A multi-sensor fault detection strategy for axial piston pump using the Walsh transform method

The axial piston pump is a key component of the industrial hydraulic system, and the failure of pump can result in costly downtime. Efficient fault detection is very important for improving reliability and performance of axial piston pumps. Most existing diagnosis methods only use one kind of the discharge pressure, vibration, or acoustic signal. However, the hydraulic pump is a typical mechanism–hydraulics coupling system, all of the pressure, vibration, and acoustic signals contain useful information. Therefore, a novel multi-sensor fault detection strategy is developed to realize more effective diagnosis of axial piston pump. The presence of periodical impulses in these signals usually indicates the occurrence of faults in pump. Unfortunately, in the working condition, detecting the faults is a difficult job because they are rather weak and often interfered by heavy noise. Therefore, noise suppression is one of the most important procedures to detect the faults. In this article, a new denoising method based on the Walsh transform is proposed, and the innovation is that we use the median absolute deviation to estimate the noise threshold adaptively. Numerical simulations and experimental multi-sensor data collected from normal and faulty pumps are used to illustrate the feasibility of the proposed approach.

[1]  Jiawei Xiang,et al.  A two-step method using Duffing oscillator and stochastic resonance to detect mechanical faults , 2016, 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[2]  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.

[3]  Wenyuan Lv,et al.  A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis , 2015 .

[4]  Gang Tang,et al.  A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition , 2014, PloS one.

[5]  Chin-Feng Lai,et al.  A green data transmission mechanism for wireless multimedia sensor networks using information fusion , 2014, IEEE Wireless Communications.

[6]  Wei Wei,et al.  Optimization of the structure of water axial piston pump and cavitation of plunger cavity based on the Kriging model , 2016 .

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

[8]  Kenneth George Beauchamp,et al.  Applications of Walsh and related functions : with an introduction to sequency theory , 1985 .

[9]  Jiawei Xiang,et al.  Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter , 2015 .

[10]  Wei Hou,et al.  Effect analysis of silencing grooves on pressure and vibration characteristics of seawater axial piston pump , 2017 .

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

[12]  N. Huang,et al.  A Novel Preprocessing Method Using Hilbert Huang Transform for MALDI-TOF and SELDI-TOF Mass Spectrometry Data , 2010, PloS one.

[13]  Ming Liang,et al.  Experimental investigation of frequency-based multi-damage detection for beams using support vector regression , 2014 .

[14]  Viliam Makis,et al.  Fault severity recognition of aviation piston pump based on feature extraction of EEMD paving and optimized support vector regression model , 2017 .

[15]  Long Quan,et al.  Development of an asymmetric axial piston pump for displacement-controlled system , 2014 .

[16]  Shuja A. Abbasi,et al.  A Novel Complete Set of Walsh and Inverse Walsh Transforms for Signal Processing , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[17]  Haifeng Gao,et al.  A hybrid fault diagnosis method using morphological filter–translation invariant wavelet and improved ensemble empirical mode decomposition , 2015 .

[18]  Yong Chen,et al.  Monte Carlo Methods for Reliability Evaluation of Linear Sensor Systems , 2011, IEEE Transactions on Reliability.

[19]  Zhengjia He,et al.  Vibration signals denoising using minimum description length principle for detecting impulsive signatures , 2014 .

[20]  Jing Li,et al.  Parametric analysis of thermal effect on hydrostatic slipper bearing capacity of axial piston pump , 2016 .

[21]  Yung-Hung Wang,et al.  On the computational complexity of the empirical mode decomposition algorithm , 2014 .

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

[23]  David L. Donoho,et al.  De-noising by soft-thresholding , 1995, IEEE Trans. Inf. Theory.

[24]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[25]  Jiawei Xiang,et al.  A Novel Personalized Diagnosis Methodology Using Numerical Simulation and an Intelligent Method to Detect Faults in a Shaft , 2016 .

[26]  T. Sasao,et al.  Unified algorithm to generate Walsh functions in four different orderings and its programmable hardware implementations , 2005 .

[27]  N. P. Mandal,et al.  Effects of flow inertia modelling and valve-plate geometry on swash-plate axial-piston pump performance , 2012, J. Syst. Control. Eng..

[28]  Jiawei Xiang,et al.  Rolling element bearing fault detection using PPCA and spectral kurtosis , 2015 .

[29]  Yanyang Zi,et al.  A Comparative Study on the Local Mean Decomposition and Empirical Mode Decomposition and Their Applications to Rotating Machinery Health Diagnosis , 2010 .

[30]  Samah Maalej,et al.  Modeling of the EHD effects on hydrodynamics and heat transfer within a flat miniature heat pipe including axial capillary grooves , 2017 .

[31]  Rajesh Kumar,et al.  Time-frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump , 2017 .

[32]  M. R. Mitchell,et al.  Testing and evaluation of water hydraulic components by acoustic emission and wavelet analysis , 2008 .

[33]  Jiawei Xiang,et al.  A novel model for predicting thermoelastohydrodynamic lubrication characteristics of slipper pair in axial piston pump , 2017 .

[34]  Jiawei Xiang,et al.  A fault detection strategy using the enhancement ensemble empirical mode decomposition and random decrement technique , 2017, Microelectron. Reliab..

[35]  Ludmila I. Kuncheva,et al.  Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy , 2003, Machine Learning.