Condition Monitoring is helping industrial firms cut costs significantly
By combing through the vast amount of data collected by IoT sensors, a new paradigm called “predictive maintenance” anticipates when equipment will need replacement. Leveraging the power of machine learning, these systems can notice signs and signals no human could ever pick up on.
Despite digital technology replacing older technologies, it’s often too costly to replace heavy machinery and other devices in certain fields. Many of these large assets have been embedded with smart sensors for years. Maintenance is a major cost of running businesses in heavy industry, and the benefits of predictive maintenance can let companies replace components before they fail, leading to less downtime and round the clock utilization. In certain cases, a failing component can lead to catastrophic failure, damaging other components along the way; predictive maintenance can prevent these failures and shut down assets in an instant.
One example of how predictive maintenance can improve operations is the agricultural field. Predictive maintenance can relay information about when a certain component is going to fail, letting farmers schedule replacement at a time when machinery is already shut down. Over time, predictive maintenance can determine how much a component tends to last at a specific farm, giving farmers elsewhere better information for scheduling maintenance.
Some of the other instances where predictive maintenance is useful are fairly obvious; a drop in vacuum pressure in a component, for example, clearly indicates that it’s not operating optimally. Sophisticated predictive maintenance, however, can also use non-obvious metrics that, based on past performance, correlate with future failure. While humans operate on logic and intuition, AI systems and predictive analytics rely on past performance, even if it seems counter-intuitive.
One of the benefits of predictive maintenance is that it works with existing technologies that are already coming to many industries. If a company is already investing in IoT technology, incorporating predictive maintenance capabilities is relatively straightforward. Advanced predictive maintenance technology can work well with software that monitors and tweaks machine performance, leading to more robust data and a leaner operation, whether on a farm, in a factory, or elsewhere.
We spoke to Hewlett Packard Enterprise’s EMEA Chief Technologist Andy Watson to talk about trends in industrial IoT, predictive maintenance and edge computing. Watch above.