Jeremy Sheldon

A Comprehensive Prognostics Approach for Predicting Gas Turbine Engine Bearing Life

Development of practical and verifiable prognostic approaches for gas turbine engine bearings will play a critical role in improving the reliability and availability of legacy and new acquisition aircraft engines. In addition, upgrading current United States Air Force (USAF) engine overhaul metrics based strictly on engine flight hours (EFH) and total accumulated cycles (TAC) with higher fidelity prognostic models will provide an opportunity to prevent failures in engines that operate under unusually harsh conditions, and will help avoid unnecessary maintenance on engines that operate under unusually mild conditions. A comprehensive engine bearing prognostic approach is presented in this paper that utilizes available sensor information on-board the aircraft such as rotor speed, vibration, lube system information and aircraft maneuvers to calculate remaining useful life for the engine bearings. Linking this sensed data with fatigue-based damage accumulation models based on a stochastic version of the Yu-Harris bearing life equations with projected engine operation conditions is implemented to provide the remaining useful life assessment. The combination of health monitoring data and model-based techniques provides a unique and knowledge rich capability that can be utilized throughout the bearing’s entire life, using model-based estimates when no diagnostic indicators are present and using the monitored features such as oil debris and vibration at later stages when failure indications are detectable, thus reducing the uncertainty in model-based predictions. A description and initial implementation of this bearing prognostic approach is illustrated herein, using bearing test stand run-to-failure data and engine test cell data.Copyright

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

Prognostics/Diagnostics for Gas Turbine Engine Bearings

Development of robust in-flight prognostics or diagnostics for oil wetted gas turbine engine components will play a critical role in improving aircraft engine reliability and maintainability. Real-time algorithms for predicting and detecting bearing and gear failures are currently being developed in parallel with emerging flight-capable sensor technologies including in-line oil debris/condition monitors, and vibration analysis MEMS. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data, with probabilistic component models to achieve the best decisions on the overall health of oil-wetted components. By utilizing a combination of health monitoring data and model-based techniques, a comprehensive component prognostic capability can be achieved throughout a components life, using model-based estimates when no diagnostic indicators are present and monitored features such as oil debris and vibration at later stages when failure indications are detectable. Implementation of these oil-wetted component prognostic modules will be illustrated in this paper using bearing and gearbox test stand run-to-failure data.Copyright

Recent case studies in bearing fault detection and prognosis

This paper updates current efforts by the authors to develop fully-automated, online incipient fault detection and prognosis algorithms for drivetrain and engine bearings. The authors have developed and evolved ImpactEnergytrade, a feature extraction and analysis driven system that integrates high frequency vibration/acoustic emission data, collected using accelerometers and other sensors such as a laser interferometer to assess the health of bearings and gearboxes in turbine engines. ImpactEnergy combines advanced diagnostic features derived from waveform analysis, high-frequency enveloping, and more traditional time domain processing like root mean square (RMS) and kurtosis with classification techniques to provide bearing health information. The adaptable algorithm suite has been applied across numerous air vehicle relevant programs for the Air Force, Navy, Army, and DARPA. The techniques presented in this paper are tested and validated in a laboratory environment by monitoring multiple bearings on test rigs that replicate the operational loads of a turbomachinery environment. The capability of the software on full-scale test rigs at major OEMs (original equipment manufacturer) locations will be shown with specific data results. The team will review developments across these multiple programs and discuss specific implementation efforts to transition to the fleet in a variety of manned and unmanned platforms

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.

Novel Joint Time Frequency Vibration Diagnostics of Turbine Engine Accessories

Traditional engine health management development has focused on major gas turbine engine components (i.e., disks, blades, bearings, etc.) due to the fact that these components are expensive to maintain and their failures frequently have safety implications. However, the majority of events that lead to standing down of aircraft arise from gas turbine accessory components such as pumps, generators, auxiliary power units, and motors. Common vibration diagnostics, which are based on frequency domain analysis that assumes the monitored signal is “stationary” during the analysis period, are not effective for these components. This is true because operating conditions are often non-stationary and evolving, which leads to spectral smearing and erroneous analysis that can cause missed detections and false alarms. Traditionally, this is avoided by defining steady state operating conditions in which to perform the analysis. Although this may be acceptable for major engine components, which are typically highly loaded during normal steady operation, many engine accessories are only high loaded during transients, especially startup. For example, an engine starter or fuel pump may be more highly loaded and therefore susceptible to damage during engine start up, typically avoided by traditional vibration analysis methods. More importantly, certain component faults and their progression can also lead to non-stationary vibration signals that, because of the smearing they induced, would be missed by traditional techniques. As a result, the authors have developed a novel engine accessory health monitoring methodology that is applicable during non-stationary operation through application of joint time-frequency analysis (JTFA). These JTFA approaches have been proven in other disciplines, such as speech analysis, radar processing, telecommunications, and structural analysis, but not yet readily applied to engine accessory component diagnostics. This paper will highlight the results obtained from applying JTFA techniques, including Short-Time Fourier Transform, Choi-Williams Distribution, Continuous Wavelet Transform, and Time-Frequency Domain Averaging, to very high frequency (VHF) vibration data collected from healthy and damaged turbine engine accessory components. The resulting accuracy of the various approaches were then evaluated and compared with conventional signal processing techniques. As expected, the JTFA approaches significantly outperformed the conventional methods. On-board application of these techniques will increase prognostics and health management (PHM) coverage and effectiveness by allowing accessory health monitoring during the most life influencing regimes regardless of operating speed and reducing inspection and replacement costs resulting in minimizing the vehicle down time.Copyright

Smart phone machinery vibration measurement

This paper presents a computationally economical algorithm that provides RMS value of vibration velocity calculated using the accelerometer measurements embedded in modern smartphones. This measurement extends the use of smartphones to quickly diagnose the health status of machinery. 1 2

Integrating Oil Health and Vibration Diagnostics for Reliable Wind Turbine Health Predictions

The Department of Energy’s (DOE) goal for wind energy is that it comprise 20% of the nation’s energy by 2030. For this to be achieved, so called “distributed wind” and off-shore wind farms will be required. However, to date, operating & maintenance and unscheduled outage costs make such applications risky [1]. A potential risk mitigation strategy is implementation of Prognostics and Health Management (PHM). Prognostics promise great benefits to parts order/handling, logistic planning, maintenance scheduling, which ultimately reduces the cost of ownership/operation. Successful prognostics require that faults be detected at the earliest possible stage. However, to fully realize the benefit of the investment, PHM systems must provide early detection of precursors for failure modes. Incipient fault detection is critical to increasing reliability and lowering Operation and Maintenance (O&M) costs for wind turbine gearboxes. The combination of this critical incipient fault detection capability with prognostics will allow wind turbine owners to reap the promised PHM benefits. It is possible to generalize gearbox faults into two areas: mechanical and lubricant related faults. To provide adequate coverage to both generalized areas, the authors will show how two primary sensing technologies can be combined to provide the necessary detection horizon for wind turbine gearboxes. The authors will introduce a generalized PHM architecture that can be adapted for a broad range of mechanical systems, especially wind turbine gearboxes. Various sensors and diagnostic techniques that can be integrated into the architecture will be discussed. Finally, the authors will show how the architecture, sensors, and techniques can be applied to a subscale test, including example results.Copyright