Abstract
The bearing is an essential component of a rotating machine. Sudden failure of the bearing may cause an unwanted breakdown of the manufacturing plant. In this paper, an intelligent fault diagnosis technique was developed to diagnose various faults that occur in a deep groove ball bearing. An experimental setup was designed and developed to generate faulty data in various conditions, such as inner race fault, outer race fault, and cage fault, along with the healthy condition. The time waveform of raw vibration data generated from the system was transformed into a frequency spectrum using the fast Fourier transform (FFT) method. These FFT signals were analyzed to detect the defective bearing. Another significant contribution of this paper is the application of a machine learning (ML) algorithm to diagnose bearing faults. The support vector machine (SVM) was used as the primary algorithm. As the efficiency of SVM heavily depends on hyperparameter tuning and optimum feature selection, the particle swarm optimization (PSO) technique was used to improve the model performance. The classification accuracy obtained using SVM with a traditional grid search cross-validation (CV) optimizer was 92%, whereas the improved accuracy using the PSO-based SVM was found to be 93.9%. The developed model was also compared with other traditional ML techniques such as k-nearest neighbor (KNN), decision tree (DT), and linear discriminant analysis (LDA). In every case, the proposed model outperformed the existing algorithms.
Highlights
Machines are the heart of any industrial unit or manufacturing plant
A data-driven intelligent fault diagnosis technique was developed for early fault detection of the deep groove ball bearing
The time wave data were captured in image format
Summary
Machines are the heart of any industrial unit or manufacturing plant. The profit of any production plant is highly dependent on the available runtime of machines. A reduction in downtime is essential to increase the company’s profit margin because maintenance costs carry about 15–20% of total production costs [1]. The sudden collapse of machine components may lead to substantial production losses. Proper condition monitoring of these components is essential to ensure the uninterrupted operation of industries. Various forms of information such as vibration, noise, temperature, current drawn by the motor, and lubricating oil conditions are obtained from the machines during this process. This information can play a significant role in developing a suitable maintenance strategy
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