Toyota Motor Corp. is no different than any other company. Like the rest, the automaker wants maximum uptime at the least cost possible. But it differs from many manufacturers in that it has an aggressive program aimed at cutting costs by tens of percentage points. And intelligent predictive maintenance is an important element of its strategy for doing so.

Toyota management proved the value of investing in maintenance on a machining line in Japan a few years ago. A team of engineers, technicians and operators there was able to cut downtime due to maintenance problems in half by applying the fundamentals of predictive maintenance. The machining line had been running at about 82 percent of the time that it was supposed to operate. “About 12 percent of the downtime was due to maintenance problems,” says Mark Rucker, power systems specialist at Toyota Motor Manufacturing, in Georgetown, Ky. “That 12 percent was cut by more than half within a year of doing that kind of analysis.”

Attention grabber

As one might imagine, this kind of success can attract quite a bit of attention. Toyota’s management, for example, now wants to install more intelligent maintenance systems to repeat the success in its plants worldwide. Other manufacturers in a variety of industries also have taken note, and are trying to implement the basic principles in their factories.

Toyota’s plant in Georgetown is complying with corporate continuous-improvement mandates by working with researchers at the National Science Foundation Industry-University Cooperative Research Center for Intelligent Maintenance Systems (IMS Center). The research is a multi-campus endeavor involving the University of Cincinnati and the University of Michigan, in Ann Arbor, as well as more than 40 global companies. The goal is to develop technologies and tools to help factories to reduce the number of breakdowns on their machines and systems to nearly zero.

The researchers plan to achieve their goal by learning to predict failures, rather than simply monitoring the status of machinery and reacting to problems as they develop. “Today, machine field services depend on sensor-driven management systems that provide alerts, alarms and indicators,” explains Jay Lee, Ph.D., founding director of the IMS Center. “The moment the alarm sounds, it’s already too late to prevent the failure.” So he advocates monitoring the important sources of degradation over time, using trends in feedback to forecast problems before they develop, and scheduling maintenance when it becomes necessary—that is, neither too early nor too late.

These intelligent maintenance systems would make predictions based both on real-time data from sensors on the machine and on quality and historical information already resident in enterprise-wide computer systems. “The goal is to predict product and machine health in the same way that the weather is forecasted,” says Lee. “We really don’t care about how precise the temperature prediction is. We care about the trend—cold to hot or clear to rainy.” Such a forecast would allow users to establish priorities and create a plan for maximizing asset utilization.

Interpretation is key

Research at the IMS Center includes finding the appropriate metrics and sensors for measuring them. Although Rucker expects Toyota to benefit from this research, he believes that his company will benefit even more from the efforts to transform the collected data into useful information in real time. The reason is that the controllers on today’s automation already collect tremendous volumes of data. “There’s an incredible amount of data coming off the lines in terms of machine and line performance,” says Rucker. “It’s just sitting there.”

In many cases, the problem is not the lack of technology for gathering the data, but the algorithms for interpreting it. “We have a lot of people in the company that deal with statistical quality analysis, but that’s only half the story,” says Rucker. “There are other signal processing techniques that the IMS Center can bring to bear on the problem.”

For example, researchers at the IMS Center are helping Toyota’s facilities engineers in a yearlong project to look for ways of saving money in the operation of the plant’s air compressors. Because rebuilding these 6,000-cubic feet per minute centrifugal units costs tens of thousands of dollars, the team is developing two models, one to predict bearing wear given the varying loads that the compressors experience throughout the day, and the other to control surges and damaging back flow. The goal is to generate cost efficiencies by finding the right parameters to measure, and then developing software to monitor and control them.

Right now, the vibration monitors on the ...