Exploiting sound signals for fault diagnosis of bearings using decision tree

Amarnath, M.; Sugumaran, V.; Kumar, Hemantha

doi:10.1016/j.measurement.2012.11.011

Cited by 133 publications

(76 citation statements)

References 15 publications

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“… C4.5 (rule based), which builds decision trees consisting of sets of ordered rules based on information entropy (Amarnath et al, 2013). Then, the decision tree that best matches a new observation in terms of highest weight reflects the type of fault.…”

Section: Classificationmentioning

confidence: 99%

Fault diagnosis of chemical processes with incomplete observations: A comparative study

Askarian

Escudero

Graells

et al. 2016

Computers & Chemical Engineering

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An important problem to be addressed by diagnostic systems in industrial applications is the estimation of faults with incomplete observations. This work discusses different approaches for handling missing data, and performance of data-driven fault diagnosis schemes. An exploiting classifier and combined methods were assessed in TennesseeEastman process, for which diverse incomplete observations were produced. The use of several indicators revealed the trade-off between performances of the different schemes.Support vector machines (SVM) and C4.5, combined with k-nearest neighbourhood (kNN), produce the highest robustness and accuracy, respectively. Bayesian networks (BN) and centroid appear as inappropriate options in terms of accuracy, while Gaussian naïve Bayes (GNB) is sensitive to imputation values. In addition, feature selection was explored for further performance enhancement, and the proposed contribution index showed promising results. Finally, an industrial case was studied to assess informative level of incomplete data in terms of the redundancy ratio and generalize the discussion.

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Section: Classificationmentioning

confidence: 99%

Fault diagnosis of chemical processes with incomplete observations: A comparative study

Askarian

Escudero

Graells

et al. 2016

Computers & Chemical Engineering

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show abstract

“…Bearing fault diagnosis using a decision tree has been proven to have good performance in terms of classification [18,19]. The decision tree is a tree-like model that predicts the value of a target variable by learning simple decision rules inferred from the data's features.…”

Section: Fault Diagnosis Using Decision Treementioning

confidence: 99%

Induction motor bearing fault diagnostics using i-kaz™ and decision tree classification

Othman

Nuawi²,

Mohamed³

2016

Int. J. Automot. Mech. Eng.

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In this paper, the I-kaz™ method is proposed for the detection of induction motor bearing faults using vibration signals, which has not been presented so far. The purpose of the study is to compare this new technique with the classical kurtosis method in the time domain and to validate the performance of the proposed I-kaz coefficient using a decision tree classification. Three bearing conditions are investigated; i.e. normal, ball fault and inner race fault; with a very small fault size (0.1778 mm). All faulty bearings are artificially damaged using electro-discharge machining and placed in the motor drive end side. The experimental test rig consists of a 2 HP induction motor, a torque transducer, a dynamometer and control electronics. Vibration data is obtained using an accelerometer and analyzed using MATLAB software for the time domain analysis which include the Ikaz graphical and coefficient comparison with time waveform and kurtosis values for all bearing conditions. Then, both features are used to train a conditional inference tree (CTree) fault classifier separately. The proposed I-kaz coefficient provides higher percentage differences between all faulty and normal bearings compared to kurtosis. However, the I-kaz graphic presents similar identification as the time waveform where only the inner race fault is distinguished from the normal bearing. The training classification results also revealed that the I-kaz coefficient is significantly better with an accuracy of 99.64% and a Kappa value of 0.9946 compared to kurtosis at only 63.57% and 0.4536, respectively. Furthermore, all test data were classified accordingly using the I-kaz coefficient whereas for kurtosis, only 65% are correctly classified with the 0.475 Kappa value. It is proved that the I-kaz™ method is suitable for induction bearing fault detection and is recommended as a classification feature, especially for the diagnostics of ball fault which is the most difficult to diagnose.

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“…The decision tree is a tree based on the knowledge methodology used to represent classification rules, and it has been widely utilized to develop the classification models [30,31] but has not yet been applied in welding quality evaluation for RSW. A standard decision tree consists of one root node, a number of internal nodes, and leaves.…”

Section: Decision Tree Classifiermentioning

confidence: 99%

A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier

Zhang

Hou²,

Zhang

et al. 2014

Int J Adv Manuf Technol

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To develop an effective nondestructive evaluation method for the welding quality of the resistance spot welding, the electrode displacement signal during the resistance spot welding process is monitored, and the acquisition data of the signal are innovatively presented as the radar chart format. Some geometric features of the radar charts are extracted to reflect the welding quality. The decision tree classification technique is adopted to build a classifier and to provide a visible and intuitive diagnostic procedure for welding quality assessment. Test results of the decision tree classifier show that it is feasible and reliable to evaluate weld quality based on the graphics features of the radar chart. The features and the weld quality are closely related, and their extraction avoids complex algorithm. Meanwhile, when there are small samples, the decision tree classifier can identify good or bad weld accurately and rapidly, even though the weld is from an abnormal welding process, such as expulsion, current shunting, greasy surface, and small edge distance condition.

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Exploiting sound signals for fault diagnosis of bearings using decision tree

Cited by 133 publications

References 15 publications

Fault diagnosis of chemical processes with incomplete observations: A comparative study

Fault diagnosis of chemical processes with incomplete observations: A comparative study

Induction motor bearing fault diagnostics using i-kaz™ and decision tree classification

A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier

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