In heavy rotating machines and assembly lines, bearing failure in any one of the rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems avoids breakdown and saves preventive and corrective maintenance time and cost. This research paper proposes advanced strategies in early fault detection of taper rolling bearings. In view of this, a mathematical model based- fault diagnosis and support vector machine (SVM) is proposed in this work. The mathematical model using dimension analysis by matrix method (DAMM) and SVM is developed to predict the vibration characteristic of the rotor bearing system. Various types of defects created using an electric discharge machine (EDM) are analyzed by correlating dependent and independent parameters. Experiments were performed to classify the rotor dynamic characteristic of healthy and unhealthy bearing. Experimental results are used to validate the model obtained by DAMM and SVM. Experimental results showed that vibration characteristics are evaluated by using a theoretical model and SVM. This contribution to the service life extension and efficiency improvement, so as to reduce bearing failure. Thus, the automatic online diagnosis of bearing faults is possible with a developed model-based by DAMM and SVM.

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Condition monitoring of rotor dynamic is recognized as an advanced preventative maintenance technique for fault-free operation. Faulty bearings in rotating machines may cause severe problems and even untimely breakdowns. This work demonstrates the power of the finite element analysis (FEA) model and dimension analysis technique (DAT) to analyze the effect of the depth and slope angle of surface faults on the bearing contact characteristic. Experimentation is performed to investigate the vibration characteristics of ball bearings. The FEA, DAT, and experimentation show that vibration amplitude is a vital function of surface fault size. The current approach of FEA with DAT reflects their reliability and accuracy for the diagnosis of rotor systems. The present method was found effective in predicting vibration amplitude and defect frequency within acceptable error.

In heavy rotating machines and assembly lines, bearing failure in any one of the rotating machines results in shut down of many other machines and affects the overall cost and quality of the product. Condition monitoring of bearing systems avoids breakdown and saves preventive and corrective maintenance time and cost. This research paper proposes advanced strategies in early detection and analysis of taper rolling bearings. In view of this, mathematical model-based fault diagnosis and support vector machine (SVM) is proposed in this work. Mathematical model using dimension analysis by matrix method (DAMM) and SVM is developed that can be used to predict the vibration characteristics of the rotor-bearing system. Various types of defects are created using electric discharge machine (EDM), analysed and correlation is established between dependent and independent parameters. An experiments were performed to evaluate the rotor dynamic characteristics of healthy and unhealthy bearing. Experimental results are used to validate the model obtained by DAMM and SVM. Experimental results showed that vibration characteristics could be evaluated by using theoretical model and SVM. This contributes to the service life extension and the efficiency improvement, so as to reduce bearing failure. Thus, the automatic online diagnosis of bearing faults is possible with developed model-based by DAMM and SVM.

No abstract