As a part of the digitalization process of manufacturing, the industrial internet of things (IIoT) is the use of smart sensors and AI/ML algorithms to enhance production and industrial processes. Every machine speaks with several measures such as vibration, temperature, voltage, temperature, pressure, sound, etc. The only way to understand them is by converting the raw data into meaningful diagrams through data analysis. This is what the whole predictive maintenance (PdM) concept is trying to do.
First of all, what is the aim of these two different maintenance methods before going into detail? In parallel with the definition of maintenance, Predictive and Preventive Maintenance methods are designed for increasing asset reliability and minimizing the cost of failures through the process of monitoring the condition of the machines. However, their approach is different. When we look at the current attitudes towards maintenance in the industry, the most preferred method is preventive maintenance. Preventive maintenance includes regular and routine maintenance to prevent downtimes and reduce the possibility of unexpected machine failures. This method relies on past statistics and lifetime data, while predictive maintenance focuses on monitoring and analyzing data from the current condition of the machine in the field or operation.
The starting point of predictive maintenance is the same problem as preventive maintenance, which includes efficiency loss due to downtimes and unexpected stops in the production facilities. On the other hand, predictive maintenance uses data analytics to analyze machine health via vibration, pressure, and temperature data in order to predict machine malfunctions before unexpected stops occur. When the AI algorithms detect the anomaly in the dataset, in other words, observe irregular behavior from the standard parameters, the system alerts technicians in the field to check the condition of the machine.
Main Benefit of Predictive Maintenance
Cost reduction seems to be the key benefit of condition-based predictive maintenance. Since it does not require skilled staff and maximizes the efficiency of the monitored machine, it directly saves money by creating planned maintenance optimization. However, this cost-saving is not the most significant benefit of predictive maintenance; the bathtub explains it. This theory says that if any equipment is newly assembled, it starts working with all the risks in the early failure zone, and the risk of failure is high until it moves into the safe operating zone. If you do not carry out your planned maintenance activities when necessary, you increase the risk of failure every time. The production facilities that have the technology to predict when equipment could fail with condition monitoring techniques do not only save money through efficient production capacity also predictive maintenance directly extends the lifetime of the machines used.
Obviously, the side benefit of predictive maintenance is the continual and steady production process. Since the system predicts the machine malfunctions before it happens, required maintenance and repair actions are taken, and the production line does not stop anytime.
Regular and periodic operations with preventive maintenance have been rapidly abandoned, and it seems that it will not be able to take place in this market no longer again. In order to maintain their competitiveness level, manufacturing companies need to take action immediately regarding the digital transformation in their production facilities. In short, digitalization in manufacturing with predictive maintenance starts to cease as a choice and becomes indispensable.