Condition-based Maintenance (CBM)

Condition-based Maintenance (CBM) strategies are fundamentally designed to identify immediate, or soon to occur, equipment faults.  In a CBM environment, an automatic process is established to generate an alert when an equipment fault has occurred (or is soon going to occur) within a system.  The automatic process may also diagnose the cause of the fault or expected fault. CBM implementation should enhance the reliability, safety, and maintainability of variable duty cycle machines and reduce the cost of their overall maintenance and support.  CBM is enabled by a collection of sensors, data acquisition devices and diagnostics/prognostics hardware and software modules.  Early recognition of pending faults further allow for proactive corrective maintenance to be planned, scheduled, and completed at the next available opportunity, having necessary resources identified and acquired prior in time for carrying out the required actions.

Clockwork Solutions provides software and services that support the following:

1. Modeling in support of plan/process development

Modeling and simulation of the incorporation of CBM-based decision logic into life cycle of systems support processes.  Evaluate life cycle operations, maintenance, and logistics impacts, as well as identification and quantification of ROI as related to CBM implementation.

2. Risk to Readiness model incorporation

Longer term maintenance requirements forecasting incorporates aspects of data uncertainty in regard to an analyst’s ability to ‘control’ the modeled operations and maintenance environment moving forward in time.  Although a life-cycle 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 dynamic needs and demands for the equipment. Therefore, we must rely on 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 CBM environments, enhances life cycle simulations by adjusting equipment failure rate distributions to account for ‘risk of fault’ signatures.

For more information:

SPAR Proportional Hazard Model (SPAR-PHM)

Also download the following selected documents from our Information Center:

SPAR-Propotional Hazard Model (SPAR-PHM)