Carl Byington is a consultant who advocates for condition-based maintenance (CBM), which involves monitoring equipment performance through sensors and performing maintenance only when data indicates a potential failure. CBM aims to decrease unnecessary maintenance costs while increasing operational readiness compared to traditional time-based maintenance. Implementing CBM requires predictive diagnostics using sensor data fusion to determine equipment health and predict remaining useful life. It is an emerging strategy enabled by advances in sensors, data processing, and machine learning techniques. The first step is identifying critical assets suitable for CBM monitoring.

1. Condition-Based Maintenance - Monitoring
Equipment Performance
Carl Byington

2. Introduction
Carl Byington has over thirty years developing models and analyzing data from
various equipment and critical assets. He is currently a consultant who leads
PHM Design, LLC, based in the greater Atlanta area. One operational strategy
Carl Byington employs involves condition-based maintenance (CBM). This
protocol rests on the concept that equipment failure is a process, rather than
one single event.

3. Condition-based maintenance (CBM) is a philosophy of performing
maintenance on a machine or system only when there is objective evidence of
need or impending failure. By contrast, time-based or use-based maintenance
involves performing periodic maintenance after specified periods of time or
hours of operation. CBM has the potential to decrease life-cycle maintenance
costs (by reducing unnecessary maintenance actions and greater operational
failures), increase operational readiness, and improve safety.

4. Implementation of condition-based maintenance involves predictive
diagnostics (i.e., diagnosing the current state or health of a machine and
predicting time to failure based on an assumed model of anticipated use).
CBM and predictive diagnostics depend on multisensor data—such as
vibration, temperature, pressure, and presence of oil debris—which must be
effectively fused to determine machinery health.

5. CBM is an emerging concept enabled by the evolution of key technologies,
including improvements in sensors, microprocessors, digital signal
processing, simulation modeling, multisensor data fusion, and automated
reasoning. CBM involves monitoring the health or status of a component or
system and performing maintenance based on that observed health and some
predicted remaining useful life (RUL).

6. This predictive maintenance philosophy contrasts with earlier ideologies, such
as corrective maintenance—in which action is taken after a component or
system fails—and preventive maintenance—which is based on event or time
milestones. Each involves a cost tradeoff. Corrective maintenance incurs low
maintenance cost (minimal preventative actions), but high performance costs
caused by operational failures. Conversely, preventative maintenance
produces low operational costs, but greater maintenance department costs.

7. As a predictive maintenance strategy, CBM involves assessing various facets
of an operating asset over time. Potential failure points are identified as early
as possible and steps taken to mitigate risk or performance deterioration.
Maintenance under CBM protocol takes place when the data analytics
indicates a performance decline or failure on the horizon. These have been
developed by experts specially trained in this domain, and more recently, by
data engineers using a range of machine learning (ML) and artificial
intelligence (AI) techniques.

8. For those considering implementing a CBM program, the first step is to
identify critical assets within operations and evaluate whether they involve
equipment with the type of observable performance metrics that allow for
effective condition-based maintenance.

9. Carl Byington can be contacted at his PHM Design website for more advice or
consultation. He can also be reached at his LinkedIn profile for professional
networking and recommendations.