Predictive Asset Analytics: The Future of Maintenance

Hagar Rabia
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引用次数: 0

Abstract

Major Overhauls (MOH) of major Rotating Equipment is an essential activity to ensure equipment and overall plant's productivity and reliability requirements are met. This submission summarizes Maintenance cost reduction and MOH extension benefits on an integrally geared centrifugal Instrument Air (IA) compressor through a first of its kind Predictive Maintenance (PdM) solution project in ADNOC. Appropriate planning for Major Overhauls (MOH) in accordance with OEM, company standards and international best practices are crucial steps. Digitalization continues to transform the industry, with enhancements to maintenance practices a fundamental aspect. Centralized Predictive Analytics & Diagnostics (CPAD) project is a first of its kind in ADNOC as it ventures into on one of the largest predictive maintenance projects in the oil & gas industry. CPAD enables Predictive Maintenance (PdM) through Advanced Pattern Recognition (APR) and Machine Learning (ML) technologies to effectively monitor & assess equipment performance and overall healthiness. Equipment performance is continuously assessed through the developed asset management predictive analytics tool. Through this tool, models associated with the equipment were evaluated to detect performance deviation from historical normal operating behavior. Any deviation from the historical norm would be flagged to indicate condition degradation and/or performance drop. Moreover, the software is configured to alert for subtle changes in the system behavior that are often an early warning sign of failure. This allows for early troubleshooting, planning and appropriate intervention by maintenance teams. Using the predictive analytics software solution, an MOH interval extension was implemented for an integrally geared centrifugal IA compressor installed at an ADNOC Gas Processing site. The compressor was due for MOH at its traditional fixed maintenance interval of 40,000 running hours in Nov 2019. Through this approach, the actual performance and condition of the compressor was assessed. Its process and equipment parameters (i.e. casing vibrations, bearing vibrations, bearing temperatures and lube oil supply temperature/pressure, etc.) were reviewed, which did not flag any abnormality. The compressor's performance had not deviated from the historical norm; indicating that the equipment was in a healthy condition and had no signs of performance degradation. With this insight, a 15 months extension of the MOH was achieved. Furthermore, a 30% maintenance cost reduction throughout the compressor's life cycle is projected while ensuring equipment's reliability and integrity are upheld. A total of 7 days maintenance down time including work force and materials planning for the MOH activities was deferred. The equipment remained in operation until its rescheduled date for MOH. Through the deployment of predictive analytics solutions, informed decisions can be made by maintenance professionals to challenge traditional maintenance practices, increase Mean Time Between Overhauls (MBTO), realize the full potential of a plant's process & utilities machinery and optimize operational costs of plant assets.
预测性资产分析:维护的未来
主要旋转设备的大修(MOH)是确保设备和整个工厂的生产率和可靠性要求得到满足的必要活动。通过ADNOC首个预测性维护(PdM)解决方案项目,本报告总结了整体齿轮离心式空气仪表(IA)压缩机的维护成本降低和MOH延长的好处。根据OEM,公司标准和国际最佳实践,适当规划大修(MOH)是至关重要的步骤。数字化继续改变行业,维护实践的增强是一个基本方面。集中式预测分析与诊断(CPAD)项目是ADNOC的首个此类项目,因为它涉足了油气行业最大的预测性维护项目之一。CPAD通过高级模式识别(APR)和机器学习(ML)技术实现预测性维护(PdM),以有效监控和评估设备性能和整体健康状况。通过开发的资产管理预测分析工具持续评估设备性能。通过该工具,评估与设备相关的模型,以检测与历史正常操作行为的性能偏差。任何与历史规范的偏差都将被标记为状态退化和/或性能下降。此外,该软件被配置为对系统行为中的细微变化发出警报,这些变化通常是故障的早期预警信号。这允许维护团队进行早期故障排除、计划和适当的干预。利用预测分析软件解决方案,对安装在ADNOC天然气处理现场的整体式齿轮离心式IA压缩机实施了MOH间隔延长。该压缩机应在2019年11月按传统的4万运行小时的固定维护间隔进行MOH。通过该方法,对压缩机的实际性能和状态进行了评估。检查了其工艺和设备参数(即套管振动、轴承振动、轴承温度和润滑油供应温度/压力等),未发现任何异常。压缩机的性能没有偏离历史标准;表明设备处于健康状态,没有性能下降的迹象。有了这一认识,卫生部延长了15个月。此外,在确保设备可靠性和完整性的同时,预计在压缩机的整个生命周期内,维护成本将降低30%。总共7天的维修停工时间,包括MOH活动的劳动力和材料计划被推迟。该设备一直运行到卫生部重新安排的日期。通过部署预测分析解决方案,维护专业人员可以做出明智的决策,挑战传统的维护实践,增加平均大修间隔时间(MBTO),实现工厂过程和公用事业机械的全部潜力,并优化工厂资产的运营成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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