Using Predictive Analytics for Maintenance

In the recent years, a lot of things have changed as far as big data is changed. In fact, the term big data is not very old either. It is now defined as data sets that are large and complex and that cannot be maintained, monitored and interpreted using the traditional data processing applications. Indeed there are many challenges to data analysis on large scale; to capture, curate, share, store, transfer and search and interpret big data is indeed not possible without new techniques of data analysis. Indeed, it is 'need' to analyze and interpret the big data that is now coming from a host of sources that predictive maintenance became so popular. In the recent times, the internet of things and a variety of sources big data is being churned out on daily basis. Stream analytics has emerged as a viable option to interpret and manipulate this data. Stream analytics is a step ahead of traditional analytics as it allows the data manipulation even before the data is stored in the database.

Even the corrective measures can be taken up before the actual fault occurs. Thanks to predictive analytics and new maintenance techniques.

As they say, necessity is the mother of invention. The ‘internet of things’ has led to an explosion of information of sorts and data is flowing in from all possible sources. In simple language, the Internet of Things is the network of physical objects or "things" embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. We have been fitting systems with electronics and sensors for decades – think about flow indicators, pressure indicators etc.

The internet of things makes it possible for the devices to communicate using Internet communications protocols and make the data available to be used for a range of additional uses. Besides, low-priced electronics makes it more cost effective to fit equipment with more sophisticated sensors and processors which can do more than just measure a simple parameter (such as overall vibration levels), but can also do additional analysis and diagnostics on the machine (such as calculating RMS acceleration, true peak acceleration) as well as other analysis. When these sensors are connected to a communications backbone, this greatly increases the volume of data that is available for analysis and also has the potential to enable real-time analysis.

In the past, these were not workable propositions as data could only be monitored not manipulated. The predictive analytics gives maintenance an edge as it not only alerts the data scientists and maintenance engineers to shut down a system if a fault is going to take place in future which could lead to severe damage of the whole process. In yesteryears, the maintenance engineers had no option but to either wait for the fault to occur or shut down the whole system for periodic maintenance which caused losses to enterprises and unnecessary delays.

The predictive maintenance analytics does away with the need for periodic shutdowns for overall maintenance as it predicts the bad sectors that must be taken up for maintenance before they cross the critical levels of performance. This indeed is a big boon for corporations which have immensely benefitted from the predictive maintenance analytics as it not only minimizes the wear and tear of the system but also saves important man-days that were earlier lost due to technical snags and systemic failure.

I'm Emma Thompson from Usa. Writing is my passion and I love to share anything interesting that comes my way while surfing Internet. I love to make friends and meet new people at new places.