Service-Oriented Predictive Maintenance for Large Scale Machines Based on Perception Big Data

2015 ◽  
Author(s):  
Bitao Yao ◽  
Zude Zhou ◽  
Wenjun Xu ◽  
Yilin Fang ◽  
Luyang Shao ◽  
...  
Keyword(s):  
Big Data ◽  
Preventive Maintenance ◽  
Large Scale ◽  
Complex Structure ◽  
Cloud Service ◽  
Predictive Maintenance ◽  
Global Environment ◽  
Extreme Condition ◽  
Word Cloud ◽  
Service Oriented

Large scale machines (LSMs) are always crucial equipments in manufacturing. Maintaining reliability, precision and safety for LSMs is very important. However, LSMs always work under extreme condition and are prone to degradation or failure. Therefore, maintenance is important for them. Compared with preventive maintenance, predictive maintenance is cost-saving. Besides, predictive maintenance is a more sustainable way by reducing failure and enhancing safety. Condition perception is needed in predictive maintenance. Due to the complex structure and large scale of LSMs, the perception data can be characterized as Big Data. Therefore, the storage and processing of Big Data needs to be integrated into maintenance. Considering that LSMs can be distributed all over the word, cloud service can be a proper way to support maintenance in a global environment. In this paper, a framework of service-oriented predictive maintenance for LSMs based on perception Big Data is synthesized to meet those demands. The methodologies are discussed as well. Finally, an industry case is studied to illustrate the implementing of predictive maintenance.

scholarly journals Military Simulation Big Data: Background, State of the Art, and Challenges

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Xiao Song ◽  
Yulin Wu ◽  
Yaofei Ma ◽  
Yong Cui ◽  
Guanghong Gong
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Rapid Development ◽  
Military Training ◽  
Great Success ◽  
Service Oriented Architectures ◽  
Weapon Systems ◽  
Service Oriented ◽  
High Resolution Models ◽  
Future Work

Big data technology has undergone rapid development and attained great success in the business field. Military simulation (MS) is another application domain producing massive datasets created by high-resolution models and large-scale simulations. It is used to study complicated problems such as weapon systems acquisition, combat analysis, and military training. This paper firstly reviewed several large-scale military simulations producing big data (MS big data) for a variety of usages and summarized the main characteristics of result data. Then we looked at the technical details involving the generation, collection, processing, and analysis of MS big data. Two frameworks were also surveyed to trace the development of the underlying software platform. Finally, we identified some key challenges and proposed a framework as a basis for future work. This framework considered both the simulation and big data management at the same time based on layered and service oriented architectures. The objective of this review is to help interested researchers learn the key points of MS big data and provide references for tackling the big data problem and performing further research.

Practical Secure Naïve Bayesian Classification Over Encrypted Big Data in Cloud

2017 ◽  
Vol 28 (06) ◽  
pp. 683-703 ◽  
Author(s):  
Youwen Zhu ◽  
Xingxin Li ◽  
Jian Wang ◽  
Yining Liu ◽  
Zhiguo Qu
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Large Scale ◽  
Cloud Service ◽  
Bayesian Classification ◽  
Training Data ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Security Proof ◽  
Data Owner

Cloud can provide much convenience for big data storage and analysis. To enjoy the advantage of cloud service with privacy preservation, huge data is increasingly outsourced to cloud in encrypted form. Unfortunately, encryption may impede the analysis and computation over the outsourced dataset. Naïve Bayesian classification is an effective algorithm to predict the class label of unlabeled samples. In this paper, we investigate naïve Bayesian classification on encrypted large-scale dataset in cloud, and propose a practical and secure scheme for the challenging problem. In our scheme, all the computation task of naïve Bayesian classification are completed by the cloud, which can dramatically reduce the burden of data owner and users. We give a formal security proof for our scheme. Based on the theoretical proof, we can strictly guarantee the privacy of both input dataset and output classification results, i.e., the cloud can learn nothing useful about the training data of data owner and the test samples of users throughout the computation. Additionally, we not only theoretically analyze our computation complexity and communication overheads, but also evaluate our implementation cost by leveraging extensive experiments over real dataset, which shows our scheme can achieve practical efficiency.

A Parallel Simulation Method for Performance Analysis of GNSS

2014 ◽  
Vol 522-524 ◽  
pp. 1187-1191
Author(s):  
Hong Tao Hou ◽  
Qun Li ◽  
Chao Wang ◽  
Qiang Chang ◽  
Wei Ping Wang
Keyword(s):  
Performance Analysis ◽  
Large Scale ◽  
Complex Structure ◽  
Model Development ◽  
Simulation Method ◽  
Satellite System ◽  
Parallel Simulation ◽  
Engineering System ◽  
Service Oriented ◽  
Global Navigation Satellite

In this paper, we proposed a parallel simulation method for performance analysis of the Global Navigation Satellite System (GNSS) based on simulation model portability 2(SMP2) and service-oriented modeling method. GNSS is a space engineering system with a large-scale and complex structure, and the proposed method can be used to construct large complex simulation systems to gain the reusability, composability and interoperability of heterogeneous simulation resources. Firstly, the method including the conceptual framework, system architecture and system engineering process is introduced. Then the parallel model development, composition and schedule method are detailed respectively. Finally, a distributed M&S environment based on service-oriented SMP2 is designed, and an example of navigation system volume simulation is given to validate the whole method.

scholarly journals Fleet maintenance using IOT technology

2021 ◽  
Vol 57 (9) ◽  
pp. 6251-6253
Author(s):  
Surabhi Hatagale, Dr. Ramkrishna Manatkar
Keyword(s):  
Big Data ◽  
Preventive Maintenance ◽  
Predictive Maintenance ◽  
Fleet Management ◽  
Maintenance Planning ◽  
Replacement Cost ◽  
Crucial Aspect

In fleet management, fleet maintenance is an important exposure to increase availability. Periodic and preventive maintenance is one such crucial aspect which is considered regardless of the practical faults which in sets of the need for repair and replacement cost as well time attached with it. With development in technology IOT and big data have been in talks. With all the data that is being produced predictive maintenance can be performed using this technology. IOT based predictive maintenance can increase fleet availability, stability and efficiency, reduced cost through effective maintenance  planning and eliminate unnecessary maintenance tasks.

Big Data Analytics in Cloud Platform

2021 ◽  
pp. 159-179
Author(s):  
Sathishkumar S. ◽  
Devi Priya R. ◽  
Karthika K.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
New Paradigm ◽  
Data Types ◽  
Data Intensive ◽  
Service Oriented ◽  
Big Data Computing

Big data computing in clouds is a new paradigm for next-generation analytics development. It enables large-scale data organizations to share and explore large quantities of ever-increasing data types using cloud computing technology as a back-end. Knowledge exploration and decision-making from this rapidly increasing volume of data encourage data organization, access, and timely processing, an evolving trend known as big data computing. This modern paradigm incorporates large-scale computing, new data-intensive techniques, and mathematical models to create data analytics for intrinsic information extraction. Cloud computing emerged as a service-oriented computing model to deliver infrastructure, platform, and applications as services from the providers to the consumers meeting the QoS parameters by enabling the archival and processing of large volumes of rapidly growing data faster economy models.

scholarly journals Research on Garment Mass Customization Architecture for Intelligent Manufacturing Cloud

2020 ◽  
Vol 179 ◽  
pp. 02125
Author(s):  
Li Zhe ◽  
Di Tao ◽  
Tian Huan
Keyword(s):  
Big Data ◽  
Mass Customization ◽  
Large Scale ◽  
Consumer Demand ◽  
Operation Mode ◽  
Intelligent Manufacturing ◽  
Innovation Model ◽  
Service Oriented ◽  
Traditional Manufacturing ◽  
Deep Integration

The deep integration of Internet, intelligent manufacturing and big data technology has promoted the development of products to be networked, digital, intelligent and personalized. The rapid iteration and differential segmentation of consumer demand has spawned new personalized consumer demand, transforming the traditional manufacturing model into a service-oriented manufacturing model. This paper analyses the large-scale customized operation mode of domestic and foreign clothing custom brands. In view of the transformation of traditional clothing industry, this paper proposes a solution to establish a large-scale custom clothing architecture under the vision of intelligent manufacturing cloud platform technology. This paper uses data mining and cloud computing and other methods to build an “Internet + manufacturing” innovation model with rapid collaboration under the umbrella of big data, and propose an architecture for mass customization of clothing, providing effective solutions and strategy recommendations for the transformation and upgrading of the traditional apparel industry.

A NOVEL ANALYTIC APPROACH FOR LARGE SCALE POWER PLANT WIDE PROCESSES WITH BIG DATA

2020 ◽  
Vol 9 (6) ◽  
pp. 3509-3517
Author(s):  
K. Malakonda Rayudu ◽  
A. Kumar
Keyword(s):  
Big Data ◽  
Power Plant ◽  
Large Scale ◽  
Analytic Approach

Multi Disease-Prediction Framework Using Hybrid Deep Learning: An Optimal Prediction Model (Preprint)

2020 ◽  
Author(s):  
Anusha Ampavathi ◽  
Vijaya Saradhi T
Keyword(s):  
Feature Extraction ◽  
Big Data ◽  
Deep Learning ◽  
Weight Function ◽  
Optimization Algorithm ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Disease Prediction ◽  
Health Care Decisions ◽  
Proposed Model

UNSTRUCTURED Big data and its approaches are generally helpful for healthcare and biomedical sectors for predicting the disease. For trivial symptoms, the difficulty is to meet the doctors at any time in the hospital. Thus, big data provides essential data regarding the diseases on the basis of the patient’s symptoms. For several medical organizations, disease prediction is important for making the best feasible health care decisions. Conversely, the conventional medical care model offers input as structured that requires more accurate and consistent prediction. This paper is planned to develop the multi-disease prediction using the improvised deep learning concept. Here, the different datasets pertain to “Diabetes, Hepatitis, lung cancer, liver tumor, heart disease, Parkinson’s disease, and Alzheimer’s disease”, from the benchmark UCI repository is gathered for conducting the experiment. The proposed model involves three phases (a) Data normalization (b) Weighted normalized feature extraction, and (c) prediction. Initially, the dataset is normalized in order to make the attribute's range at a certain level. Further, weighted feature extraction is performed, in which a weight function is multiplied with each attribute value for making large scale deviation. Here, the weight function is optimized using the combination of two meta-heuristic algorithms termed as Jaya Algorithm-based Multi-Verse Optimization algorithm (JA-MVO). The optimally extracted features are subjected to the hybrid deep learning algorithms like “Deep Belief Network (DBN) and Recurrent Neural Network (RNN)”. As a modification to hybrid deep learning architecture, the weight of both DBN and RNN is optimized using the same hybrid optimization algorithm. Further, the comparative evaluation of the proposed prediction over the existing models certifies its effectiveness through various performance measures.

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