Intelligent diagnosis method for machinery by sequential auto-reorganization of histogram.

This paper proposes an intelligent sequential diagnosis method for plant machinery using statistical filter (SF), signal histogram and genetic programming (GP). The SF is used to cancel noise from the measured vibration signal for raising the accuracy of fault diagnosis. Since the vibration signal measured for the condition diagnosis conforms to various probability distributions, histograms are used to reflect the signal features instead of the conventional symptom parameters (SPs). Then, the genetic programming (GP) is used to generate new variables termed "integrated symptom parameters" (GP-ISPs) from the histogram. GP-ISPs obtained by the auto-reorganized histogram can reflect features and raise the sensitivity of the fault diagnosis by the greatest amount possible. Furthermore, a sequential diagnosis algorithm using GP-ISPs is also proposed to realize precise diagnosis for distinguishing fault types. Finally, the effectiveness of the proposed method is verified by applying it to the fault diagnosis of a centrifugal blower. The proposed method has wide applicability and is practical in the field of machinery fault diagnosis.

[1]  Qi Feng,et al.  Multi-level genetic programming for fault data clustering , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[2]  N. R. Sakthivel,et al.  A method for calculation of optimum data size and bin size of histogram features in fault diagnosis of mono-block centrifugal pump , 2011, Expert Syst. Appl..

[3]  Qiang Luo,et al.  Avionics equipment failure prediction based on genetic programming and grey model , 2017, International Conference on Electronics and Information Engineering.

[4]  Michael Negnevitsky,et al.  Fuel Cell Power Management Using Genetic Expression Programming in All-Electric Ships , 2017, IEEE Transactions on Energy Conversion.

[5]  Jie Yu,et al.  A new fault diagnosis method of multimode processes using Bayesian inference based Gaussian mixture contribution decomposition , 2013, Eng. Appl. Artif. Intell..

[6]  Idriss El-Thalji,et al.  A summary of fault modelling and predictive health monitoring of rolling element bearings , 2015 .

[7]  Makarand S. Kulkarni,et al.  Bearing diagnosis based on Mahalanobis–Taguchi–Gram–Schmidt method , 2015 .

[8]  Yu Zhang,et al.  Self-adaptive fault diagnosis of roller bearings using infrared thermal images , 2017, IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society.

[9]  Saravanan Natarajan.  Vibration signal analysis using histogram features and support vector machine for gear box fault diagnosis , 2017 .

[10]  Lingli Cui,et al.  Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical–Horizontal Synchronization Signal Analysis , 2017, IEEE Transactions on Industrial Electronics.

[11]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[12]  Peng Chen,et al.  Fault diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming , 2005 .

[13]  Jianfeng Ma,et al.  Quantitative trend fault diagnosis of a rolling bearing based on Sparsogram and Lempel-Ziv , 2018, Measurement.

[14]  Lingli Cui,et al.  A three-dimensional geometric features-based SCA algorithm for compound faults diagnosis , 2019, Measurement.

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

[16]  M. S. Safizadeh,et al.  Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell , 2014, Inf. Fusion.

[17]  Linxia Liao,et al.  Discovering Prognostic Features Using Genetic Programming in Remaining Useful Life Prediction , 2014, IEEE Transactions on Industrial Electronics.

[18]  Liang Zhang,et al.  Fault classification using genetic programming , 2007 .

[19]  Diego Galar Pascual,et al.  Artificial Intelligence Tools: Decision Support Systems in Condition Monitoring and DIagnosis , 2015 .

[20]  Peng Chen,et al.  Step-by-Step Fuzzy Diagnosis Method for Equipment Based on Symptom Extraction and Trivalent Logic Fuzzy Diagnosis Theory , 2018, IEEE Transactions on Fuzzy Systems.

[21]  Huaqing Wang,et al.  Intelligent Condition Diagnosis Method for Rotating Machinery Based on Probability Density and Discriminant Analyses , 2016, IEEE Signal Processing Letters.

[22]  Peng Chen,et al.  Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery , 2018, IEEE Transactions on Instrumentation and Measurement.

[23]  Radoslaw Zimroz,et al.  Stochastic Modeling of Time Series with Application to Local Damage Detection in Rotating Machinery , 2013 .

[24]  V. Sugumaran,et al.  Fault diagnosis of automobile hydraulic brake system using statistical features and support vector machines , 2015 .

[25]  Fulei Chu,et al.  HVSRMS localization formula and localization law: Localization diagnosis of a ball bearing outer ring fault , 2019, Mechanical Systems and Signal Processing.