Abstract
Condition monitoring system monitors the system degradation and it identifies common failure modes. Several sensor signals are available for monitoring the changes in system components. Vibration signal is one of the most extensively used technique for monitoring rotating components as it identifies faults before the system fails. Early fault detection is the significant factor for condition monitoring, where Acoustic Emission ( AE ) sensor signals have been applied for early fault detection due to their high sensitivity and high frequency. In this paper, vibration and acoustic emission signals are acquired under various simulated gear and bearing fault conditions from the synchromesh gearbox. Then the statistical features are extracted from vibration and AE signals and then the prominent features are selected using J48 decision tree algorithm respectively. The best features from the vibration and AE signals are then fused using feature-level fusion strategy and it is classified using Support Vector Machine ( SVM ) and Proximal Support Vector Machine ( PSVM ) classifiers and it is compared with individual signals for fault diagnosis of the synchromesh gearbox. From the experiments, it is observed that the performance of the fault diagnosis system has been improved for the proposed feature level fusion technique compared to the performance of unfused vibration and AE feature sets.
Highlights
Gearboxes play an important role in various industries such as aerospace, automotive and heavy industries
The Acoustic emission signal features considered in this study are rise time, count, energy, duration, amplitude, AFreq, RMS, Average Signal Level (ASL), Percentile, Thr, R-Freq, I-Freq, Signal strength, and Absolute energy and each of the acoustic emission (AE) features shown in Figure 4 is described below
Application of Support Vector Machine (SVM) and Proximal Support Vector Machine (PSVM) for Fault Diagnosis for Automobile Gearbox. For both gear and bearing fault classes, six features from both the vibration and acoustic emission signals consisting of 144 experimental conditions was collected for 500 rpm, 750 rpm, and 1000 rpm. 100 samples were used in each fault classes, where 75 samples are used for training and 25 samples are engaged for testing
Summary
Gearboxes play an important role in various industries such as aerospace, automotive and heavy industries. The recent research reported (Meghdad Khazaee et al (2014)) that fault classification accuracy of fused vibration and acoustic emission signals was increased by up to 10% when compared to single sensor mode using DempsterShafer evidence theory for fault diagnosis of planetary gearbox faults. The fusion of features from vibration and sound signals using decision tree feature selection technique has proved to be an effective method for fault diagnosis of rotating machinery (Saimurugan & Ramprasad (2017)). The reports (Saimurugan & Ramprasad (2017), Saimurugan & Nithesh (2017), Praveenkumar et al (2018)) have proved the performance of a system increased by selecting the dominant features of vibration and sound signals using decision tree algorithm for fault diagnosis of rotating machinery. The fused signals were classified using SVM and PSVM classifiers and their performance were compared with the unfused individual signals
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