How much are you spending on preventive maintenance? How many components do you change for prevention although many times they are new? Surely you have thrown defective products due to the failure of a machine increasing the cost in materials, expensive repairs or with production stops even complying with the preventive maintenance plan. Predictive maintenance allows predicting failures in the production chain by showing alarms, stopping preventively or showing activity at each point through a control panel.
Isn’t predictive maintenance a priority in your company? Take a look at diagnosis 4.0, it helps to prioritize actions in digital transformation. Let’s see brief illustrations on predictive and preventive maintenance.
What Is Predictive And Preventive Maintenance?
Preventive maintenance is a set of actions necessary to keep machines running, minimizing downtime for failures, machinery breakdowns or unexpected stops. The main objective is to anticipate unexpected stops and minimize downtime in industrial companies, which include tasks such as changing parts, oils, periodic checks, etc. Some maintenance activities are unnecessary, but still remain less expensive than repair.
Predictive maintenance goes further, by monitoring the production chain we can know and monitor machine data such as temperature, humidity, pressure, uptime, etc. to assess and detect problems before they occur.
The use of Artificial Intelligence or Deep learning allows a more effective and faster predictive maintenance management.
All this data, captured in real time and historical data, can be used to improve predictive maintenance and for machines to learn and detect problems autonomously, showing alarms , indicating when a part has to be changed or, in extreme cases, preventively stop production. In order to extract this knowledge and generate predictive models that allow detecting when a production defect is going to occur, Artificial Intelligence and cognitive technologies are used, especially Deep Learning algorithms.