Analysis of a Full-Stack Data Analytics Solution Delivering Predictive Maintenance

With the developments of Industry 4.0, data analytics solutions and their applications have become more prevalent in the manufacturing industry. Currently, the typical software architecture supporting these solutions is modular, using separate software for data collection, storage, analytics, and visualization. The integration and maintenance of such a solution requires the expertise of an information technology team, making implementation more challenging for small manufacturing enterprises. To allow small manufacturing enterprises to feasibly obtain the benefits of Industry 4.0 data analytics, a full-stack data analytics framework is presented, and its performance evaluated as applied in the common industrial analytics scenario of predictive maintenance. The predictive maintenance approach was achieved by using a full-stack data analytics framework comprised of the PTC Inc. Thingworx software suite. When deployed on a lab-scale factory, there was a significant increase in factory uptime in comparison with both preventive and reactive maintenance approaches. The predictive maintenance approach simultaneously eliminated unexpected breakdowns and extended the uptime periods of the lab-scale factory. This research concluded that similar or better results may be obtained in actual factory settings, since the only source of error on predictions in the testing scenario would not be present in real world scenarios. An analysis of the effect of downtime period durations and discussion on the cost of reactive maintenance and associated breakdowns is also presented.