Gas companies and utilities around the world face the ongoing challenge of delivering reliable service to their end users. This challenge, however, is compounded by the unexpected failure of components that can lead to extended shutdowns of facilities — a potentially costly surprise. Also, with possible delays when sourcing replacement components, the overall reliability of the system is jeopardized. Traditional methods used to mitigate these issues include:
- Ordering spare parts and equipment upfront.
- Performing routine maintenance as described in original equipment manufacturer (OEM) manuals.
But these traditional methods are reactive in nature and have upfront cost impacts on a project. With high natural gas demands in the marketplace, owners are seeking better solutions that will cost less and provide better results.
One proactive solution to improving the reliability of these natural gas delivery systems is to utilize intelligent monitoring systems to gather data in real time. This data can be communicated to OEM specialists for analysis to anticipate possible component failures and schedule maintenance ahead of time. This method is called condition-based maintenance.
Leveraging Existing Instrumentation Data
In general, compressors for reciprocating engines and gas turbines already are fitted with multiple sensors, including monitors — for vibration, pressure and temperature — connected to a plantwide control system. While these plant controllers only generate alarms when the sensor values exceed predefined set points, the same instrumentation devices can be leveraged to provide an even deeper insight into the performance of the systems.
Some owners lean toward minimizing or eliminating unexpected failures by requesting OEMs guarantee the performance of the units for a longer period of time between overhauls. To achieve this goal, OEMs need access to daily data when the units are in operation. Utilizing machine-learning techniques, models and analytical software programs, the data extracted from instrumentation devices can proactively track anomalies based on the operating regime of the units.
Take, for example, an engine-driven compressor with an engine oil/coolant temperature sensor. The data obtained from its control system indicates a steady and incremental rise in the engine oil temperature. The computerized maintenance controller sends an alert that corrective action is needed before the next scheduled preventive maintenance. Technicians can perform maintenance tasks including inspection and replacement of the engine oil coolant system before it fails. This condition-based maintenance technique helps owners maximize their system uptime while reducing overall ownership costs and expensive downtime.
Future of Maintenance Programs
The use of machine learning is shifting the mentality behind maintenance programs and shaping the future of these programs to be more proactive and cost-effective. Integration of condition-based monitoring systems into existing gas facility processes will continue to expand as technology improves. As a result, operators will see improvement in minimizing unplanned downtimes, reducing operational costs and improving safety.
Natural gas is an essential commodity, but a break in its distribution network can threaten public safety and cause serious economic losses. To protect these pipelines as well as the public, utilities are evaluating systems to identify proper safety devices — and redundant overpressure protection solutions — and meet federal regulations.
