An incipient fault detection approach via detrending and denoising

Abstract An incipient fault tends to be buried by either the process trend or the measurement noise. Fault–trend ratio (FTR) and fault–noise ratio (FNR) are two main factors that impact the detection performance. An incipient fault detection approach is proposed in this paper based on the detrending and denoising techniques. There are three main phases in this approach. First, to increase FTR, a detrending algorithm is implemented. The fault detection rate can be significantly enhanced, when the normal trend is eliminated from the testing residual. Second, to increase FNR, a denoising algorithm is realized. The residual obtained from this algorithm can avoid the incipient fault being buried by the widely oscillating noise. Therefore the fault detection performance can be further improved. Third, the new detection statistic is composed based on the two algorithms. The approach is applied to a simulated process, a satellite attitude control system process, and the Tennessee Eastman process. The comparison results show that the proposed method outperforms the traditional Hotelling method in detecting incipient faults.

[1]  Yu Zhang,et al.  Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures , 2017, IEEE Access.

[2]  Steven X. Ding,et al.  Fault Detection for Non-Gaussian Processes Using Generalized Canonical Correlation Analysis and Randomized Algorithms , 2018, IEEE Transactions on Industrial Electronics.

[3]  Mohd Azlan Hussain,et al.  Fault diagnosis of Tennessee Eastman process with multi- scale PCA and ANFIS , 2013 .

[4]  Yongbo Li,et al.  Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings , 2017, IEEE Transactions on Industrial Electronics.

[5]  Dale E. Seborg,et al.  Identification of the Tennessee Eastman challenge process with subspace methods , 2000 .

[6]  Masaharu Tsubokura,et al.  Internal radiation exposure after the Fukushima nuclear power plant disaster. , 2012, JAMA.

[7]  Belle R. Upadhyaya,et al.  Incipient fault detection of motor-operated valves using wavelet transform analysis , 2008 .

[8]  Steven X. Ding,et al.  A New Soft-Sensor-Based Process Monitoring Scheme Incorporating Infrequent KPI Measurements , 2015, IEEE Transactions on Industrial Electronics.

[9]  Jiongqi Wang,et al.  A visualization approach for unknown fault diagnosis , 2018 .

[10]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[11]  Myeongsu Kang,et al.  Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm , 2015, Inf. Sci..

[12]  Lechang Cheng.  Fault detection and isolation using the local approach , 2000 .

[13]  Abdessamad Kobi,et al.  Design of experiments and statistical process control using wavelets analysis , 2016 .

[14]  Thomas J. McAvoy,et al.  Base control for the Tennessee Eastman problem , 1994 .

[15]  Yuri A.W. Shardt,et al.  Determining the state of a process control system: Current trends and future challenges , 2012 .

[16]  M.-H. Wang.  Extension neural network for power transformer incipient fault diagnosis , 2003 .

[17]  T Pohlemann,et al.  Facts about the disaster at Eschede. , 2000, Journal of orthopaedic trauma.

[18]  Jun Guo,et al.  Vibrant fault diagnosis for hydroelectric generator units with a new combination of rough sets and support vector machine , 2012, Expert Syst. Appl..

[19]  H. Abdi,et al.  Principal component analysis , 2010 .

[20]  Peter C. Young,et al.  Nonlinear and Nonstationary Signal Processing , 1998, Technometrics.

[21]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[22]  Jihong Yan,et al.  Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis , 2014, Signal Process..

[23]  Michel Verhaegen,et al.  Robust Fault Detection With Statistical Uncertainty in Identified Parameters , 2012, IEEE Transactions on Signal Processing.

[24]  Jiongqi Wang,et al.  An Improved Detection Statistic for Monitoring the Nonstationary and Nonlinear Processes , 2015 .

[25]  Steven X. Ding,et al.  Canonical correlation analysis-based fault detection methods with application to alumina evaporation process , 2016 .

[26]  Yilu Liu,et al.  Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers , 2008 .

[27]  Michèle Basseville,et al.  Early warning of slight changes in systems , 1994, Autom..

[28]  Jiongqi Wang,et al.  Maximum Correntropy Criterion Kalman Filter for α-Jerk Tracking Model with Non-Gaussian Noise , 2017, Entropy.

[29]  Claude Delpha,et al.  An optimal fault detection threshold for early detection using Kullback-Leibler Divergence for unknown distribution data , 2016, Signal Process..

[30]  Min Li,et al.  Improved diagnostics for the incipient faults in analog circuits using LSSVM based on PSO algorithm with Mahalanobis distance , 2014, Neurocomputing.

[31]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[32]  Steven X. Ding,et al.  Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems , 2014 .

[33]  Ping Zhang,et al.  A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process , 2012 .

[34]  B. Thompson.  Canonical Correlation Analysis , 1984 .

[35]  M. Bilodeau,et al.  Theory of multivariate statistics , 1999 .

[36]  Shibin Wang,et al.  Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis , 2016 .

[37]  Yanyang Zi,et al.  Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold , 2014 .

[38]  Balint Nemeth,et al.  Transformer condition analyzing expert system using fuzzy neural system , 2010, 2010 IEEE International Symposium on Electrical Insulation.

[39]  Xin Gao,et al.  An improved SVM integrated GS-PCA fault diagnosis approach of Tennessee Eastman process , 2016, Neurocomputing.

[40]  Steven X. Ding,et al.  Improved canonical correlation analysis-based fault detection methods for industrial processes , 2016 .

[41]  Jiongqi Wang,et al.  Optimal Weight and Parameter Estimation of Multi-structure and Unequal-Precision Data Fusion , 2017 .

[42]  Richard D. Braatz,et al.  Tennessee Eastman Process , 2000 .

[43]  Mohieddine Jelali,et al.  Revision of the Tennessee Eastman Process Model , 2015 .

[44]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[45]  Shen Yin,et al.  Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System , 2015, IEEE Transactions on Industrial Electronics.

[46]  Claude Delpha,et al.  Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis: Part II , 2015, Signal Process..

[47]  Byron Sun.  Tianjin port explosions , 2015 .

[48]  Hooshang Jazayeri-Rad,et al.  Incipient fault diagnosis using support vector machines based on monitoring continuous decision functions , 2014, Eng. Appl. Artif. Intell..

[49]  Jiongqi Wang,et al.  A unified framework for contrast research of the latent variable multivariate regression methods , 2015 .

[50]  Christos Georgakis,et al.  Plant-wide control of the Tennessee Eastman problem , 1995 .