Big Data Analytics for Predictive Maintenance Strategies

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.

Download Full-text

Real-Time Maintenance Policy in Manufacturing Systems With Intermediate Buffers

Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing ◽

10.1115/msec2015-9441 ◽

2015 ◽

Author(s):

Xi Gu ◽

Xiaoning Jin ◽

Jun Ni

Keyword(s):

Real Time ◽

Manufacturing Systems ◽

Maintenance Policy ◽

Discrete Time Markov Chain ◽

Markov Decision ◽

Maintenance Decision ◽

Maintenance Policies ◽

Time Buffer ◽

Buffer Capacities ◽

One Machine

Real-time maintenance decision making in large manufacturing system is complex because it requires the integration of different information, including the degradation states of machines, as well as inventories in the intermediate buffers. In this paper, by using a discrete time Markov chain (DTMC) model, we consider the real-time maintenance policies in manufacturing systems consisting of multiple machines and intermediate buffers. The optimal policy is investigated by using a Markov Decision Process (MDP) approach. This policy is compared with a baseline policy, where the maintenance decision on one machine only depends on its degradation state. The result shows how the structures of the policies are affected by the buffer capacities and real-time buffer levels.

Download Full-text

Maintenance Decision Model for a Two-Machine Production Line With Multistage Degradation Machines

Volume 3: Manufacturing Equipment and Systems ◽

10.1115/msec2018-6633 ◽

2018 ◽

Author(s):

Yunyi Kang ◽

Feng Ju

Keyword(s):

Decision Model ◽

Production Systems ◽

Maintenance Policy ◽

Multi Stage ◽

Markov Decision ◽

Optimal Maintenance ◽

Markov Decision Model ◽

Maintenance Decision ◽

Maintenance Policies ◽

Optimal Maintenance Policy

In this work, we develop preventative maintenance policies on two-machine-and-one-buffer production systems with machines subject to multi-stage degradation. Condition-based maintenance policies are generated for both machines, with consideration on both the machine degradation stages and the buffer level. Moreover, the policies are flexible, allowing a machine to be recovered to any better operating state, while merely recovering to the best operating state is possible in many previous work. A Markov decision model is formulated to find the optimal maintenance policy and computational experiments show that the policies improve the performance of a system in finite production runs.

Download Full-text

Considering machine health condition in jointly optimizing predictive maintenance policy and X-bar control chart

2017 International Conference on Grey Systems and Intelligent Services (GSIS) ◽

10.1109/gsis.2017.8077727 ◽

2017 ◽

Cited By ~ 1

Author(s):

Yaping Li ◽

Ershun Pan ◽

Zhen Chen

Keyword(s):

Control Chart ◽

Health Condition ◽

Predictive Maintenance ◽

Maintenance Policy ◽

Machine Health

Download Full-text

Novel holistic architecture for analytical operation on sensory data relayed as cloud services

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v10i4.pp4322-4330 ◽

2020 ◽

Vol 10 (4) ◽

pp. 4322

Author(s):

Manujakshi B. C ◽

K. B. Ramesh

Keyword(s):

Big Data ◽

Data Analytics ◽

Large Scale ◽

Big Data Analytics ◽

Cost Effective ◽

Cloud Services ◽

Sensor Nodes ◽

Sensory Data ◽

Energy Constrained ◽

Analytical Operation

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system.

Download Full-text

Big Data Analytics and Processing Platform in Czech Republic Healthcare

Applied Sciences ◽

10.3390/app10051705 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1705

Author(s):

Martin Štufi ◽

Boris Bačić ◽

Leonid Stoimenov

Keyword(s):

Big Data ◽

Czech Republic ◽

Data Analytics ◽

High Performance ◽

Big Data Analytics ◽

Cost Effective ◽

The European Union ◽

Production Environment ◽

The Czech Republic ◽

Data Mining Algorithms

Big data analytics (BDA) in healthcare has made a positive difference in the integration of Artificial Intelligence (AI) in advancements of analytical capabilities, while lowering the costs of medical care. The aim of this study is to improve the existing healthcare eSystem by implementing a Big Data Analytics (BDA) platform and to meet the requirements of the Czech Republic National Health Service (Tender-Id. VZ0036628, No. Z2017-035520). In addition to providing analytical capabilities on Linux platforms supporting current and near-future AI with machine-learning and data-mining algorithms, there is the need for ethical considerations mandating new ways to preserve privacy, all of which are preconditioned by the growing body of regulations and expectations. The presented BDA platform, has met all requirements (N > 100), including the healthcare industry-standard Transaction Processing Performance Council (TPC-H) decision support benchmark in compliance with the European Union (EU) and the Czech Republic legislations. Currently, the presented Proof of Concept (PoC) that has been upgraded to a production environment has unified isolated parts of Czech Republic healthcare over the past seven months. The reported PoC BDA platform, artefacts, and concepts are transferrable to healthcare systems in other countries interested in developing or upgrading their own national healthcare infrastructure in a cost-effective, secure, scalable and high-performance manner.

Download Full-text

Cost-Effective Cloud Server Provisioning for Predictable Performance of Big Data Analytics

IEEE Transactions on Parallel and Distributed Systems ◽

10.1109/tpds.2018.2873397 ◽

2019 ◽

Vol 30 (5) ◽

pp. 1036-1051 ◽

Cited By ~ 12

Author(s):

Fei Xu ◽

Haoyue Zheng ◽

Huan Jiang ◽

Wujie Shao ◽

Haikun Liu ◽

...

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Cost Effective ◽

Cloud Server

Download Full-text

Big-Data Analytics for Predictive Maintenance Modeling: Challenges and Opportunities

Journal of Petroleum Technology ◽

10.2118/1016-0071-jpt ◽

2016 ◽

Vol 68 (10) ◽

pp. 71-72 ◽

Cited By ~ 1

Author(s):

Adam Wilson

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Predictive Maintenance ◽

Challenges And Opportunities

Download Full-text

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.143-144.901 ◽

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.

Download Full-text