How Can Big Data Aid Predictive Maintenance?

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What is predictive maintenance?

Predictive maintenance performs two key functions:

  1. To analyse equipment performance, and predict when failure might occur.
  2. To act on any predicted failures by performing maintenance.

The key benefit of predictive maintenance is the ability to prevent equipment failure entirely, by performing maintenance in advance of predicted failures. Ultimately, this results in a lower maintenance frequency, as well as a significant reduction in repair costs.


What is the difference between preventive and predictive maintenance?

The two most common forms of maintenance are preventive maintenance and predictive maintenance, and people often question the difference between the two.

The key difference is that preventive maintenance is planned and scheduled regardless of equipment performance, whilst predictive maintenance is performed in response to predicted equipment failure.

Preventive maintenance aims to prevent any breakdowns or issues occurring in the future, however, its main issue is that this “regular checkup” approach can be unnecessary and costly to businesses. Predictive maintenance, on the other hand, achieves the same outcome, but only performs maintenance when actually necessary, saving businesses money, as well as preventing any equipment failures which could occur between scheduled preventive maintenance slots.


Why is predictive maintenance important?

The major benefit of predictive maintenance is that it saves organisations money. It achieves this through lowering maintenance costs, by only performing maintenance when necessary, and minimising repair costs, by performing necessary maintenance before any equipment failures occur. Predictive maintenance also helps extend equipment life, reduce downtime and improve production quality by addressing problems before they cause equipment failures.

However, in order to function properly, and achieve the above results, predictive maintenance needs a supporting digital infrastructure, to facilitate equipment monitoring, analysis, and maintenance.

Predictive maintenance also draws on past data, and so it’s important to view the process as a journey, rather than a one-off fix. It starts with identifying the right set of data points, integrating with the machine to ingest real-time data and improving the data quality through live tracking of machine failures. Data preparation and data quality are the key inputs for any predictive model. The higher the quality of data fed into the predictive model, the better its accuracy.


How can big data aid predictive maintenance?

As experts in building management, we have a great deal of experience with predictive maintenance and have found that accurate data, and how it is analysed, is crucial to success. With Big Building Data, we are able to accurately monitor all aspects of building and equipment performance, allowing for fully-optimised predictive maintenance.


What is Big Data?

The term “big data” is fairly self-explanatory – it refers to a large volume of data gathered by an organisation for analysis. As first articulated by industry analyst Doug Laney, big data can be broken down into the “three Vs”:


Organisations collect data from a variety of sources, including business transactions, social media and information from sensor or machine-to-machine data. In the past, storing it would’ve been a problem – but new technologies have eased the burden.


Data streams in at an unprecedented speed and must be dealt with in a timely manner. RFID tags, sensors and smart metering are driving the need to deal with torrents of data in near-real time.


Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, email, video, audio, stock ticker data and financial transactions.


How can it help?

Big data is hugely beneficial to organisations looking to improve their operating costs, as it provides useful insights which lead to better decisions and strategic business moves.

In terms of predictive maintenance, big data is hugely beneficial. Firstly, a greater volume of data leads to more accurate predictive maintenance, as well as the ability to analyse trends in equipment performance. Secondly, the speed at which big data is delivered ensures that predictive maintenance is carried out as soon as potential equipment failure is detected, minimising downtime.


Get in touch

The volumes of data now available from buildings and their services can truly be categorised as ‘big building data’. The advent of this data has opened up huge opportunities to provide accurate advanced analytics, allowing building managers and operators to predict and pre-empt problems that degrade a building’s operational efficiency and energy performance. Get in touch with Next Controls to discover how we can utilise big data to improve your building performance and save your business money on operational costs.

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