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ОглавлениеA Brief Introduction to Machinery Monitoring
To survive, a processing facility must be profitable. To thrive, a processing facility must incessantly strive to become more and more profitable in order to sell products for a lower price than their competitors. To maintain a sustainable competitive edge, process owners must always be on the lookout for smarter ways to increase yields while reducing raw material costs, energy needs, maintenance costs, etc. This book is written for those working in organizations that wish to thrive and become industry leaders.
Because maintenance costs represent a significant portion of an organization’s expenses, maintenance budgets are constantly under scrutiny during budget review times. Everyone knows that lowering maintenance costs can have a major effect on the bottom line. However, blindly cutting maintenance efforts without carefully weighing the potential effects can be costly to the overall bottom line and drastically affect the site’s risk profile. Every organization must choose whether they will maintain their process facility proactively or reactively.
Rotating machinery represents a major source of expense to a maintenance organization due to both their complexity and their labor-intensive nature. The machinery maintenance budgets are often seen as having “low hanging fruit” opportunities; therefore, they are targeted for review. Any modifications to a machinery maintenance program must be carefully evaluated and approved by machinery professionals. Poorly managed rotating machinery can devastate a process organization by adversely affecting process availability, safety, and efficiency.
A powerful methodology called Reliability Centered Maintenance, RCM, is aimed at establishing a safe minimum level of maintenance and focusing key maintenance resources specifically toward mission critical equipment, such as process machinery. Reliability centered maintenance is an engineering framework that helps establish a complete maintenance philosophy and organization. It employs a structured framework for analyzing the functions and potential failures for a physical asset (such as a pump, compressor, or gas turbine). Its primary focus is preserving system functions rather than preserving equipment. The promise of RCM is reduced maintenance costs and improved equipment availability.
Some key steps of RCM include:
1.Identifying key machine functions
2.Determining machine criticality
3.Identifying functional failure modes and effects
4.Identifying failure consequences
5.Identifying how failures can be prevented and predicted
6.Identify the causes of failure
7.Selecting maintenance 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. A gradually increasing trend plot may indicate either a deteriorating internal or external component.
Figure 2.3 Trend Examples
4.Critical monitoring systems may also have built-in protection schemes, which can be readily programmed. These systems can provide either a remote or local alarm or alert whenever an undesirable condition has been detected.
5.Finally, for a monitoring system to be complete, assessment criteria are required to determine when the machine owner should be concerned or if action must be taken immediately. Without assessment criteria, there would be no need for monitoring systems because their outputs would be meaningless. If assessment criteria are set too low, then time and money are wasted. However, if assessment criteria are set too high, then human health, environment, and equipment are placed in jeopardy. One of the primary goals of this book is to provide proven and accepted assessment criteria to owners of process machinery.
Let’s put our newly acquired information about machinery monitoring knowledge to use by studying a hospital heart monitor (see Figure 2.4). Many of us have seen them in hospitals beeping and flashing incessantly. Their purpose is to continuously monitor key health parameters of patients in serious or critical conditions. If any of the key indicators are found to be outside normal values, an alarm will sound at a nurse’s station.
Figure 2.4 Typical heart monitor
Here, the patient is analogous to a machine; the heart monitoring system is analogous to a machine’s monitoring system, complete with remote alarms. Because this application is considered a critical monitoring one, i.e., a seriously ill human, continuous monitoring is employed with local and remote alarming capabilities.
Heart monitors typically include the following functions:
•Heart rate (dynamic data)—A heart monitor is a device that measures the heartbeat rate of a patient in real time. A pad with electrodes is placed on the patient’s chest. These electrodes must contact the skin directly in order to monitor the heart’s electrical voltages. Sensors detect a series of heart beats that are sent as raw signals to a signal processor, which processes the information and calculates the beat frequency. This heartbeat rate and digital waveform information are then sent to the monitor for display and storage.
•Blood pressure (dynamic data)—A blood pressure monitor must measure and report two key values that reflect blood pressure in a fractional form; one number on top and one on the bottom, e.g., 128/82. The number on top is called the systolic pressure, which is the pressure inside your blood vessels at the moment your heart beats. The number on the bottom is your diastolic pressure, which is the pressure in your blood vessels between heartbeats, when your heart is resting. The signal processor must be capable of detecting the maximum and minimum pressure in pressure waveform, storing the data, and then displaying on the monitor.
•Temperature (static data)—Temperature is probably the oldest indicator of health. By placing a thermometer, i.e., sensor, in the mouth or other location, we can assess the general health of the human body. It is common knowledge that 98.6°F (37°C) is an expected normal body temperature. Furthermore, any temperature over 100°F (37.8°C) is not expected and could be taken as an alarm, or alert, level. It is also useful to trend temperature to see when the body temperature began to rise. Gathering this information often requires some form of internal memory so that the data can be trended over time.
Together these functions provide a comprehensive package of monitors that work together to provide extensive protection. Like the heart monitor, individual monitoring systems can be combined to provide broader protection. It’s usually not enough to monitor only one parameter because people, as well as machines, are susceptible to failure in many different ways. We know from experience that some condition indicators are capable of detecting impending problems earlier than others. With prior knowledge of how people and machines announce their maladies, we can better design effective monitoring systems.
You don’t have to fully understand how monitoring systems sense, process, display, and save information in order to utilize their outputs. As a decision maker, you only need to ask some basic questions:
•What is normal for this machine parameter? Historical data is invaluable when answering this question.
•What has changed? Each machine parameter provides a different insight into what is happening. A baseline vibration spectrum, like the one shown in Figure 2.5, is invaluable when determining what has changed inside a given machine.
Figure 2.5 Machine system and signal sources
•What is the information telling me about the machine? If vibration levels have increased on the drive-end of a newly installed motor, you might want to check alignment.
•How quickly is the change occurring? A step change in temperature might be signaling a loss of cooling, whereas a gradually increasing trend might be telling you a bearing is beginning to fail.
•How quickly do I need to react to this information? Is the measurement value at the alarm or danger level?
We hope that the rest of this book will help you answer these questions and make better decisions.
