Close the Loop, Eliminate Data Silos

Technology that integrates data from all assets, while preserving context, unlocks visibility into plant health.

Modern plants have a wide variety of assets and each one contributes to overall plant health, safety, and efficiency. Each data point generated by those assets is a critical piece of a greater puzzle that helps maintenance and reliability personnel keep a finger on the pulse of the plant, and, in the case of larger organizations, the enterprise.

While a common complaint is that plants have too much data, a closer look often reveals that data volume is not the real problem. In many cases, the issue is that data is often trapped in silos—disparate machines, individual and incompatible systems, file cabinets full of paper records—that keep it from being useful.

Digital transformation and Industry 4.0 technologies have long been the path to breaking down these data silos, but that path has been hard to follow in the reliability space. As more and more plants move toward a boundless automation vision for extracting full value from their digital transformations—driving data seamlessly from the field through the edge and into the cloud—personnel must find a way to close the loop on maintenance and reliability. If they can find that path, they will break down the silos that impede progress. Fortunately, the strategies and technologies exist to do just that.

Automation barriers

The strategies for breaking down maintenance and reliability data silos are simple in theory. Teams need to bring disparate data sources together without losing or destroying the context around those data points. Implementing this strategy is complicated by the various modalities in the maintenance and reliability space. Vibration, thermography, lubrication, and corrosion are all collected in different ways. As such, each information type requires its own sets of experts for collection and analysis.

When a reliability team collects these different data points and must manually pull everything into one trend or analysis, bottlenecks arise. Those same people also must have time to focus on understanding what the data means in context of how all the processes operate. That’s a big ask in an era of widespread retirements and workforce shortages.

As organizations operate with leaner staffs, technology is stepping up to close the gap with devices that add context to break down data silos. These devices are designed to transform data into contextualized information at every step of the process, democratizing it to be used by cross-functional teams at any layer of the organization.

Wireless sensors

The most obvious way data is siloed is when it is not collected at all. No plant wants to leave asset health data on machines. If data must wait until someone travels out to collect it, that information may be invisible for a long time. Small, rugged wireless sensors are more affordable than ever, making it easy to move stranded data into the fold, regardless of how often personnel are available to walk around the plant on manual rounds.

Not all wireless sensors are created equal. The simplest sensors only collect and deliver raw data. While that data is helpful, it still requires extensive overhead. Not only do plant personnel need to be experienced enough to read the spectrums and waveforms in raw data, but they must also have the time to focus on that task. If data can only be interpreted by a select group of highly skilled people, it’s still siloed.

Intelligent wireless sensors automatically add context to collected values, helping identify low-frequency faults such as imbalance, misalignment, resonance, and looseness. Yet many of these devices leave out impacting, a critical indicator of equipment faults that most commonly shut down machines. The most advanced wireless sensors provide comprehensive context by also rating impacting to help technicians at any skill level intervene when problems are still small.

Context at the edge

Much like wireless sensors, edge-analytics devices can monitor vibration and provide an asset health score, including impacting. Edge-analytics devices further reduce data silos by monitoring variables such as temperature and pressure. These edge devices then use onboard analytics to paint a picture of the root cause behind emerging problems.

The most advanced edge-analytics devices can also incorporate data from external systems, such as a plant’s control system, using open communication protocols. Teams taking advantage of these capabilities gain deeper insights into the issues affecting their assets, empowering them to identify ways that changing process variables affect asset health.

Reliability teams wanting to further reduce the number of data silos in a plant are implementing advanced machinery-health software packages. Even the best wireless sensors and edge-analytics devices are still independent sources of data, so teams use machinery-health software to collate the data from these and other devices, perform local analysis, and export critical values. In the most advanced solutions, all device information is presented in a single, intuitive dashboard, helping users save significant time by not sifting through multiple applications to track and trend information.

Closing The Loop

Modern asset-health collaboration platforms can help teams further close the maintenance and reliability loop. These software solutions take all the data that has been contextualized by the team’s wide array of intelligent devices and connect it with existing expert information. The collaboration platforms facilitate integration with external clients, historians, computerized maintenance-management systems, business systems, and development environments to collect and organize data into a standardized, scalable and, most important, usable pool. Instead of a data lake full of unrelated values, an asset-health collaboration platform breaks down even more silos to provide actionable information, produced by using layers of analytics, context, and institutional knowledge.

From the plant to the enterprise, there’s a long pipeline that data can travel. The more context data collects on its journey to and through the enterprise, the better decisions personnel can make at every level. Closing that loop is a process, but it starts with small steps and intuitive technology investments. EP

By Erik Lindhjem, Emerson

Erik Lindhjem is Vice President and General Manager of Emerson’s Reliability Solutions business, St. Louis (emerson.com). He is focused on driving digital transformation through plant asset management, enabling clients to reach top-quartile performance. He earned a B.S. in mechanical engineering from the Univ. of Virginia and an MBA from Wake Forest Univ.

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