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A Risk-Averse Remaining Useful Life Estimation for predictive maintenance

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 412-422页

作者： Chuang Chen Ningyun Lu Bin Jiang Cunsong Wang College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Remaining useful life(RUL)prediction is an advanced technique for system maintenance *** of existing RUL prediction methods are only interested in the precision of RUL estimation;the adverse impact of overestimated RUL on maintenance scheduling is not of *** this work,an RUL estimation method with risk-averse adaptation is developed which can reduce the over-estimation rate while maintaining a reasonable under-estimation *** proposed method includes a module of degradation feature selection to obtain crucial features which reflect system degradation ***,the latent structure between the degradation features and the RUL labels is modeled by a support vector regression(SVR)model and a long short-term memory(LSTM)network,*** enhance the prediction robustness and increase its marginal utility,the SVR model and the LSTM model are integrated to generate a hybrid model via three connection *** designing a cost function with penalty mechanism,the three parameters are determined using a modified grey wolf optimization *** addition,a cost metric is proposed to measure the benefit of such a risk-averse predictive maintenance *** is done using an aero-engine data set from *** results show the feasibility and effectiveness of the proposed RUL estimation method and the predictive maintenance strategy.

关键词： Long short-term memory(LSTM)network predictive maintenance remaining useful life(RUL)estimation risk-averse adaptation support vector regression(SVR)

An Ordinal Multi-Dimensional Classification(OMDC)for predictive maintenance

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Computer Systems Science & Engineering 2023年第2期44卷 1499-1516页

作者： Pelin Yildirim Taser Izmir Bakircay University Department of Computer EngineeringIzmir35665Turkey

predictive maintenance is a type of condition-based maintenance that assesses the equipment's states and estimates its failure probability and when maintenance should be *** machine learning techniques have been frequently implemented in this area,the existing studies disregard to the nat-ural order between the target attribute values of the historical sensor ***,these methods cause losing the inherent order of the data that positively affects the prediction *** deal with this problem,a novel approach,named Ordinal Multi-dimensional Classiﬁcation(OMDC),is proposed for estimating the conditions of a hydraulic system's four components by taking into the natural order of class *** demonstrate the prediction ability of the proposed approach,eleven different multi-dimensional classiﬁcation algorithms(traditional Binary Relevance(BR),Classiﬁer Chain(CC),Bayesian Classiﬁer Chain(BCC),Monte Carlo Classiﬁer Chain(MCC),Probabilistic Classiﬁer Chain(PCC),Clas-siﬁer Dependency Network(CDN),Classiﬁer Trellis(CT),Classiﬁer Dependency Trellis(CDT),Label Powerset(LP),Pruned Sets(PS),and Random k-Labelsets(RAKEL))were implemented using the Ordinal Class Classiﬁer(OCC)***,seven different classiﬁcation algorithms(Multilayer Perceptron(MLP),Support Vector Machine(SVM),k-Nearest Neighbour(kNN),Decision Tree(C4.5),Bagging,Random Forest(RF),and Adaptive Boosting(AdaBoost))were chosen as base learners for the OCC *** experimental results present that the proposed OMDC approach using binary relevance multi-dimensional classiﬁcation methods predicts the conditions of a hydraulic system's multiple components with high ***,it is clearly seen from the results that the OMDC models that utilize ensemble-based classiﬁcation algorithms give more reliable prediction performances with an average Hamming score of 0.853 than the others that use traditional algorithms as base learners.

关键词： Machine learning multi-dimensional classiﬁcation ordinal classiﬁcation predictive maintenance

Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

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Advances in Manufacturing 2015年第2期3卷 97-104页

作者： Ke-Sheng Wang Zhe Li JФrgen Braaten Quan Yu Knowledge Discovery Laboratory Department of Production and Quality EngineeringNorwegian University of Science and Technology Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University

It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article re- ports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks （ANNs）. The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.

关键词： Backlash error Artificial neural network(ANN) Machine centers predictive maintenance

Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers： Industry 4.0 scenario

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Advances in Manufacturing 2017年第4期5卷 377-387页

作者： Zhe Li Yi Wang Ke-Sheng Wang Department of Mechanical and Industrial Engineering Norwegian University of Science and TechnologyTrondheim Norway School of Business Plymouth University Plymouth UK

Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules： sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.

关键词： Data mining (DM) Machine centers predictive maintenance Industry 4.0

Data-Driven predictive maintenance Approach for Spinning Cyber-Physical Production System

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Journal of Shanghai Jiaotong university(Science) 2020年第4期25卷 453-462页

作者： FAROOQ Basit BAO Jinsong LI Jie LIU Tianyuan YIN Shiyong College of Mechanical Engineering Donghua UniversityShanghai 201600China

The fundamental process of predictive maintenance is prognostics and health management,and it is the tool resulting in the development of many algorithms to predict the remaining useful life of industrial equipment.A new data-driven predictive maintenance and an architectural impulse,based on a regularized deep neural network using predictive analytics,are proposed successfully for ring spinning *** paradigm shift in computational infrastructures enormously puts pressure on large-scale linear and non-linear automated assembly systems to eliminate and cut down unscheduled downtime and unexpected *** sensor network designed for the scheduling process comprises different critical components of the same spinning machine frames containing more than thousands of spindles attached to *** established a genetic algorithm based on multi-sensor performance assessment and prediction procedure for the spinning *** show that it operates with a relatively less amount of training data sets but takes advantage of larger volumes of *** integrated system aims to prognosticate abnormalities,disturbances,and failures by providing condition-based monitoring for each component,which makes it more accurate to locate the defined component failures in the current spinning spindles by using smart agents during the operations through the neural sensing network.A case study has provided to demonstrate the feasibility of the proposed predictive model for highly dynamic,high-speed textile spinning system through real-time data sensing and signal processing via the industrial Internet of Things.

关键词： predictive maintenance prognostics and health management smart spinning manufacturing cyberphysical production system

Developing a predictive maintenance model for vessel machinery

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Journal of Ocean Engineering and Science 2020年第4期5卷 358-386页

作者： Veronica Jaramillo Jimenez Noureddine Bouhmala Anne Haugen Gausdal Department of Maritime Operations University of South-Eastern Norway Kristiania University College

The aim of maintenance is to reduce the number of failures in equipment and to avoid breakdowns that may lead to disruptions during *** objective of this study is to initiate the development of a predictive maintenance solution in the shipping industry based on a computational artificial intelligence model using real-time monitoring *** data analysed originates from the historical values from sensors measuring the vessel´s engines and compressors health and the software used to analyse these data was *** results demonstrated key parameters held a stronger influence in the overall state of the components and proved in most cases strong correlations amongst sensor data from the same *** results also showed a great potential to serve as inputs for developing a predictive model,yet further elements including failure modes identification,detection of potential failures and asset criticality are some of the issues required to define prior designing the algorithms and a solution based on artificial intelligence.A systematic approach using big data and machine learning as techniques to create predictive maintenance strategies is already creating disruption within the shipping industry,and maritime organizations need to consider how to implement these new technologies into their business operations and to improve the speed and accuracy in their maintenance decision making.

关键词： maintenance in Shipping industry Big Data Analytics Vessel Machinery Sensor Systems Sensor Data Condition Based maintenance predictive maintenance

predictive maintenance and its applications in civil engineering structures:A review

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Journal of Southeast University(English Edition) 2024年第3期40卷 245-256页

作者： Shan Jiazeng Zhang Xi Loong Cheng Ning Liu Yanzhe Hu Xinyue College of Civil Engineering Tongji UniversityShanghai 200092China Shanghai Engineering Research Center for Resilient Cities and Intelligent Disaster Mitigation Shanghai 200092China Department of Civil and Environmental Engineering the Hong Kong University of Science and TechnologyHong Kong 999077China

Structural health monitoring and performance prediction are crucial for smart disaster mitigation and intelligent management of structures throughout their *** advancements in predictive maintenance strategies within the industrial manufacturing industry have inspired similar innovations in civil engineering,aiming to improve structural performance evaluation,damage diagnosis,and capacity *** review delves into the framework of predictive maintenance and examines various existing solutions,focusing on critical areas such as data acquisition,condition monitoring,damage prognosis,and maintenance *** from real-world applications of predictive maintenance in civil engineering,covering high-rise structures,deep foundation pits,and other infrastructure,are *** challenges of implementing predictive maintenance in civil engineering structures under current technology,such as model interpretability of data-driven methods and standards for predictive maintenance,are *** research prospects within this area are also discussed.

关键词： predictive maintenance civil engineering structural health monitoring machine learning

Seeing around the corner:an analytic approach for predictive maintenance using sensor data

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Journal of Management Analytics 2015年第4期2卷 333-350页

作者： Zhongju Zhang Pengzhu Zhang Department of Operations and Information Management University of Connecticut School of BusinessStorrsUSA Department of Management Information Systems Antai College of Economics and ManagementShanghai Jiaotong UniversityShanghai 200030China Department of Information Systems W.P.Carey School of BusinessArizona State UniversityTempe85287

Technological advancements such as the industrial Internet of Things now allow companies to continuously monitor the operating conditions of expensive equipment using *** the tremendous amount of sensor data flowing in continuously,equipment makers are seeking innovative analytical solutions to turn operational data to help guide their tactical and strategic *** sensor data on wind turbine operations and service records from a top Fortune 100 company in the energy industry,we showcase techniques to map out operational-level data for analysis,and develop several analytical models(a sequence analysis,a logistic regression and a survival model)to help predict and evaluate equipment failure *** analyses highlight the significant value propositions of sensor data in the big data *** implications as well as extensions of the proposed predictive models are discussed.

关键词： predictive maintenance data analytics survival analysis business intelligence

An Efficient IIoT-Based Smart Sensor Node for predictive maintenance of Induction Motors

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Computer Systems Science & Engineering 2023年第10期47卷 255-272页

作者： Majida Kazmi Maria Tabasum Shoaib Arshad Aziz Hashim Raza Khan Saad Ahmed Qazi Faculty of Electrical and Computer Engineering NED University of Engineering and TechnologyKarachi75270Pakistan Neurocomputation Lab National Center of Artificial IntelligenceKarachi75270Pakistan Department of Electrical Engineering PNECNational University of Sciences and Technology(NUST)Karachi75350Pakistan

predictive maintenance is a vital aspect of the industrial sector,and the use of Industrial Internet of Things(IIoT)sensor nodes is becoming increasingly popular for detecting motor faults and monitoring motor *** integrated approach for acquiring,processing,and wirelessly transmitting a large amount of data in predictive maintenance applications remains a significant *** study presents an IIoT-based sensor node for industrial *** sensor node is designed to acquire vibration data on the radial and axial axes of the motor and utilizes a hybrid approach for efficient data processing via edge and cloud *** initial step of signal processing is performed on the node at the edge,reducing the burden on a centralized cloud for processing data from multiple *** proposed architecture utilizes the lightweight Message Queue Telemetry Transport(MQTT)communication protocol for seamless data transmission from the node to the local and main *** broker’s bridging allows for data backup in case of connection *** proposed sensor node is rigorously tested on a motor testbed in a laboratory setup and an industrial setting in a rice industry for validation,ensuring its performance and accuracy in real-world industrial *** data analysis and results from both testbed and industrial motors were discussed using vibration analysis for identifying *** proposed sensor node is a significant step towards improving the efficiency and reliability of industrial motors through realtime monitoring and early fault detection,ultimately leading to minimized unscheduled downtime and cost savings.

关键词： IIoT sensor node condition monitoring fault classification predictive maintenance MQTT