Decision Tree-Based Classification for Planetary Gearboxes’ Condition Monitoring with the Use of Vibration Data in Multidimensional Symptom Space

Abstract

Monitoring the condition of rotating machinery, especially planetary gearboxes, is a challenging problem. In most of the available approaches, diagnostic procedures are related to advanced signal pre-processing/feature extraction methods or advanced data (features) analysis by using artificial intelligence. In this paper, the second approach is explored, so an application of decision trees for the classification of spectral-based 15D vectors of diagnostic data is proposed. The novelty of this paper is that by a combination of spectral analysis and the application of decision trees to a set of spectral features, we are able to take advantage of the multidimensionality of diagnostic data and classify/recognize the gearbox condition almost faultlessly even in non-stationary operating conditions. The diagnostics of time-varying systems are a complicated issue due to time-varying probability densities estimated for features. Using multidimensional data instead of an aggregated 1D feature, it is possible to improve the efficiency of diagnostics. It can be underlined that in comparison to previous work related to the same data, where the aggregated 1D variable was used, the efficiency of the proposed approach is around 99% (ca. 19% better). We tested several algorithms: classification and regression trees with the Gini index and entropy, as well as the random tree. We compare the obtained results with the K-nearest neighbors classification algorithm and meta-classifiers, namely: random forest and AdaBoost. As a result, we created the decision tree model with 99.74% classification accuracy on the test dataset.

Optimisation and evaluation of the random forest model in the efficacy prediction of chemoradiotherapy for advanced cervical cancer based on radiomics signature from high-resolution T2 weighted images

“…In this study, the selection criteria of two optimal partitioning attributes, namely, information gain ratio and Gini index, were compared. The Gini index focuses on selecting attributes that make the partitioned data set purer, whereas the information gain ratio focuses on selecting attributes that provide more information [ 40 ]. In this study, the chemoradiotherapy efficacy prediction model with Gini index as the optimal partition attribute selection standard had slightly better performance.…”

Multidimensional Data Interpretation of Vibration Signals Registered in Different Locations for System Condition Monitoring of a Three-Stage Gear Transmission Operating under Difficult Conditions

“…Continuous vibration separation was used to overcome the modulation effect caused by planetary movements, as well as restraining noise and asynchronous signal components, minimum entropy deconvolution was used to enhance fault-induced impulses if exist. Another approach was presented in [ 44 ]. Applications of novel decision trees for the classification of spectral-based 15D vectors of diagnostic data were developed.…”

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

“…Rauber_2021 [219] Samuel_2005 * [33] Vives_2020 [216] Random Forest Rauber_2021 [219] Li_2016b [249] Fuzzy predictive model Hadroug_2021 [250] Malla_2019 * [3] Str ączkiewicz_2015 [251] Saravanan_2009 [252] Da Silva_2017 [253] Sharma_2021 * [25] Decision Trees (DTs) Lipinski_2020 [254] Joshuva_2017a [112] Tabaszewski_2020 [255] Yang_2005 [256] Yang_2000 [257] Song_2018 [181] Dempster-Shafer (D-S) evidence theory Khazaee_2014 [258] Khazaee_2012 [259] Multi-Sensor Data fusion Safizadeh_2014 [260] Khazaee_2012 [259] Stief_2017 [261] Sharma_2021 * [25] Hybrid classifier based on SVM and ANN Sharma_2021 * [25] Hybrid classifier based on Principal Component Analysis (PCA) and ANN Liu_2008 [262] Devendiran_2015 [104] De Moura_2011 [263] Bendjama_2010 [264] Others Stefanoiu_2019 [265] Yan_2019 [199] Liu_2014 [266] Zhang_2021b [267] Avendaño-Valencia_2017 [268] Jayaswal and Wadhwani, in 2009 [31], reviewed the techniques successfully implemented for the automated fault diagnosis of bearings until that time, and refer to expert systems developed with multilayer perceptron (MLP), radial basis function (RBF) and probabilistic neural network (PNN). More recently, Tao et al, in 2019 [222], adopted a multilayer gated recurrent unit (MGRU) method for gear fault diagnosis; a comparison with long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM) LSTM, MLSTM, GRU, and SVM models, based on an experimental analysis, revealed improved accuracy with the MGRU network.…”