Browse Topic: Flight recorders
With the advancement of automotive industries, the need for wireless connectivity between vehicle and smartphone is increasing. To meet the demand for wireless connectivity, Bluetooth plays a vital role. Testing Bluetooth systems is challenging and complex when development cycles of the system involve multiple partners. The system under test must fulfil consumers expectation of Bluetooth functionality paired with their personal devices. Despite many advances and existence of a few reliable systems, hardware limitation, and lack of standardization in Bluetooth test system are some of the prolonged issues. Throughout the course, various capabilities and existing traditional Bluetooth testing system practice were researched, which majorly at a system level (Black box). The gap of such testing is the escape of defect which involves the interoperability of multiple profiles like AVRCP, HFP, and A2DP. This paper focuses on a reliable testing approach which is based on packet level testing common to BLE and Bluetooth Classic. Research shows there is no common approach for BLE and Bluetooth Classic, some research provides BLE based solution for packet level tracing but lacks to bring it in Bluetooth Classic. The work here will help us in packet sniffing, processing, logging for both BT Classic and BLE profiles. Hence the need to test Bluetooth system to detect and deliver defect-free products would save development cost in terms of time involved in iterative development and testing cycle, and gain customer’s trust. This paper intends to introduce a humble solution to the testing community a step before the functional testing of Bluetooth system through validation of transmitted Bluetooth profile packet-id between the devices.
Machine learning is used for the research and development of ITS services and the rider assistance for on-road motorcycle racing. Meanwhile, rider assistance systems for off-road motorcycles have yet to be developed, partly due to the complexity of the measurement conditions, as described in the previous paper. This research aims to create a reliable AI which is capable of classifying typical jump behaviors in off-road riding by machine learning to create a rider assistance system for off-road motorcycles. Motorcycle manufacturers and certain research institutes use motion sensors to collect data, but the data is obtained from a limited number of vehicles and riders. The creation of a rider assistance system requires a large amount of validation data. Furthermore, it is desirable to achieve the target with data that can be measured in mass-produced vehicles, which will make it possible to collect data even from general users. In addition, recent machine learning models are black boxes because it is difficult for people to understand the entire process, and it is necessary to evaluate the validity of the results. The approaches are as follows. (1) Using data that can be measured in mass-produced vehicles, the number of features was increased as a preprocessing step. (2) The validity of the machine learning model was evaluated by focusing on the SHAP value, one of the XAI techniques. As a result, (1) classification ability has improved. (2) correspondence between the number of features with large SHAP values and physical phenomena has been obtained. In other words, it has been confirmed that appropriate number of features have been selected for classification. These results have indicated that the created AI has a certain level of classification ability and that the judgment results can be trusted.
Spatial Disorientation (SD) mishaps account for the greatest loss of lives in both military and civilian aviation worldwide. When no mechanical cause of a mishap is identified, mishap investigators can use flight data recorder information to populate perceptual models with aircraft flight parameters in order to confirm or deny that pilot SD was the probable cause of the mishap. Current perceptual model weaknesses include the inability to analyze hover and hover-transition mishaps and not accounting for sensory inputs from the auditory and somatosensory systems. The authors have conducted in-flight helicopter perceptual threshold studies to extend the model envelop to include hover as well as a series of tactile cueing in-flight studies in fixed-wing aircraft to permit the inclusion of somatosensory information into the model. This expanded model, by including all sensory modalities, now provides a probable solution to prevention of SD mishaps by continuously maintaining spatial orientation via multisensory cueing. Examples of application of the model to recent high-profile mishaps are included.
Rotorcrafts are generally subject to a higher fatal accident rate than other segments of aviation, including commercial and general aviation. The safety improvement for rotorcrafts would directly improve the efficiency of air traffic control, since rotorcrafts operate primarily within low-level airspace; an area that is becoming increasingly complex with new entrants, such as unmanned aircraft systems and urban air mobility. The recent impact of artificial intelligence and deep learning algorithms on various aspects of our lives has led to the investigation of the application of these algorithms in the aviation domain; as it may offer a prime opportunity to enhance safety within the aviation community. In this research, we explore the efficacy, reliability, and, more importantly, the explainability of modern deep learning algorithms. We use machine learning models to predict the attitude (pitch and yaw) of rotorcrafts using video data recorded with ordinary cameras. The cameras were mounted inside the helicopter cockpit and recorded outside view through windshield continually during the flight. We train four different architectures of convolutional neural networks (CNNs), i.e., VGG16, VGG19, ResNet50, and Xception. The models achieved 90%, 91%, 88%, and 88%, respectively, average attitude prediction accuracy on the test video dataset. Furthermore, we use gradient class activation maps (grad-CAM) to ascertain the features and regions of the image that influenced the model to make a specific prediction. We show that CNNs learn to focus on similar features as human operators (pilots), i.e., the natural horizon curve. Our findings demonstrate the feasibility of using deep learning models for attitude prediction from f light videos recorded using ordinary inexpensive cameras. The proposed video analytics framework provides a cost-effective means to supplement traditional Flight Data Recorders (FDR); a technology that is often beyond the financial reach of most general aviation rotorcraft operators.
As the premier agency for promoting and insuring aviation safety, the Federal Aviation Administration (FAA) continues to promote and highlight the importance of participating in aviation Flight Data Monitoring (FDM) programs to improve flight safety and operational efficiency. Indeed, recorder safety is one of the agency's top 10 most wanted list of safety improvements in 2017-2018. The FAA, National Transportation Safety Board (NTSB), and the United States Helicopter Safety Team (USHST) are strong proponents of recorder use. These organizations and other industry partners are working together to implement a helicopter safety enhancement that promotes the use of flight data recorders as a mechanism to reduce the helicopter fatal accident rate. However, despite these best efforts to reduce the fatal accident rate with this lifesaving technology, barriers to implementation exist. These include initial costs of flight data recorders which can range from 9,000 - 50,000, on average. These costs can be significant for small operators and they combine to prohibit the widespread adoption of FDM by the rotorcraft community. Thus, rotorcraft, in general, typically have a lower participation rate in FDM programs than other forms of aviation (i.e. commercial fixed-wing or part 121 airline operations). On the other hand, even small helicopter operators often have access to or the financial means to purchase one or more off-the-shelf video cameras, which can be mounted inside the cockpit. These cameras offer an alternative to traditional flight data recorders as well as a means to augment them with supplementary data not always available depending on the type of Flight Data Recorder (FDR) installed in the helicopter. On board video data offers several possibilities for improving safety including flight replay, as well as the ability to extract information from the external scene such as readings of instrument panel gauges. As part of our research approach, we analyzed video data from cameras recording the instrument panel and compared these values against ground truth data from the flight data recorder. These values formed the training dataset for our video analytic framework. To analyze this information, we first cropped the gauge of interest (i.e. airspeed indicator, tachometer, engine oil temperature/pressure) in each frame of every video. The gauge image, extracted from all videos, were subsequently fed to train a deep Convolutional Neural Network (CNN) using the FDR measurements as ground truth. We trained Resnet50 CNN models for airspeed, engine oil temperature/pressure, and tachometer gauges. These models obtained 78%, 89%, 89%, and 88% validation accuracy on airspeed, engine oil temperature/pressure, and tachometer gauges, respectively. To further demonstrate the feasibility, we used the trained models to retrieve airspeed and engine oil values from the complete flight profile. We observed that the our models predicted trajectories for gauges closely follow the actual sensory values recorded by FDR. Such solution results in an effective flight data analysis tool as well as improved safety and operational efficiency of rotorcraft. These results demonstrate the feasibility of an inexpensive cockpit camera solution that would facilitate participation in FDM programs even for legacy helicopters that may otherwise require significant installation work.
The CH146 Griffon helicopter is a Federal Aviation Administration (FAA) and Transport Canada certified commercial helicopter used by Canadian Armed Forces in military role, which is distinguished from the original design intent of the helicopter for commercial use. A regime based Structural Usage Monitoring (SUM) program is developed by Bell to meet the Canadian Government requirements documented in the Technical Airworthiness Manual (TAM). As part of the CH146 SUM, helicopter usage data recorded by the Flight Data Recorder (FDR) system is collected and flight conditions (regimes) are defined by using dedicated software tools. Helicopter usage is then compared with the baseline and by using the actual helicopter data on selected critical components and fatigue damage accumulation is performed. Finally, the calculated damage is evaluated to define recommended structural maintenance actions. Development of the CH146 SUM is completed and more than 50,000 flight hours accumulated FDR data is processed. The acceptance of the SUM program by the Canadian Department of Defence (DND) is expected in the summer of 2019.
This paper presents the results from several load estimation methods developed at the National Research Council Canada (NRC) which enable the estimation of helicopter loads and tracking load exceedances and fatigue damage for a targeted component using computational intelligence techniques. The approach relies only on flight state and control system (FSCS) parameters, such as those recorded by a flight data recorder (FDR), and can also be applied to legacy aircraft or to those aircraft not equipped with HUMS. The methodologies adapt to the input data available so are not constrained to one particular system or platform, and enable the estimation of loads through the duration of a manoeuvre instead of assuming a constant load for an entire manoeuvre. So far, the three methods have been tested on data obtained from two different helicopter platforms, the S-70A-9 Australian Black Hawk and the CH-146 Griffon (Bell 412). Significant improvements are made over previous results presented for the S-70A-9 Black Hawk using the NRC developed Signal Approximation Method (SAM) while using uniquely FSCS parameters obtained from a FDR to obtain full manouevre dynamic load signals in time. Furthermore, the application of the technology to a different platform and different original equipment manufacturer (OEM), namely the CH-146 Griffon, demonstrates the possibility of applying this methodology and its adaptability across different platforms.
The US Army Condition Based Maintenance program collects data from Health and Usage Monitoring Systems, Flight Data Recorders, Maintenance Records, and Reliability Databases. These data sources are not integrated, but decisions regarding the health of aircraft components are dependent upon the information stored within them. The Army has begun an effort to bring these data sources together using Machine Learning algorithms. Two prototypes will be built using decision-making machines: one for an engine output gearbox and another for a turbo-shaft engine. This paper will discuss the development of these prototypes and provide the path forward for implementation. The importance of determining applicable error penalty methods for machine learning algorithms for aerospace applications is explored. The foundations on which the applicable dataset is built are also explored, showing the importance of cleaning disparate datasets. The assumptions necessary to generate the dataset for learning are documented. The dataset is built and ready for unsupervised and supervised learning techniques to be applied.
The analysis of accidents can be complicated and often smaller aircraft and helicopters are not equipped with a Flight Data Recorder. Radar data can be inadequate due to terrain reflections when the aircraft flew in very low altitude. This means that in some cases little information can be available and taking witness testimonies into account can be time and cost consuming today. A new method called Immersive Witness Interview (IWI®) has been developed to support the analysis of accident investigation, taking witness statements into account. Based on the witness reports that are transformed in three dimensional, a flight path of an observed aircraft can be reconstructed including all potential errors. IWI® has been evaluated in the beginning of 2009 within a test in real circumstances in cooperation with the Federal Armed Forces Flight Safety Division supported by the German Air Force. Further the German Federal Bureau of Aircraft Accident Investigation (BFU) has used the new method for accident investigation in October 2009. This paper will focus on the method, results and benefits using IWI® within accident investigation.
There is great concern in the U. S. Navy/Marine Corps aviation community regarding out-year operating costs. Simply put, there may not be sufficient funds for the services to execute their mission goals. Studies and initiatives have been undertaken to reduce Operational and Support Costs, with a keen interest in Condition Based Maintenance (CBM) and a re-structuring of the logistics infrastructure. A key artifact of CBM, Structural Health and Usage Monitoring (SHUM), is vital to the Chief of Naval Operations (CNO) vision of "the right readiness, at the right cost." The Structural Appraisal of Fatigue Effects (SAFE) program at the Naval Air Systems Command (NAVAIR) has been providing structural tracking information to maintain the structural integrity of US Navy aircraft for over 30 years. The SAFE program uses parametric data from onboard flight recorders to accrue component life consumption via actual flight data vice an assumed usage spectrum. The objective is to determine if an aircraft is flown more or less severely than designed, and to provide benefit in the form of either safety or economy. Although most of the beneficiaries from the SAFE program have been fixed wing aircraft, NAVAIR has been working to implement the first rotorcraft fleet in SAFE, the Integrated Mechanical Diagnostics System (IMDS) equipped CH-53E helicopter. Seven CH-53E components selected upon criticality, perceived benefit, and expense, were evaluated via SAFE. There are two ways to execute SHUM. The first is to implement a CH-53E SAFE program to provide a Fatigue Life Expended (FLE) metric based upon component specific aircraft usage. The second is to update the aircraft usage spectrum based upon fleet-wide aircraft usage. The CH-53E program has funded both of these paths to maximize benefit. This paper discusses these paths and respective tasks including regime recognition, event counting, damage rate and mapping, and the institution of a novel gross weight prediction tool developed by Naval Surface Warfare Center (NSWC) Carderock. Mitigation strategies for obstacles such as loss-of-data and component configuration management are highlighted in this paper.
The V-22 tilt-rotor features an advanced HUMS system that will enable more efficient maintenance and more accurate structural life assessments based on individual aircraft usage. The primary system elements include on-board data recording, ground station data processing and structural life assessment database. This paper describes the analytical process of developing the damage tracking model for an early V-22 production NLG design that was determined to be life-limited during fatigue test. In order to maximize utility of the spares inventory, reduce life-cycle costs and increase operational availability, the V-22 NLG was identified as a top priority for early fleet damage tracking. The analytical process included development of landing and ground maneuver recognition logic, development and correlation of stress, fatigue and crack growth models for the critical keyway slot design feature, mapping of the processed flight recorder data to control point stresses and implementation of a strain-life damage calculation model on the SAFE system to enable calculation of FLE by serial number for affected fleet NLG.
Helicopter maintenance is currently based on aircraft flight hours. Each component's retirement time is derived using the Prime Contractors / Original Equipment Manufacturer's (OEM's) safe life methodology which typically includes fatigue testing of production components to determine a working S/N curve, measurements of maximum loads from flight load surveys, and construction of a composite usage spectrum from pilot surveys. Efforts are being made under the Condition Based Maintenance (CBM) program to implement a usage monitoring system, and evaluate the actual aircraft usage based on the time spent in specific maneuvers or missions. Goals of the usage monitoring program are to understand actual aircraft usage spectrums and apply this knowledge to improve safety, decrease component maintenance cost, and increase availability. This paper outlines a recommended Condition Based Maintenance (CBM) approach to helicopter fleet management. The different stages of the approach to CBM discussed in the following sections rely upon the Rotorcraft Structural Integrity Plan (RSIP) along with Usage Monitoring Systems that are based on flight recorder data. The RSIP establishes the structural performance, design development, and verification for rotorcraft structural components. A term that is used frequently throughout this report is fleet management. Fleet management refers to an approach to safety and preventative maintenance that, as with CBM, utilizes the individual aircraft's usage information to ultimately determine the level of damage accrued by critical structural components during routine operational usage. This can only be achieved when both the frequency and severity of the load can be determined from the aircraft's usage information. This paper also introduces concepts for the development of the actual operational Usage Spectrum from the flight recorder data. Issues associated with establishing reliability, mission spectrums and usage spectrums are discussed as a first step in implementing solutions.
Panoramic detection systems (PDSs) are developmental video monitoring and image-data processing systems that, as their name indicates, acquire panoramic views. More specifically, a PDS acquires images from an approximately cylindrical field of view that surrounds an observation platform. In example of a major class of intended applications, a PDS mounted on top of a motor vehicle could be used to obtain unobstructed views of the surroundings (see Figure 1). In another such example, a PDS could be mounted above a roadway intersection for monitoring approaching and receding vehicles in order to provide image data on the vehicles as input to an automated traffic-control system. In either application, a running archive of the image data acquired by the PDS could be maintained as a means of reconstructing the events leading up to a vehicular collision: used in this way, a PDS would be analogous to an aircraft "black box" data recorder.
A computational method and software to implement the method have been developed to sift through vast quantities of digital flight data to alert human analysts to aircraft flights that are statistically atypical in ways that signify that safety may be adversely affected. On a typical day, there are tens of thousands of flights in the United States and several times that number throughout the world. Depending on the specific aircraft design, the volume of data collected by sensors and flight recorders can range from a few dozen to several thousand parameters per second during a flight. Whereas these data have long been utilized in investigating crashes, the present method is oriented toward helping to prevent crashes by enabling routine monitoring of flight operations to identify portions of flights that may be of interest with respect to safety issues.
This paper will demonstrate, with costs and results, that it is possible to produce a HUMS/HOMP/FDR device, appropriate for General Aviation, which is very low cost and that could yet provide a wealth of useful information; to operators about their day to day operations, to maintenance engineers in the form of diagnostics, and to accident investigators in the event of an incident. It is not being suggested that GA aircraft should ever be burdened with further compulsory regulation and costs from the Federal Aviation Authority but it is being suggested that there could be positive benefits for the operators of some GA aircraft to choose to invest in such devices. The research project behind this paper aims to deliver reference designs for low-cost light aircraft (Helicopter or Aeroplane) solid-state flight data recorders utilising modern technologies and to produce new flight data analysis software tools
A spacecraft may be unable to communicate critical data associated with a serious or catastrophic failure. A brief report proposes a system, somewhat like a commercial aircraft "black box," for retrieving these data. A microspacecraft attached to the prime spacecraft would continually store recent critical data from that spacecraft. If either spacecraft detected certain serious conditions of the prime spacecraft, the microspacecraft would separate from the prime spacecraft and independently transmit the stored data to Earth. Supplemental data, acquired from sensors onboard the microspacecraft, could be added to this transmission. For example, the orientation and angular rates of the prime spacecraft immediately before separation as well as pictures taken of the prime spacecraft after separation could be included. Functional enhancements over aircraft black boxes include the separation from the prime vehicle (which gains independence from the fate of that vehicle), wireless transmission of data (making physical black box recovery unnecessary), and the optional acquisition of supplemental sensor data.
A neural network based low airspeed sensor is developed using flight test data from an SH-60 helicopter specifically collected for this purpose. Inputs to the network are parameters that are typically monitored by a flight data recorder or a health and usage monitoring system. Approximately five percent of the data is utilized for network training. Prediction results indicate that accurate prediction of low airspeed magnitude and direction are possible with a root-mean-square error just under three knots. The neural network based low airspeed sensor can be utilized in real time to improve pilot situational awareness or implemented as a non-real time algorithm to improve assignment of fatigue damage through enhanced structural usage monitoring in the low airspeed flight regime. The neural network based low airspeed sensor provides a viable and affordable alternative to advanced mechanical low airspeed sensor systems.
A Common system is defined as “a system architecture with published interfaces that, based on its widespread acceptance by suppliers, integrators, and operators, becomes a de facto standard.” Several organizations are pursuing common and/or non-proprietary Open Systems to meet their needs. Government initiatives, in particular, have opened the door to the procurement of Commercial Off-The-Shelf (COTS) equipment by the U.S. military. While it was the military that led early flight recorder and monitoring system development on fixed-wing aircraft such as the F-16’s Standard Flight Data Recorder, the F-18 MDRS/ECAMS, and later the V-22 VSLED/AMEGS, it was the commercial sector that sought technically and economically feasible systems to meet the special requirements of the North Sea environment that could be fitted to all helicopter types. Third generations of these systems have already been developed by HUMS suppliers in cooperation with airframe manufacturers. Single board HUMS modules are ready for integration. The Canadian Forces has fielded a COTS derivative for the 412CF Utility Aircraft. The U.K. MOD, the U.S. Navy as part of its COSSI initiative, the U.S. Army Digital Source Collector program, the Joint Services JAHUMS program, and others are pursuing COTS derivatives to meet their own helicopter HUMS requirements. The question arises as to how these efforts might come together, what the true common requirements are, and how unique requirements can be dealt with. These issues are pivotal to the development of next-generation helicopter diagnostic systems. HUMS requirements are multi-dimensional and must meet a broad set of user needs. These requirements fall into the categories of functional requirements, airborne processing requirements, cockpit interface requirements, “At Aircraft” requirements, line maintenance HUMS ground station requirements, and up-line maintenance management system requirements. This paper will review the requirements, propose possible approaches, develop a generic system architecture with top-level system requirements, and finally propose a common set of user interface requirements and functions for use in the cockpit, at the aircraft, and within the ground station/ logistics system Interfaces.
With the high cost of life-limited components, many manufacturers are studying and implementing monitoring systems on both commercial and military aircraft to optimize retirement times. A Vibration, Structural Life, and Engine Diagnostics recorder, called VSLED, was in the original specification of the V-22. This paper addresses the structural life aspects of VSLED as it is now implemented. Using the transducer signals available on the MIL-STD-1553B bus, VSLED calculates and records time in maneuver, component synthetic load histograms, peak and valley history, and aircraft usage data. A ground-based computer program, compatible with the Navy’s SAFE program, is being developed to analyze VSLED data. The ground-based system will be used to track fatigue damage accumulation on an individual aircraft basis. Flight condition recognition data will be used in conjunction with load values from flight load survey data to calculate high-cycle fatigue damage on rotor components. Peak and valley data will be used to calculate low-cycle damage accumulation on the airframe and landing gear structure. When component synthetic load algorithms have been developed to a sufficient accuracy level, they will be used to evaluate loading levels on fleet aircraft and compared to loading levels in the flight stress survey data. Inspection intervals for components will be evaluated based on measured, rather than estimated, usage data. The VSLED represents the high end of flight data recorders both in functionality and complexity. It has the potential to greatly enhance safety of flight by providing a mechanism to identify problem areas with flight critical components. By tracking each aircraft’s flight profile on a flight-by-flight basis, many component lives will be extended beyond that obtained from a traditional generic fleetwide spectrum. As an added benefit, the Navy will have enhanced asset management capabilities through the use of component usage projections based on historical data.
Real-time rotor system load monitoring algorithms have been developed and evaluated in a flight test of an instrumented SH-60 helicopter. Vibratory loads are predicted using an artificial neural network that utilizes input parameters which are typically measured and stored on a flight data recorder. These load prediction algorithms were developed using an extensive SH-60 flight data set which includes approximately 9 flight hours of data and a wide variety of SH-60 usage spectrum maneuvers. The neural network based load monitoring capability was demonstrated in a flight test evaluation where the algorithms were embedded in an on-board processor and flight data recorder for real-time on-board load predictions. Prediction results are compared with strain gauge measurements from two separate flight test aircraft. Assessment of load predictions shows good results and indicates that the neural network algorithms developed using flight data from one aircraft can be used to predict vibratory loads on a second aircraft. The flight test results also demonstrate the potential to use the algorithms for real-time pilot cueing to minimize operation in fatigue damaging flight conditions.
A Health and Usage Monitoring System (HUMS) has been developed for the digital cockpit version of the International CH-47D and is currently being operated by the Royal Netherlands Air Force (RNLAF). The system architecture is unique in that the Air Vehicle signals are digitized for the pilots by dual Air Vehicle Integrated System Processors (AVISPs) which drive cockpit Multi-Function Displays (MFDs) for management of the air vehicle systems. Each AVISP sends many of the digitized signals to the HUMS computer, called a Modular Data Acquisition Unit (MDAU), via a dedicated ARINC 429 data bus. The MDAU reformats the data as required and sends it to a Combined Voice and Flight Data Recorder (CVFDR), as well as to a separate set of internal processing cards which calculate rotor track and balance, exceedance monitoring, usage monitoring, and transmission diagnostics (sensors for transmission diagnostics are not installed on the Digital International CH-47Ds at this time). Digitization of over 200 signals by the AVISPs greatly simplified the HUMS installation, however, unique integration and validation techniques had to be developed due to the signal conditioning, asynchronous sampling, and propagation delays associated with those signals, performed within the AVISPs, which were developed by a different supplier. Extensive rotor track and balance testing was performed to demonstrate the ability of the HUMS to accurately suggest recommended adjustments as required by routine maintenance as well as after major overhaul of the rotor system. Tests were conducted with single and multiple seeded faults on multiple aircraft. The HUMS successfully demonstrated the ability to correct the condition with data collected from a single flight on many occasions. Operational issues associated with record-keeping within a military organization which has an extensive computerized logistic system are being resolved in the field. A maturity plan is the mechanism being utilized to enable progressive utilization of data with the existing deployed system as well as specification and development of additional capabilities which are determined to be desirable.
Concept formulation, detailed design, and initial flight test validation of the flight regime recognition algorithms incorporated in the MH-47E Structural Usage Monitoring System (SUMS) are discussed. These regime recognition algorithms categorize aircraft flight time into regimes for which damage accumulation rates of critical life limited parts are available. The SUMS regime recognition approach is (1) to obtain high quality flight data measurements through the use of innovative wild point editing and forward/backward filtering techniques, (2) to define the maneuver state of the aircraft in terms of a comprehensive set of fundamental maneuvers, and (3) to determine the MH-47E basic fatigue profile flight regime which best describes the maneuver state of the aircraft. Data conditioning, filtering, and failure management techniques are discussed. The use of wind direction and magnitude estimation and inertial/air data blending to obtain high fidelity airspeed estimation at low speeds is described, Maneuver identification algorithms and criteria are presented and validated using flight test data. Methodology for mapping aircraft maneuver state into the MH-47E Basic Fatigue Profile flight regimes in a manner which ensures a conservative, yet realistic, assessment of critical component life expenditure is described. Trades between flight data recorder data storage requirements, regime recognition software complexity and processing time, acquisition of redundant data for failure management, and operational considerations are also discussed.
Sikorsky Aircraft has a program in place for helicopter Health and Usage Monitoring Systems (HUMS). This includes both the development and fielding of a HUMS for the S-76 and S-92 helicopters, and support of operator installed HUMS. The paper discusses the background and reasons for developing the Sikorsky HUMS2. In response to Sikorsky requirements, the hardware and software for this HUMS have been developed by GEC-Marconi, based upon their experience in fielding their Integrated Health and Usage Monitoring System (IHUMS). The Sikorsky HUMS2 includes airborne processing for most functions combined with a ground station which provides extensive capability including uploading and downloading of data, display of faults detected on aircraft, trending of pertinent parameters to guide the maintainer in planning his maintenance actions, and the ability to evaluate in depth the fault indications found by the HUMS2. HUMS2 can provide output to a combined cockpit voice and flight data recorder (CVFDR) with solid state memory. The functions in HUMS2 include basic operational usage, structural usage, rotor track and balance, drive train monitoring, automated power assurance, and exceedance monitoring. The implementation of these capabilities is discussed. In addition, Sikorsky has set up a program to support those operators flying S-61 and S-76 helicopters equipped with HUMS. This ongoing cooperation with the operators has provided both parties with correlation between the faults indicated by HUMS and the evidence found during teardown of the gearboxes to help increase the confidence in the fielded HUMS.
This paper describes a forward looking on-board vehicle detection and driver alert system that provides a distance indication and alert tone to the driver. The system, called VORAD (Vehicular On-board Radar), is the first of its kind to be fielded. The system can be programmed to function in different operating modes, allowing customization to the user's requirements. Some possible operating modes include simply providing an alert to the driver, providing following distance indication measured in seconds based on the vehicles' speeds, or providing following distance measured in feet. The VORAD System also has optional features to enhance its usefulness, including a blind spot alert system and a built-in event recorder. The blind spot system provides additional information to the driver regarding the presence of vehicles in his blind spot for use in making lane changes. The event recorder can act as a “flight recorder” for accident reconstruction, safety training and fleet management activities. The current version of the system does not interface with any vehicle controls. New applications under development include advanced control systems such as Intelligent Cruise Control. In addition, future systems can be designed to provide vehicle to vehicle and vehicle to roadside communication functions.
Significant changes in fatigue evaluations are now occurring in the helicopter industry. These changes are being driven by requirements for quantified reliability and damage tolerant design; the introduction of new materials such as composites; and advancing technology such as fracture mechanics, flight data recorders, and increased computer capability for fight test data. The AHS Fatigue and Damage Tolerance Subcommittee has completed an initial round robin investigation of the reliability issue, and is preparing to investigate the other changes. This paper identifies the issues associated with these changes, provides a general road map for the subcommittee to investigate these issues, and defines a new set of round robin problems addressing reliability, fracture mechanics, and damage tolerant design.
The Tactical Mission Analysis Station (TMAS) is a graphics-oriented, interactive computer model designed for use on Silicon Graphics engineering workstations. TMAS was developed to satisfy the needs of analysts in the survivability and human factors communities by allowing quick, interactive creation of missions that take place in a nap-of-the-earth combat environment. TMAS models the fundamental elements of a combat mission, including terrain, performance of rotorcraft and fixed-wing aircraft, and the capabilities of friendly and threat weapon systems. Terrain and feature information is generated from Defense Mapping Agency Digital Terrain Elevation Data and Digital Feature Analysis Data. Weapon, sensor, and air vehicle performance parameters are stored in data files, allowing the model itself to remain unclassified. This feature allows easy substitution of alternate parameter sets when conducting tradeoff studies and sensitivity analyses. Simulation data are stored in a flight data recorder for use in post-mission analysis and display functions.
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