Leverage of Multiple Predictive Maintenance Technologies in Root Cause Failure Analysis of Critical Machineries

CATEGORY: PREDICTIVE MAINTENANCE
Leverage of Multiple Predictive Maintenance Technologies in Root Cause Failure Analysis of Critical Machineries

Type	Journal Article
Author	Pramod K. Behera
Author	Bhabani S. Sahoo
URL	Free Full Text Source:  http://www.sciencedirect.com/science/article/pii/S1877705816303605
Series	International Conference on Vibration Problems 2015
Volume	144
Pages	351-359
Publication	Procedia Engineering
Date	2016
Abstract	Globally, the process/manufacturing industries are undergoing a traumatic transition from buoyancy a few years back, to the paroxysm of low off take, rising input price and an adverse business climate menacing their growth prospect. Maximizing asset productivity and ensuring that the associated processes are as efficient as possible are key aspects for organizations striving to gain their strong financial returns. Maintenance costs are one of the largest factors impacting a plant's budget. Attempts to reduce these costs have led to the development of several maintenance strategies. To achieve reliability of process/system, effective utilization of predictive maintenance has proven to be very effective and beneficial. Predictive maintenance can contribute a lot in improving the bottom line through eliminating unscheduled outages of machine/processes, optimizing the process parameters, improving reliability and productivity. This paper showcases the odyssey of successful implementation of various Predictive maintenance technologies adopted in critical plant assets.

Identifying Maintenance Actions Using Portable Lubrication Analytical Instrumentation for Maintenance Application

CATEGORY: PREDICTIVE MAINTENANCE
Identifying Maintenance Actions Using Portable Lubrication Analytical Instrumentation for Maintenance Application

Type	Book Section
Author	Adrian Chaplin
Author	Frances Hardiman
URL	http://link.springer.com/chapter/10.1007/978-981-10-1088-0_23
Publisher	Springer Singapore
Pages	301-315
Date	2016
Abstract	Machine efficiency can be improved by adapting maintenance practices. Organizations have adapted a proactive approach to maintenance by incorporating predictive maintenance and condition monitoring techniques. Machinery and equipment must operate continuously for organizations to achieve maximum capabilities. Recent advanced analytical instrumentation for machinery maintenance has become more compact and portable, and is able to withstand the harsh continuous cycling of machinery. Authors describe one of the latest and most sophisticated analytical instruments, namely, a portable Fourier Transform Infrared (FTIR) oil analysis instrument. It can be incorporated into a predictive maintenance program. They present a case study on how maintenance personnel perform when analyzing used engine oils. Post training interviews established that experienced maintenance personnel struggled to identify a maintenance action from the results of the portable instrument. Consequently, further training was devised to overcome these difficulties. The research reveals a deficiency in maintenance training regimes.
Book Title	Transactions on Engineering Technologies

Methodology of Predictive Maintenance of Pipelines

CATEGORY: PREDICTIVE MAINTENANCE
Methodology of Predictive Maintenance of Pipelines

Type	Book Section
Author	Sviatoslav Timashev
Author	Anna Bushinskaya
URL	http://link.springer.com/chapter/10.1007/978-3-319-25307-7_9
Series	Topics in Safety, Risk, Reliability and Quality
Pages	361-405
Date	2016
Series Number	30
Abstract	Practical predictive maintenance and repair methodology for pipeline systems is based on the theory of the two-(multilevel) policy of managing reliability of mechanical systems proposed in Timashev, (Reliability of Large Mechanical Systems), for various conditional and physical (real-life) failure criteria.

Application of Artificial Neural Networks in Condition Based Predictive Maintenance

CATEGORY: PREDICTIVE MAINTENANCE
Application of Artificial Neural Networks in Condition Based Predictive Maintenance

Type	Book Section
Author	Jiri Krenek
Author	Kamil Kuca
URL	http://link.springer.com/chapter/10.1007/978-3-319-31277-4_7
Series	Studies in Computational Intelligence
Pages	75-86
Date	2016
Series Number	642
Abstract	Authors review various techniques of maintenance, artificial neural networks (ANN) and their various applications in fault risk assessment and early fault detection analysis. The predictive maintenance focuses on production facilities supplying in long supplier chains of automotive industry to ensure the reliable and continuous production and on-time deliveries. ANN offer a powerful tool to evaluate machine data and parameters which can learn from process data of fault simulation. Their review concludes with examples of the use of ANN in specific predictive maintenance cases.

Predictive maintenance of power industrial electronic equipment

CATEGORY: PREDICTIVE MAINTENANCE
Predictive maintenance of power industrial electronic equipment

Type	Journal Article
Author	George Robert Şişman
Author	Mihai Oproescu
URL	Free Full Text Source:  http://jeeeccs.net/index.php/journal/article/view/18
Volume	1
Issue	2
Pages	21-30
Publication	Journal of Electrical Engineering, Electronics, Control and Computer Science
Date	2016/02/25
Abstract	Presents the concepts and equipment which is used in achieving predictive maintenance. Two case studies are presented here. The first shows the concept of maintenance for a single phase inverter based on tree analysis technique. This technique is based on the relation between the symptoms, the causes and the defects for the inverter, but can be extrapolated to another type of electronic equipment as well. The second study presents the equipment for achieving the predictive maintenance for a combination of electrical and electronic equipment. The SIMOCOD software module has the capability to make the on-line diagnosis. The diagnosis module for a 3 phase motor asynchronous is detailed in this study, highlighting the features and advantages in use it.