Predictive Maintenance Tools

Machinery, equipment and physical assets are at the core of innumerable industrial enterprises. Whether you’re a solar power plant where solar panels are critical to daily operations or a polymer manufacturer that depends on reactors for streamlined production, reliable physical infrastructure is crucial to the success of such enterprises. 

However, equipment failure is a plant’s worst nightmare. Of course, planned outages and downtime are a part of any industrial business’s operations. However, unexpected and unplanned failures can result in disastrous consequences — at best, a headache, and at worst, sunk costs and customer dissatisfaction. 

Predictive maintenance tools help companies work more strategically by helping anticipate equipment failure and schedule maintenance and repairs accordingly. By using tools such as CMMS maintenance software and predictive maintenance analytics, industrial enterprises gain a more in-depth understanding of their equipment status to minimize unexpected outages and the corresponding consequences.


Predictive analytics for smarter maintenance

Predictive maintenance tools are intelligent, data-fueled tools used to anticipate equipment failure and outages to prevent unplanned downtime and loss of productivity. Categorized as condition-monitoring tools, predictive maintenance tools detect any potential equipment problems so organizations can take a proactive approach to equipment maintenance. 

The power of predictive maintenance tools is twofold. First, predictive maintenance tools detect any signs of equipment deterioration that may indicate the need for maintenance or repairs. Organizations can then capitalize upon insights gleaned through these tools to run analytics to predict potential asset failure. 

Predictive maintenance tools work by using powerful data analytics, machine learning and IoT predictive maintenance technology to accurately gauge equipment status and estimate the likelihood of failure or outages. Companies can then use this information to plan and schedule maintenance accordingly to maximize throughput and reduce the chances of unnecessary downtime.


Types of predictive maintenance tools

There are several main types of predictive maintenance tools that are implemented across an array of industries. These include:

  • Ultrasonic analysis - This tool involves using a microphone to pick up high-frequency sounds and converting these into recordings for further analysis. Organizations can analyze these audio recordings to determine if there are any possible issues and identify best maintenance practices for assets.

  • Vibration analysis - This tool uses sensors to detect and measure equipment vibrations. Any changes in these vibrations may indicate a need for maintenance to prevent major repairs.

  • Oil analysis - This routine process involves analyzing the composition of oil to assess its health and its impact on machine performance. Any contaminants detected in the oil may affect the asset’s ability to properly function and must be appropriately addressed.

  • Infrared analysis - This tool monitors any changes in infrared radiation emitted from assets to assess equipment status. Changes in temperature may indicate a potential problem and a need for maintenance.

  • Motor circuit analysis - This involves measuring supply voltage along with operating current to detect any potential problems. Using motor circuit analyzers as predictive maintenance tools helps enterprises detect faults in electric motors, which can result in significant downtime if left unaddressed.

  • Laser-shaft alignment - Misaligned shafts are responsible for a significant amount of equipment failures. Laser-shaft alignment involves examining shaft alignment to verify correct installation to prevent potential performance issues in the future.

Depending on the industry, some organizations may prioritize certain predictive maintenance tools over others. However, all these tools can work in conjunction to help enterprises gain a more comprehensive understanding of equipment performance. By gathering a diverse array of asset data, businesses can better estimate potential failures and take a more strategic approach to maintenance management.


Predictive maintenance solutions for a proactive approach

Predictive maintenance tools are often integrated with larger maintenance management solutions so companies can easily track and address any potential asset performance issues. Some examples of solutions that integrate with predictive maintenance tools include:

Predictive maintenance tools are useful for gathering important asset information that could otherwise go undetected. By collecting and analyzing this critical data, companies take a more strategic and informed approach to asset maintenance to increase uptime and profitability.



What are predictive maintenance tools?
Predictive maintenance tools consist of software, systems and other tools that are used to help businesses better anticipate any necessary asset maintenance to prolong asset lifecycle. Predictive maintenance tools also help enterprises accurately predict when certain assets will fail or require maintenance to prevent equipment downtime.

How do you do predictive maintenance?
Predictive maintenance involves using asset data to measure performance and detect any issues that may eventually result in outages or equipment failure. Predictive maintenance typically requires assistance from predictive maintenance software, a powerful tool that integrates data-driven IoT predictive maintenance technology to anticipate potential equipment failure and consequential downtime.

What is predictive maintenance strategy?
A predictive maintenance strategy takes a proactive approach to maintenance as opposed to a reactive approach. Predictive maintenance involves using predictive maintenance tools to forecast outages and asset failure and creating a preventative maintenance plan around these events. By weaving predictive maintenance tools into their smart manufacturing strategies, enterprises can improve productivity, increase efficiency and accelerate output.