If you polled manufacturers, seven out of 10 would tell you they’re interested in using predictive maintenance technologies in their plant floor automation systems to help machinery run productively and to help detect potential equipment failures. Yet, only three out of 10 have actually deployed this technology. Why the disparity?
The main reason is that most manufacturers lean on both operations and maintenance departments to collect equipment performance data, prioritize issues and decide on a course of action. When the recommendations don’t match, confusion and disagreements between those departments abound. The traditional approach to deploying predictive maintenance technologies also can be too difficult or expensive to be widely adopted.
But, by using condition monitoring and vibration analysis systems that are directly integrated into the control system, manufacturers can take an operations-driven approach to machine maintenance to help increase productivity while reducing costs.
The great divide
When a plant establishes a vibration-measurement system, the maintenance department typically is responsible for the entire implementation. Operations personnel only receive a report advising on the status of the machines. This produces two independent systems (Figure 1).
This schematic represents the traditional, isolated, maintenance-based approach to implementing a reliability system.
The main problem with this isolated approach is in getting data from the maintenance side to the operations side. The architecture produces an unequal balance of data that doesn’t transfer properly or accurately across the divide from one department to another. In addition, these two departments use multiple software applications (five to six on average) to collect, analyze and share information. As a result, engineers must configure systems manually in multiple places, which is time-consuming and needlessly complex.
Because these systems are standalone and server-based, the analysis function typically is isolated from the day-to-day operations of the facility and plant operators usually don’t understand or accept the resulting recommendations. For example, consider a plant-floor operator whose responsibilities include working with a critical pumping station that feeds raw slurry into the manufacturing process. During a routine check, the operator sees a blinking red light that indicates a problem with the pump. The next stop is the operator station to reference the operator screens, which indicate the pump is vibrating excessively. Finally, someone in the maintenance group is notified of the problem.
After asking a few diagnostic questions about noise and leaks, the maintenance team collects pump data. As it isn’t safe to crawl across the machine to collect tombstone data from the motor information plate, the maintenance technician references various equipment manuals and the gearbox reduction ratio to determine the speed at which the machine is running.
Eventually, the maintenance technician concludes the pump is running at 950 RPM, and the vibration analyzer tells him that the peak is 1,900 RPM. For clarification, he calls the analyzer manufacturer’s customer support line and discovers this could mean that something on the pump is loose. The maintenance technician and the operator return to the pumping station and, after crawling around the running machine, discover that the concrete base on the front drive side of the pump is cracked and the anchor bolt has worked loose. The maintenance technician reports the findings and schedules a temporary shutdown to retighten the bolt and review the cracking problem.
This so-called traditional method is both time-consuming and inefficient. Instead, plants need a way to incorporate vibration measurements into an operations-centric model and deliver diagnostic information to the machine operator quickly and efficiently.
The great bridge
Some plants are instead using an automated operations approach to collect and analyze data automatically on a routine basis. As a result, more operators interact with and understand the condition monitoring information and recommendations. This is known as an operations-driven reliability program, and has been proven to increase the effectiveness of predictive maintenance over traditional isolated reliability approaches (Figure 2).
The value of an early warning appears in many forms.
This operations-driven approach uses analysis tools and software that integrate vibration measurement into a plant’s control system. Machinery diagnostic instructions can be inserted in the programmable automation controller to automate the routine predictive maintenance functions. These instructions are coupled with diagnostic faceplates for standard operator interface visualization software to stage the complex vibration data for detailed analysis. This not only helps increase machine reliability, but also helps reduce the cost to implement a system as compared to standalone, proprietary approaches.
Applying this integrated approach in the example above simplifies finding a solution when the operator sees that blinking red light indicating a pump problem. The operator merely pages through various operator screens, one of which shows a diagnostic message for high vibration readings and suggests that the pump might be loose. The operator then surveys the machine and notices the crack in the concrete pump foundation and the loose anchor bolt. The operator then calls the maintenance team, which asks a few questions and schedules a temporary shutdown to tighten the bolt and review the cracking problem. Obviously, this scenario using an integrated approach is preferable. The operator and the maintenance technician obtained valuable information regarding the problem quite quickly and easily, which allows for better, faster decision-making.
Integration for better business
Every new technology reintroduces the fear of change, particularly the fear of being downsized. Some vibration analysts might wonder, “If this function gets automated, doesn’t that mean I’m out of a job?”
On the contrary: Integrated condition monitoring systems are a valuable complement to vibration analysis, not a replacement. These automated systems allow the analyst to offload routine activities, such as data collection and routine analysis, and concentrate on more value-add responsibilities.
For example, another key benefit of integrated predictive maintenance is the ability to track historical machinery condition data. This knowledge improves almost any maintenance activity because maintenance technicians can better understand the equipment for which they are responsible, and more easily identify and remedy future problems. Understanding the routine matters lets the maintenance department tune or upgrade the machinery to be more efficient or operate faster.
Implementing an integrated condition monitoring program makes sense for plants that are serious about competing in the global marketplace. Condition monitoring systems help to keep plant floors running productively by detecting potential equipment failures earlier. The data produce better analysis, which leads to better decision-making at every level – from plant floor to enterprise.
Richard Schiltz is business manager of Integrated Condition Monitoring at Rockwell Automation, Mayfield Heights, Ohio. Contact him at email@example.com and (440) 646-4211.