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Cognitive computing preventive maintenance: How enterprises can benefit

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Enterprise asset management is tricky business. Under-maintenance and over-maintenance – both create problems for almost everyone, right from the maintenance staff to the end users of the IT assets. Under-maintenance, which most enterprises are guilty of, causes productivity loss, creates avoidable security gaps, and often results in expedited costs. On the other hand, over-maintenance creates unnecessary instances of downtime, increases labor costs, and causes severe operational challenges to teams that use the systems.

A huge challenge faced by enterprises, in terms of their IT asset-management practices, is that investing in maintenance is expensive. The kind of resourcing, time, and effort that maintenance activities need are too much for any enterprise to spend without strong reasons. This dilemma is what gives wings to the questions we’re exploring in this guide.

How does an enterprise balance the maintenance and operations when it comes to its assets? What’s the best way to achieve this balance? How do modern cognitive computing-powered tools come into the picture? Read on to know the answers.

Cognitive computing preventive maintenance ahead of reactive maintenance

The reactive maintenance motto of “if it ain’t broke, don’t fix it” does not cut it anymore, even for small and medium-sized businesses, let alone enterprises. Preventive maintenance, on the other hand, is pretty much the norm, because it helps enterprises realize cost savings by preventing asset breakdown and ensures safer operations and reduced risks of hazards. Other benefits delivered by preventive maintenance are increased equipment efficiency, decreased equipment downtime, more reliability, and prolonging of asset lifetime.

Basics of cognitive computing preventive maintenance

Cognitive computing tools have the potential to help enterprises manage asset health for highly complicated and expansive asset portfolios. The basic mechanism can be broken into three steps:

  • The first phase is data recording; there are several sensors in play, feeding a lot of data to the cognitive computing engine in real time.
  • Then, the data is used in conjugation with historical data that contains information about baseline health statistics of different assets.
  • Streaming asset health data is then compared to the predefined thresholds to identify potential situations where asset performance could fall below required standards.

This three-step procedure helps enterprises accelerate preventive maintenance.

IoT and cognitive computing

IoT has the potential to significantly alter and improve the way enterprises conduct asset health monitoring and maintenance. By using hundreds of sensors and channels of information exchange, IoT can be an effective solution to the decade old asset management problem for enterprises. Cloud-based analytics tools and edge computing-based real-time analysis tools can quickly transform data into knowledge. The kinds of insight on asset health, alerts on when to perform maintenance tasks, and rich information about asset performance – IoT enables these like no technology ever did earlier. This means that enterprises can combine the cognitive capabilities of advanced tools and IoT to infuse a sense of intelligence in physical objects.

Preventive maintenance vs. periodic maintenance

Traditionally, even enterprises that invest a lot in asset maintenance choose to stick with the idea of periodic preventive maintenance. The core idea here is to agree on a suitable time period, after which equipment is taken offline for preventive maintenance. However, with cutthroat competition, unimaginable pressure on assembly lines, and suffocating cost pressures, no organization can afford to take equipment offline unnecessarily. This is where the idea of conducting preventive maintenance when absolutely necessary comes to the fore.

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Of course, preventive maintenance can’t ever wait till the time the equipment actually breaks down. This contemporary idea of preventive maintenance is centered on:

  • Analyzing the failure state of equipment.
  • Measuring all micro factors at the failure stage.
  • Measuring these properties in the real-life situations.
  • Raising alarms when the readings become too close to those at the failure stage.

This approach overcomes the biggest flaws of traditional periodic preventive maintenance – ignoring the failure conditions! By creating insights about the real factors that cause assets to fail, the contemporary cognitive computing preventive maintenance approach ensures that downtimes are minimized, and the contribution to uptime metrics is improved.

Sensors: The core of cognitive computing preventive maintenance

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Most industrial equipment manufactured these days incorporates a lot of built-in sensors responsible for transmitting massive amounts of data in real time. These sensors can be integrated with building management systems, supervisory control and data acquisition systems, and programmable logic controllers. Also, these sensors are available off the shelf and can be fitted with older enterprise assets. These information streams provide adequate data inputs for cognitive tools to make use of and help enterprises realize early-warning indicator systems. The cognitive systems grow smarter with time, and can use the ever-expanding base of data and outcomes to fine tune its preventive maintenance alerts, creating a lot of insight for the enterprise in the process.

Improving operational effectiveness with cognitive computing preventive maintenance

Cognitive computing tools take a fully datacentric approach toward preventive maintenance. The data continues to be available for more analyses using other technologies owned by the enterprise. For instance:

  • Reliability engineers, maintenance professionals, and technicians can use real-time data to optimize preventive maintenance.
  • Cloud technologies help enterprises easily store the data originating from their assets, and feed the same data to analytical engines that can create a lot of insight on asset performance.
  • Even traditional and unstructured data, such as video feeds, can be captured and analyzed to uncover hidden patterns that can then be used to predict machine failure.

Can’t be ignored

Asset downtimes are massive headaches for modern enterprises. Thankfully, IT can help by leveraging the power of cognitive computing tools and to perform preventive maintenance. The cost savings, asset efficiencies, and asset conservation benefits of a cognitive computing preventive maintenance approach are too good for any enterprise to ignore.

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