The manufacturing industry has been going through rapid development, and a lot of new trends have been set in the past few years. Manufacturers have started to move from reactive to “proactive” (i.e. regular preventive) maintenance. Most recently, the concept of predictive maintenance has been introduced and has instantly become one of the buzz words of smart manufacturing.
More than a few steps ahead of standard computerized maintenance management systems (CMMS), predictive maintenance systems use machine learning models and advanced algorithms to predict asset failures. In simple words, you don’t just respond to problems: you know about them before they even happen. This way you have enough time to schedule maintenance, prevent asset downtime – and save costs.
Connectivity is a key concept related to Industry 4.0 and IoT. By connecting machines to the IoT platform via sensors, we are able to monitor parameters (such as temperature or vibration) that give us information about the actual condition of the devices. Predictive maintenance systems process these data and, using advanced analytics, they are able to forecast how the parameters might change based on similar patterns of behaviour in the past.
A good CMMS application, such as Productoo Maintenance Control, allows you to manage and track maintenance operations in your plant. It also gives you access to information about all assets, spare parts as well as maintenance staff. Most importantly, it offers you a clear overview of the current status of all corrective and preventive (i.e. regular planned) work orders, breakdowns and predictive maintenance tasks. Work orders related to predictive maintenance are automatically generated based on triggers coming from the machine sensors.
Productoo collects not only data from the machines but also data inserted manually by your technicians. Using predictive algorithms, this data is analyzed and compared with history. If the results indicate there might be a risk of a failure, a maintenance notification is issued automatically.