Data science and AI have become the word of the day in every industry from gaming, fintech, and engagement to healthcare, HR, and beyond, kicking them into evolutionary overdrive.
One of the slower sectors to adopt AI, however, has been the manufacturing and process sector, including food & beverage, oil & gas, automotive, and chemical industries. While embracing various technologies towards smart manufacturing and Industry 4.0, these industries still seem to lag behind when it comes to maintenance.
With countless manufacturing processes and assets – machines, pumps, steam traps, valves, flanges, heat exchangers, pipes, and the list goes on – these industries are rife with potential trouble spots that can critically impact their operational and financial cycles. Even the slightest malfunction in a small asset can mean a general shutdown due to regulatory restrictions on environmental pollution levels, resulting in tremendous downtime costs.
Predictive maintenance
Industry 4.0 uses data science to perform predictive maintenance: proactive decision-making based on production floor data, derived from IoT sensors that can detect what happens inside specific industrial assets.
Machine learning and deep learning models are able to identify performance issues in real time, allowing for preventive, cost-effective maintenance by fixing issues ahead of asset failure or damage.
The move towards AI
As the manufacturing sector develops, AI is increasingly used for predictive maintenance. Since many of the target industries for Industry 4.0 are low-tech by nature, there’s a learning curve when it comes to AI, and to predictive (rather than reactive) maintenance in general.
Historically, a technician in these industries would follow scheduled, predefined maintenance cycles, and failures would only be discovered during routine maintenance, or if a real-time failure occurred, shutting down the production line.
A universal shift in concept
Today, the advent of smart solutions has given rise to two-way communication between machines and maintenance staff. This requires a conceptual adjustment by everyone from technicians up to management. With malfunctions flagged in real time, reactions need to be quicker across the entire chain of remediation.
But change is a delicate balancing act, and no industry can be transformed in a day. Thus, for solution providers like Feelit, predictive maintenance AI modeling isn’t just about spotting malfunctions, but also about fine-tuning the conditions in which an alert – i.e., engaging the technician – is warranted within a particular industrial operation.
The better technology matches pace with the industry, the smoother evolution can take place.
Photo by Simon Kadula on Unsplash
