The Shift from Scheduled to Condition-Based
Maintenance
Gas utility operators have long depended on
calendar-based maintenance schedules—inspecting equipment at fixed intervals
regardless of actual condition. This approach creates two problems: equipment
can fail between scheduled checks, and resources are spent servicing assets
that don’t yet require attention.
The industry is moving toward condition-based
strategies using real-time monitoring to determine when maintenance is actually
needed. This shift requires investment in sensors, data infrastructure, and
analytics, but operational benefits are becoming clear.
Understanding the Cost of Reactive Maintenance
When equipment fails unexpectedly, costs extend
beyond the repair itself. Emergency response requires immediate technician
dispatch at premium rates. Service interruptions trigger regulatory penalties
and damage reputation. Component failures can cascade to connected systems,
while critical parts must be stocked “just in case,” tying up
capital.
Industry analysis shows unplanned maintenance
events cost utilities 3-5 times more than planned interventions when accounting
for all direct and indirect expenses.
How Predictive Maintenance Works
Predictive maintenance relies on three core
elements:
· Continuous Monitoring: Sensors track
vibration, temperature, pressure, flow rate, and acoustic signatures,
transmitting data at intervals ranging from seconds to hours.
· Pattern Recognition: Analytics platforms
establish baseline performance profiles. Deviations trigger alerts, while
machine learning identifies subtle degradation trends that precede failure.
· Maintenance Scheduling: When analysis
indicates developing issues, work orders are created with appropriate priority,
allowing maintenance during planned windows rather than emergency responses.
Practical Applications for Gas Distribution
Networks
· Pipeline Integrity Monitoring
Pressure and flow sensors along distribution
lines detect anomalies suggesting corrosion, third-party damage, or joint
degradation. Investigation can begin before leaks develop.
· Compressor Station Reliability
Vibration sensors identify bearing wear,
misalignment, or imbalance weeks before audible symptoms appear. Temperature
monitoring detects cooling issues or excessive friction, while oil analysis
reveals contamination.
· Regulator Performance
Monitoring upstream and downstream pressure,
valve position, and response time helps identify diaphragm wear, spring
fatigue, or control drift before regulation becomes erratic.
· Odorization Equipment
Flow meters and concentration sensors ensure
proper odorant injection—a safety requirement and regulatory obligation—with
alerts when levels fall outside acceptable ranges.
· Metering Infrastructure
Monitoring large commercial meters for pressure
drops, temperature variations, or flow irregularities indicates mechanical
issues, calibration drift, or potential tampering.
Implementation Considerations
· Start with High-Impact Assets
Begin with equipment where failure has the
highest consequences: assets serving critical customers, equipment in
difficult-to-access locations, components with poor reliability history, and
high-value infrastructure approaching end of design life.
· Data Integration
Sensor data must flow into systems for analysis
and action. This requires connecting field devices to SCADA or asset management
platforms, establishing secure communication networks, developing data
infrastructure, and creating workflows routing alerts to appropriate personnel.
· Establishing Baselines
When sensors are first installed, allow several
weeks to establish normal operating ranges before relying on anomaly detection
across various operating conditions.
· Balancing Sensitivity
Alert thresholds require calibration. Too
sensitive creates false positives; too conservative misses problems. Expect an
adjustment period refining parameters based on experience.
· Measurable Outcomes
Organizations implementing predictive
maintenance programs report quantifiable improvements:
– **15-25% lower maintenance spending** as unnecessary preventive work is eliminated
– **10-20% extensions in service life** for monitored assets
– **30-40% reduced repair times** through
planned maintenance windows
– **20-30% fewer safety events** on
monitored infrastructure
These figures represent industry
observations—actual outcomes depend on implementation quality, asset condition,
and operational discipline.
Building Internal Capabilities
Technology alone doesn’t create value. Field
technicians need training to recognize alerts and validate findings.
Maintenance planners must prioritize work based on condition data rather than
calendars. Engineers should develop data analysis and trend interpretation
skills. Management requires understanding of program metrics for informed
investment decisions.
Regulatory and Compliance Support
Predictive maintenance supports compliance
through detailed documentation of asset condition and maintenance activities,
demonstrating proactive pipeline safety approaches, providing context for
incident investigations, and justifying evidence-based spending in regulatory
proceedings.
However, predictive approaches don’t eliminate
traditional inspection requirements. Most regulations still mandate periodic
physical inspections regardless of monitoring data.
Common Implementation Pitfalls
· Over-Engineering: Starting with complex
analytics and comprehensive coverage leads to delays and budget overruns.
Simpler pilots targeting specific problems generate faster returns.
· Ignoring Data Quality: Sensors require
calibration, cleaning, and replacement. Poor data quality undermines the
system. Establish maintenance protocols for monitoring equipment itself.
· Lack of Integration: Predictive insights lose
value without connection to work management processes. Alerts via email that
don’t create work orders get overlooked.
· Insufficient Change Management: Shifting from
calendar-based to condition-based maintenance represents cultural change. Clear
communication about approach changes and decision-making prevents resistance.
Looking Forward
Predictive maintenance technology continues
evolving. Edge computing enables sensor-level analysis, reducing data
transmission requirements. Improved battery technology extends sensor life in
remote locations. Machine learning models become more accurate with larger
datasets.
For gas utilities, the question is how to
implement effectively. Starting with focused pilots, building internal
expertise, and scaling based on demonstrated results provides a practical path
forward.
The goal isn’t eliminating all equipment
failures—that’s unrealistic. Rather, it’s shifting the balance from reactive
responses toward planned interventions, reducing costs and risks while
maintaining reliable service