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Predictive Maintenance -  Dispelling Common Myths 

For plants operating at capacity, equipment is running at maximum throughput and maintenance departments are under relentless pressure to keep it that way. The problem is that the motors and bearings that are run faster and at a higher duty cycles than rated have a high risk of premature failure and unscheduled downtime. In response, many organizations just expand their preventative maintenance efforts.

Unfortunately, adding tasks to a preventative maintenance schedule doesn’t necessarily reduce downtime. It does increase cost, time, and effort spent on maintenance. Indeed, intensifying a predictive preventative maintenance program can make things worse, increasing worker fatigue and the chance of error. The solution is predictive maintenance.

Preventative vs Predictive Maintenance

  • Preventative maintenance uses modeling, failure analysis, and best practices to preempt failures by cleaning, adjusting, lubricating, and replacing assets according to schedule and/or usage.
  • Predictive maintenance uses continuous condition-monitoring and data analysis to determine when assets require maintenance or replacement and use targeted maintenance to preempt failures and avoid premature replacement.

Predictive maintenance leverages data about asset health to perform maintenance strategically. This enables engineering departments to strategically allocate maintenance dollars to optimize the performance of equipment throughout the facility. Depending on the equipment involved, diagnostics and troubleshooting can take place remotely, enabling companies to work with third-party reliability experts without paying those people to be on site.

In this scenario, maintenance staff spend their shifts preempting problems rather than performing rote tasks that may or may not deliver benefit. With predictive maintenance, companies no longer replace healthy assets unnecessarily. They can monitor even troubled assets to maximize useful lifetime without the risk of downtime or catastrophic failure the sensors will alert operators when issues arise. Maintenance becomes a scheduled operation.

Predictive Maintenance: The Myths And The Realities

So why are so many companies are unwilling or unable to make the switch to predictive maintenance? The answer lies in the gap between the common perception of predictive maintenance and the reality of modern predictive maintenance technology and practices.

Myth #1: Predictive Maintenance is Expensive

The condition-monitoring equipment traditionally used for predictive maintenance has been expensive. As a result, it was reserved for only high-value assets. Although this helps protect the investment in that equipment, such limited deployment doesn’t yield efficient ROI. Nor does it necessarily improve uptime. The reason is that in many cases, the choke-points are not high-value assets but more mundane components like pumps and blowers that cost a few hundred dollars. That makes it hard to justify buying a monitoring system costing five or six figures, especially when several of those choke-points exist.

Today’s Reality: Predictive Maintenance is Affordable

Today, an emerging class of condition monitoring systems is affordable enough for deployment on any asset type, including smaller assets such as fans, pumps, and blowers. Broad deployment across the facility. In many cases, they provide more effective results and fast ROI.

The best way to determine whether this approach can work for your facility is to conduct a small pilot project. Learn more about how to get started with a pilot project here

Myth #2: Predictive Maintenance is Time Consuming and Complex

The traditional, high-value systems can take days simply to deploy. If the equipment needs to be linked to the factory network, IT may need to get involved, which can introduce further headaches. At the end of the day, many end-users and even integrators simply lack the engineering resources to apply to the problem. Manual monitoring has many drawbacks but all too often, companies default to it because they think is the faster, easier option. The problem is that it only costs more time and money in the long run. Fortunately, the legacy systems are not the only option.

Today’s Reality: Predictive Maintenance is Streamlined

The newest condition-monitoring systems are designed for fast deployment that takes advantage of existing communications infrastructure. These attributes significantly reduce the engineering hours involved. Users can begin collecting data within a few minutes and configure the devices to send alerts to maintenance only when certain conditions are violated.

Myth #3: Predictive Maintenance Takes Analysis Expertise We Don’t Have

Installing the equipment and gathering data is just the start. Sensor readings need to be analyzed to yield actionable information. The need for these skills can discourage many organizations.

Today’s Reality: Analysis Can be Performed Off-Site

Fortunately, the modern emphasis on cloud deployment and analytics brings virtual expertise to the factory. Companies no longer have to educate staff. The third-party service providers can access the data in real-time as though they were standing in front of the machine. They can analyze machine performance using web tools and make recommendations on solutions. The approach enables organizations to focus their engineering efforts on the aspects of development that differentiate them from their competitors while leaving reliability monitoring to outside experts.

Myth #4: Go Big or Go Home

Companies sometimes decide to adopt predictive maintenance across their entire facility. Very soon, they are drowning in data, most of which indicates that the equipment is fine. The plant network is overwhelmed, the reliability technicians can’t keep up, and management doesn’t see any of the promised results.

Today’s Reality: Easy Does It, You Can Start Small And Get Results

By executing a pilot project, organizations can demonstrate success and uncover pitfalls that will help them more effectively make the switch for the rest of the plant. See how to choose the right predictive maintenance approach and develop a pilot project here

Predictive Maintenance in Action

For one food manufacturer, this new breed of affordable condition motors helped prevent extended downtime and major loss of revenue. The facility that bottled the company’s signature barbecue sauce was served by a single rooftop blower with a history of failure. Their third-party service provider installed a Dynapar OnSite Condition Monitoring System monitor the unit 24/7 from an off-site location. The actual installation process took less than 30 minutes and the unit began capturing data immediately.

In less than a month, changes in the vibration spectrum indicated a developing bearing defect. At this point, the OnSite demonstrated its true value. The manufacturer had a production quota that they couldn’t meet if they shut down the line for repairs. Because the OnSite monitored the blower around the clock, operations could continue production, secure in the knowledge that the unit would warn of any sudden changes to bearing condition in plenty of time to preempt failure.

Download the full case study here

Predictive maintenance has great potential for increasing productivity and uptime. All too often, however, companies are reluctant to make the switch, based on misconceptions and outdated information. Using the latest generation of condition-monitoring equipment, organizations can monitor assets across their facilities, preventing downtime easily and economically. The approach lets them spend less time repairing equipment and more time doing what they do best manufacturing quality product to delight their customers.

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Additional Resources:

Learn more about vibration analysis best practices and continuous vibration monitoring here

Learn how to choose the best vibration sensors for rotating equipment

Learn how to develop a predicate maintenance program by starting small and scaling here

Learn how to evaluate cloud-based condition monitoring security