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.

For a client in the health insurance domain, a health recommendation engine is crucial. We integrated this engine, after the design and development phase, on top of an existing health trajectory prediction application. Our goal was to ensure the client could offer recommendations on treatment and disease prevention by lowering their potential risk.

With only the company name available, how would you learn more about a given business—or even gather leads to get in touch with that business? We were approached by a client to build a comprehensive database not only covering certain companies’ individual characteristics, but also extending their data with information from unstructured sources in the WWW. Using cloud technologies and web-scraping frameworks, we built a data-mart that provided an extensive overview of different companies.

For a client in the mining industry, we developed machine learning models that continuously predict the wear of a complex machine over the next days and weeks. The AI can learn which factors and machine conditions drive the highest wear, all while explaining how it came to certain predictions in a clear and concise way. Through this approach, we’ve not only managed to predict future wear (therefore enabling an optimized maintenance schedule), but we’ve also created optimal machine setting guidelines to reduce future wear.

For a client in the medical domain, we converted a significant amount of unstructured health records into structured insights. Challenges included navigating discrepancies involving how health records might look, and addressing the fact that relevant data can reside anywhere—in tables, as free text, or even in handwritten notes. Yet with our solution, the client can tap into a so-far-unused dataset and drive novel use cases.

For a client in the mining industry, we developed machine learning models that understand and learn machine states and behaviors without human intervention. In a data explorer tool, these states and behaviors were visualized in a way that enabled discussion with subject matter experts, with the goal of deriving optimized machine configurations.

For a client in the insurance industry, data is essential. This is where SPRYFOX’s machine learning models come in. Using our engine, the client can get a better sense of pets’ health trajectories. Our models allow insurance experts to predict their furry customers’ likelihood of contracting specific conditions, providing vital insights that can help pets live longer, healthier lives.