scholarly journals A DATA DRIVEN DECISION MAKING APPROACH FOR LONG-WALL MINING PRODUCTION ENHANCEMENT

Mining Scince ◽  
2019 ◽  
Vol 26 ◽  
Author(s):  
Amid Morshedlou ◽  
Hesam Dehghani ◽  
Hadi Hoseinie
Keyword(s):  
Preventive Maintenance ◽  
Conveyor Belt ◽  
Data Driven ◽  
Computational Results ◽  
Production Chain ◽  
Maintenance Schedule ◽  
Average Production ◽  
Drum Shearer ◽  
Critical Components ◽  
Production Losses

Machine failures have destructive effects on continuity of operation and lead to production losses in long-wall mines, making proper maintenance scheduling essential. This paper models the reliability of the whole production chain in an Iranian long-wall mine including the drum shearer, Armored Face Conveyor (AFC), hydraulic powered supports, Beam Stage Loader (BSL), and main conveyer belt. Analyzing the computational results and failure frequencies, we rank the critical components and develop a reliability-based preventive maintenance schedule for all equipment. In respect to the data classification, conveyor belt with failure abundance of 41.5 percent is the most critical, while powered supports with the failure abundance of 1.2 percent shows the best performance. Approximately, the reliability of the production process after four hours reaches nearly to zero. Implementing the schedule, computational results suggest an increase of approximately 67.7 percent in the average production per shift.

scholarly journals Vulnerability based management of water resources systems

2004 ◽  
Vol 6 (2) ◽  
pp. 133-156 ◽  
Author(s):  
V. K. Kanakoudis
Keyword(s):  
Water Supply ◽  
Preventive Maintenance ◽  
Water System ◽  
Supply System ◽  
Water Supply System ◽  
Maintenance Policy ◽  
Supply Networks ◽  
Water Resources Systems ◽  
Maintenance Schedule ◽  
Preventive Maintenance Policy

Must the water networks be fail-proof or must they remain safe during a failure? What must water system managers try to achieve? The present paper introduces a methodology for the hierarchical analysis (in time and space) of the preventive maintenance policy of water supply networks, using water supply system performance indices. This is being accomplished through a technical–economic analysis that takes into account all kinds of costs referring to the repair or replacement of trouble-causing parts of the water supply network. The optimal preventive maintenance schedule suggested by the methodology is compared with the empirically based maintenance policy applied to the Athens water supply system.

scholarly journals Coal Flowability Control on Preventing Spillage on Conveyor Belt Through Modeling and Simulation for Improving its Performance

2021 ◽  
Vol 90 (1) ◽  
pp. 130-145
Author(s):  
Sabri Bahrun ◽  
Mohd Shahrizan Yusoff ◽  
Mohamad Sazali Said ◽  
Azmi Hassan
Keyword(s):  
Bulk Material ◽  
Extraction Process ◽  
Conveyor Belt ◽  
Underground Mines ◽  
Mining Site ◽  
Dem Simulation ◽  
Conveyor System ◽  
Average Production ◽  
And Performance ◽  
Belt Conveyors

Belt conveyors are generally used in mining plant areas, both surface and underground mines. The belt conveyor is mainly applied to transport the extracted bulk material from the mining site to delivery. The effectiveness of the extraction process depends on the reliability and durability of the conveyor belt system. In addition, conveyor performance is very important specially to control material flowability to prevent spills or other operational disturbances to optimize production throughput. However, the transfer chute and settling zone can cause some problems during the transfer process, such as material spills. This problem can reduce the function and performance of the conveyor belt. This paper discusses a design model to reduce the problem of spillage in the settling zone. The model was developed by compiling the previous defecting data from the durability of the conveyor system, then analyzed using Discrete Element Method (DEM) software and compared with bulk characteristics. The initial performance of certain conveyors is only capable of serving with an average production of 76% of the designed capacity while energy is consumed at full load. By applying the DEM simulation result, the blade gate can reduce the peak angle break in the depositional zone before exiting. After the analysis is completed using DEM, the conveyor increases the average production to 95% of the designed capacity. In conclusion, controlling the maximum belt load without spillage will reduce interruption on conveyor belt operation and maintenance costs therefore increase plant reliability and availability.

A Dynamic Reliability-Centered Maintenance Analysis Method for Natural Gas Compressor Station Based on Diagnostic and Prognostic Technology

2015 ◽  
Vol 138 (6) ◽  
Author(s):  
Dengji Zhou ◽  
Huisheng Zhang ◽  
Yi-Guang Li ◽  
Shilie Weng
Keyword(s):  
Natural Gas ◽  
Preventive Maintenance ◽  
Condition Based Maintenance ◽  
Equipment Management ◽  
Analysis Method ◽  
Dynamic Reliability ◽  
Maintenance Schedule ◽  
Reliability Centered Maintenance ◽  
Maintenance Decision ◽  
Maintenance Model

The availability requirement of natural gas compressors is high. Thus, current maintenance architecture, combined periodical maintenance and simple condition based maintenance, should be improved. In this paper, a new maintenance method, dynamic reliability-centered maintenance (DRCM), is proposed for equipment management. It aims at expanding the application of reliability-centered maintenance (RCM) in maintenance schedule making to preventive maintenance decision-making online and seems suitable for maintenance of natural gas compressor stations. A decision diagram and a maintenance model are developed for DRCM. Then, three application cases of DRCM for actual natural gas compressor stations are shown to validate this new method.

Optimizing Maintenance for Multi-Unit Industrial Gas Turbine Installations

2010 ◽  
Author(s):  
Markus Bohlin ◽  
Mathias Wa¨rja
Keyword(s):  
Natural Gas ◽  
Gas Turbine ◽  
Preventive Maintenance ◽  
Gas Turbines ◽  
Economic Effects ◽  
Negative Effects ◽  
Component Level ◽  
Maintenance Schedule ◽  
Natural Gas Pipelines ◽  
Oil Platforms

High levels of availability and reliability are essential in many industries where production is subject to high costs due to downtime. Examples where gas turbines are used include the mechanical drive in natural gas pipelines and power generation on oil platforms, where it is common to use redundant gas turbines to mitigate the effects of service outage. In this paper, component-level maintenance of parallel multi-unit systems is considered, allowing production at a reduced level when some of the units are not operational. Units are themselves assumed to be composed out of components in a serial configuration; maintenance of one component implies shutdown of the unit. Parallel installations allow maintenance to be performed on one or a few gas turbines without taking down the entire installation. This allows maintenance to be optimized even further than in a serial system. However, the maintenance optimization process is made more complicated, since there now exist both positive and negative grouping effects. The positive grouping effects come from shared setup activities and costs, and the negative effects come from resource limitations, in this case the limited number of gas turbines which can be maintained at the same time. In the approach presented in this paper, each component has its individual preventive maintenance schedule, which is updated at inspections, changes in production and when indicated using remote condition monitoring. A minimal repair model for noncritical routine inspections and service tasks is assumed, which does not affect component state. In addition, previously developed procedures for estimating and measuring residual component lifetime for individual components during operation are used. The procedures are based on a Retirement For Cause (RFC) approach where components are not replaced until a potential failure has been detected. To maximize revenues for an operator, the available information is evaluated using software where scenario analysis and optimization is performed. To show the possible economic effects, gas turbine operation data is used together with maintenance and operator requirements as input for optimization of a production line consisting of a natural-gas compressor station having three SGT-600 gas turbines. Savings can be substantial compared to a traditional preventive maintenance plan.

Modular Design of Data-Driven Analytics Models in Smart-Product Development

2017 ◽  
Author(s):  
Yunpeng Li ◽  
Utpal Roy ◽  
Jeffrey S. Saltz
Keyword(s):  
Product Development ◽  
Modular Design ◽  
Unmanned Aircraft ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Product Development Process ◽  
Smart Product ◽  
Critical Components ◽  
Aircraft System ◽  
Task Oriented

Data-driven analytics models have been built as critical components of a smart product to enable product autonomy and intelligence. Due in part to the dynamic nature of the machine-learning algorithms used in data-driven analytics models, the configuration of a smart product is frequently refined, often in a real-time context. Hence, a smart product requires a continuous evolution of its architecture. This paper proposes a systematic method to facilitate the modularization of an analytics model architecture, so that a modular smart-product architecture can be achieved. Productizing an analytics model transforms conventional task-oriented data analytics activities into a data product development process. Issues related to the standardization of analytics models, the modular design approaches, the modularity quantification, and their impacts on the overall smart product design, are discussed. The proposed method is applied to an unmanned aircraft system (UAS) design so that a modular UAS architecture can be configured for various mission applications.

scholarly journals Optimizing preventive maintenance policy: A data-driven application for a light rail braking system

2017 ◽  
Vol 231 (5) ◽  
pp. 534-545 ◽  
Author(s):  
Francesco Corman ◽  
Sander Kraijema ◽  
Milinko Godjevac ◽  
Gabriel Lodewijks
Keyword(s):  
Preventive Maintenance ◽  
Data Driven ◽  
Sequential Optimization ◽  
Maintenance Cost ◽  
Light Rail ◽  
Maintenance Policy ◽  
Maintenance Costs ◽  
Braking System ◽  
Optimal Maintenance Policy ◽  
Preventive Maintenance Policy

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.

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