What Is Predictive Maintenance and How Is It Transforming Manufacturing?

Written by: Leah Gourley

Read Time: 2 min

Predictive maintenance is a type of maintenance that directly tracks an asset’s health, status, and performance in real time. Predictive maintenance is aimed at reducing costly, unexpected breakdowns and offers the manufacturer the opportunity to plan maintenance around their own production schedule.

How does predictive maintenance work?

Through a combination of real-time data collected through the industrial internet of things (IIoT), predictive maintenance continuously analyzes the condition of equipment during normal operations to reduce the likelihood of unexpected machine failure.

With predictive maintenance, organizations can monitor and test various indicators such as slow bearing speed, lubrication, or temperature. Using condition-based monitoring and IIoT technology, these tools detect abnormalities during normal operations and send real-time alerts to the machine owner that indicate a potential future failure. More specific types of predictive maintenance including:

Vibration analysis: This is a common type of predictive maintenance used inside manufacturing plants with rotating machinery. It can detect imbalance, misalignment, or loose parts of equipment. 

Infrared analysis: Using temperature as an indicator, issues related to airflow, cooling, and motor stress can be identified.

Sonic acoustical analysis: Sounds can be converted to an auditory or visual signal that can be heard or seen by a technician, indicating conditions such as worn or under-lubricated bearings in both low and high-rotating machinery.

When is predictive maintenance suitable?

Equipment manufacturers in all types of industries, including medical devices, high volume pumps, packaging equipment, and data centers, have implemented the IIoT in production and achieved initial value. But when IIoT is used alongside predictive maintenance and service, organizations can make the most of their data to increase uptime. All kinds of manufacturers can achieve great value by unlocking the full potential of data to deliver business benefits.

What are the three types of maintenance?

  • Corrective Maintenance

Corrective maintenance occurs when equipment is repaired after it has failed. This strategy identifies the failure and restores the system to operational condition. The time between identifying the failure and rectifying often results in downtime for the customer, as machinery and lines cannot be run.

  • Condition-Based Maintenance

Condition-based maintenance uses sensors to collect real-time measurements from equipment about various conditions, such as temperature, pressure, or vibration. It can then work alongside other maintenance strategies such as preventative maintenance to keep maintenance costs – and unplanned downtime – at a minimum.

  • Preventative Maintenance

Preventative maintenance takes place on a regular basis to reduce the chances of equipment failure and unplanned downtime in the future. While predictive maintenance is a type of condition-based maintenance, it uses the same constant stream of IIoT sensor data to accurately learn and predict the root cause of failures, rather than a preset schedule.

Predictive maintenance vs. preventive maintenance

Although often used interchangeably, there are significant differences between predictive and preventive maintenance. 

  • Preventive maintenance occurs at regular intervals based on the machine’s lifecycle, regardless of usage, to ensure that no issues emerge. For preventative maintenance, the only variable used to predict failure is a span of time since the previous maintenance was conducted.
  • Predictive maintenance is scheduled based on equipment conditions. Data produced during the machine’s normal operations is analyzed to reduce the probability of failures, and provides a richer understanding of the causes, likelihood, and time to failure if an asset is left unserviced.  

The difference between condition-based maintenance and predictive maintenance

Although both forms of proactive maintenance are aimed at preventing machine failure, there is a significant difference between “condition-based maintenance” and “predictive maintenance.” 

  • Condition-based maintenance (CBM) automatically tells service teams when installed equipment is not operating to specification or needs attention. Service is then delivered only once the condition status demands it—i.e. when your machine has hit a specified threshold parameter level.
  • Predictive maintenance allows technicians to catch potential issues even earlier, so service can be scheduled more efficiently.

While predictive maintenance is a type of condition-based maintenance, it uses the constant stream of IIoT sensor data on a much larger scale. Rather than only taking the condition status into account, predictive maintenance leverages big data methodology to predict machine degradation based on asset history and related data. Further, condition-based maintenance often runs the risk of multiple machines requiring service at the same time, making it difficult to coordinate service without disrupting the normal operations of your manufacturing floor.

5 technological advantages of predictive maintenance

Predictive maintenance is a transformative application of the IIoT with tremendous advantages. Below, we explore five benefits that can serve as differentiators for your organization:

Decreased downtime

Predictive maintenance enables technicians to detect issues in advance and resolve problems before equipment failure can occur, so you can:

  • Cut unplanned downtime by as much as up to 30%Schedule multiple service procedures at one time
  • Avoid the risk of reputation-damaging outages
  • Reduce costly truck rolls required by unexpected downtime

Greater worker productivity

There is no need to disrupt worker productivity for an unexpected malfunction or breakdown. Predictive maintenance plans around workers’ schedules, and:

  • Enables up to 83% faster service time-to-resolution
  • Maximizes uptime and prevents productivity lags
  • Increases asset utilization

Reduced field service costs

By anticipating machine maintenance, service departments can generate major cost-savings and increased ROI through: 

  •  Reduction of costly service truck rolls
  •  Increased first-time fix rates
  •  Streamlined maintenance costs through reduced labor, equipment, and inventory costs

Improved Product Design

Harnessing the power of IIoT data collected through your machine’s sensors, product designers can use this vital information to:

  • Extend asset lifespans
  • Improve equipment durability and reliability
  • Build more efficient machines in the future

Improved worker safety

An unexpected breakdown or malfunction can lead to hazardous working conditions for your employees. By predicting when a malfunction may occur, you can ensure:

  • Employees are nowhere near a machine when it breaks down
  • Technicians can carry out service before a machine becomes dangerous

How to implement a successful predictive maintenance strategy

To reap the benefits of a predictive maintenance program, you must lay the groundwork, prioritize critical assets, and start small with high-value use cases that can be scaled up over time. Here’s how to begin your service transformation: 

  1. Design your program: Gain buy-in from upper management and outline the benefits and goals of your predictive maintenance program. Identify equipment that has experienced a history of high failure and the associated causes.

  2. Install IIoT devices: Machines equipped with sensors, connected to an IIoT platform, such as PTC’s Thingworx, can be leveraged to carry out predictive maintenance.

  3. Perform system integration: Leverage IIoT tools to turn condition-monitoring activity into action. The connectivity established by IIoT-driven condition monitoring can be leveraged for analytics, automation, and integration between OT and IT to further increase efficiency.

  4. Schedule maintenance: Empower your service teams with insights created by smart algorithms sent via real-time alerts to coordinate maintenance.

What challenges stem from predictive maintenance? 

  • Detailed planning process: Preparing to implement IoT on your manufacturing floor can be a complex, time-consuming process and involves a big upfront investment. It is important to communicate your plan clearly so that everyone has a strong understanding of the process moving forward.
  • Integration with current assets: Especially when it comes to older equipment and technology, it is imperative to make sure your predictive maintenance hardware and software can communicate effectively.
  • Bringing staff up to speed: Ensuring your staff understands how the new technology works is the key to unlocking great value. By utilizing predictive maintenance technology to its fullest potential, workers can draw the insights they need to reduce downtime and increase productivity. IIoT can deliver role-based notifications that enable front line employees to modify their usage of assets and equipment, if they are operating out of spec or at risk of a mechanical failure.

Looking forward to the future of predictive maintenance

The value in predictive maintenance in its ability to eliminate downtime on manufacturing floors with minimal disruption – and it’s been pretty successful so far. By catching failures before they occur, predictive maintenance is helping all types of manufacturing floors become more efficient and cost-effective. Industry 4.0 and the constant improvements being made in the world of artificial intelligence (AI) will only bolster the capabilities of predictive maintenance alongside automation, real-time analytics, and true enterprise-wide connectivity. Further, predictive maintenance will help support a more sustainable future by reducing the amount of energy used on the manufacturing floor as well as increasing the useful life of equipment.

Utilize IoT-enabled predictive maintenance to gain a competitive edge

Predictive maintenance offers many benefits that invest in the IIoT—from reduced downtime and fewer productivity lags to cost-savings advantages. The benefits of predictive maintenance will extend beyond your service department to become enterprise-wide. Don’t wait until machines are down to fix them. Start being proactive at your plant with predictive maintenance and optimize your workforce efficiency. Kickstart your business’s transformation with PTC’s eBook on how to anticipate machine maintenance with the IIoT. 

Reduce Truck Rolls and Increase Savings

How Spending Less Time on Service Boosts Uptime and Customer Satisfaction Read Now
Tags: Predictive Analytics

About the Author

Leah Gourley

Leah Gourley is a Digital Content Marketing Specialist based out of PTC's Boston office. She enjoys creating and sharing content surrounding the latest technologies that are transforming industries, including augmented reality and the industrial internet of things.