How Predictive Maintenance Analytics Can Make Your Business Profitable

Posted by Emma Thompson on May 28th, 2018

In every business regardless of the nature of their business profit can only be made if the cost of production is kept under check and the product is sold at reasonable price.  Indeed the cost of the production depends upon the factors which are beyond the control of the entrepreneur like demand in the market, competitors pricing, cost of taxes etc. But the costing of the product is definitely under the control of the company to a large extent. Keeping the machines in good condition and reducing the downtime not only saves the money spent on repairs but also reduces the downtime thereby improving productivity.

When it comes to maintenance of machinery, equipment manufacturers, EPCs and power and process plant owners are hard-pressed to keep their fleet, machinery, and other assets in effective working condition.  Predictive maintenance is the technology based on real-time big data analytics to forecast when functional equipment will fail so that its maintenance and repair can be scheduled before the failure actually occurs. The underlying architecture of a preventive maintenance model is fairly uniform irrespective of its end applications.

By identifying potential issues, companies can deploy their maintenance services more effectively and improve equipment up-time. The critical features that help to predict faults or failures are often buried in structured data such as year of production, make, model, warranty details, and in other unstructured data.

Real-time big data analytics is the most happening analytics as it is fast and does not wait for the data to reach the storage. The data scientists can access the data on the go in real time and take corrective measures. Information from millions of log entries, sensors, error notifications, pressure, current, voltage   generates the data that is valuable for predictive maintenance and can be effectively used to predict the problems in the assembly line well before they actually occur.

By using predictive maintenance analytics, the information derived from such sources can be turned into meaningful and actionable insights for pro-active maintenance of assets. This naturally results in asset downtime due to accidents and failures of key components. Apart from this predictive maintenance analytics does away with the need for regular checkups which is the norm in the industries which do not base Apache Spark based real-time big data analytics.

The predictive maintenance employs non-intrusive testing techniques to evaluate and computer asset performance trends. The methods used can be thermodynamics, acoustics, vibration analysis, and infrared analysis and so on. The continuous development of big data, machine-to-machine communication, and cloud technology has created new possibilities of studying the information emanated by industrial assets. The bigger players are already using it for more than a decade. The small and medium-sized companies in the manufacturing sector can also reap its advantages by using it.