Aided by big data
Big Data refers to the large volume of diverse data sets generated at speed by heterogeneous sources. This data comes from a wide range of cloud and enterprise applications, websites, computers, sensors, cameras and much more — all coming in from different formats and protocols. In the manufacturing industry, there are many different types of data to take into consideration, including the data coming from production equipment fitted with sensors and databases from ERP, CRM and MES systems.
Breakdowns and errors in critical machines can disrupt the entire production chain of a manufacturing organization. Preventing these machines from breaking down or becoming a liability is key. It is crucial to ensure all machines are working optimally, all the time. The manufacturing industry is now counting on the help of machine learning- and historical data to create prediction models which can prevent downtime. Fixing the problem before it occurs is what it’s all about.
The rise of smart devices together with increase of cost efficiency of storage solutions unlocked the ability of storing an endless amount of data, thus allowing to approach problems in a data- driven way. But how?
Predictive maintenance aims to help in solving these challenges. In manufacturing, Big Data can help in improving the efficiency of processes and reducing production costs. Transforming data into actionable insights is one of the keys to stay ahead in competition, for instance increasing sales, thanks to the ability to follow market trends or the use of recommendation engines on the e-commerce portal.
Predictive maintenance is a type of maintenance that directly tracks an asset’s health, status, and performance in real time. It is aimed at reducing costly, unexpected breakdowns and offers the manufacturer the opportunity to plan maintenance around their own production schedule. Through a combination of real-time data collected through the IOT, predictive maintenance tooling continuously analyzes the condition of equipment during normal operations to reduce the likelihood of unexpected breakdowns.
Organizations can monitor and test various indicators such as slow bearing spread, or temperature. By using condition-based monitoring, these tools detect abnormalities during operations and send real-time alerts to machine owners that indicate a potential future failure.
Predictive maintenance techniques aim to determine when maintenance should be performed tracking an asset’s health, status and performance.