scholarly journals Automation of Predictive Maintenance Using Internet of Things (IoT) Technology at University-Based O&M Project

2021 ◽  
Vol 11 (7) ◽  
pp. 332-336
Author(s):  
James Ryan Fernandez ◽  
◽  
Yogi Tri Prasetyo ◽  
Satria Fadil Persada ◽  
A. A. N. Perwira Redi
Keyword(s):  
Internet Of Things ◽  
Mobile Applications ◽  
Performance Indicator ◽  
Predictive Maintenance ◽  
Fault Prediction ◽  
Maintenance Cost ◽  
Maintenance Planning ◽  
Significant Deterioration ◽  
Sustainable Operations ◽  
Planning Framework

Predictive Maintenance can be defined as a type of advanced maintenance that detects the onset of system degradation allowing causal stressors to be eliminated or controlled prior to any significant deterioration in component physical state. Thru Internet of Things (IoT) Technology, automation, and implementation of Predictive Maintenance are possible. The purpose of this study is to propose the implementation of Predictive Maintenance using IpT Technology at University-based Operation & Maintenance Project that aims to transform the current Key Performance Indicator (KPI) of PM to CM Ratio from 80:20 to 90:10. Six Sigma DMAIC Methodology and Data-Driven Predictive Maintenance Planning Framework were utilized as the methodology of this research. Research’s results show that KPI, 90:10 (PM to CM Ratio) is achievable and maintenance cost can significantly reduce from 25% to 30%. Other valuable benefits are return of investment (10X), elimination of breakdown (70 - 75%), reduction in downtime (35% - 45%) and increase of production (20% - 25%). The proposed concept can be utilized in other industries to achieve high customer satisfaction percentages, sustainable operations, fault prediction, and online monitoring using PC or mobile applications.

scholarly journals A Study on the Predictive Maintenance Method of Fully Mechanized Mining Equipment based on Cost and Time Factors

2021 ◽  
Author(s):  
Xiangang Cao ◽  
Tianbo Xu ◽  
Youjun Zhao ◽  
Jiangbin Zhao ◽  
Yan Wang
Keyword(s):  
Time Factors ◽  
Predictive Maintenance ◽  
Maintenance Cost ◽  
Maintenance Planning ◽  
Mining Equipment ◽  
Cost Rate ◽  
Maintenance Time ◽  
Maintenance Cycle ◽  
Optimal Maintenance ◽  
Single Objective

Abstract In view of the problems of excessive maintenance and insufficient utilization of equipment service life caused by preventive maintenance of fully mechanized mining equipment with fixed cycle, a predictive maintenance method is proposed. Firstly, based on Weibull distribution function and evolution rules of equipment decay, the evolution model of equipment failure rate is established; Then, the single-objective decision-making models of equipment maintenance cost rate and maintenance downtime rate are established respectively. On this basis, the multi-objective predictive maintenance planning model of fully mechanized mining equipment with comprehensive cost and time factors is established, and the optimal predictive maintenance cycle planning sequence is obtained. Combined with the coal production continuation plan, this paper puts forward a method to determine the optimal maintenance time by making suitable choices between advance maintenance and delay maintenance. The result confirms the effectiveness and superiority of the proposed method.

scholarly journals A hybrid of convolutional neural network and long short-term memory network approach to predictive maintenance

2022 ◽  
Vol 12 (1) ◽  
pp. 721
Author(s):  
Ahmed Nasser ◽  
Huthaifa AL-Khazraji
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Predictive Maintenance ◽  
Fault Prediction ◽  
Gradient Boosting ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

<p>Predictive maintenance (PdM) is a successful strategy used to reduce cost by minimizing the breakdown stoppages and production loss. The massive amount of data that results from the integration between the physical and digital systems of the production process makes it possible for deep learning (DL) algorithms to be applied and utilized for fault prediction and diagnosis. This paper presents a hybrid convolutional neural network based and long short-term memory network (CNN-LSTM) approach to a predictive maintenance problem. The proposed CNN-LSTM approach enhances the predictive accuracy and also reduces the complexity of the model. To evaluate the proposed model, two comparisons with regular LSTM and gradient boosting decision tree (GBDT) methods using a freely available dataset have been made. The PdM model based on CNN-LSTM method demonstrates better prediction accuracy compared to the regular LSTM, where the average F-Score increases form 93.34% in the case of regular LSTM to 97.48% for the proposed CNN-LSTM. Compared to the related works the proposed hybrid CNN-LSTM PdM approach achieved better results in term of accuracy.</p>

R4 Model for Case-Based Reasoning and Its Application for Software Fault Prediction

2016 ◽  
Vol 8 (3) ◽  
pp. 19-38 ◽  
Author(s):  
Ekbal Rashid
Keyword(s):  
Software Quality ◽  
Similarity Measures ◽  
Fault Prediction ◽  
Maintenance Cost ◽  
Quality Level ◽  
Case Based Reasoning ◽  
Quality Prediction ◽  
Error Level ◽  
Software Fault Prediction ◽  
Case Based

Making R4 model effective and efficient I have introduced some new features, i.e., renovation of knowledgebase (KBS) and reducing the maintenance cost by removing the duplicate record from the KBS. Renovation of knowledgebase is the process of removing duplicate record stored in knowledgebase and adding world new problems along with world new solutions. This paper explores case-based reasoning and its applications for software quality improvement through early prediction of error patterns. It summarizes a variety of techniques for software quality prediction in the domain of software engineering. The system predicts the error level with respect to LOC and with respect to development time, and both affects the quality level. This paper also reviews four existing models of case-based reasoning (CBR). The paper presents a work in which I have expanded our previous work (Rashid et al., 2012). I have used different similarity measures to find the best method that increases reliability. The present work is also credited through introduction of some new terms like coefficient of efficiency, i.e., developer's ability.

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