Prof. Dr. JUN WU

School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Prof. Dr. JUN WU

School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

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Academic Career Length

21Years (2006-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2020 in ISA Transactions

Journal Article

Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network

JUN WU

Kui Hu

Yiwei Cheng

Haiping Zhu

Xinyu Shao

Yuanhang Wang

15 February 2018 in International Journal of Production Research

Journal Article

Selective maintenance scheduling under stochastic maintenance quality with multiple maintenance actions

Chaoqun Duan

Chao Deng

Abolfazl Gharaei

JUN WU

Bingran Wang

Many systems are required to perform a series of missions with finite breaks between any two consecutive missions. To improve the probability of system successfully completing the next mission, maintenance action is carried out on components during the breaks. In this work, a selective maintenance model with stochastic maintenance quality for multi-component systems is investigated. At each scheduled break, a set of maintenance actions with different degrees of impact are available for each component. The impact of a maintenance action is assumed to be random and follow an identified probability distribution. The corresponding maintenance cost and time are modelled based on the expected impact of the maintenance action. The objective of selective maintenance scheduling is to find the cost-optimal maintenance action for each component at every scheduled break subject to reliability and duration constraints. A simulated annealing algorithm is used to solve the complicated optimisation problem where both multiple maintenance actions and stochastic quality model are taken into account. Two illustrative numerical examples and a real case study have been solved to demonstrate the performance of the proposed approach. A comparison with deterministic maintenance shows the importance of considering the proposed stochastic quality in selective maintenance scheduling.

1 March 2018 in IEEE Transactions on Industrial Electronics

Journal Article

Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines

Time-to-failure (TTF) prognostics plays a crucial role in predicting remaining lifetime of electrical machines for improving machinery health management. This paper presents a novel three-step degradation data-driven TTF prognostics approach for rolling element bearings (REBs) in electrical machines. In degradation feature extraction step, multiple degradation features, including statistical features, intrinsic energy features, and fault frequency features, are extracted to detect the degradation phenomenon of REBs using complete ensemble empirical mode decomposition with adaptive noise and Hilbert-Huang transform methods. In degradation feature reduction step, the degradation features, which are monotonic, robust, and correlative to the fault evolution of the REBs, are selected and fused into a principal component Mahalanobis distance health index using dynamic principal component analysis and Mahalanobis distance methods. In TTF prediction step, the degradation process and local TTF of the REBs are observed by an exponential regression-based local degradation model, and the global TTF is predicted by an empirical Bayesian algorithm with a continuous update. A practical case study involving run-to-failure experiments of REBs on PRONOSTIA platform is provided to validate the effectiveness of the proposed approach and to show a more accurate prediction of TTF than the existing major approaches.

1 December 2019 in Computers in Industry

Journal Article

Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform

Pengfei Liang

Chao Deng

JUN WU

Zhixin Yang

Jinxuan Zhu

Zihan Zhang

22 August 2018 in IEEE Transactions on Industrial Informatics

Journal Article

Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks

Machine health monitoring is of great importance in industrial informatics field. Recently, deep learning methods applied to machine health monitoring have been proven effective. However, the existing methods face enormous difficulties in extracting heterogeneous features indicating the variation until failure and revealing the inherent high-dimensional features of massive signals, which affect the accuracy and efficiency of machine health monitoring. In this paper, a novel data-driven machine health monitoring method is proposed using adaptive kernel spectral clustering (AKSC) and deep long short-term memory recurrent neural networks (LSTM-RNN). This method include three steps: First, features in time domain, frequency domain and time-frequency domain are respectively extracted from massive measured signals. And, an Euclidean distance based algorithm is designed to select degradation features. Second, AKSC algorithm is introduced to adaptively identify machine anomaly behaviors from multiple degradation features. Third, a new deep learning model (LSTM-RNN) is constructed to update and predict the failure time of the machine. The effectiveness of the proposed method is validated using a set of test-to-failure experimental data. The results show that the performance of the proposed method is competitive with other existing methods.

1 July 2018 in Applied Soft Computing

Journal Article

Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system

JUN WU

Yongheng Su

Yiwei Cheng

Xinyu Shao

Chao Deng

Cheng Liu

About Me

Prof. Jun Wu earned his PhD degree in Mechanical Engineering from Huazhong University of Science and Technology in 2008. He is currently a professor and doctoral supervisor at the School of Naval Architecture and Ocean Engineering of Huazhong University of Science and Technology. He is also the director of the Institute of Intelligent Operation and Maintenance of Marine Equipment, director of the System Integration Institute of the National Intelligent Design and CNC Technology Innovation Center, IEEE Senior Member, visiting scholar at Stanford University, executive director of the Rotor Dynamics Professional Committee of the Chinese Vibration Engineering Society, director of the Equipment Structural Health Monitoring and Early Warning Branch of the Chinese Instrument Society, member of the Industrial Big Data and Intelligent System Branch of the Chinese Mechanical Engineering Society, and member of the Youth Work Committee of the Chinese Artificial Intelligence Society. He has long been committed to the research of online monitoring, anomaly detection, state assessment, fault diagnosis, life prediction, and maintenance decision-making of complex electromechanical equipment, involving key technologies such as smart hardware, smart Internet of Things, industrial big data analysis, artificial intelligence algorithms, edge computing, cloud computing, digital twins, AI big models, and industrial Internet platforms.

Prof. Dr. JUN WU

Prof. Dr. JUN WU

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