scholarly journals Computer Prediction Model for Equipment Maintenance Using Cloud Computing and Secure Data-sharing

2021 ◽  
Vol 2083 (4) ◽  
pp. 042042
Author(s):  
Kun Pan ◽  
Yuchen Jiang
Keyword(s):  
Cloud Computing ◽  
Feature Fusion ◽  
Big Data Analytics ◽  
Health Condition ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Computer Prediction ◽  
Useful Life ◽  
The Internet Of Things ◽  
Electrical Data

Abstract A With the popularization of automation in the industrial field, productivity has been greatly improved. However, manufacturing corporations are facing a data tsunami which brings new challenges to predictive maintenance (PdM). In recent years, many approaches and architecture for predictive maintenance have been proposed to solve some of these problems to varying degrees. This paper introduces a general framework based on the Internet of Things, cloud computing and big data analytics for PdM of industrial equipment. In this framework, smart sensors are installed on the device to obtain electrical data, which is then encrypted and uploaded to the cloud platform to predict the health condition by deep learning methods. Several working instances including feature selection, feature fusion, and Remaining Useful Life (RUL) prediction are provided. The effectiveness of the proposed methods is demonstrated by real-world cases.

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.

An Intelligent Prognostic System for Gear Performance Degradation Assessment and Remaining Useful Life Estimation

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Dong Wang ◽  
Qiang Miao ◽  
Qinghua Zhou ◽  
Guangwu Zhou
Keyword(s):  
Health Condition ◽  
Performance Degradation ◽  
Dynamic Bayesian Networks ◽  
Remaining Useful Life ◽  
Accelerated Life Test ◽  
Monte Carlo Algorithm ◽  
Residual Lifetime ◽  
Mechanical Transmission ◽  
Prognostic System ◽  
Useful Life

Gears are widely used in machines to transmit torque from one shaft to another shaft and to change the speed of a power source. Gear failure is one of the major causes for mechanical transmission system breakdown. Therefore, early gear faults must be immediately detected prior to its failure. Once early gear faults are diagnosed, gear remaining useful life (RUL) should be estimated to prevent any unexpected gear failure. In this paper, an intelligent prognostic system is developed for gear performance degradation assessment and RUL estimation. For gear performance degradation assessment, which aims to monitor current gear health condition, first, the frequency spectrum of gear acceleration error signal is mathematically analyzed to design a high-order complex Comblet for extracting gear fault related signatures. Then, two health indicators called heath indicator 1 and health indicator 2 are constructed to detect early gear faults and assess gear performance degradation, respectively, using two individual dynamic Bayesian networks. For gear RUL estimation, which aims to predict future gear health condition, a general sequential Monte Carlo algorithm is applied to iteratively infer gear failure probability density function (FPDF), which is used to predict gear residual lifetime. One case study is investigated to illustrate how the developed prognostic system works. The vibration data collected from a gearbox accelerated life test are used in this paper, where the gearbox started from a brand-new state, and ran until gear tooth failure. The results show that the developed prognostic system is able to detect early gear faults, track gear performance degradation, and predict gear RUL.

A Big Data implementation of the MANTIS reference architecture for predictive maintenance

2019 ◽  
Vol 233 (10) ◽  
pp. 1361-1375
Author(s):  
Felix Larrinaga ◽  
Javier Fernandez-Anakabe ◽  
Ekhi Zugasti ◽  
Iñaki Garitano ◽  
Urko Zurutuza ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Reference Architecture ◽  
Implementation Support ◽  
Physical Systems ◽  
Root Cause ◽  
Hadoop Distributed File System ◽  
Useful Life

This article presents the implementation of a reference architecture for cyber-physical systems to support condition-based maintenance of industrial assets. It also focuses on describing the data analysis approach to manage predictive maintenance of clutch-brake assets fleet over the previously defined MANTIS reference architecture. Proposals for both the architecture and data analysis implementation support working on Big Data scenarios, due to the usage of related technologies, such as Hadoop Distributed File System, Kafka or Apache Spark. The techniques are (1) root cause analysis powered by attribute-oriented induction clustering and (2) remaining useful life powered by time series forecasting. The work has been conducted in a real use case within the H2020 European project MANTIS.

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.

scholarly journals Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 932
Author(s):  
Ziqiu Kang ◽  
Cagatay Catal ◽  
Bedir Tekinerdogan
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Continuous Production ◽  
Case Study Research ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Production Lines ◽  
Important Concern ◽  
Proposed Model ◽  
Useful Life

Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

scholarly journals Semi-supervised Constrained Hidden Markov Model Using Multiple Sensors for Remaining Useful Life Prediction and Optimal Predictive Maintenance

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xinyu Zhao ◽  
Yunyi Kang ◽  
Hao Yan ◽  
Feng Ju
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Remaining Useful Life ◽  
Predictive Maintenance ◽  
Multiple Sensors ◽  
Degradation Data ◽  
State Identification ◽  
Markov Decision ◽  
Useful Life

Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we develop a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is effective in estimating the RUL, while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov Decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method.

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