SPAR-Proportional Hazard Model (SPAR-PHM™)

Quantifying the relationships between equipment conditions described by quantitative health indices, mission readiness as affected by the deterioration of equipment health indices, and the application of logistic support elements in the form of parts and labor, requires a high-fidelity model.

Since machinery events and human actions-based processes often exhibit variability and uncertainty, a time based modeling method must be employed that can closely emulate these random behaviors. Clockwork Solutions utilizes an effective methodology based on the Monte Carlo simulation method applied to a reliability block diagram representation of the systems, expanded with supporting process rules (planning, scheduling, materials ordering, work execution, etc.). This permits simulating expected equipment behavior without ignoring its probabilistic nature alongside with logistic support infrastructure, to predict economic and performance metrics of the systems over time. With the availability of on-line, real-time data systems providing machinery performance and condition assessment related information, the simulation model can provide dynamically changing predictions, allowing for more intelligent decision management throughout the system life cycle.

Longer term maintenance requirements forecasting through simulation provides a very sounds basis for understanding the cause and effect relationships associated with pending supportability decision. However, long term forecasts may introduce aspects of data uncertainty in regard to an analyst’s ability to ‘control’ the modeled operations and maintenance environment moving forward in time. Although the model includes expected operations, repair concepts, component aging and repair effectiveness, decision are made every day to deviate from earlier plans in order to respond to moving needs and demands for the equipment. Therefore, we must mitigate uncertainty by incorporating condition indicators of the equipment to give us a sense for current equipment state, even though a fault has not yet been recognized, nor is one foreseen in the immediate future. A ‘risk of fault’ projection, using data obtained from Condition Based Maintenance environments, can enhance the life cycle simulation by adjusting equipment failure rate distributions to account for ‘risk of fault’ signatures.

Clockwork Solutions applies the Proportional Hazard Methodology (PHM) as a probabilistic survival analysis tool to support the periodic adjustment/refinement of equipment failure rates. This technique provides a basis for identifying factors (covariates) influencing aging and degradation of equipment. Inclusion of these types of covariates within a PHM analysis actually produces a family of lifespan distributions that portrays the probability of survival across time of a sub-population within the overall population.

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SPAR-PHM applies PHM principles by incorporating a PHM-based model for each failure mode within the system. Each PHM model will have one or more covariates that are used to project the variation in probability to failure over previous values determined from previous covariate readings. As covariates change with time so also will the probability associated with the ‘prediction’ of an equipment failure mode occurrence. Ultimately, SPAR-PHM will continuously evaluate covariate readings with the associated equipment/failure mode PHM models, and predict future system failure probability as a function of current equipment state and planned operations or scenarios of use.

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SPAR-PHM provides additional insight into criticality of equipment components and sensitivity of components to overall system performance.

Condition Based Maintenance is an automatic process established to generate an alert when an equipment fault has occurred (or is soon going to occur) within a system.SPAR-PHM™ further enhances CBM-enabled processes by providing enhanced capabilities to pre-plan for required logistics support by providing risk based notifications of pending faults, against any level of risk identified as unacceptable. Finally, advanced simulation-based life cycle performance prediction tools, such as ATLAST™, provide a comprehensive, mathematically sound means of forecasting requirements in order to better control and manage life cycle uncertainty.

For more information:

Condition Based Maintenance and Defense Industry Solutions

Also download the following selected documents from our Information Center:

Clockwork Solutions Raytheon Partnership an Integral Componet of ReadiLog "Sense and Respond Team"

SPAR Proportional Hazard Model-(SPAR-PHM)

Total Life Cycle Readiness Prediction – LAV Fact Sheet

Advanced Residual Life Estimation for Aircraft Engines

Fleet Management Analysis System Fact Sheet

The Marine Corps, Sense and Respond-Enabling Seabased Logistics

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SPAR-Proportional Hazard Model (SPAR-PHM™)