The predictive maintenance process in the age of industry 4.0
Industrial Internet of Things (IIoT) and artificial intelligence algorithms are transforming and simplifying the process of managing the maintenance of critical equipment in factories.
Below are the new evolutionary steps in Jim’s maintenance process since the implementation of a 4.0 predictive maintenance system. Jim is the supervisor of a manufacturing plant in Quebec. He is responsible for the follow-up and maintenance of the critical machines of this plant.
Step 1 : Machine conditions monitoring
Professionals in the field advised Jim on the choice of sensors and on the choice of an IIoT platform.
Jim installs the sensors on the machines and then connects them to his IIoT platform.
The sensors allow it to monitor in real time the critical parameters of its machines (vibration, temperature, amperage, etc.). The cost of IIoT sensors makes data collection very accessible for manufacturing SMEs.
Step 2 : Setting up the data history
The sensors provide vital machine data in real time. Already, Jim sees trends he didn’t see before. The data history is starting and soon he will have enough data to push his analyses further.
Step 3 : Conditional maintenance
Jim has been tracking and analyzing his data for five weeks now. He makes sure that his machines are in good condition. He added alarm levels on his platform according to the established values, following his analysis. It automatically receives an SMS or email when one of these values reaches a critical alarm level.
Step 4 : Construction of the predictive model
The machine learning module builds a mathematical model based on the data history. This model is used to estimate the number of useful days remaining before the next breakage. A good model reaches a 95% confidence interval and specifies which machine component is most at risk of causing machine failure.
Step 5 : Predictive maintenance
The predictive maintenance module notifies Jim that a predictive maintenance requirement is required more than 90 days in advance (when the amount of data accumulated is sufficient). Jim prepares his work request and plans the materials and resources required for the work.
Step 6 : Predictive maintenance integrated into CMMS
The predictive maintenance system is now connected to the plant’s computer-aided maintenance management system (CMMS). The maintenance request is sent to the CMMS automatically by the system of the machine in need.
Jim validates and approves the request. Fred, the electrotechnician receives his work request and prepares in advance to perform the maintenance work.
According to recent studies on the subject, predictive maintenance is one of the most profitable areas of industry 4.0 for municipal, agri-food, mining or manufacturing plants. It simplifies your maintenance process and greatly improves the overall efficiency rate (RER) of the machines (more than 90%).
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