Achieving high availability levels of a deteriorating system by optimizing condition based maintenance policies

2013 ◽  
pp. 829-837
Author(s):  
A Platis ◽  
A Platis ◽  
S Malefaki
Keyword(s):  
High Availability ◽  
Condition Based Maintenance ◽  
Maintenance Policies

Mathematical Optimization Models for the Maintenance Policies in Production Systems

2018 ◽  
pp. 252-268
Author(s):  
Alperen Bal ◽  
Sule Itir Satoglu
Keyword(s):  
Preventive Maintenance ◽  
Production Systems ◽  
Mathematical Optimization ◽  
Condition Based Maintenance ◽  
Future Research ◽  
Reliability Level ◽  
Simulation Based ◽  
Maintenance Activities ◽  
Maintenance Policies ◽  
Critical Components

This chapter initially presents a brief information about production systems. At these systems, different types of maintenance policies are developed to cope with wear out failures. Mainly used maintenance policies can be classified as corrective, preventive, and condition-based maintenance. In the corrective maintenance, repair or replacement is applied whenever components of the machine breakdown. In the preventive maintenance approach maintenance activities are applied to the critical components on a periodic basis. On the other hand, maintenance activities are applied whenever critical reliability level is reached or exceeded. These types of maintenance policies are modeled using mathematical modeling techniques such as linear programming, goal programming, dynamic programming, and simulation. A review of current literature about the mathematical models, the simulation-based optimization studies examining these maintenance policies are categorized and explained. Besides, the solution methodologies are discussed. Finally, the opportunities for future research are presented.

Automated Train Brake Effectiveness (ATBE) Test Process at Canadian Pacific

2012 ◽  
Author(s):  
Abe Aronian ◽  
Michelle Jamieson ◽  
Kim Wachs
Keyword(s):  
Visual Inspection ◽  
Condition Based Maintenance ◽  
Braking Performance ◽  
Health Monitoring System ◽  
Detection Technology ◽  
Class 1 ◽  
Detector Output ◽  
Maintenance Policies ◽  
Technology Focus ◽  
Test Process

In 2011, Canadian Pacific (CP) implemented a new Automated Train Brake Effectiveness (ATBE) process for coal trains which replaces the visual Class 1 (No.1) Air Brake test required under Canada’s Department of Transport (Transport Canada – TC) regulations. The ATBE process relies on Wayside Detector technology to assess the operation of brakes on each railcar under dynamic conditions. CP began analyzing wayside detector information in 2008 as the basis for evaluating the braking performance of coal trains in Canadian Export service, specifically targeting existing Hot Box / Hot Wheel Detectors strategically situated alongside the track. Using the wayside detector output, the new ATBE process improves upon the visual No.1 Brake Test by evaluating brake effectiveness. The wayside detector information is automatically transmitted to a central Equipment Health Monitoring System after each train passing, where train brake effectiveness is evaluated and results published to mechanical maintenance facilities and train crews. The published results constitute the completed ATBE Test for the train. Given the substantial number of mechanical components requiring visual inspection each day by railway train inspectors, and taking into account the considerable investment CP has made into Wayside Detection technology, focus has moved towards Technology Driven Train Inspections (TDTI), preferring predictive, proactive maintenance practices and condition-based maintenance policies instead of the traditional reactive maintenance approach.

scholarly journals Condition-based maintenance policies for systems with multiple dependent components: A review

2017 ◽  
Vol 261 (2) ◽  
pp. 405-420 ◽  
Author(s):  
Minou C.A. Olde Keizer ◽  
Simme Douwe P. Flapper ◽  
Ruud H. Teunter
Keyword(s):  
Condition Based Maintenance ◽  
Maintenance Policies

Condition-based maintenance: an industrial application on rotary machines

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Antonio Acernese ◽  
Carmen Del Vecchio ◽  
Massimo Tipaldi ◽  
Nicola Battilani ◽  
Luigi Glielmo
Keyword(s):  
Ad Hoc ◽  
Rolling Bearing ◽  
Operating Conditions ◽  
Predictive Maintenance ◽  
Condition Based Maintenance ◽  
Training Data ◽  
Content Type ◽  
Rotary Machines ◽  
Maintenance Policies ◽  
Condition Indicator

PurposeThe purpose of this paper is to describe a model for the design and development of a condition-based maintenance (CBM) strategy for the cutting group of a labeling machine. The CBM aims to ensure the quality of labels' cut and overall machine performances.Design/methodology/approachIn developing a complete CBM strategy, two main difficulties have to be overcome: (1) appropriately dealing with incomplete and low-quality production database and (2) selecting the most promising predictive model. The first issue has been addressed applying data cleansing operations and creating ad hoc methodology to enlarge the training data. The second issue has been handled developing and comparing an empirical model with a machine learning (ML)-based model; the comparison has been performed assessing capabilities thereof in predicting erroneous label cuts on data obtained from an operating plant located in Italy.FindingsResearch results showed that both empirical and ML-based approaches exhibit good performances in detecting the operating conditions of the cutting machine. The advantage of adopting an ML-based model is that it can be used not only as a condition indicator (i.e. a model able to continuously provide the health status of an asset) but also in predictive maintenance policies (i.e. a CBM carried out following a forecast of the degradation of the item).Research limitations/implicationsThe study described in this manuscript has been developed on the practices of a labeling machine developed by an international company manufacturing bottling lines for beverage industry. The proposed approach might need some customization in case it is applied to other industries. Future researches can validate the applicability of such models on different rotary machines in other companies and similar industries.Originality/valueThe main contribution of this paper lies in the empirical demonstration of the benefits of CBM and predictive maintenance in manufacturing, through the overcoming of a specific production issue. The large number of variables involved in thin label cutting lines (film thickness between 30 and 38 µm), the high throughput and the high costs due to production interruptions render the prediction of non-conforming labels an economically relevant, albeit challenging, goal. Moreover, despite the large scientific literature on CBM in rolling bearing and face cutting movements, papers dealing with rotary labeling machines are very unusual and unique.

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