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Since the 1980s, we have seen the rise of instrumentation that can provide maintenance departments with information on real-time conditions. These include vibration sensors for rotating equipment, corrosion monitoring, and the use of ultrasound and infrared sensing. Initially, maintenance personnel carrying sensing equipment gathered most of that data. On trips to the field or the plant floor, maintenance personnel would do onsite monitoring of devices and download the data into maintenance systems once the job was completed.

In recent years, sensors to detect those conditions have been mounted on machinery or are embedded in the device itself and have delivered their data via network to centralized asset-management applications. These applications can alert maintenance and operations personnel to variability in their performance that could be causing a decrease in production rate and/or product quality. These applications also can interact with computerized maintenance management systems (CMMS) to automatically trigger work orders.

Such technologies and predictive capabilities at the hardware and software levels have added value to maintenance processes in recent years and can save users significant sums of money. However, numerous users, vendors, and industry analysts claim most users are not gaining the benefits that they had hoped.

Automated predictive monitoring has been gaining value as plants have reduced the size of their maintenance staffs, says Jim McGlone, global sales manager for Rockwell Automation's maintenance business. By using predictive techniques on a steady flow of data from the field, maintenance staffs can devote limited time to critical plant assets actually in need of service.

In the end, to maximize the benefits these tools can deliver, users must understand where predictive maintenance can be used to best advantage, select the appropriate device and/or software, and make necessary changes in business processes.

With that in mind, consider the following, recent technological developments that have increased the power of predictive maintenance.

Smart instrumentation. With the rise in onboard intelligence, instrumentation field devices are able to relay diagnostic information to plant personnel. 'A device as innocuous as a photoelectric eye now has the ability to tell you if it has a problem such as a dirty lens. So instead of the maintenance department routinely sending someone around to clean the photo eyes on a weekly basis, now the photo eye can just alert an appropriate person nearby, who can then just wipe off the lens,' says McGlone.

Faster, more capable networks. Whereas older analog communications networks were limited in the amount of data they could carry, others are superseding them. These include hybrid analog/digital network protocols like HART and all-digital field networks such as FOUNDATION fieldbus, Profibus, ControlNet, and DeviceNet. In addition, many networks are capable of high-speed data transfer, providing users with a high-resolution picture of their processes and improving their ability to spot potential problems. For example, GE Fanuc's Control Memory Xchange is an embedded technology that enables devices to share large quantities of control data over a fiber-optic network. Once data are written to shared memory, they are immediately and automatically broadcast to all other nodes on the network at rates up to 174 megabytes per second.

'In machine control, many problems occur around sequence of operations,' says Jeff Bartoletti, GE Fanuc market development manager. 'Engineers can look at data and see that limit switches 1 and 2 both have fired, but because of the relatively infrequent updates, engineers are not necessarily able to see the order in which the firing has occurred. But if you have a network that's fast enough to identify the sequence, you can spot potential problems. Users actually wind up improving the performance and yield of their processes because they've optimized their machines as a consequence of this maintenance process.'

The rise of plant reliability.Once it became apparent that plant performance data could lead to increased plant uptime and better return on assets, users sought ways to integrate it with systems and applications that would enable plant managers to make better business decisions. As a result, industry has been moving from a narrow focus on plant maintenance to overall plant reliability.

'Based on the knowledge that a system could fail within a week, the questions plant managers now face are: When is the best time to bring the system down? What is the economic impact on the operations so you can optimize your production, minimize your downtime and its impact on profitability?' says Houghton LeRoy, ARC Advisory Group research director. 'Or, let's say a piece of equipment is working at only 85% efficiency instead of the normal, which might be 95%. You'd have to decide whether you want to do repairs on the system now, or whether it makes more economic sense at the moment to keep running at this level of efficiency.'

Barry Kleine, manager of reliability and maintenance techniques for ABB Performance Services notes that Strategic Corporate Assessment Systems' RCM Turbo package (reliability centered maintenance) lets users enter extensive information about the way each piece of equipment is used, the conditions under which it's run, and the amount of time it takes to inspect the equipment. Once it has all of that information, it recommends appropriate maintenance.

'I might tell the system, 'I want to do vibration analysis once every two months,' and the system might disagree with that decision. Not only that, but it will tell you why it recommends that you do otherwise,' says Kleine. RCM Turbo also provides information needed to make trade-offs between risk of equipment failure and the cost of maintenance, Kleine adds.

Leading reliability systems also can display past performance data stored in process and plant historians as well as profiles of optimal equipment performance. This enables users to quickly compare current performance with past and/or ideal performance, thus helping personnel identify sub-optimal performance quickly.

To facilitate data sharing among systems, MIMOSA, the OPC Foundation and ISA recently formed a joint operating committee to develop a new standard, Open Operate & Maintain (Open O&M), for integrating diagnostic, prognostic, control, and maintenance applications within the enterprise.

Despite the potential for cost savings and improved return on assets, vendors and analysts largely agree that predictive maintenance simply isn't yet living up to its potential. The problem, they say, lies not in the hardware and software, but in the way in which predictive maintenance systems are purchased, implemented, and supported.

Perhaps the biggest problem, says Kleine, lies in companies purchasing maintenance software without a clear idea of what they are trying to achieve or why. 'Companies will bring a preventive maintenance application on site, they'll give it to someone and just say, 'go implement it.' That approach is guaranteed to fail,' he says.

Instead, plants should determine what they want to accomplish with the software and then determine which type of software is right for them.

'There are many different types of software out there—software that captures device condition data, analyzes it, and prompts you for the types of maintenance to conduct. You have to understand what you want,' says Kleine.

Stuart Harris, Emerson Process Management's vice president for asset optimization, notes that, with the abundance of available data on the condition of plant assets, it isn't a good idea to try to pay equal attention to all of them. Instead, he says, plants need to consider which assets are most critical and focus on them.

'You want to focus on those assets that are going to have the greatest economic effect on your operations,' says Harris.

In addition, says Harris, plant staffs have to alter their work processes to make best use of predictive maintenance systems. Too often, plants install predictive systems, but then wind up ignoring them because their existing work processes don't take their information into account. 'If you ask people in plants to show you the work lists generated by their CMMS, you're going to find nine times out of 10 that there's no mention of the plant's predictive diagnostics tools,' says Harris.

Kleine also notes that, as good as predictive maintenance and reliability systems are, no one should blindly accept the data they generate. 'You need to audit the inputs and outputs of those systems and periodically validate them.