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Anatomy of CBM

Having understood, from Figure 6‑4 (page 87), that the decision to perform CBM flows from a fundamental analysis of the physical asset’s maintenance requirements, we turn our attention to the composition of a CBM task. We keep the over-riding concerns in mind. That is, we elect to conduct only applicable and effective CBM procedures. Figure 7‑1 portrays three distinct CBM sub-processes, each of which must satisfy the applicability and effectiveness criteria in order for CBM to add value to a maintenance program.

Figure 7-1: CBM sub-processes

Data Acquisition

Data acquisition is the first and, one might assert, the easiest of the three CBM sub-processes to implement. Assisted by advanced sensor, signal transmission, and storage technologies, we can, without too much effort, implement systems that collect and store impressive amounts of data. The predictive maintenance industry has organized[1] to provide communication standards and protocols endowing their products with unprecedented capability to share process and condition monitoring data. Because commercial-off-the-shelf (COTS) data acquisition hardware and software products can be used across a range of industries, data acquisition enjoys more commercial exposure than do the other sub-processes of CBM. Some maintenance technology consumers imagine that, once they set up elaborate data acquisition, storage, and display systems, they will have overcome the major hurdle to effective CBM. Some pay scant attention to the choice of the data they decide to collect, adopting a when-in-doubt-collect-it-anyway-it-might-be-useful attitude. Their data choices are influenced largely by the capabilities of the technology rather than by a pre-assessment of how well the collected data will reflect an evolving failure mode.

By way of illustration, there are two important reasons why bearings fail :

• Overheating – the most common cause being over-lubrication, and
• Contaminants in the bearing oil – water being the dominant one

Were we to consider Condition Based maintenance a form of maintenance inspection (rather than a hi-tech maintenance process), we would demand that monitored data relate clearly to the failure modes with which we are most concerned. Moreover, from an information management perspective, we would require that our CBM and CMMS databases store, in the case of centrifugal pumps, for example, such “mundane” types of data as:

1. Bearing oil reservoir levels and bearing case temperatures.[2]
2. Incidences of leakage from the stuffing box, gaskets, bearing seals, cracks or holes in the piping or pump casting.
3. Abnormal noise such as that sometimes heard when air is leaking into a mechanical seal or pipefitting. (Vacuum leaks can be checked with smoke.)
4. Odors indicating high temperature.
5. Colors indicating a component has been subjected to abnormal heat.[3]
6. Blackened oil indicating that it has been subjected to high temperatures.
7. Excessive vibration detected either with the use of instruments, or by one of the senses.
8. Malfunctioning environmental controls on stuffing boxes, discernable by measuring the temperature difference between the inlet and outlet lines.
9. Positions of control and isolation valves throughout the system while the pump is running steadily.
10. Flow, differential pressure, power consumption, temperatures (in the volute and stuffing box), shaft speed, liquid levels (sight glasses)
11. In cartridge seals, estimates of face loads by measurement of the gap that held the retention clips.

Tradespersons and operators make these types of observations routinely. Sometimes, they take approriate corrective action. Seldom, however, do the observation or the failure mode[4] discovered as a result of the observation, appear methodically as records in the maintenance history database. Invaluable sources of reliability data such as these, elude most maintenance information record keeping processes. Rather, those historical records contain, mainly, descriptions of activities performed, without reference to the conditions that inspired the actions. The McNalley institute[5] enumerates the possible causes of the elevated temperatures in the stuffing box as:

• Loss of circulation in the stuffing box cooling jacket.
• Loss of cooling in the bearing case cooling sump.
• Something is cooling the outside of the bearing casing causing the outside diameter of the bearing to shrink, increasing the load.
• The bearing was installed incorrectly.
• The bearing is over lubricated. The oil level is too high or there is too much grease in the bearing.
• The lubricating oil is contaminated with water.
• The shaft is overloaded because the pump is operating off of the B.E.P. (best efficiency point).
• There is too much axial thrust of the shaft.
• Misallignment, unbalance, etc.

Oil sampling will indicate the following conditions that are a prelude to (or an indication of) serious failure.

• Water is getting into the oil.
• Oil additives are no longer present and functioning.
• The oil is carbonizing due to high temperature.
• Solids due to corrosion, bearing-cage destruction, or some other reason are present.

By monitoring pump suction and discharge pressure in concert with product flow and motor amperage, the following failure modes may be detected:

• Wrong size pump.
• Pump operating far from best efficiency point raising the likelihood of shaft deflection.
• Motor close to an overload condition.
• Impeller needs adjustment or the wear rings need replacement.
• Poor operating practices.
• Source product tank at wrong level or suction lines are clogging.
• Getting close to cavitation.

Most failure modes occur randomly rather than by a wearing out of a component. For example, were wear the dominant failure mode in bearings, they would, on the average, survive 50 or even 100 years. But, industrial bearings undergo accelerated wear initiated by randomly occurring internal or environmental events, for example a shock load, excessive heat, or water ingress causing lubricant failure. Bearing life is, in addition, highly influenced by initial conditions, for example, how it was stored and handled prior to installation, and how it was installed.

Randomness, being the rule, rather than the exception, is it reasonable for us to assume that we will usually find a monotonically rising trend of some monitored variable throughout a component’s lifecycle, from which we may predict its failure? A more reasonable approach to CBM would be to monitor the equipment and its operating context for signs of conditions causing abnormal stress, that if allowed to persist, will be destructive. Doctors monitor cholestrol to determine whether our arteries are in danger of clogging. At a certain level, they order a corrective action, usually a change in lifestyle. Maintainers monitor oil levels to avoid the consequences of over- or under-lubrication. Vibration analysts determine a condition of foundation weakness, shaft misalignment or of rotor imbalance, that, if uncorrected, will lead to serious failure.

These examples illustrate that Condition Based maintenance is a viable maintenance strategy for avoiding failure altogether. Yet CBM can also track and predict some failure modes from some point in time after their random initiation to their ultimate functional failure. It has been estimated[6] that twenty precent of failure modes proceed in a predictable enough manner following their detection (their potential failure), that a repair action may be planned and executed prior to the loss of asset functionality. A spalled bearing, for example, emits bearing tones that can be detected automatically through processing of the spectral data assisted by cepstrum analysis. The bearing may continue to operate adequately from this point for several months prior to a failure that would render it non-functional.

It seems, then from the preceding, that there are two classes of CBM:

a. the detection of abnormal stresses[7] on a system that, if uncorrected, will provoke a failure that has not yet initiated, and
b. the detection of a failure that has already begun, but has not progressed to the point where a required function has been lost.

In either situation, CBM is said to be effective, as long as the consequences of failure are reduced (or avoided entirely) at an acceptable cost. In the case of the first CBM class, and, pursuing our example of a centrifugal pump, we might notice a rising trend in the temperature of the stuffing box. If it gets too hot, we are going to have problem. We had better correct the condition if we do not want to experience a premature (random) seal failure. The McNally Institute describes the following seal failure modes that will be provoked by excessive stuffing box temperatures:

• The product can change its state, insofar as ceasing to act as a lubricant, but partially transforming into a destructive solid.
• The product can vaporize, expand and blow the seal faces open leaving solids between the faces.
• The product can become viscous, interfering with the free movement of the springs and bellows.
• The product can become an adherent, gluing the lapped faces together or making the moveable components inoperable.
• The product can crystallize interfering with the moving parts of the seal.
• Excessive heat can cause the product to build a film on the faces (hot oil as an example) impeding sliding of the components and making them inoperable.
• Corrosion increases with increasing temperatures.
• Thermal expansion may cause seal faces to go out of flat, loosening of pressed-in carbon faces in their holder, and sticking of the bellows’ vibration dampers to the shaft sleave and opening the faces.
• Heat can damage the faces of the plated materials and filled carbon face types.
• Expansion of air pockets in some carbon faces can cause pits in the lapped faces.
• High heat levels can cause elastomers to experience compression set problems, resulting in leakage or in some cases complete failure.

A change in stuffing box pressures can cause:

• The product to vaporize opening the lapped faces.
• O-rings and other elastomer designs to extrude and jam the sliding components.
• Lapped seal faces to distort and go out of flat.
• A stuffing box vacuum that can blow open unbalanced seals.
• A differential pressure across the elastomer that can cause ethylene oxide to penetrate into the elastomer and destroy it as it expands in the lower pressure side.

When monitoring temperature and pressure in the stuffing box area we will note these changes. Then, by applying our knowledge based rules, we will have adeqate time to react before seal failure occurs. Knowledge based rules form our CBM policy. Without a CBM policy, regardless of the number of sensors scattered throughout our process, the amount of data storage capacity, or the sophistication of the software “shell”, our CBM program will ultimately prove ineffective.