Continuous use of mechanical systems requires precise maintenance. Automatic monitoring of such systems generates a large amount of data which require intelligent mining methods for processing and information extraction. The problem is to predict the faults generated with ball bearing which severely degrade operating conditions of machinery. We develop a distributed fault prediction model based on big data intelligence that extracts nine essential features from ball bearing dataset through distributed random forest. We also perform a rigorous simulation analysis of the proposed approach and the results ensure the accuracy/correctness of the method. Different types of fault classes are considered for prediction purpose and classification is done in a supervised distributed environment. © Springer Nature Switzerland AG 2018.