Physical asset managers attempt to implement policies that maintain the functionality of machinery and other production assets at a level required by their users, owners, and by society at large. They select "proactive maintenance" as their first line of defense against the causes of equipment failure. By applying routine inspection (condition based maintenance aka CBM) or periodic renewal (preventive maintenance aka PM), they seek to avoid the consequences of failure. Of the two approaches they prefer the former because it is less frequently intrusive. Although data can be collected and processed in every situation, CBM is appropriate only when it is both technically feasible and economically justifiable. Technical feasibility implies that there is available a non-ambiguous indicator of failure initiation.

Preventive maintenance is the routine renewal of physical assets or their components. Condition based maintenance is the routine inspection of a physical asset to determine whether a failure process is underway. If failure has begun, the goal is to take an action which will somehow avoid or reduce the consequences of failure. If the remedial action (for example a cleaning or adjustment) can be performed on the spot, at the time of the inspection, most companies consider the inspection activity as belonging to their preventive maintenance (PM) program.

Condition based maintenance (aka on-condition maintenance, predictive maintenance, and others) first appeared in the late 1940's in the Rio Grande Railway Company, to detect coolant and fuel leaks in a diesel engine's lubricating oil. They achieved outstanding economic success in reducing engine failure by performing maintenance whenever "any" glycol or fuel was detected in the engine oil. The U.S. army, impressed by the relative ease with which physical asset availability could be improved, adopted those techniques and developed others. During the 50's, 60's, and early 70s CBM grew in popularity and a vibrant CBM technology industry emerged providing training, products, and services which came to be known as "predictive maintenance".

Commercialization of CBM coincided with the dawn of the "information age" and CBM took on a new "flavor". Technology entrepreneurs conjectured that, if simple physical measurements, such as vibration amplitude or oil viscosity, could provide such useful benefits, then collecting the data in computers and trending it over time would, likely, provide a far deeper insight into the state of a machine's health. Hence the 1980s and 1990s witnessed a soaring rise in the use of computers, software, and data collectors in maintenance shops throughout the industrial world.

In reality, even in the midst of impressive information technology growth, most day-to-day CBM success stories still derive from the basic application of the original, uncomplicated form of CBM. For example; the detection of unbalance in a rotating machine, of glycol or fuel in an engine oil, or of mechanical looseness, soft foot, or shaft misalignment seldom require the degree of sophistication (and related expense) of the variety of technology bells and whistles happily proffered by the CBM industry.

At the same time (as the growth of CBM), the information technology revolution impacted another part of physical asset management - the computerized control of maintenance materials, labor, and historical records. These products became known as computerized maintenance management systems (CMMS). There was, however, a striking difference between the CBM and CMMS approaches.

While CBM technology vendors required their clients to adhere to highly structured procedures for data collection and storage, CMMS vendors, on the other hand, hailed the concept of 'flexibility' and extolled their products' "ease of adaptation" to their clients' existing business processes. As a consequence of their much vaunted "user friendliness" no common practices of data classification gathered sufficient critical mass to achieve standardization - not even within a given organization, let alone in an industry or in the physical asset management community at large.

It is in this context that the second millennium, the age of connectivity, finds the state of maintenance information. Maintenance technology vendors are poised to inject the latest generation of "integration technology" into their traditional market. But the lack of a common data model impedes smooth penetration.

The Maintenance Information Management Open Systems Alliance (MIMOSA) was formed in 1994 by key CBM and maintenance technology vendors to address the problem. The result of their labors in the past 10 years is the impressive common relational information system (CRIS) and associated enabling tools. The CRIS accommodates many physical asset management concepts within its data structure and has the flexibility to adapt as required. It is continuously maintained and updated by MIMOSA (www.mimosaa.org).

Hence we may foretell the day when disparate production and physical asset management systems will communicate seamlessly thanks to MIMOSA and other standardized information protocols such as OSA-CBM (Open Systems Alliance - Condition Based Maintenance), STEP (standard exchange for model product data), OPC (OLE for process control), OAG (Open Applications Group), and others.

Connectivity to this degree of intimacy implies that process and maintenance information from multiple platforms will materialize in a universally accessible format (CRIS) and, in that homogenized form, may be intelligently processed for optimum decision making. Optimization seeks to achieve some objective: the lowest average cost of maintenance, highest asset availability, or a specified effective reliability. It is onto this stage that the "CBM Optimizing Intelligent Agent" enters.

EXAKT, a CBM optimizing software, developed by the CBM Laboratory at the University of Toronto is an intelligent agent. More precisely, it is a platform for developing intelligent agents that are designed to interpret condition data (CBM measurements) in combination with corresponding historical data from the CMMS. The agent reduces both data sets to a clear decision - i.e. whether to intervene and perform maintenance at this time or to allow the equipment to continue operating. It does so by considering the economic consequences of failure, the cost of repair, and the risk of failure in an upcoming period. It generates, a recommendation that supports a currently stated management objective - either to minimize cost or to maximize the asset's availability or to achieve a particular desired ratio of planned-to-breakdown maintenance.

What does the future have in store for CBM? The CBM process consists of three sub-processes: data acquisition, signal processing, and decision making. Data acquisition is already highly technologically advanced. "Signal processing" in CBM filters out of the data operational and environmental data so that what is left is a "condition indicator" that reflects the degree of deterioration of some targeted failure mode. New signal processing methodologies based on a variety of disciplines (wavelet analysis, principal component analysis, inference engines, and neural net classifiers to name a few) are being developed in research institutions and universities around the world. Their effect will be to make it technically feasible to track and manage ever increasing numbers of failure modes.

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