Process Plant Equipment Operation Control And Reliability Pdf
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- Reliability Engineering Principles for the Plant Engineer
- Process Plant Reliability and Maintenance Strategies - REL-5
- Process Plant Equipment – Mixers
Increasingly, managers and engineers who are responsible for manufacturing and other industrial pursuits are incorporating a reliability focus into their strategic and tactical plans and initiatives. With its origins in the aviation industry, reliability engineering, as a discipline, has historically been focused primarily on assuring product reliability. More and more, these methods are being employed to assure the production reliability of manufacturing plants and equipment — often as an enabler to lean manufacturing.
Reliability Engineering Principles for the Plant Engineer
Increasingly, managers and engineers who are responsible for manufacturing and other industrial pursuits are incorporating a reliability focus into their strategic and tactical plans and initiatives. With its origins in the aviation industry, reliability engineering, as a discipline, has historically been focused primarily on assuring product reliability.
More and more, these methods are being employed to assure the production reliability of manufacturing plants and equipment — often as an enabler to lean manufacturing. This article provides an introduction to the most relevant and practical of these methods for plant reliability engineering, including:.
The origins of the field of reliability engineering, at least the demand for it, can be traced back to the point at which man began to depend upon machines for his livelihood. Utilizing hydraulic energy from the flow of a river or stream, the Noria utilized buckets to transfer water to troughs, viaducts and other distribution devices to irrigate fields and provide water to communities. If the community Noria failed, the people who depended upon it for their supply of food were at risk.
Survival has always been a great source of motivation for reliability and dependability. While the origins of its demand are ancient, reliability engineering as a technical discipline truly flourished along with the growth of commercial aviation following World War II. It became rapidly apparent to managers of aviation industry companies that crashes are bad for business.
Karen Bernowski, editor of Quality Progress , revealed in one of her editorials research into the media value of death by various means, which was conducted by MIT statistic professor Arnold Barnett and reported in Barnett evaluated the number of New York Times front-page news articles per 1, deaths by various means. He found that cancer-related deaths yielded 0. The cost and high-profile nature of aviation related accidents helped to motivate the aviation industry to participate heavily in the development of the reliability engineering discipline.
Likewise, due to the critical nature of military equipment in defense, reliability engineering techniques have long been employed to assure operational readiness. Many of our standards in the reliability engineering field are MIL Standards or have their origins in military activities. Reliability engineering deals with the longevity and dependability of parts, products and systems.
More poignantly, it is about controlling risk. Reliability engineering incorporates a wide variety of analytical techniques designed to help engineers understand the failure modes and patterns of these parts, products and systems. Traditionally, the reliability engineering field has focused upon product reliability and dependability assurance. In recent years, organizations that deploy machines and other physical assets in production settings have begun to deploy various reliability engineering principles for the purpose of production reliability and dependability assurance.
These same organizations are beginning to adopt life cycle cost-based design and procurement strategies, change management schemes and other advanced tools and techniques in order to control the root causes of poor reliability. However, the adoption of the more quantitative aspects of reliability engineering by the production reliability assurance community has been slow. This is due in part to the perceived complexity of the techniques and in part due to the difficulty in obtaining useful data.
The quantitative aspects of reliability engineering may, on the surface, seem complicated and daunting. In reality, however, a relatively basic understanding of the most fundamental and widely applicable methods can enable the plant reliability engineer to gain a much clearer understanding about where problems are occurring, their nature and their impact on the production process — at least in the quantitative sense.
Used properly, quantitative reliability engineering tools and methods enable the plant reliability engineering to more effectively apply the frameworks provided by RCM, RCA, etc. However, engineers must be particularly clever in their application of the methods. The operating context and environment of a production process incorporates more variables than the somewhat one-dimensional world of product reliability assurance.
Despite the increased difficulty in applying quantitative reliability methods in the production environment, it is nonetheless worthwhile to gain a sound understanding of the tools and apply them where appropriate.
This article will provide an introduction to the most basic reliability engineering methods that are applicable to the plant engineer that is interested in production reliability assurance. It presupposes a basic understanding of algebra, probability theory and univariate statistics based upon the Gaussian normal distribution e. It should be made clear that this paper is a brief introduction to reliability methods.
It is by no means a comprehensive survey of reliability engineering methods, nor is it in any way new or unconventional. The methods described herein are routinely used by reliability engineers and are core knowledge concepts for those pursuing professional certification by the American Society for Quality ASQ as a reliability engineer CRE. Several books on reliability engineering are listed in the bibliography of this article. The author of this article has found Reliability Methods for Engineers by K.
Krishnamoorthi and Reliability Statistics by Robert Dovich to be particularly useful and user-friendly references on the subject of reliability engineering methods. Both are published by the ASQ Press. Before discussing methods, you should familiarize yourself with reliability engineering nomenclature.
For convenience, a highly abridged list of key terms and definitions is provided in the appendix of this article.
For a more exhaustive definition of reliability terms and nomenclature, refer to MIL-STD and other related standards. Many mathematical concepts apply to reliability engineering, particularly from the areas of probability and statistics. Likewise, many mathematical distributions can be used for various purposes, including the Gaussian normal distribution, the log-normal distribution, the Rayleigh distribution, the exponential distribution, the Weibull distribution and a host of others.
In the interest of brevity and simplicity, important mathematical concepts such as distribution goodness-of-fit and confidence intervals have been excluded.
The purpose for quantitative reliability measurements is to define the rate of failure relative to time and to model that failure rate in a mathematical distribution for the purpose of understanding the quantitative aspects of failure. The most basic building block is the failure rate, which is estimated using the following equation:. For example, if five electric motors operate for a collective total time of 50 years with five functional failures during the period, the failure rate is 0.
For items that are simply thrown away and replaced, we use the term MTTF. The computations are the same. The basic calculation to estimate mean time between failure MTBF and mean time to failure MTTF , both measures of central tendency, is simply the reciprocal of the failure rate function.
It is calculated using the following equation. The MTBF for our industrial electric motor example is 10 years, which is the reciprocal of the failure rate for the motors. Incidentally, we would estimate MTBF for electric motors that are rebuilt upon failure. For smaller motors that are considered disposable, we would state the measure of central tendency as MTTF.
The failure rate is a basic component of many more complex reliability calculations. The importance of failure rate vs. Figure 1. Different Failure Rates vs. Time Scenarios. Individuals that have received only basic training in probability and statistics are probably most familiar with the Gaussian or normal distribution, which is associated with familiar bell-shaped probability density curve. The Gaussian distribution is generally applicable to data sets where the two most common measures of central tendency, mean and median, are approximately equal.
Surprisingly, despite the versatility of the Gaussian distribution in modeling probabilities for phenomenon ranging from standardized test scores to the birth weights of babies, it is not the dominant distribution employed in reliability engineering.
The Gaussian distribution has its place in evaluating the failure characteristics of machines with a dominant failure mode, but the primary distribution employed in reliability engineering is the exponential distribution. Regrettably, the bathtub curve has been harshly criticized in the maintenance engineering literature because it fails to effectively model the characteristic failure rate for most machines in an industrial plant, which is generally true at the macro level.
We rarely see systemic time-based failures in industrial machines. Despite its limitations in modeling the failure rates of typical industrial machines, the bathtub curve is a useful tool for explaining the basic concepts of reliability engineering.
Figure 2. The human body is an excellent example of a system that follows the bathtub curve. People, and other organic species for that matter, tend to suffer a high failure rate mortality during their first years of life, particularly the first few years, but the rate decreases as the child grows older. Assuming a person reaches puberty and survives his or her teenage years, his or her mortality rate becomes fairly constant and remains there until age time dependent illnesses begin to increase the mortality rate wearout.
These factors can be metaphorically compared to factors that influence machine life. The exponential distribution, the most basic and widely used reliability prediction formula, models machines with the constant failure rate, or the flat section of the bathtub curve. Most industrial machines spend most of their lives in the constant failure rate, so it is widely applicable. Below is the basic equation for estimating the reliability of a machine that follows the exponential distribution, where the failure rate is constant as a function of time.
In our electric motor example, if you assume a constant failure rate the likelihood of running a motor for six years without a failure, or the projected reliability, is 55 percent. It is worth reiterating at this point that these calculations project the probability for a population.
Any given individual from the population could fail on the first day of operation while another individual could last 30 years.
That is the nature of probabilistic reliability projections. A characteristic of the exponential distribution is the MTBF occurs at the point at which the calculated reliability is In our motor example, after 10 years, In other words, the survival rate is We often speak of projected bearing life as the L10 life.
In reality, only a fraction of the bearings actually survive to the L10 point. The probability density function pdf , or life distribution, is a mathematical equation that approximates the failure frequency distribution. It is the pdf, or life frequency distribution, that yields the familiar bell-shaped curve in the Gaussian, or normal, distribution. Below is the pdf for the exponential distribution. In our example, if we assume a constant failure rate, which follows the exponential distribution, the life distribution, or pdf for the industrial electric motors, is expressed in Figure 3.
Yes, the failure rate is constant, but the pdf mathematically assumes failure without replacement, so the population from which failures can occur is continuously reducing — asymptotically approaching zero.
Figure 3. The Probability Density Function pdf. The cumulative distribution function cdf is simply the cumulative number of failures one might expect over a period of time. For the exponential distribution, the failure rate is constant, so the relative rate at which failed components are added to the cdf remains constant. However, as the population declines as a result of failure, the actual number of mathematically estimated failures decreases as a function of the declining population.
Much like the pdf asymptotically approaches zero, the cdf asymptotically approaches one Figure 4. Figure 4. Failure Rate and the Cumulative Distribution Function. The declining failure rate portion of the bathtub curve, which is often called the infant mortality region, and the wear out region will be discussed in the following section addressing the versatile Weibull distribution.
Originally developed by Wallodi Weibull, a Swedish mathematician, Weibull analysis is easily the most versatile distribution employed by reliability engineers.
Process Plant Reliability and Maintenance Strategies - REL-5
A round-the-clock operation of heavy equipment in harsh, gritty conditions is the nature of the coal mining and production business. Equipment failure is not an option for an industry that services customers around the world. Some of the components cost hundreds of thousands of dollars each and production downtime losses can be immeasurable. Figure 1: Equipment failure is not an option in surface mining operations. Reliability has long been in focus at one surface operation in the U. Condition monitoring of equipment began there 15 years ago to provide early detection of deterioration and avoid costly failures.
PROCESS PLANT EQUIPMENT. Operation, Control, and Reliability. Edited by. MICHAEL D. HOLLOWAY. CHIKEZIE NWAOHA. OLIVER A. ONYEWUENYI.
Process Plant Equipment – Mixers
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. The productivity of these investments is a fundamental element of competition among companies and nations. Events that slow or interrupt the manufacturing process or degrade the product impair the competitiveness of a manufacturing enterprise.
Ronald Frend X MR. He rose to a senior position in Shell International Middle East before opening a worldwide engineering consultancy based in England and a senior position in a global training enterprise. His entire career has been concerned with practical applications of maintenance and engineering from a solid business foundation. Ron is experienced in a variety of maintenance analytical techniques as well as possessing management skills suitable to a large multi-national corporation working in the oil and gas industry. Ron has also undergone specialized training on the following topics: management techniques, non-destructive testing, oil tanker cargo operations, instrumentation and control, resistance and gas welding, vibration analysis, infrared thermography, and ultrasonics.
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