East Hartford, CT, United States

Qualtech Systems, Inc.

www.teamqsi.com
East Hartford, CT, United States
SEARCH FILTERS
Time filter
Source Type

Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: STTR | Phase: Phase II | Award Amount: 748.88K | Year: 2014

Wrong decisions during the missions can lead to an unsafe condition or immediate failure, while correct decisions can help continue the missions even from faulty conditions. In view of the lessons learned from mishaps, i.e., failed space missions, it is imminent that reliability analysis and risk assessment are kept in sync with space system design as it evolves from the concept through preliminary design, detailed design, production, and operations. From the successful proof-of-concept demonstration for the proposal solution in Phase I, Qualtech Systems, Inc. (QSI) in collaboration with Dr. John Sheppard from Montana State University (MSU) proposes to architect the solution for continuous real-time health monitoring and diagnosis, automatically generating current risk assessment for Loss of Mission, Loss of Crew, Loss of Vehicle during vehicle operations while taking into account the current health of the vehicle and operational modes and phases in Phase II. The QSI-MSU team plans to emphasize advancement in the six following areas: (a) enhancement of the existing EPS model/modeling a new target system, (b) dynamic generation of fault-tree by TEAMS-RDS®, (c) expansion of risk modeling and learning, (d) expansion of risk assessment capabilities, (e) Automatic information exchange between TEAMS-RDS® reasoner and CTBN reasoner for both design-time and run-time, and (f) enhancement and incorporation of the risk visualization tool capability into web-based TEAMS-RDS® dashboard. The solution architecture will provide the ability for the crew to assess and select the "right" mitigation option for component failures and subsequently update the health diagnosis and risk assessment given the executed mitigation plan.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 125.00K | Year: 2016

Fault Management (FM) is a key enabler of system autonomy critical to reducing overall operations costs of increasingly complex science missions while ensuring their success. NASA has invested significant effort and has developed a draft FM Handbook to improve FM design, development, V&V and operations processes. While the FM Handbook provides rules and guidelines, those can be effectively followed for realizing the above mentioned goals with the aid of advanced Model-Based Systems Engineering (MBSE) software tools. NASA uses a variety of such tools to conduct its FM activities. However, these tools are varied and disjoint, and often require manual intervention to transfer data from the output of one tool to the input of another. This process is tedious and error-prone and scales poorly for large, complex systems. This prevents SHM engineers from gaining insight into the overall system level design and characteristics that are key to transparency, verifiability and efficiency of implementing and testing FM. To address these challenges QSI-DST team plans to develop techniques and concomitant software tools to (1) capture diverse and disjoint data products and multi-domain modeling information into TEAMS for standardizing FM Techniques and Activities, (2) conduct Architecture Trade Studies focusing on failure detection (abort trigger) effectiveness with corresponding sensor suite selection, and (3) introduce ancillary capabilities in TEAMS to support the main tasks such as assessment of Failure Effect Propagation timing (FEPT). The proposed effort seeks to aid the evaluation and V&V of FM of system(s) in multiple usage scenarios through utilizing existing capabilities and introducing added capabilities to TEAMS for the computation and evolution of relevant FM analyses. The added capabilities include information integration; extending the system modeling capabilities; and assessing the effect of implementing diagnostic decisions on overall functionality of the system.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 749.70K | Year: 2016

Functional robustness, resulting from superior engineering design, along with appropriate and timely mitigating actions, is a key enabler for satisfying complex mission goals, and for enhancing mission success probability. Fault Management (FM) is a crucial mechanism to ensure system functionality from system design through the operational phase of a mission. FM is implemented with spacecraft hardware, on-board autonomous software that controls hardware, software and information redundancy, ground-based software and procedures. Given that most NASA missions require highly complex systems, at least a basic level of fault detection and isolation capability is almost always added on to them to protect against thousands of potential failure modes. It is therefore imperative to treat FM like any other engineering discipline and formalize the tools, metrics and best practices to ensure a uniformly high quality of implementation of FM across all NASA missions. The proposal to utilize recent advances in the theory and practice of FM, and in particular in the theory and practice of FM metrics, to enhance the ability of system and FM engineers and operators to measure and document the value, cost and risks associated with the FM design. This SBIR is aims to utilize existing capabilities of TEAMS toolset and extending it as necessary to enable it to compute a range of FM metrics, quantitative assessment of an FM design and V&V of the FM activities. As schedule and resource pressures build, there comes a need to reduce the amount of planned testing while guaranteeing a degree of confidence in FM design. By defining a methodical approach to identifying and assigning priorities to tests, one can define a minimum set of tests required to certify FM (i.e., incompressible test list). This SBIR also seeks to develop a Prioritized Validation Test Suite that ensures that critical risks are detected and appropriate FM Mitigation Strategies are employed to minimize the risk.


Grant
Agency: Department of Defense | Branch: Navy | Program: SBIR | Phase: Phase II | Award Amount: 984.65K | Year: 2016

Reliable operation of LCS computing systems are paramount to mission operations because unanticipated failures and system degradations result in the ships crew and subject matter experts (SMEs) spending extended time on debugging and resolving system issues. The need for reliability of large and complex computing environments has led to the development of network monitoring systems (NMS). These systems allow network administrators to make use of the logged data on a managed network in order to assess the integrity of software services they provide, and present an overview of the current state of the system, including alarms raised when issues arise. However, such alarms constitute only the annunciations of detected problems. One needs to determine the root cause behind the alarms in order to assess their impact on network operations and identify suitable corrective actions. To address these "gaps", QSI-LM seeks to create a seamless, intelligent sense, anticipate and respond solution with Condition Based Maintenance (CBM) and remote diagnosis capabilities, that mines, analyzes and consolidates operational data from shipboard Network Monitoring Systems (NMS) tools and functions as a decision-aid for the SME and the crew for determination of the MPCE and TSCE health condition, and hence its incipient fault/failure conditions.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 122.00K | Year: 2016

NASA has invested significant effort in the past decade in developing and maturing technologies that enable efficient and effective use of Next-generation (NextGen) Vertical Lift (VL) systems for a broad class of missions and operations. One of the key barriers it faces to the widespread use of VL vehicles within the National Airspace is the cost of maintenance on the vehicles to keep them safe and reliable. Qualtech Systems, Inc (QSI) in collaboration with Lockheed Martin - Mission Systems and Training (LM-MST) seeks to address these maintenance challenges by fielding a predictive Condition Based Maintenance Plus (CBM+) solution leveraging a diagnostic reasoner TEAMS-RDS (Testability Engineering And Maintenance System Remote Diagnosis Server) and prognostic algorithms. CBM+ involves inferring, tracking and forecasting of system degradation based on state awareness acquired from monitored data through fault detection, isolation, identification, diagnosis and prognosis techniques and to proactively plan maintenance actions to improve system availability and safety. QSI-LM's CBM+ solution will furnish the ability to keep the vehicle health status continually ahead of an advancing failure accumulation through a predictive maintenance strategy geared towards replacement-while-in-operation before the ensuing failures render the VL vehicle inoperable. Diagnosis will focus on current health state identification through detection, isolation, root cause analysis and identification of faults that have already occurred, while prognosis will leverage the current health state identification and forecast performance degradation, incipient component failures and probability density (or moments) of remaining useful life (RUL) or Time to Maintenance (TTM) or Time to Failure (TTF). It is anticipated that the CBM+ solution will leverage the currently existing communication capabilities between the aircraft, the pilot and ground-support personnel in a seamless and automated manner.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.98K | Year: 2016

Autonomous, avionic and robotic systems are used in a variety of applications including launch vehicles, robotic precursor platforms, etc. Most avionic innovations are based on software-embedded systems, and this has resulted in an increase in the number of interactions (coupling) among heterogeneous subsystems. Avionic systems degrade in performance due to gradual development of anomalies and unanticipated failures ranging from issues affecting a single hardware or software subsystem to issues occurring as a result of coupling among multiple subsystems. In addition, system usage and operating conditions may lead to different failure modes necessating multiple recovery procedures possibly causing conflicts and deadlocks among recovery steps. QSI intends to address these challenges by leveraging the current capabilities of model-based fault management and supportability solutions of TEAMS to efficiently sequence individual steps within each procedure, including adding/deleting steps, and resolve conflicts and deadlocks in recovery procedures. TEAMS-RT, the real-time inference engine, has multiple fault diagnosis capability built-in. Additionally, TEAMS-RDS (TEAMS-remote diagnostic server) already exploits commonalities among test steps during guided troubleshooting, where each test is represented as a chain of pre-setup, post-setup and action nodes with Do and Undo steps interspersed. The proposed effort will extend this to more general digraphs of test and recovery/repair procedures and also embed this capability in a solution linked to enhanced TEAMS-RT for automated /crew-initiated recovery and resolution of conflicts and deadlocks in recovery procedures. This proposal aims to enhance QSI?s existing probabilistic inference engine to handle multiple, intermittent and coupled failure scenarios and developing an ISHM response engine module that dynamically assembles feasible and near-optimal recovery procedures to handle multiple failure scenarios.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 124.04K | Year: 2015

Functional robustness, resulting from superior engineering design, along with appropriate and timely mitigating actions, is a key enabler for satisfying complex mission goals, and for enhancing mission success probability. Fault Management (FM) is a crucial mechanism to ensure system functionality from system design through the operational phase of a mission. FM is implemented with spacecraft hardware, on-board autonomous software that controls hardware, software and information redundancy, ground-based software and procedures. A major issue in the development and operation of Fault Management (FM) is the determination of the value of the various components of FM design within a system. Without comprehensive measures of value, FM designers and system engineers are left with qualitative arguments often tied to fault tolerance requirements (for example, single fault tolerance, fail-operational-fail safe) or one-off, ad hoc analyses to estimate the risks associated with particular failures and design measures to mitigate them. Qualtech Systems, Inc., in collaboration with Dr. Stephen B. Johnson of University of Colorado at Colorado Springs (UCCS) and President of Dependable System Technologies, LLC, proposes to develop techniques and concomitant software tools for evaluating FM metrics by using TEAMS® as the underlying platform. This proposal aims to utilize recent advances in the theory and practice of FM, and in particular in the theory and practice of FM metrics, to enhance the ability of system and FM engineers and operators to measure and document the value, cost and risks associated with the FM design. In turn, this provides the information needed to compare alternative FM designs, quantitatively evaluate how well a system is achieving its goals, and enables more effective verification and validation (V&V) of selected FM design.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2015

There has been a renewed push across NASA centers and programs to make Systems Engineering & Integration (SE&I) processes more efficient and results-oriented than the current cumbersome and expensive cross-checking processes using text documents, and transition to a repeatable and a cost-effective process of Model Based Systems Engineering (MBSE). In parallel, Systems Health management (SHM), with its operational subset Fault Management (FM), has also been developing with rigorous model-based practices, but largely separate from the mainstream of Systems Engineering and Design activities in NASA and the DoD. The technical and knowledge gap between the SE&I and SHM processes results in significant inefficiencies during product design, verification and validation, and excessive operational maintenance costs, collectively yielding unacceptably high life cycle costs and failure rates. To address these challenged, QSI with Dr. Stephen Johnson, intends to develop tool neutral architecture, processes and interfaces for integration of model-based SE&I designed in SysML (Systems Modeling Language), with SHM modeling and analysis performed in TEAMS® (Testability Engineering And Maintenance System). It is our intention to reduce the duplicative and disjoint effort by NASA's subject matter experts in the development of systems engineering and design models as well as systems health management/fault management models. The benefits realized through this effort are (a) Reduced systems engineering and fault management costs, combined with improved quality and traceability, as well as enhanced communication and coordination among stakeholders, (b) Improved quality of SE&I and SHM products by having inherent traceability across models and ability to catch defects in design and FM earlier, and (c) Establishment of modeling recommendations for NASA community as it develops its MBSE approaches and models.


Grant
Agency: National Aeronautics and Space Administration | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 750.00K | Year: 2015

In Phase II, the QSI-Vanderbilt team seeks to develop a system-level diagnostics and prognostic process that incorporates a "sense and respond capability," which first uses error codes and discrete sensor values to correctly diagnose the system health including degradations and failures of sensors and components, and then invokes appropriate prognostics routines for the assessment of RUL and performance capability. The QSI-Vanderbilt team plans to emphasize advancement in the following five areas: (a) leverage extensive LADEE telemetry data to further enhance and develop online degradation profiles, performance analysis and remaining useful life (RUL) computation algorithms, (b) develop/implement degradation detection algorithms to compute time-to-alarm (TTA) and time-to-maintenance (TTM) predictions and correlate with alarm/maintenance events, (c) develop reusable library of models and tests, (d) verification and validation of the resulting solution, and (e) demonstrate the proposed solution on LADEE's and other spacecraft subsystems. Once fully developed, outcomes of this effort will lower the cost of developing prognostics and provide maximum critical system availability, smarter scheduling of maintenance, overall logistics support cost, and optimal match of assets to missions. The proposed offering will also provide a cost-effective and pragmatic solution to our commercial customers who want to reduce unscheduled downtime by practicing condition based maintenance, but cannot justify the cost of developing prognostic methods in the conventional way.


Grant
Agency: Department of Defense | Branch: Army | Program: SBIR | Phase: Phase I | Award Amount: 99.86K | Year: 2015

This proposal describes a method to reduce the acoustic noise and torque ripple of a switched-reluctance machine by solving a multi-objective optimization problem. The physical limitations of the machine, namely voltage and temperature, are used as constraints, and these are related to the objectives of noise and torque variation. Electrical, mechanical, and thermal models are used to form an analytical association, while finite element analysis forms the core of a complimentary numerical approach. Final accuracy is determined by experimental testing, and iterative improvements are used to improve the accuracy of poorly performing methods. The motor will be extensively instrumented for the experimental validation. Vibration sensors are the primary means of measuring stator displacement, with noise explicitly recorded from a high-fidelity microphone. Strain gauges will be installed to allow direct measurement of radial forces on the poles of the stator to allow for accurate comparison with analytical and numerical results. Mechanical torque is measured using a dynamometer having a high sampling rate to allow for determination of the tangential force as a function of rotor angle. With sufficiently accurate analytical models and numerical methods, the original optimization problem will be solved to yield the desired control.

Loading Qualtech Systems, Inc. collaborators
Loading Qualtech Systems, Inc. collaborators