Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM

Fault diagnosis of rope tension is significantly important for hoisting safety, especially in mine hoists. Conventional diagnosis methods based on force sensors face some challenges regarding sensor installation, data transmission, safety, and reliability in harsh mine environments. In this paper, a novel fault diagnosis method for rope tension based on the vibration signals of head sheaves is proposed. First, the vibration signal is decomposed into some intrinsic mode functions (IMFs) by the ensemble empirical mode decomposition (EEMD) method. Second, a sensitivity index is proposed to extract the main IMFs, then the de-noised signal is obtained by the sum of the main IMFs. Third, the energy and the proposed improved permutation entropy (IPE) values of the main IMFs and the de-noised signal are calculated to create the feature vectors. The IPE is proposed to improve the PE by adding the amplitude information, and it proved to be more sensitive in simulations of impulse detecting and signal segmentation. Fourth, vibration samples in different tension states are used to train a particle swarm optimization–support vector machine (PSO-SVM) model. Lastly, the trained model is implemented to detect tension faults in practice. Two experimental results validated the effectiveness of the proposed method to detect tension faults, such as overload, underload, and imbalance, in both single-rope and multi-rope hoists. This study provides a new perspective for detecting tension faults in hoisting systems.

[1]  E. Mucchi,et al.  Diagnosis of Localized Faults in Multistage Gearboxes: A Vibrational Approach by Means of Automatic EMD-Based Algorithm , 2017 .

[2]  Cristina Castejón,et al.  EMD-Based Methodology for the Identification of a High-Speed Train Running in a Gear Operating State , 2018, Sensors.

[3]  Mohammadsadegh Mobin,et al.  Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring , 2018, Measurement.

[4]  Weidong Zhang,et al.  Multi-fault diagnosis for rolling element bearings based on intrinsic mode function screening and optimized least squares support vector machine , 2016 .

[5]  Gordon Morison,et al.  Entropy-Based Feature Extraction for Electromagnetic Discharges Classification in High-Voltage Power Generation , 2018, Entropy.

[6]  Yaguo Lei,et al.  Application of the EEMD method to rotor fault diagnosis of rotating machinery , 2009 .

[7]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[8]  Tomasz Figlus.  A Method for Diagnosing Gearboxes of Means of Transport Using Multi-Stage Filtering and Entropy , 2019, Entropy.

[9]  Xiaoguang Zhang,et al.  A novel tension monitoring device of multi-rope friction hoister by using acoustic filtering sensor , 2016 .

[10]  Zhang Yu,et al.  Fault Diagnosis for Planetary Gearbox Based on EMD and Deep Convolutional Neural Networks , 2019, Journal of Mechanical Engineering.

[11]  Yang Wang,et al.  Fault Diagnosis of Rotary Parts of a Heavy-Duty Horizontal Lathe Based on Wavelet Packet Transform and Support Vector Machine , 2019, Sensors.

[12]  Eberhard F. Kochs,et al.  Permutation Entropy: Too Complex a Measure for EEG Time Series? , 2017, Entropy.

[13]  Massimiliano Zanin,et al.  Permutation Entropy and Its Main Biomedical and Econophysics Applications: A Review , 2012, Entropy.

[14]  Ziming Kou,et al.  Research and optimization of intelligent diagnosis algorithm based on rope tension , 2019 .

[15]  Cristina Castejón,et al.  Condition monitoring of critical mechanical elements through Graphical Representation of State Configurations and Chromogram of Bands of Frequency , 2019, Measurement.

[16]  Abdel-Ouahab Boudraa,et al.  EMD-Based Filtering Using Similarity Measure Between Probability Density Functions of IMFs , 2014, IEEE Transactions on Instrumentation and Measurement.

[17]  David Cuesta-Frau,et al.  Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures , 2018, Entropy.

[18]  Fulei Chu,et al.  Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples , 2013 .

[20]  Q. Gan,et al.  Unified Practical Formulas for Vibration-Based Method of Cable Tension Estimation , 2015 .

[21]  David,et al.  A study on helicopter main gearbox planetary bearing fault diagnosis , 2017, Applied Acoustics.

[22]  Hua Ding,et al.  Nonlinear Blind Source Separation and Fault Feature Extraction Method for Mining Machine Diagnosis , 2019, Applied Sciences.

[23]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[24]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[25]  Fengshou Gu,et al.  A Novel Fault Detection Method for Rolling Bearings Based on Non-Stationary Vibration Signature Analysis , 2019, Sensors.

[26]  Joo-Ho Choi,et al.  Gear fault diagnosis using transmission error and ensemble empirical mode decomposition , 2018, Mechanical Systems and Signal Processing.

[27]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[28]  O. Weck,et al.  A COMPARISON OF PARTICLE SWARM OPTIMIZATION AND THE GENETIC ALGORITHM , 2005 .

[29]  Rafik Djemili,et al.  Application of empirical mode decomposition and artificial neural network for the classification of normal and epileptic EEG signals , 2016 .

[30]  Yitao Liang,et al.  A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM , 2015 .

[31]  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.

[32]  Qianli D. Y. Ma,et al.  Modified permutation-entropy analysis of heartbeat dynamics. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[33]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[34]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[35]  Hamed Azami,et al.  Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation , 2016, Comput. Methods Programs Biomed..

[36]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[37]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[38]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[39]  A. S. Sekhar,et al.  Fault detection in rotor bearing systems using time frequency techniques , 2016 .

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

[41]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[42]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[43]  FanĀ Jiang,et al.  Lifting load monitoring of mine hoist through vibration signal analysis with variational mode decomposition , 2017 .

[44]  Minqiang Xu,et al.  A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery , 2019, Entropy.

[45]  Xiaoguang Zhang,et al.  Study on Dynamic Monitoring of Wire Rope Tension Based on the Particle Damping Sensor , 2019, Sensors.

[46]  Wenlong Fu,et al.  Fault Diagnosis for Rolling Bearings Based on Fine-Sorted Dispersion Entropy and SVM Optimized with Mutation SCA-PSO , 2019, Entropy.

[47]  Bong-Hwan Koh,et al.  Fault Detection of Bearing Systems through EEMD and Optimization Algorithm , 2017, Sensors.