SBIR-STTR Award

Ridgetop will develop an advanced reasoning-based troubleshooting algorithm to identify the failed components in complex mechanical/electrical systems. The reasoning-based algorithm will be used in an Autonomic Reasoning-based Troubleshooting (ART) management system which will provide support for maintenance personnel in the following areas: (1) the integration with Condition Based Maintenance (CBM) activities; (2) the interpretation of the components interactions; (3) reduction of uncertainty by updating CBM information with in-situ measurement; and (4) an easy-to-use tool with a user-interface and automation support. The significance of the innovation is that expert troubleshooting systems are needed to achieve significant savings by learning what diagnostic actions lead to correct outcomes and minimize wasted time and effort while achieving reduced maintenance costs. Studies show that it is possible to achieve 20% savings on deployed electronic systems. In Phase I, Ridgetop will develop and deliver the design of an ART systems including: - Mapping the fault tree into a reasoning-based troubleshooting algorithm - Interpretation of the component interactions through hierarchical, spatial and temporal information - Reduction of uncertainty with in-situ measurement - Design of an easy-to-use tool including a user-interface and autonomic capability - Validation of the algorithm to quantify the potential benefits through appropriate metrics

Benefit:
For today’s complex military aircraft, rigorous routine inspection and maintenance procedures are performed to ensure the health of the plane’s numerous mechanical and electronic systems. While vital, this constant process has seen significant cost increases over the past 10 years as various cost components such as labor, parts, and aircraft downtime rise in conjunction with the increasing complexity of these systems. The proposed autonomic reasoning-based troubleshooting (ART) analysis model can drastically reduce these costs via the rapid identification of faults and failures which typically take significant amounts of time to repair. In addition, Ridgetop’s innovation will provide an intelligent, user-friendly interface supplying not only the visual diagnosis, but an optimal action plan and rationale to correct the issue as well. Specifically designed for aircraft such as the C-130 and F-15, the innovation can also be applied to the ECSS, F-35, NASA NextGen Shuttles, UAV’s, commercial aircraft, and commercial automotive applications.

Keywords:
Fault-Tree Analysis, Dynamic Case Based Reasoning, Model Based Reasoning, Bayesian Belief Networks, Built In Test (Bit), Built In Test Equipment (Bite)

Ridgetop Group, Inc. will design and develop a prototype of an electronic warfare (EW) “Expert Troubleshooting and Repair System” that has the following characteristics: (1) troubleshooting support that presents a prioritized list of methods; (2) a list of test-dependent action(s), including recommended repair actions; and (3) an Analog-Degradation Detection and Analysis (ADDA) tester. Troubleshooting methods and repair actions will be a hybrid causal-based and knowledge-based system that is populated and trained using existing historical maintenance data from the WR-ALC LEAN Depot Maintenance System (LDMS). The prioritization will be based on fault analysis of recorded maintenance and repair actions to effect a reduction in mean-time-to-repair (MTTR) rates and an increase in mean-time-between-maintenance events (MTBME), with emphasis on (1) using analog-degradation analysis of digital data as a leading contributor to intermittent problems and increase in CND (could not duplicate) repair codes; and (2) identifying “bad actor” chains wherein one component degrades, then causes other components to subsequently fail.

Benefit:
There is a large installed base of complex aerospace systems in service requiring rapid repair times as failures occur. Conventional methods of determining the problem root cause do not take full advantage of relevant historical data to improve the process of repairing the system, especially at the Circuit Card Assembly (CCA) level . The anticipated benefits of an Expert Troubleshooting System (ETSS) using an Analog-Degradation Detection and Analysis (ADDA) tester are the following: 1. Mean-time-to- repair (MTTR) rate is reduced. 2. Mean-time-between-maintenance events (MTBME) is increased. 3. Reliability is increased because a “bad actor chain” (when a single failure cascades to cause subsequent downstream components to be stressed and fail) is repaired in a single maintenance event. 4. Could not duplicate (CND) repair codes are reduced because ETSS and ADDA provide a method of detecting analog degradation of digital data, and such degradation causes both intermittent and solid failures in the field. Commercial applications include the Depot-Level repair of electronic systems such as office automation equipment, automated teller machines, automotive electronic modules, and industrial machine tools having electronic controls.

Keywords:
EW system, analog degradation, MTTR, expert troubleshooting, CND, SRU, LRU, CCA