Automated data inspection in jet engines

Rolls Royce accumulate a large amount of sensor data throughout the testing and deployment of their engines. The availability of this rich source of data offers exciting opportunities to automate the monitoring and testing of the engines. In this thesis we have developed statistical models to make meaningful insights from engine test data. We have built a classification model to identify different types of engine running in Pass-Off tests. The labels can be used for post-analysis and highlight problematic engine tests. The model has been applied to two different types of engines, in which it gives close to perfect classification accuracy. We have also created an unsupervised approach when there are no defined classes of engine running. These models have been incorporated into Rolls Royce systems. Early warnings for potential issues can enable relatively cheap maintenance to be performed and reduce the risk of irreparable engine damage. We have therefore developed an outlier detection model to identify abnormal temperature behaviour. The capabilities of the model are shown theoretically and tested on experimental and real data. Lastly, in a test decisions are made by engineers to ensure the engine complies with certain standards. To support the engineers we have developed a predictive model to identify segments of the engine test that should be retested. The model is tested against the current decision making of the engineers, and gives good predictive performance. The model highlights the possibility of automating the decision making process within a test

Vehicle level health assessment through integrated operational scalable prognostic reasoners

Today’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS

A method for the architectural design of distributed control systems for large, civil jet engines: a systems engineering approach

The design of distributed control systems (DCSs) for large, civil gas turbine engines is a complex architectural challenge. To date, the majority of research into DCSs has focused on the contributing technologies and high temperature electronics rather than the architecture of the system itself. This thesis proposes a method for the architectural design of distributed systems using a genetic algorithm to generate, evaluate and refine designs. The proposed designs are analysed for their architectural quality, lifecycle value and commercial benefit. The method is presented along with results proving the concept. Whilst the method described here is applied exclusively to Distributed Control System (DCS) for jet engines, the principles and methods could be adapted for a broad range of complex systems

Structural Health Monitoring System Trade Space Analysis Tool with Consideration for Crack Growth, Sensor Degradation and a Variable Detection Threshold

Structural Health Monitoring (SHM) systems face many obstacles and gaps that have resulted in the slow implementation in real-world applications. These obstacles include technology performance, implementation issues and a solid business case that justifies the investment in a SHM system. The presentation of a solid business case for the SHM system is a great challenge and arguably is the main factor contributing to the slow implementation of this technology. The research intent of this dissertation is to focus on the business case by providing a tool to aid decision makers. Simulated aging aircraft flight data are used in this effort due to the fact that many aging military aircraft will be flying beyond their initially intended design life. An analytical model was developed to address the business case and the integration of the SHM system into Condition Based Maintenance (CBM). The model aids the calculation of the cost of Life Cycle (LC) events resulting from the implementation of the SHM system on an aging aircraft. In addition, the model captures the events and effect on aircraft availability due to different SHM detection threshold settings and replacement of degraded sensors. The model captures false alarm rates, crack growth, probability of detection, and sensor degradation amongst other parameters. The proposed analytical model is a useful tool that provides the decision makers the confidence to either implement the SHM system on an aging military aircraft or not. Two models were developed; one was the SHM system model with no degradation and the second was the SHM system model with simulated degrading sensors. Three major subcomponents of the SHM model will be the sensor detection component, the crack growth component and the sensor degradation component (second model only)

Gas in engine cooling systems: occurrence, effects and mitigation

The presence of gas in engine liquid cooling systems can have severe consequences for engine efficiency and life. The presence of stagnant, trapped gases will result in cooling system hotspots, causing gallery wall degradation through thermal stresses, fatigue and eventual cracking. The presence of entrained, transient gases in the coolant flow will act to reduce its bulk thermal properties and the performance of the system s coolant pump; critically the liquid flow rate, which will severely affect heat transfer throughout the engine and its ancillaries. The hold-up of gas in the pump s impeller may cause the dynamic seal to run dry, without lubrication or cooling. This poses both an immediate failure threat should the seal overheat and rubber components melt and a long term failure threat from intermittent quench cooling, which causes deposit formation on sealing faces acting to abrade and reduce seal quality. Bubbles in the coolant flow will also act as nucleation sites for cavitation growth. This will reduce the Net Positive Suction Head available (NPSHA) in the coolant flow, exacerbating cavitation and its damaging effects in locations such as the cylinder cooling liners and the pump s impeller. This thesis has analysed the occurrence of trapped gas (air) during the coolant filling process, its behaviour and break-up at engine start, the two-phase character of the coolant flow these processes generate and the effects it has on coolant pump performance. Optical and parametric data has been acquired in each of these studies, providing an understanding of the physical processes occurring, key variables and a means of validating numerical (CFD) code of integral processes. From the fundamental understanding each study has provided design rules, guidelines and validated tools have been developed, helping cooling system designers minimise the occurrence of trapped air during coolant filling, promote its breakup at engine start and to minimise its negative effects in the centrifugal coolant pump. It was concluded that whilst ideally the prevention of cooling system gases should be achieved at source, they are often unavoidable. This is due to the cost implications of finding a cylinder head gasket capable of completely sealing in-cylinder combustion pressures, the regular use of nucleate boiling regimes for engine cooling and the need to design cooling channel geometries to cool engine components and not necessarily to avoid fill entrapped air. Using the provided rules and models, it may be ensured stagnant air is minimised at source and avoided whilst an engine is running. However, to abate the effects of entrained gases in the coolant pump through redesign is undesirable due to the negative effects such changes have on a pump s efficiency and cavitation characteristics. It was concluded that the best solution to entrained gases, unavoidable at source, is to remove them from the coolant flow entirely using phase separation device(s)

Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these methods may not perform well when applied to data from new units of a fleet that follow different operating conditions than those they were trained on. This is also known as domain shifts. Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features. However, DA methods do not distinguish between the different phases of operation, such as steady states or transient phases. This can result in misalignment due to under- or over-representation of different operation phases. This paper proposes two novel DA approaches for RUL prediction based on an adversarial domain adaptation framework that considers the different phases of the operation profiles separately. The proposed methodologies align the marginal distributions of each phase of the operation profile in the source domain with its counterpart in the target domain. The effectiveness of the proposed methods is evaluated using the New Commercial Modular Aero-Propulsion System (N-CMAPSS) dataset, where sub-fleets of turbofan engines operating in one of the three different flight classes (short, medium, and long) are treated as separate domains. The experimental results show that the proposed methods improve the accuracy of RUL predictions compared to current state-of-the-art DA methods.Comment: 18 pages,11 figure

Human Optimization and Performance Enhancement in Flight via Real-time Biofeedback (Project HAVE HOPE)

A four-phase, chronological, and build-up approach was implemented that commenced with basic hardware testing in a centrifuge and culminated in flights augmented by real-time biofeedback displays. A prototype Portable Electrocardiogram Unit (PECGU) was designed and proven to accurately measure heart rate (HR) and display percent heart rate reserve (%HRR). Results showed that %HRR was not a sole predictor of cognitive state. Cognitive responses indicated some correlation with %HRR, but were influenced by environment (centrifuge vs. flight). Subjective perceived exertion levels did not show statistically significant changes during test with biofeedback. A G-tracking task was evaluated during centrifuge and flight tests. One of four subjects showed statically significant improvement during the centrifuge while one of three subjects statistically improved during airborne G-tracking. This research marked the first time pilot HR was accurately measured and processed in flight, yielding a real-time biofeedback display. Overall, results could not be characterized by a single HR metric. A wide range of biosensors is needed to define operator physiological and cognitive (PC) state. There is hope for future development of an individualized, all-inclusive, and data-driven complex biofeedback algorithm, which ultimately presents a streamlined and intuitive PC state index with potentially tremendous enduring benefits to the warfighter

NASA Tech Briefs, September 2008

Topics covered include: Nanotip Carpets as Antireflection Surfaces; Nano-Engineered Catalysts for Direct Methanol Fuel Cells; Capillography of Mats of Nanofibers; Directed Growth of Carbon Nanotubes Across Gaps; High-Voltage, Asymmetric-Waveform Generator; Magic-T Junction Using Microstrip/Slotline Transitions; On-Wafer Measurement of a Silicon-Based CMOS VCO at 324 GHz; Group-III Nitride Field Emitters; HEMT Amplifiers and Equipment for their On-Wafer Testing; Thermal Spray Formation of Polymer Coatings; Improved Gas Filling and Sealing of an HC-PCF; Making More-Complex Molecules Using Superthermal Atom/Molecule Collisions; Nematic Cells for Digital Light Deflection; Improved Silica Aerogel Composite Materials; Microgravity, Mesh-Crawling Legged Robots; Advanced Active-Magnetic-Bearing Thrust- Measurement System; Thermally Actuated Hydraulic Pumps; A New, Highly Improved Two-Cycle Engine; Flexible Structural-Health-Monitoring Sheets; Alignment Pins for Assembling and Disassembling Structures; Purifying Nucleic Acids from Samples of Extremely Low Biomass; Adjustable-Viewing-Angle Endoscopic Tool for Skull Base and Brain Surgery; UV-Resistant Non-Spore-Forming Bacteria From Spacecraft-Assembly Facilities; Hard-X-Ray/Soft-Gamma-Ray Imaging Sensor Assembly for Astronomy; Simplified Modeling of Oxidation of Hydrocarbons; Near-Field Spectroscopy with Nanoparticles Deposited by AFM; Light Collimator and Monitor for a Spectroradiometer; Hyperspectral Fluorescence and Reflectance Imaging Instrument; Improving the Optical Quality Factor of the WGM Resonator; Ultra-Stable Beacon Source for Laboratory Testing of Optical Tracking; Transmissive Diffractive Optical Element Solar Concentrators; Delaying Trains of Short Light Pulses in WGM Resonators; Toward Better Modeling of Supercritical Turbulent Mixing; JPEG 2000 Encoding with Perceptual Distortion Control; Intelligent Integrated Health Management for a System of Systems; Delay Banking for Managing Air Traffic; and Spline-Based Smoothing of Airfoil Curvatures

Mining Technologies Innovative Development

The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research