement techniques. Specifically, the technology has been demonstrated on two subscale test stands. The first is a generator test rig that was equipped with a laser vibrometer and two high-frequency accelerometers. Various mechanical and electrical faults were seeded, with an emphasis on generator bearing faults. Initial results show very good detection capability in frequency bands well above those used in traditional vibration analysis. Another focus, accessory gearbox systems, was addressed for feasibility through a gearbox test rig, which was instrumented with high bandwidth accelerometers and wideband and narrowband acoustic emissions (AE) sensors. Baseline, seeded fault, and fault progression tests were conducted, including tests with various levels of gear tooth corrosion. Successful detection of this fault was then demonstrated using a number of new, innovative approaches. A statistical analysis was also performed to compare the approaches, with narrowband acoustic emission and high frequency vibration features performing the best.

Distributed, Integrated PHM and Control via Smart Engine Accessories for Future Modern Aircraft

Today’s modern aircraft have strict Prognostics and Health Management (PHM) requirements for each module and line replaceable component (LRC). Despite these requirements, there is limited on-board processing capability for health monitoring of even the major safety critical engine components. Additionally, loss of central processing and monitoring may lead to loss of the functionality or even the aircraft. Therefore, instead of locating all component PHM algorithms in a single box (i.e., FADEC), the authors have developed a ‘Distributed PHM’ (DPHM) architecture that distributes the PHM algorithms onto processing hardware that is embedded within the component. This approach would allow the component to be aware of its own current and future health state by monitoring and processing its own sensor data. Furthermore, aircraft engine distributed control systems (DCS) offer numerous advantages to the aviation industry, including: weight reduction through simplified power harness and minimization of a centralized controller, a faster and cheaper certification process, and implementation of advanced multivariable controls and active component control that will maximize the performance and efficiency of the engine. Integration of the DPHM and DCS philosophies would result in self-diagnosing components with integrated control capabilities that would enable concepts such as adaptive fault tolerant control. This distributed, nodal approach would greatly reduce the aircraft level data communication and process burden. Instead of needing to transfer and process high bandwidth data, the aircraft level computer simply receives low bandwidth health indicators from all smart components. As such, this solution has the potential to address some of the processing limitations experienced by current ‘Centralized’ PHM and control approaches. Key to this integration is the implementation of an open-systems architecture that would allow seamless integration of components manufactured by different vendors into a common architecture. In this work, the authors explore the implementation of such an approach on engine accessory components.

Automated Health Management for Gas Turbine Engine Accessory System Components

Traditional engine health management development has focused on major gas turbine engine turbomachinery components, such as disks, blades, and main bearings, because these components are expensive to maintain and their failures frequently have safety implications. However, the majority of the events that compromise mission success and equipment availability in military aircraft arise from the degradation or failures of engine accessory system components, such as valves, pumps, and actuators. Failure or statistical-based maintenance of these components fails to account for unanticipated and extreme operating scenarios, which are a major cause of unscheduled maintenance events. U.S. military systems are thus moving toward condition-based maintenance (CBM), wherein maintenance is performed as and when required, thus improving asset availability and contributing significantly to mission success. The authors have developed low-overhead diagnostics and prognostics techniques, which would enable a shift toward CBM of engine accessory components. The current work focused on aircraft fuel and lubrication systems. Model- based and data-driven techniques were developed to provide reliable health assessments of hydraulic pumps and valves, which are essential components on these systems.