What is Predictive Maintenance?

Predictive Maintenance continuously analyzes the condition of connected assets and equipment to reduce the likelihood of unplanned downtime or machine failure. It is a transformative application of the IIoT (Industrial Internet of Things) with tremendous advantages for your organization.

Decreased downtime: Predictive maintenance enables technicians to detect issues in advance and resolve problems before equipment failure can occur.

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

Reduced field service costs: By anticipating machine maintenance, service departments can generate major cost-savings and increased ROI (Return on Investment).

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

Improved worker safety: An unexpected breakdown or malfunction can lead to hazardous working conditions for your employees. By predicting when a malfunction may occur, service can be carried out before a machine becomes dangerous.

How Predictive Service Works and Why Is It Important?

Through a combination of real-time collected data, predictive maintenance continuously analyzes the condition of equipment during normal operations to identify potential machine failure.

Watch this 3-minute video to find out how organizations can monitor and test various indicators such as bearing speed, lubrication, and temperature using predictive maintenance, and learn more about the latest trends in service operations in the report linked below.

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Predictive Maintenance vs Condition Based Monitoring

Although both forms of proactive maintenance are aimed at preventing machine failure, there is a significant difference between condition-based and predictive maintenance. Condition-based maintenance uses sensors to collect real-time measurements from equipment about various conditions, such as temperature, pressure, or vibration. While predictive maintenance is a type of condition-based maintenance, it uses the constant stream of IIoT sensor data on a much larger scale.

Learn about the 4 phases of AI adoption and how service leaders are applying service optimization strategy to transform the predictive maintenance process in Part 1 and Part 2 of this Emerj report.

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Benefits of Predictive Maintenance

Predictive maintenance is the process of predicting a machine’s performance, status, and real-time health. With predictive maintenance, machines are equipped with sensors that are connected to IoT-enabled software that gives users updates, alerts, and notifications. The IoT-enabled software will collect vast amounts of data. This data will then be run through various algorithms that can be used to accurately predict when a future breakdown may occur. This form of machine learning allows predictive maintenance to prevent running unnecessary maintenance checks, further reducing waste in terms of time and effort.

Reduce Time to Production

Reduced Downtime

Detect issues and resolve problems before they occur, cutting unplanned downtime up to 30%

Reduce Operational Costs

Reduced Cost

Anticipate equipment maintenance by reducing truck rolls and increasing first time fix rates

Improved Productivity

Reduce disruptions caused by unexpected malfunctions or breakdowns by planning around shifts, maximizing uptime and increasing asset utilization

Transform Service Outcomes With Predictive Maintenance

Enable true predictive service by analyzing data to create user-specific alarms and alerts that help you prevent problems before they ever occur. Service leaders rely on PTC solutions to prevent problems proactively, reduce the need for truck rolls, and respond with agility. Our customers achieve:

Up to a

30%

Drop in Unplanned Downtime

Up to

83%

Faster Service Resolutions

Up to

75%

Less time on site

Combine Machine Data and AI to Deliver Predictive Maintenance

Unplanned downtime reduces your customers’ productivity and requires costly truck rolls to resolve. Predict and prevent failures before they happen, coordinate onsite visits, and minimize disruptions with a machine learning predictive maintenance strategy. PTC’s technology helps you monitor performance with IoT connectivity, use AI to predict problems, and even simulate conditions during the design process—making your products more reliable in the field.

Predict with what you know

Predict with what you learn

Predict with simulation

Predict with what you know

Merge your historical performance data, engineering specs, and real-time analytics to create user-specific, condition-based alarms and alerts—so you can fix an issue before it occurs.

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Predict with what you learn

Create a predictive maintenance strategy from AI and machine learning, which builds knowledge over time and identifies triggers that predict downtime and can be proactively resolved.

Build Service Intelligence

Predict with simulation

Simulate stress that causes performance problems during the design process to ensure they stand up to real-world conditions and determine predictive alarm and alert points.

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The Speaking of Service Podcast

The ways in which the Industrial Internet of Things (IIoT) can maximize your organization’s value are often overlooked, especially when it comes to service outcomes. While big topics such as the implementation of predictive maintenance and remote condition monitoring can seem daunting, PTC’s Speaking of Service podcast series features industry experts breaking down practical ways to grow service revenue, manage costs, and improve satisfaction.

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Predictive Maintenance Case Studies

Elekta

Elekta improved their connected service operations by using digital twins of deployed products in the field to improve equipment uptime and reliability.

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