Gas companies and utilities around the world have a goal of delivering reliable service to their customers. With high natural gas demands in the marketplace, owners are seeking better solutions that cost less and provide better results.
One proactive solution for improving the reliability of natural gas delivery systems, including compressor stations, is to utilize intelligent monitoring systems to gather data in real time, also called condition-based maintenance.
Traditional methods of addressing equipment wear may involve upfront costs and the risk of machinery and equipment nonavailability. In addition, based on the size, high speed and high capital costs of gas compressors, it is generally the case that no installed spare equipment or machinery components enable better methods to continuously monitor compressor conditions.
As a result, many utilities and operators are moving to more robust maintenance programs to further reliability.
Solutions that address the whole life cycle of the machinery are the best way to achieve the highest reliability of the machines as described in the original equipment manufacturer, or OEM, manuals.
For example, will this equipment need minor retrofitting or major overhauls within the next five, 10 or 20 years? Changes in natural gas demand can exacerbate wear and tear of a facility’s components; have these conditions been accounted for?
It is expected that all major components within a compressor skid will need to be replaced at or near the end of their design life cycles.
One predictive solution for 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 relayed to OEM specialists for analysis to anticipate possible component replacement and schedule condition-based maintenance ahead of time.
OEM maintenance groups currently monitor the performance and condition of equipment during normal operations by assessing changes in the operating regimes that can lead to different outputs. Key indicators such as temperature, vibration and other metrics allow the OEMs to predict when equipment updates might need to occur. This data is important in avoiding unplanned maintenance.
Gas compressors and their drivers—gas and steam turbines, large electric motors and auxiliary equipment such as gear speed increasers—are generally equipped with velocity probes, temperature transmitters and vibration monitoring systems. The signals from these devices are transmitted to suitable monitoring devices, with alarm and shutdown settings in compliance with applicable standards. As problems occur, operations personnel are called to intervene and fix the issue, but, again, this may cause delays and might not address issues with other devices and components.
While these plant controllers only generate alarms when the sensor values exceed predefined points, the same instrumentation devices can be leveraged to provide deeper insight into the performance of the systems. The output from these monitors can be shared with the OEMs to better understand the daily operations of the engines and compressors, predict any issues in the systems and get advice in a timely manner. These systems can be used to monitor steady-state data and transient data related to process upsets, and the data are recorded automatically.
Owners typically expect high reliability and availability of the machines and request that OEMs guarantee both. For their part, OEMs generally can guarantee performance and reliability of machines for the design cases stipulated by the owner/operators prior to purchase. However, the operational reality often is different than what was anticipated at the time equipment and machinery were procured. The machines may operate at different regimes, resulting in unexpected flow modes. This can make the OEMs’ maintenance recommendations difficult to follow and implement.
A condition-based monitoring system could be implemented as a prudent step to address these situations. This requires that OEMs have access to daily operating data when the units are in operation, allowing them to assess and monitor the overall health of the machines.
The proactive nature of this program results in greater machine uptime while optimizing the overall operations and maintenance costs through condition-based maintenance and general overhauls.
Utilizing machine-learning techniques, models and analytical software programs, analysts can use the data extracted from instrumentation devices such as an engine oil/coolant temperature sensor to proactively track anomalies. For example, if the data obtained from the control system indicates a steady and incremental rise in the engine oil temperature, an alert is sent, indicating 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 problems occur. This condition-based maintenance technique helps owners maximize their system uptime while reducing overall ownership costs and downtime expense.
The main goal of installing an online condition-based monitoring system is to protect the safety of personnel, assets and environment. It also provides critical data that operators and engineers can use to assess the equipment’s condition, determine machine availability and make informed decisions.
Before implementing condition-based monitoring systems, the types of machines need to be known, and design parameters and devices to be installed must be identified. Additional sensors may need to be added to the system.
The online condition-monitoring system must be designed for the type of machinery and its historical update parameters. For example, with reciprocating compressors, more than 90% of unplanned shutdowns are known, with more than 35% related to compressor valves, more than 10% related to pressure packing, 5% to piston rings and 5% to instrumentation. The parameters to be monitored need to be identified, and the OEM and owner must agree if additional sensors/transmitters need to be installed to better monitor the machine.
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.
With today’s evolving system demands, implementing online condition-based monitoring systems in conjunction with procurement of the units is highly recommended as a step to stay ahead of inevitable system updates.
