The Six Principles of Predictive Maintenance
Disruptive innovation has radical and lasting effects on the way in which companies operate in their industry. Industrial engineering – more specifically, maintenance of industrial machinery – has gone through several disruptions in the last decades.
In the early days, maintenance involved little more than waiting for a machine to fail and fixing it on demand, hence the retrospectively applied term reactive maintenance. Due to the obvious shortcomings of this approach engineers transitioned to preventive maintenance, with scheduled maintenance sessions, always trying to stay ahead of failures.
The industry-wide adoption of new technologies for the collection of large amounts of data from operating machinery brought about a new era in the sector of industrial maintenance. Whereas preventive maintenance focuses on the earliest possible failure to be expected for an entire class of equipment, predictive maintenance determines when and how a given machine needs to be maintained based on its individual attributes.
The market potential for applications in predictive maintenance is expected to increase by a factor of ten in the course of the next decade. However, while the basic ideas and methodologies required to bring out this potential have been proven to succeed in other domains, various challenges hold predictive maintenance back from prospering in the industrial domain. Those include the lack of high-quality data, missing data on failure cases, regulatory aspects, and many more.
FLSmidth and Spryfox have joined forces to tackle the challenges described above. With a focus on FLS’ High Pressure Grinding Rolls (HPGR), we successfully applied predictive analytics to take predictive maintenance to the next level.
HPGRs are used for comminution (i.e., reduction to minute particles) of raw materials and minerals like ores and coal. The surface of the rolls which are used to grind the materials wear off over time which leads to the rolls being replaced regularly. As is the case with all large machinery, failures and downtimes produce large costs and their early prediction can yield considerable cost savings. As part of our work, we have identified six principles which drove the success of our application and its use in practice.(more…)