What is predictive maintenance? How it works

What is predictive maintenance?

Predictive maintenance uses data and algorithms to determine when equipment might fail so you can perform maintenance right before that happens. It helps reduce unplanned downtime and costs associated with emergency repairs.

How this happens: 

Data and algorithms: Predictive maintenance relies on analyzing historical data and using algorithms to identify patterns and predict future equipment failures.

Right before failure: The goal is to schedule maintenance interventions just before a breakdown occurs, preventing unplanned downtime.

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How predictive maintenance works with robotic systems

Predictive maintenance uses data and analytics to determine the condition of robots and schedule maintenance before issues pop up. Sensors on the robots monitor performance and alert technicians to potential problems. 

Robots are fitted with sensors that continuously monitor crucial performance metrics. These sensors track:

• Vibration patterns: Unusual vibrations can signify wear and tear in motors, bearings, or joints.
• Temperature: Overheating can indicate electrical faults, lubricant breakdown, or impending component failure.
• Power consumption: Changes in power draw can reveal excessive strain or degradation within specific parts of the robot.
• Performance data: Tracking cycle time, precision, and repeatability helps detect subtle deviations that might indicate developing issues.

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Predictive maintenance software analyzes this real-time sensor data using algorithms and machine learning models. The software looks for:

• Anomalies: Sudden spikes or drops in sensor readings that suggest an immediate problem.
• Long-term trends: Gradual changes that signal a component is degrading, allowing for prediction of when it might fail completely.

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The system generates actionable insights:
Early warnings: The system triggers alerts when analysis points to a potential future problem.

• Maintenance scheduling: It recommends when to perform maintenance to prevent breakdowns, balancing this with minimizing unnecessary downtime.
• Specific diagnoses: The system might pinpoint the exact component likely to fail, allowing for targeted repairs.

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Key predictive maintenance technologies

Several innovative technologies have enabled predictive maintenance for robotics. Sensors are a key part of predictive maintenance, as they gather data on the operating conditions and performance of robotic systems.

Let’s go a bit further: 

• Condition monitoring sensors track factors like vibration, temperature, and pressure to detect early signs of potential issues. These sensors can identify tiny changes that indicate a component is starting to fail or malfunction before it impacts operations. Robots are often outfitted with these sensors, which provide constant health monitoring.
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• The data from condition monitoring sensors would be useless without analytics. Advanced analytics, machine learning, and AI sift through the sensor data to detect patterns, identify anomalies, and predict when and what maintenance will be required. Software analyzes trends in the data to determine if a robotic component or system will fail within a given time frame.
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• Augmented reality. Augmented reality, or AR, is an emerging technology for predictive maintenance. AR overlays digital information onto the physical world. Technicians can use AR glasses to view sensor data, alerts, and repair recommendations displayed on the robot they’re servicing. This makes predictive maintenance more efficient by providing technicians with information at the point of service.

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And while that’s the main technology, there are others that play a supporting role. 

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They are: 

• Cloud computing provides scalable computing power and storage, essential for the volume of data generated in predictive maintenance systems. Cloud platforms can handle the analysis of massive datasets, particularly when monitoring numerous robots or tracking high-resolution sensor data.
  
  
• Internet of Things (IoT) infrastructure enables real-time transmission of sensor data and remote monitoring of robot health. Secure and reliable IoT networks ensure continuous data flow, allowing for immediate detection of anomalies and timely alerts for technicians.
  
  
• Digital twins allow for virtual simulation of the robot's behavior, including potential failure scenarios. This helps test maintenance strategies and optimize predictive models without the need to experiment on the physical robot itself, minimizing downtime and preventing costly interruptions.

Predictive maintenance technology benefits in robotics

As robotic systems become increasingly sophisticated and autonomous, predictive maintenance becomes even more important for optimizing performance and reducing downtime. 

Here's how this translates into tangible benefits:

• Maximized productivity: Predictive maintenance minimizes unexpected breakdowns, allowing robots to work consistently and meet demanding production schedules. This translates into increased throughput and lower operating costs.
  
  
• Better safety: By preventing catastrophic failures, predictive maintenance reduces the risk of robot malfunctions that could damage equipment or injure workers. It ensures robots operate safely within their design parameters.
  
  
• Extended asset lifespan: Proactive maintenance extends the working life of expensive robotic components. This protects your investment and avoids the costs associated with premature equipment replacement.
  
  
• Up goes quality: Predictive maintenance helps catch subtle wear or misalignment that could affect a robot's output. By addressing these issues early, manufacturers can ensure consistent product quality.
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• More adaptability: Predictive maintenance data provides insights into how robots are used in real-world conditions. This information can be used to optimize maintenance schedules further and even inform future robot designs, leading to more efficient and robust systems.
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• Reduced maintenance costs: By targeting repairs and replacements proactively, predictive maintenance avoids more expensive emergency repairs and the potential for secondary damage caused by cascading failures. This translates to lower overall maintenance costs over time.
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• Environmental sustainability: Predictive maintenance can contribute to environmental goals by reducing unplanned downtime, which often leads to wasted materials and energy. Additionally, extending equipment lifespan reduces the need for premature replacement and its associated environmental impact.

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Implementing a predictive maintenance program in robotics operations

To implement an effective predictive maintenance program for your robotic systems, you’ll need to put in some time and effort upfront. But trust us, you’ll save money and increase productivity in the long run: 

• Collect historical data. First, collect as much historical data as possible about your robotic equipment, like operating hours, repair records, sensor readings, and error logs. The more data you have, the better your algorithms can detect patterns that indicate decreased performance or impending failure.
  
  Work with your robotics vendors and tech teams to pull reports and ensure you have a process to capture this data on an ongoing basis.
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• Be mindful about the tools you pick. Next, you’ll need tools to analyze all that data. Predictive maintenance software uses algorithms and machine learning to detect anomalies, identify trends, and predict when parts or systems need replacement or repair.
  
  Explore options that integrate with your specific robotic platforms and operations. These tools don’t have to be complex or expensive, but should provide easily interpreted insights and alerts.
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• Set key performance indicators. Key performance indicators (KPIs) help determine what “normal” operation looks like for your robotic systems. By monitoring factors like operating temperatures, error rates, throughput, and downtime, you’ll establish baseline metrics to measure against.
  
  Any deviation from normal KPI ranges could indicate a need for maintenance or repair before a failure occurs. Review KPIs regularly to catch issues early.
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• Put monitoring procedures into place. With your software and KPIs in place, develop procedures to monitor your robotic equipment on an ongoing basis. Check that data is being captured properly. Review reports and dashboards regularly for any anomalies or downward trends. Stay on the lookout for alerts from your predictive maintenance tools.
  
  Make monitoring a habitual part of your daily or weekly tasks to maximize the benefits.

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Summing up

As you can probably gather by now, predictive maintenance offers a proactive way to monitor the health of your robotic systems and catch issues before they cause downtime. (And make your hair gray) 

So, by using AI and advanced analytics, you can detect early warning signs and schedule maintenance at just the right time. 

This approach helps improve uptime, optimize operational costs, and avoid unplanned outages. 

Carefully evaluate your data infrastructure and team capabilities as you give some thought to implementing this powerful strategy. With the right prep work, predictive maintenance can help your robots keep running in tip-top shape.

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Next steps

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