Enabling a holistic approach to Predictive Maintenance

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Used across industries, rotating equipment is mission-critical in manufacturing. From compressors to pumps to motors, plant profitability revolves around equipment productivity. That’s why reliability is essential. Rotating equipment users employ condition-monitoring strategies to observe the health of machinery components and keep unexpected breakdowns to a minimum by making repairs or replacements before failures occur.

The next generation of condition monitoring goes beyond ‘preventing’ failures to ‘predicting’ failures. Predictive maintenance strategies take advantage of modern sensor technology combined with artificial intelligence technology to offer unprecedented levels of insight and predictive analysis.

The Challenge

Considering the scope of pressure points and torsional stress points involved at every connection of rotating equipment, it comes as little surprise that proper maintenance is a priority to keeping runtimes steady and uninterrupted. Reliability is the ultimate goal of keeping rotating machinery at peak production, but ensuring reliability is a constant challenge for numerous reasons.

Firstly, maintaining alignment is crucial for collinear shafts to ensure minimal stress on bearings, couplings, and other machine components. Angular misalignment of just 0.08 of a degree has been shown to cut the life of a bearing in half. Keeping the cyclical forces on bearings, shafts, and equipment structure under control requires maintaining a balance standard such as ISO 21940-11 (rotor balancing). If a rotor is out of balance or falling out of balance, it will impose high strain levels on bearings and severely diminish their lifespan.

Studies also show that mistakes in cleaning, tightening, and lubrication are the root cause for some 70% of mechanical failures in rotating machinery. Dirt, oil, and debris in oil sumps and bearings are frequently the cause of premature failure.

Maximizing Efficiencies

Early warning systems are essential in rotating equipment to escape cascading damage chains that lead to exponential damage, repair costs, and, ultimately, failure. Maximizing efficiencies while minimizing downtimes is the name of the game.

Predictive Maintenance (PdM) is one of the newest iterations of maintenance strategy – harnessing the power of digital technology and IIoT implementation. And yet, it is one of the fastest-growing modes of maintenance strategy, given its long-term financial benefits. This mode of maintenance utilizes modern analysis (AI) advancements derived from data provided by implemented sensors and historical data.

By actively monitoring equipment performance with comparative analytics, equipment managers can predict when and where an asset will fail, thus allowing service professionals to take steps to correct an issue before it reaches the point of failure. A data-forward model of predictive maintenance enables a holistic overview of interconnected technologies and assets rather than a fragmented one where symptoms are repaired instead of addressing core problems.

A report by the US Department of Energy on “Achieving Operational Efficiency” has revealed that average ROI is up to 10 times higher in predictive versus preventive measures. Reduction in maintenance costs ranges from 25-30% higher. Elimination of breakdowns is especially impressive at 70-75% higher than preventive strategies. Downtimes are reduced by 35-45%, and production levels are increased by 20-25% using predictive methods and technologies rather than preventive ones.

The initial investment in IIoT and digital solutions can be relatively easy to justify when considering the long-term savings in damage avoidance and minimized replacement costs.

Enabling Holistic System Maintenance

Instead of an individualistic and fragmented maintenance solution, systems experts recommend holistic service systems that enable maintenance staff to find root causes earlier and with better precision. For instance, in a pump system used in a hydrocarbon processing plant, a wide range of complex, interconnected assets need to be considered for interdependencies rather than their singular function alone.

The advantage of a holistic predictive maintenance solution is its custom application. Individual components are assessed for their value in the entire production chain, and sensors are applied accordingly. A holistic approach has been shown to ensure the maximum potential for early warning analysis and root cause identification in rotary equipment systems.

Service Strategies with Equipment-as-a-Service

Asset managers have come to realize that costs over the lifecycle of a piece of machinery weigh heavily to the side of the operation and service costs rather than the initial cost of the equipment itself – roughly speaking at a ratio of 20:80 with the majority side in service.

The realization that service and maintenance in the life of rotating equipment contain far more value than initial capital investment has prompted the emergence of the subscription or Equipment-as-a-Service (EaaS) model. By externalizing the risks and costs around equipment service to specialists, EaaS promises to revolutionize the industrial landscape at large and empower the users of rotating equipment.

OEMs who see the benefits and unlocked value in EaaS have been able to externalize costs around service, maintenance, operations, finance, and warranties – offering a new range of previously unavailable services to their clients. Both manufacturers and users of rotating equipment utilize IIoT implementation combined with digital solutions to maximize uptime and minimize failure rates in their machinery operations.

If you want to learn more about the future of maintenance, download relayr’s white paper on predictive maintenance of rotating equipment.