MC-2017 Learning Session - 40:59
by Marcus Corley and Scott Lane, Central Arizona Project.
Timing maintenance outages depends on factors such as PM window, asset health indicators, and customer impact among others. All designed to maximize service life and availability. Customer impact of an outage is usually discussed in terms of immediate loss of availability. However, the potential impact of events such as late service return, simultaneous breakdowns, and inefficient power usage are risks often overlooked or subjective due to lack of data. Quantifying criticality of all assets using actual operational metric provides the data needed for timing scheduled outages with the lowest risk.
CMMS, SCADA, Operations, and Engineering records provided our raw data set. Ten metrics extracted from the data set form the basis for calculating criticality of each pump unit such as, service factor, pump capacity, max flow rates, power impact, and customer demand. Statistical analysis of each metric transformed the data set to a common one-to-five range that when added together with an applied weighting factor produces a criticality score. The criticality score represents customer risk of scheduling an outage on a pump unit relative to all other pump units.
Our analysis produced an interactive graphical model of the relative criticality of every pump unit in our system. A weighting factor to account for unique attributed as well as seasonal factors for shifting customer demand can be applies to every unit. A quick reference heat map provides a visual of the relative criticality across the system.
This exercise showed unit criticality, or customer risk, across our aqueduct system is variable by season, geographic region, customer demand, plant design, unit, etc. When a choice is possible, scheduling outages on units with a relatively low criticality score can reduce customer risk.
