In the appendix of Reliability-Centered Maintenance II, John Moubray lists 102 proactive maintenance techniques in six categories:
1. Dynamic effects: monitoring of vibration, pulses and acoustic emissions (17 techniques)
2. Particle effects: monitoring of particles in the component’s operating environment, e.g. lubricant (15 techniques)
3. Chemical effects: monitoring of chemical elements in the component’s operating environment, e.g. lubricant (27 techniques)
4. Physical effects: monitoring of surface cracks, fatigue, wear, etc (24 techniques)
5. Temperature effects: thermography (4 techniques)
6. Electrical effects: (15 techniques)
While extensive, Moubray’s list is by no means exhaustive. And new predictive techniques are being devised or invented all the time. But as Joel Levitt points out in his book Complete Guide to Preventative and Predictive Maintenance, the most fundamental of all predictive maintenance techniques is the visual inspection.
With this in mind, some of the things to check and look out for when inspecting a hydraulic machine include:
- The level of the hydraulic oil in the tank and its appearance. It should be ‘clear and bright’.
- Weeps and leaks – from component bodies, shaft seals, rod seals and connectors
- External surface condition of pipes, tubes and hoses
- Condition of cylinder rod-wiper seals
- Surface of cylinder rods (for dents, nicks and scores)
- Position of filter clogging indicators (pop-up type)
- Operating pressure readings (requires permanently installed gauges or test-points for quick checking)
- Operating oil temperature (requires a heat gun or installed thermocouple)
- Heat exchanger effectiveness (requires a heat gun–see page 103-104 of The Hydraulic Troubleshooting Handbook for a detailed procedure).
- Listen for abnormal noises
- Observe for smooth operation and record machine cycle times (requires a timer/stop-watch)
The other point about predictive maintenance tasks which is important to keep in mind, is that regardless of whether data is gathered through the use of a sophisticated piece of equipment, or from human senses alone, it is how the information is interpreted and reasoned from, which makes the task ‘predictive’. And not the execution of the task itself.
For example, a routine inspection of a hydraulic machine has determined pump noise level has increased and actuator (cylinder) movement is no longer smooth. One interpretation could be that the two symptoms are related, and the system has an air ingression problem. From this interpretation it is possible to predict the machine performance and reliability issues to follow: loss of lubricity, metal erosion resulting from bubble collapse, accelerated oxidation of the oil and dieseling in the cylinder resulting in damage to its seals. So the process is: data collection (task) > data interpretation > predictive reasoning.
Note too that data interpretation and predictive reasoning depend on context. Say the daily visual inspection reveals a filter’s visual clogging indicator has popped-up. Normally, no big deal. Unless that filter element was changed yesterday. Same data; different context; changed reasoning.
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