A simple predictive maintenance definition would be that it is a technique which makes use of various tools of data analysis to detect probable defects in the machinery before they fail. The use of predictive maintenance in various sectors of industry is common from the 1990s. With the use of artificial intelligence (or künstliche Intelligenz (KI) in German) and machine learning along with the integration of the IOT devices, determining the malfunctioning condition of any components well before it permanently breaks down has become very accessible.
The various advantages of PDM:
- Reduced plant downtime – Knowing about the faulty condition of any component before it finally stops working would definitely benefit because that would help in repairing the particular without much hindrance to the working of the plant. However, if the component shuts down, all the co-related parts would also prove to be unproductive. This would prove to be a massive financial loss for the company on most occasions. PDM also allows the systematized scheduling of the optimal maintenance and inspection routine to reduce unplanned downtime and avoidable costs.
- Increase in lifespan of equipment – The determination of any malfunctioning component in some machinery with the help of machine learning, which is a subset of artificial intelligence (künstliche Intelligenz (KI) in German) contributes to the lifespan of that component positively. This is because minor maintenance of the part could make it work in an efficiently once again. For instance, if some bearing needs a bit of oiling, it would be evident in the vibration patterns and would also be pointed out by the experts. This would also in turn reduce your need to purchase new components again and again.
- Increased savings – The fact that the plant would not face long downtimes and at the same time would not need to purchase new components, would reduce the amount of expenditure. The plant would be manufacturing more which would imply an increase in income and savings. Since there are numerous associated costs to each asset in an industry, an unforeseen failure could be a huge burden to the total cost of owning an asset. What needs to be taken care of is that the IOT devices are properly integrated with the predictive maintenance because they use historical information from various sources including the devices themselves to predict asset health accurately, thus following its definition. This would enable the specific authorities to take necessary actions on the basis of this information.
- Increased optimization and productivity – A well-maintained machine would help employees to make best use of those. This would increase productivity by spending lesser time on it. This is how revenue would be increased. PDM helps in identifying if the delays are caused by something internal or external and also assist in setting up processes addressing these factors.
- Safety – PDM would be highly beneficial in the determination of possible safety threats and pre-empt potential issues before they affect the employees. The organization can take immediate and appropriate actions to negate safety risks since they would be able to analyse the data from both internal and external sources along with the data from IOT devices and sensors.
A well-integrated system of Internet of Things and predictive maintenance would definitely help a plant in increasing their productions and also ensure a safer environment which is in accordance with the regulations for their workers.