  In the era of the fourth industrial revolution, it is essential to automate
fault detection and diagnosis of machineries so that a warning system can be
developed that will help to take an appropriate action before any catastrophic
damage. Some machines health monitoring systems are used globally but they are
expensive and need trained personnel to operate and analyse. Predictive
maintenance and occupational health and safety culture are not available due to
inadequate infrastructure, lack of skilled manpower, financial crisis, and
others in developing countries. Starting from developing a cost-effective DAS
for collecting fault data in this study, the effect of limited data and
resources has been investigated while automating the process. To solve this
problem, A feature engineering and data reduction method has been developed
combining the concepts from wavelets, differential calculus, and signal
processing. Finally, for automating the whole process, all the necessary
theoretical and practical considerations to develop a predictive model have
been proposed. The DAS successfully collected the required data from the
machine that is 89% accurate compared to the professional manual monitoring
system. SVM and NN were proposed for the prediction purpose because of their
high predicting accuracy greater than 95% during training and 100% during
testing the new samples. In this study, the combination of the simple algorithm
with a rule-based system instead of a data-intensive system turned out to be
hybridization by validating with collected data. The outcome of this research
can be instantly applied to small and medium-sized industries for finding other
issues and developing accordingly. As one of the foundational studies in
automatic FDD, the findings and procedure of this study can lead others to
extend, generalize, or add other dimensions to FDD automation.
