Aspen Mtell® Machine Learning Finds Cause of Compressor Failures at LNG Facility
Read how this LNG facility used Aspen Mtell prescriptive maintenance to provide up to 61 days advance notice of catastrophic compressor failures, preventing an economic loss of more than $40M USD per occurrence. Quick to implement and readily scalable, the solution provided key insights into the root cause of the failures.
Saras拥有地中海最复杂的炼油厂，每天的炼油产能为30万桶。作为数字化项目的一部分，他们正在评估如何提高资本和资产密集型炼油厂运营的可靠性。他们选择了AspenMtell，基于一个竞争性试点项目选择过程，最初的重点集中在关键炼油设备上，比如大型压缩机和水泵。 Aspen Mtell通过挖掘历史和实时操作以及维护数据来发现资产性能下降和故障发生之前的精确特征，预测未来故障并制定详细的行动以缓解或解决问题。
Saras Drives Innovation in Predictive Maintenance via Digital Transformation Program
When developing its highly successful digital transformation program, Saras turned to Aspen Mtell for predictive maintenance. After deploying Aspen Mtell on more than 50 large assets in a refinery, Saras began testing the technology at a wind farm and had five early successes in predicting gear box failures.
Prescriptive Maintenance Software Helps Saras Improve Business Performance and Drive Operational Excellence
As part of an effort to drive reliability in its refinery operations, Saras turned to Aspen Mtell® prescriptive maintenance to improve equipment uptime and decrease maintenance costs.