这个脱胎于工业4.0的技术飘在空中已经很久了，转一篇文章梳理一下，关于大数据的价值挖掘和应用，这是个基础。
What is predictive maintenance?
Predictive maintenance uses historical, available data to foresee when equipment failure is likely to occur so that you can proactively address that potential failure with maintenance. This maximizes efficiency and reduces downtime.
Predictive maintenance vs preventive maintenance: What’s the difference?
Let’s be clear: predictive maintenance is different than preventive maintenance.
Preventive maintenance generally involves inspecting a machine or piece of equipment and doing some sort of maintenance on it. It’s usually performed on the basis of time (for example, seasonal HVAC inspections) or usage (for example, rotating a vehicle’s tires at 25000 miles).
Predictive maintenance, on the other hand, can be continuously monitored and acted on when conditions fall out of optimal parameters.
Why predictive maintenance now?
What’s driving this move to predictive maintenance is the Industry 4.0 revolution. The Internet of Things (IoT) is one of the main enablers of Industry 4.0, as it allows machine-to-machine connection and communication where it was not possible or practical before. IoT sensors that are attached to industrial machinery generate data which is then captured, collected, and analyzed.
The collected data is the jumping off point for predictive maintenance. The data that’s needed for predictive maintenance is time-series data, meaning it’s collected at specific, discrete times. With that information in hand, you can start to build out machine learning models to predict when machines are likely to fail.