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Purpose: Early detection of bearing faults is an essential task of machine health monitoring. The bearings are one of the vital components of rotary machines. Various methods and techniques were used over the time to diagnose and improve the bearing fault analysis. This review paper includes a fourfold study on the fault analysis. First, the decomposition methods used for signal processing and extracting the required features are discussed under vibrational analysis for health monitoring. Second, the analysis on oil–air lubrication and thermal models used to study the temperature variations are studied. Third, analysis on sensor placement to select an optimal position in collecting the original signals with least possible disturbances was discussed in detail. Finally, in the fourth section, neural networks used for the fault diagnosis is reviewed and analysed. Methods: The convolutional neural network (CNN), Bayesian neural network (BNN), and probabilistic neural network (PNN) are the techniques most commonly used by researchers recently. Among the networks, CNN with modifications is found to provide high accuracy and robustness in identifying and classifying bearing faults even in noisy environments. Results: Discrete wavelet transform (DWT) method for the feature extraction. Daubechies four (db4) with five-layer decomposition method was used to identify the severity of bearing defects. Also, Orthogonal Fuzzy Neighbourhood Discriminant (OFND) features were extracted. This survey analysed existing works on fault diagnosis on spherical roller bearing in different perspectives. Conclusion: Among all the decomposition methods, variational mode decomposition was the most commonly used method and also found to be very effective compared to other methods. The intrinsic time scale method also finds a reasonable part in many research works. Also, various types of neural networks were used in which CNN and modifications to CNN were mostly used to automatically acquire the sensitive fault information from the decomposed signal data. The time-domain waveforms are used widely for feature extraction.