Global competitive pressures have increased demands to keep plants running better, longer and more cost effectively by reducing unscheduled downtime and boosting uptime for machinery assets. Much of the responsibility to optimize asset efficiency falls on maintenance staffs. Yet they face numerous challenges in achieving the goal. For a variety of reasons personnel may not be able to follow precision maintenance practices to the letter; equipment maintenance is becoming more complicated; and environmental and safety laws have grown stricter.
The result has been sustained interest in proactive maintenance programs to help achieve equipment reliability objectives. Condition Monitoring (CM) can play a crucial role in proactive maintenance.
CM involves regularly measuring physical parameters such as vibration, noise, lubricant properties and temperature via non-invasive methods, usually during normal operation of equipment. CM makes it possible to detect machine and component problems before they can result in unexpected downtime and the high costs associated with interruptions in production.
CM ultimately can serve as a platform for implementing a condition-based maintenance program — scheduling maintenance, inspection and overhaul based on machine condition instead of the calendar. The goal is to trend and analyze data to identify troublesome conditions and detect early stages of component degradation. Then, remedial action can be taken to prevent failures and reduce unanticipated downtime.
Many plants already rely on some CM methods, particularly overall vibration monitoring and lubricant analysis. However, the CM toolbox also includes under-appreciated techniques such as time domain analysis and bump testing. So, we’ll look at what various methods involve and the insights they provide.
The sidebar gives some pointers for making the most of these techniques. They (and others) can help promote successful CM programs.
Vibration can be defined as the behavior of a machine’s mechanical components in response to internal or external forces. Because most rotating-equipment problems cause excessive vibration, this operating parameter generally is considered the best to initially assess a machine’s condition. Vibration monitoring can detect fault conditions such as imbalance, misalignment, rolling bearing degradation, mechanical looseness, structural resonance and soft foundation.
When analyzing vibration, frequency and amplitude of the signal should be evaluated.
The frequency at which the vibration occurs indicates the type of fault (certain types of faults typically occur at certain frequencies). By establishing the frequency, a clearer picture can emerge regarding cause.
Amplitude typically determines the severity of the fault (the higher the amplitude, the higher the vibration and the bigger the problem). Amplitude depends on the size of the machine and must be considered relative to the vibration level of the fully functioning equipment.
A typical starting point is to trend a machine’s overall vibration level. This is the total vibration energy measured within a specific frequency range. In the case of a rotor, for example, the overall vibration would be measured and then compared with its normal value to assess any inconsistencies. A higher-than-normal overall vibration reading would indicate that “something” is affecting the rotor. Further analysis can identify the actual cause.
Figure 1. This small hand-held monitoring tool provides a convenient means to collect data on overall vibration.
Hand-held units such as low-cost vibration pens (Figure 1), overall vibration meters or more sophisticated portable data collectors (Figure 2) and related instruments combining compact size with data storage capabilities make data collection for overall vibration analysis easy. Other options include online surveillance systems to perform round-the-clock monitoring of machinery, regardless of equipment location. This type of technology has been highly engineered to collect data continuously (or at a predetermined data-collection frequency) from permanently installed sensors. Findings then are transmitted to a host computer for subsequent analysis.