tasks
Once an RCM analysis is complete, there are several principle risk management strategies that are recommended. Some of these are:
•On-condition maintenance tasks, i.e. condition monitoring
•Scheduled restoration or replacement maintenance tasks, i.e. preventive maintenance
•Failure-finding maintenance tasks, for example, checking a steam turbine overspeed trip system to ensure it is functioning properly
•One-time changes to the “system” (e.g., changes to hardware design, operations)
•Run to failure
The approved risk management strategies are then judiciously folded into an integrated maintenance plan that will provide an acceptable level of process reliability, with an acceptable level of risk, in an efficient and cost-effective manner. These scheduled maintenance plans usually include predictive maintenance program definition, such as vibration collection and analysis, and time-based maintenance activities, such as oil and filter replacements.
RCM emphasizes a combination of predictive maintenance (PdM) techniques with applicable and traditional preventive measures. These preventive measures include activities such as cleanings, inspections, oiling, and adjustments. The goal of PdM, or condition-based maintenance, is to assess the condition of equipment by performing periodic inspections—such as vibration analysis, temperature monitoring, oil analysis, ultrasonic analysis—or by using continuous (online) equipment such as vibration or temperature sensors. The primary tenet of the PdM philosophy is that it is more cost-effective to perform maintenance at a scheduled point in time than to risk running equipment until it loses performance capability and adversely affects the process. This view is in contrast to a time-based maintenance approach, where a piece of equipment gets maintained (i.e., overhauled or refurbished) at a prescribed time interval, whether it is warranted or not. Time-based maintenance is usually labor intensive and ineffective in identifying problems that develop between scheduled inspections; it has been proven not be cost-effective.
The purpose of most process machines is to transport liquids and gases efficiently from one point in the process to another. This action typically requires raising a fluid stream’s overall energy state by increasing its elevation, pressure, or velocity—or a combination of these fluid energy forms. Many different designs are utilized in process machinery; each design depends on the fluid being transported, the flow volumes required by the process, or the horsepower required for the task at hand. However, all machines are imperfect. Therefore, they are less than 100% efficient, which means that some of the horsepower provided by the driver (e.g., motor or turbine) is always converted into unusable forms of energy, such as vibration, pulsation, or heat. These tell-tale signs give us clues about the condition of operating machinery.
The majority of this book is dedicated to proven predictive maintenance techniques that can be employed on industrial machines. However, the aim of this book is to go beyond simple PdM methodologies by endorsing the machinery assessment approach. This more general term machinery assessment, frequently referred to in this book, is defined as a holistic approach that uses multiple predictive maintenance techniques and inspection methodologies to better evaluate and classify the condition of operating machines. Rarely does one machine condition parameter paint an accurate picture of overall health. The central belief of the machinery assessment approach is that a synergy is gained by using multiple evaluation methods to determine a machine’s mechanical condition. It is only by building a comprehensive view of the machine in its overall operating context that one can begin to understand if the machine is truly fit for its intended service.
Predictive maintenance methods depend heavily on monitoring systems that have the ability to accurately sense and report one or more key equipment condition indicators. Most monitoring systems have several distinct components (see Figure 2.1). The intent of the monitoring system is to take a physical event and convey that change so it can be observed over time; a decision can then be made as to the proper action to take.
1.They all have some type of sensor that detects and transmits a signal, usually a current or voltage, to the signal processor.
2.Next is a signal processor which receives the signal and converts it to a usable output signal. Signal processing can include filtering out unwanted portions of the input signal, converting the signal to a digital set of values, or calculating the average, maximum, or minimum value of a series of inputs. The design of signal processors are numerous and varied in purpose. In the end, you want the output of the signal processor to provide a useful output that can be displayed or used in a protection system or scheme.
Figure 2.1 Monitoring system schematic
Signal processors are designed to handle static and/or dynamic signals. An example of a static signal is temperature. If you plot a temperature over time, you typically get a gradually changing series of points that can visually be studied and analyzed. Static signals do not carry any rapidly changing, i.e. dynamic, components. On the other hand, dynamic signals can vary rapidly with time, as seen in Figure 2.2. Dynamic signals, also called dynamic waveforms, require more complex signal processing to determine their properties. Processing speed is critical when high speeds or high frequencies are involved. Typical waveform properties are frequency, peak amplitude, root mean square amplitude, and phase. In some cases, both static and dynamic information are extracted from the raw sensor data.
In Figure 2.2, Waveform A is the complex wave detected by the sensor that is to be processed. Waveforms B1, B2, and B3 are the fundamental sine wave components of the original complex wave that are determined by the signal analysis process.
Figure 2.2 Complex dynamic waveform decomposed into sine wave components
3.The signal processor then sends an output signal to the display or monitor that receives the output from the signal processor and displays in a way that is easy to interpret, as seen in the heart monitor in Figure 2.4. Displays can use dials, scales, simple numerical displays, or waveforms to communicate the status of variable being measured.
Sophisticated monitoring systems can also have internal storage capabilities in order to provide a means of trending and comparing the present status with the past. This capability is a must in critical applications.
Data presentation
Figure 2.3 illustrates two trend plot examples. One plot is that of a gradually increasing value whereas the other shows a step change in a measured value. Trend plots are useful because they provide visual representations of the measured parameter over time; this representation can help in the troubleshooting process. Suppose a step change occurred at the same time as a change in the process; there may be a correlation between the two events that should be investigated.
