An exciting trend in the world of CMMS is the increasing sophistication of condition-based maintenance (CBM) features and functions vendors offer and maintenance professionals actually use. Perhaps we’re finally turning a corner on the age-old firefighting mentality, replacing it with a more planned environment. CBM, a form of proactive, preventive or predictive maintenance, can be defined simply as maintenance initiated on the basis of an asset’s condition. Physical properties or trends are monitored on a periodic or continuous basis for attributes such as vibration, particulates in the oil, wear and so on. CBM is an alternative to failure-based maintenance initiated when assets break down, and use-based maintenance triggered by time or meter readings.
Vendors have incorporated CBM into their CMMS offerings in a number of ways. The simplest packages allow manual input of data such as condition readings for triggering PM routines. The more sophisticated CMMS software connects online to PLCs or other shop-floor devices for automated data collection. The software then analyzes incoming data to ensure that trends are on target and within user-defined control limits. When data strays outside limits, the software initiates a work order or takes some other action. It tracks variance from target as well as the worst and best readings.
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Condition monitoring versus control
Although condition monitoring is, in most cases, better than waiting for a breakdown, CBM isn’t the ideal solution. Wherever possible, implement automated control systems, as they minimize human error and significantly improve service levels.
For example, suppose a critical piece of equipment is monitored continuously to ensure that some temperature is within an acceptable range. If the temperature rises above the upper limit, a control loop can activate a fan to cool the overheated area until the temperature returns to an acceptable range.
This is clearly superior to a condition-monitoring system that merely alerts a human that the temperature was too high. It’s then up to the human to eliminate the variance condition effectively and efficiently.
However, it isn’t always possible to determine the root cause of a variance automatically. Nor is it always possible or cost-effective to take automatic action. In such cases, human intervention is desirable, making a condition-monitoring system preferable over an automated control system.
For example, when a sensor detects a machine vibration level above the upper control limit by a user-defined amount for a user-defined period, it can initiate an alarm condition. A human might be required to determine the many possible root causes of excessive vibration, such as operator error, raw material problems, jammed parts, machine wear and so on. A human might also be required to determine the most appropriate corrective action. Therefore, it’s impractical to automate the root-cause detection and subsequent control loop to fix the problem.
Six giant steps
There are many permutations and combinations to evaluate when trying to select and prioritize the conditions to monitor, how often, for which components, leading to what actions. Many companies have spent considerable time and money on internal and external resources to make these determinations, and some have been frustrated to the point of abandoning the exercise.
To make the process less onerous, prioritize the assets for which CBM might make sense based on what happens when an asset or component fails. If the consequences of failure are catastrophic (large loss of production, major safety risk), then CBM might be appropriate. Compare the cost of failure or use-based maintenance with CBM for a given asset, and factor in the approximate value of the asset failing to prioritize candidate CBM assets. Apply the six steps below to your prioritized short-list of assets and components. The example provided is for a cooling water system where out-of-range water temperature may have catastrophic consequences.