Dynamic Predictive Maintenance Model Based on Data-Driven Machinery Prognostics Approach

2011 ◽  
Vol 143-144 ◽  
pp. 901-906
Author(s):  
W.Z. Liao ◽  
Y. Wang
Keyword(s):  
Paradigm Shift ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Computational Results ◽  
Maintenance Policy ◽  
Model Based ◽  
Proposed Model ◽  
Maintenance Model

As an increasing number of manufacturers realize the importance of adopting new maintenance technologies to enable systems to achieve near-zero downtime, machinery prognostics which enables this paradigm shift from traditional fail-and-fix maintenance to a predict-and-prevent paradigm has arose interests from researchers. Machine's condition and degradation estimated by machinery prognostics approach can be used to support predictive maintenance policy. This paper develops a novel data-driven machine prognostics approach to assess machine's health condition and predict machine degradation. With this prognostics information, a predictive maintenance model is constructed to decide machine's maintenance threshold and predictive maintenance cycles number. Through a case study, this predictive maintenance model is verified, and the computational results show that this proposed model is efficient and practical.

Big Data Analytics for Predictive Maintenance Strategies

2017 ◽  
pp. 50-74 ◽  
Author(s):  
C. K. M. Lee ◽  
Yi Cao ◽  
Kam Hung Ng
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Maintenance Policy ◽  
Failure Patterns ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Faulty Component

Maintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.

scholarly journals A study of production and harvesting planning for the chicken industry

2018 ◽  
Vol 64 (No. 7) ◽  
pp. 316-327 ◽  
Author(s):  
You Peng-Sheng ◽  
Hsieh Yi-Chih
Keyword(s):  
Sensitivity Analysis ◽  
Production Planning ◽  
Programming Model ◽  
Computational Results ◽  
Mathematical Programming Model ◽  
Planning Period ◽  
Hybrid Heuristic ◽  
Proposed Model ◽  
Parameter Variations

To order to raise chickens for meat, chicken farmers must select an appropriate breed and determine how many broilers to raise in each henhouse. This study proposes a mathematical programming model to develop a production planning and harvesting schedule for chicken farmers. The production planning comprises the number of batches of chickens to be raised in each henhouse, the number of chicks to be raised for each batch, what breed of chicken to raise, when to start raising and the duration of the raising period. The harvesting schedule focuses on when to harvest and how many broilers to harvest each time. Our aim was to develop proper production and harvesting schedules that enable chicken farmers to maximise profits over a planning period. The problem is a highly complicated one. We developed a hybrid heuristic approach to address the issue. The computational results have shown that the proposed model can help chicken farmers to deal with the problems of chicken-henhouse assignment, chicken raising and harvesting, and may thus contribute to increasing profits. A case study of a chicken farmer in Yunlin County (Taiwan) was carried out to illustrate the application of the proposed model. Sensitivity analysis was also conducted to explore the influence of parameter variations.

Data-Driven Model Based Computation and Analysis of Operability Sets Using High-Dimensional Continuation: A Plant-Wide Case Study

2020 ◽  
Vol 59 (21) ◽  
pp. 10043-10060
Author(s):  
Utkarsh Konge ◽  
Abhishek Baikadi ◽  
Jayanth Mondi ◽  
Sivakumar Subramanian
Keyword(s):  
Data Driven ◽  
High Dimensional ◽  
Model Based

scholarly journals A multi-objective evolutionary algorithm-based soft computing model for educational data mining

2017 ◽  
Vol 12 (1) ◽  
pp. 106-123
Author(s):  
Choo Jun Tan ◽  
Ting Yee Lim ◽  
Chin Wei Bong ◽  
Teik Kooi Liew
Keyword(s):  
Student Achievement ◽  
Evolutionary Algorithm ◽  
Soft Computing ◽  
Real World ◽  
Classification Accuracy ◽  
Content Type ◽  
Computing Model ◽  
Model Based ◽  
Proposed Model

Purpose The purpose of this paper is to propose a soft computing model based on multi-objective evolutionary algorithm (MOEA), namely, modified micro genetic algorithm (MmGA) coupled with a decision tree (DT)-based classifier, in classifying and optimising the students’ online interaction activities as classifier of student achievement. Subsequently, the results are transformed into useful information that may help educator in designing better learning instructions geared towards higher student achievement. Design/methodology/approach A soft computing model based on MOEA is proposed. It is tested on benchmark data pertaining to student activities and achievement obtained from the University of California at Irvine machine learning repository. Additional, a real-world case study in a distance learning institution, namely, Wawasan Open University in Malaysia has been conducted. The case study involves a total of 46 courses collected over 24 consecutive weeks with students across the entire regions in Malaysia and worldwide. Findings The proposed model obtains high classification accuracy rates at reduced number of features used. These results are transformed into useful information for the educational institution in our case study in an effort to improve student achievement. Whether benchmark or real-world case study, the proposed model successfully reduced the number features used by at least 48 per cent while achieving higher classification accuracy. Originality/value A soft computing model based on MOEA, namely, MmGA coupled with a DT-based classifier, in handling educational data is proposed.

A Unifying Model for Mechatronic Systems Specification

2015 ◽  
Vol 789-790 ◽  
pp. 803-811
Author(s):  
Sabah Al-Fedaghi ◽  
Ahmed Abdullah
Keyword(s):  
Coherent System ◽  
System Specification ◽  
Mechatronic Systems ◽  
Model Based ◽  
Development Teams ◽  
Proposed Model ◽  
Baggage Handling ◽  
Specification Analysis

Over the years, mechatronic systems have witnessed an increase in complexity. To address this issue, a model-based approach has been utilized to produce coherent system specification. In model-based engineering, a system is depicted graphically and textually at various levels of granularity and complexity. For this purpose, Systems Modeling Language (SysML) is designed to support development stages in systems, including specification, analysis, design, and validation, and to generate specifications in a single language for use by heterogeneous development teams. Nevertheless, an underlying tool is lacking that would express the totality of a system’s processes and concepts, including mechanical, electrical, and informational aspects. SysML introduces a variety of diagrams and tools that are heterogeneous in notation and terms, e.g., use cases, blocks, activities, components, parameters, sequence, and so forth. This paper proposes a diagrammatic methodology to specify a unified conceptual map for mechatronic systems that can play the role of blueprint for a whole system at different stages of development. The paper focuses on using the proposed methodology as a specification tool, offering a new model that captures the dynamic behaviors of the system. The claim is that this proposed model for specification provides a nontechnical map of the system without a multiplicity of representations as in SysML. To demonstrate the viability of the model, it is applied to a case study of an airport baggage handling system.

Predictive Maintenance Information Systems

2020 ◽  
Vol 16 (2) ◽  
pp. 22-37 ◽  
Author(s):  
Michael Möhring ◽  
Rainer Schmidt ◽  
Barbara Keller ◽  
Kurt Sandkuhl ◽  
Alfred Zimmermann
Keyword(s):  
Information Systems ◽  
Predictive Maintenance ◽  
Data Driven ◽  
Service Provisioning ◽  
Process Automation ◽  
Research And Practice ◽  
Maintenance System ◽  
Advanced Analytics

Predictive maintenance has the potential to improve the reliability of production and service provisioning. However, there is little knowledge about the proper implementation of predictive maintenance in research and practice. Therefore, we conducted a multi-case study and investigated underlying conditions and technological aspects for implementing a predictive maintenance system and where it leads to. We found that predictive maintenance initiatives are triggered by severe impacts of failures on revenue and profit. Furthermore, successful predictive maintenance initiatives require that pre-conditions are fulfilled: Data must be available and accessible. Very important is also the support by the management. We identified four factors important for the implementation of predictive maintenance. The integration of data is highly facilitated by Cloud-based mechanisms. The detection of events is enabled by advanced analytics. The execution of predictive maintenance operations is supported by data-driven process automation and visualization.

Data-driven predictive maintenance policy based on multi-objective optimization approaches for the component repairing problem

2020 ◽  
pp. 1-20 ◽  
Author(s):  
Ornella Pisacane ◽  
Domenico Potena ◽  
Sara Antomarioni ◽  
Maurizio Bevilacqua ◽  
Filippo Emanuele Ciarapica ◽  
...  
Keyword(s):  
Predictive Maintenance ◽  
Data Driven ◽  
Multi Objective Optimization ◽  
Maintenance Policy ◽  
Multi Objective

Big Data Analytics for Predictive Maintenance Strategies

Web Services ◽  
2019 ◽  
pp. 1646-1665
Author(s):  
C. K. M. Lee ◽  
Yi Cao ◽  
Kam Hung Ng
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Maintenance Policy ◽  
Failure Patterns ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Faulty Component

Maintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.

INTEGRATED PRODUCTION MAINTENANCE MODEL UNDER IMPERFECT AGE-BASED MAINTENANCE POLICY AND NON-NEGLIGIBLE MAINTENANCE TIMES

2010 ◽  
Vol 27 (04) ◽  
pp. 539-558 ◽  
Author(s):  
SAMI EL-FERIK ◽  
MOHAMMED BEN-DAYA
Keyword(s):  
Model Parameters ◽  
Optimal Solutions ◽  
Maintenance Policy ◽  
Fixed Amount ◽  
Integrated Production ◽  
Production Run ◽  
Proposed Model ◽  
Production Period ◽  
Maintenance Model ◽  
Random Nature

This paper deals with an integrated production maintenance model where preventive maintenance (PM) activities are carried out at the end of each production run or at failure, whichever of them occurs first. PM activities take a fixed amount of time. However, due to the random nature of failures, the stock built during the production period may deplete before the next production run starts, leading to some loss in demand. After N production runs, the process is replaced (or undergoes a major repair), which brings it to the "as-good-as-new" condition. A mathematical model is derived for this problem and a model analysis conducted to characterize optimal solutions. Numerical examples are used to illustrate the proposed model and conduct a sensitivity analysis with respect to key model parameters.

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