Graph-based change detection for condition monitoring of industrial machinery: an enhanced framework for non-stationary condition signals

[1]  Gaigai Cai,et al.  Reliability estimation for cutting tools based on logistic regression model using vibration signals , 2011 .

[2]  Hamid Reza Karimi,et al.  Data-driven design of robust fault detection system for wind turbines , 2014 .

[3]  Kwok-Leung Tsui,et al.  Condition monitoring and remaining useful life prediction using degradation signals: revisited , 2013 .

[4]  Zhiqiang Ge,et al.  Fault detection in non-Gaussian vibration systems using dynamic statistical-based approaches , 2010 .

[5]  Sunil K. Narang,et al.  Signal processing techniques for interpolation in graph structured data , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[6]  Guoliang Lu,et al.  Graph-based structural change detection for rotating machinery monitoring , 2018 .

[7]  Antoine Grall,et al.  A modeling framework for deteriorating control system and predictive maintenance of actuators , 2015, Reliab. Eng. Syst. Saf..

[8]  Jérôme Antoni,et al.  Separation of combustion noise in IC engines under cyclo-non-stationary regime , 2013 .

[9]  Zhigang Tian,et al.  An Integrated Prognostics Method Under Time-Varying Operating Conditions , 2015, IEEE Transactions on Reliability.

[10]  Yasushi Makihara,et al.  Phase Estimation of a Single Quasi-Periodic Signal , 2014, IEEE Transactions on Signal Processing.

[11]  Lina Bertling Tjernberg,et al.  An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings , 2015, IEEE Transactions on Smart Grid.

[12]  Yaguo Lei,et al.  An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.

[13]  Jie Liu,et al.  Adaptive Change Detection for Long-Term Machinery Monitoring Using Incremental Sliding-Window , 2017 .

[14]  Guoliang Lu,et al.  A novel framework of change-point detection for machine monitoring , 2017 .

[15]  Melinda Hodkiewicz,et al.  Classifying machinery condition using oil samples and binary logistic regression , 2015 .

[16]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[17]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[18]  Jian Ma,et al.  An approach to fault diagnosis for gearbox based on reconstructed energy and support vector machine , 2017 .

[19]  Jérôme Antoni,et al.  The spectral analysis of cyclo-non-stationary signals , 2016 .

[20]  Johan A. K. Suykens,et al.  LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines , 2015, Eng. Appl. Artif. Intell..

[21]  Radoslaw Zimroz,et al.  A new feature for monitoring the condition of gearboxes in non-stationary operating conditions , 2009 .

[22]  T. Popescu Blind separation of vibration signals and source change detection: Application to machine monitoring , 2010 .

[23]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[24]  Abhisek Ukil,et al.  Abrupt Change Detection in Power System Fault Analysis using Adaptive Whitening Filter and Wavelet Transform , 2015, ArXiv.

[25]  Mogens Blanke,et al.  Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection , 2015 .

[26]  Mohammad Esmalifalak,et al.  A data mining approach for fault diagnosis: An application of anomaly detection algorithm , 2014 .

[27]  S. H. Upadhyay,et al.  Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform , 2013, Soft Computing.

[28]  T. Gasser,et al.  Alignment of curves by dynamic time warping , 1997 .

[29]  Peng Qian,et al.  Integrated data-driven model-based approach to condition monitoring of the wind turbine gearbox , 2017 .

[30]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[31]  Zeeshan Syed,et al.  Using Adaptive Downsampling to Compare Time Series with Warping , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[32]  Andrés Bustillo,et al.  An SVM-Based Solution for Fault Detection in Wind Turbines , 2015, Sensors.

[33]  Radoslaw Zimroz,et al.  Two simple multivariate procedures for monitoring planetary gearboxes in non-stationary operating conditions , 2013 .

[34]  Zhigang Tian,et al.  A framework for predicting the remaining useful life of a single unit under time-varying operating conditions , 2013 .

[35]  Rames C. Panda,et al.  Neural network model for condition monitoring of wear and film thickness in a gearbox , 2013, Neural Computing and Applications.

[36]  Bo-Suk Yang,et al.  Estimation and forecasting of machine health condition using ARMA/GARCH model , 2010 .

[37]  Kan Liu,et al.  Parameter Estimation for Condition Monitoring of PMSM Stator Winding and Rotor Permanent Magnets , 2013, IEEE Transactions on Industrial Electronics.