|Acquisition Program: ||Autonomic Logistics (potential I), Embedded Platform Logistics System (IV)|
| ||RESTRICTION ON PERFORMANCE BY FOREIGN CITIZENS (i.e., those holding non-U.S. Passports): This topic is “ITAR Restricted”. The information and materials provided pursuant to or resulting from this topic are restricted under the International Traffic in Arms Regulations (ITAR), 22 CFR Parts 120 - 130, which control the export of defense-related material and services, including the export of sensitive technical data. Foreign Citizens may perform work under an award resulting from this topic only if they hold the “Permanent Resident Card”, or are designated as “Protected Individuals” as defined by 8 U.S.C. 1324b(a)(3). If a proposal for this topic contains participation by a foreign citizen who is not in one of the above two categories, the proposal will be rejected.|| Objective: ||This topic seeks technology to identify maintenance trends in both platforms/components and prognostics to determine the likelihood that any one particular vehicle/system or tactical grouping of vehicles/systems will complete its assigned mission.
|| Description: ||Aging systems, spare parts shortages, and high operating tempo are placing increasing pressure on the material readiness of our force. The long-term sustainability of the equipment is a continuing challenge requiring new initiatives to effectively address both equipment and mission readiness. With the implementation of the Embedded Platform Logistics System (EPLS) on USMC ground tactical vehicles, the monitoring and collection of system/subsystem/assembly mission critical data elements is now available for further analysis. To realize the full potential of this data, further research and development of tools to analyze datasets for trends and projected performance is needed. The ability to mine, process, mechanically model and statistically analyze this aggregated data from a fleet perspective to determine maintenance trends, failure mechanisms, support system lifecycle management, and better understand the probability that an item of equipment will/will not fail over the course of a mission has significant benefit to the operating forces. Such prognostic and trend analysis tools would significantly enhance the capabilities provided by the Autonomic Logistics Program of Record and provide the warfighter with an application-level tool to support tactical decision making and improve mission performance of sensor-equipped vehicles.
|| ||PHASE I: 1) Develop and construct a set of statistical analysis tools (algorithms, models, data mining techniques, etc.) for identifying maintenance trends. 2) Develop and construct a set of prognostic tools (algorithms, models, etc.) for determining remaining useful life.
|| ||PHASE II: Develop proof-of-concept demonstrators based on a set of realistic, operationally based scenarios and mission profiles.
|| ||PHASE III: Integrate proof-of-concept demonstrators with existing application(s).
PRIVATE SECTOR COMMERCIAL POTENTIAL/|| ||DUAL-USE APPLICATIONS: Equipment reliability and availability are significant aspects of commercial product success. Application of statistical trending and/or prognostic tools during both the design process and/or as an embedded attribute of the final product leads to a product line that incorporates machine learning to improve overall product performance.
|| References: ||
1. Capability Development Document for Autonomic Logistics – Marine Corps
2. Capability Development Document for Electronic maintenance Support System
3. Initial Capability Document for Sense and Respond Logistics
4. Condition Based Maintenance Plus DOD Guidebook|
|Keywords: ||Prognostics, statistical analysis, algorithm, data mining, data analysis, trending, decision support tools, prediction, remaining useful life|
Questions and Answers:
Q: Could you please provide the pdf files of all the References? Thank you very much.
A: References 1-3 (Capability Development Document for Autonomic Logistics - Marine Corps, Capability Development Document for Electronic maintenance Support System, and Initial Capability Document for Sense and Respond Logistics) are Marine Corps FOUO documents that will be made available to the SBIR awardee after non-disclosure agreements are in place. These documents are not needed to generate a proposal on this topic and are provided as source references to validate Autonomic Logistics requirements for diagnostic and prognostic tools supporting Condition Based Maintenance. The DoD CBM+ Guidebook is available at http://www.acq.osd.mil/log/mpp/cbm+/CBM_DoD_Guidebook_May08.pdf.
Q: Could you provide a list of data available from the EPLS system?
A: The data collected by EPLS is generated by sensors installed on AAV, LAV and MTVR platforms. For the most part, sensors are standard, commercially available devices used in the commercial trucking and automotive industry. Data collected is time/date stamped and stored as comma delimited text for offload from the vehicle. Typical data collected includes:
- Hours Idle
- Total Hours Idle
- Hours of Use
- Total Hours of Use
- Current Speed
- Maximum Speed During Trip
- Current RPM
- Maximum RPM
- Mileage Per Trip
- Total Miles
- Air Cleaner Pressure
- Moisture Content
- Turbo Boost Atmospheric Pressure
- Coolant Temperature
- Oil Level
- Oil Temperature
- Oil Pressure
- Oil Filter Restriction Pressure
- Oil Life
- Internal Battery Resistance
- Battery Shunt Current
- Output Voltage
- Field Voltage
- Output Amperage
- Alternator Ground Voltage
- Starter Motor Voltage
- Starter Solenoid Voltage
- Fuel Pump Voltage
- Fuel Pump Current
- Fuel Supply Pressure
- Fuel Supply Pressure
- Fuel Filter Differential Pressure
- Individual Fuel Tank Fuel Level
- Transmission Fluid Level
- Transmission Fluid Temperature
- Transmission Fluid Pressure
- Transmission Fluid Filter Restriction Pressure
- Fluid Temperature
Q: Does the EPLS system collect maintenance information as would be required by task 1) of Phase I?
A: Please see the SITIS response to the question regarding types of data collected for a description of the current data set contents. Phase 1 effort can include analysis of the data available to determine additional data needed to perform effective prognostics.
Q: Are you looking for a data compilation system, or are you also interested in Innovative methods to predict failures?
A: The monitoring and collection (data compilation) of system/subsystem/assembly mission critical data elements is performed by EPLS. This SBIR effort is intended to develop the ability to mine, process, and statistically analyze the EPLS data to determine maintenance trends, failure mechanisms, support system lifecycle management, and better understand the probability that an item of equipment will/will not fail over the course of a mission. Innovative methods to accomplish these prognostic and diagnostic tasks are encouraged.