How much does it cost to have a production line shut down in manufacturing? According to a study by Aberdeen Group, the average unplanned downtime costs $260,000 per hour.
This is a significant amount, considering that many factories operate 24/7. The financial losses in terms of lost production, labor costs, and potential damage to equipment can quickly add up. That's where predictive maintenance comes in.
So, what is predictive maintenance?
To define predictive maintenance, it anticipates equipment failures before they happen, allowing manufacturers to schedule maintenance at the most optimal time. At the heart of this technology are the Internet of Things (IoT) sensors, machine learning algorithms, and predictive maintenance analytics, all leveraging data to make informed decisions. But it's not just about collecting data—it's a dynamic interplay of techniques like regression, time series analysis, and anomaly detection.
In this article, we will explore everything you need to know about predictive maintenance in manufacturing.
Examples of Predictive Maintenance Work in Manufacturing
Let's look at some real-world predictive maintenance examples in manufacturing to understand how it works and its benefits.
Equipment health monitoring software
Health monitoring software is used in manufacturing facilities to monitor the condition and performance of equipment.
One example of equipment health monitoring software is IBM Maximo Predictive Maintenance. IBM Maximo Predict provides condition-based maintenance capabilities with operational data to help maintenance teams proactively detect, diagnose, and resolve potential equipment issues before they impact production.
The asset health score feature in IBM Maximo uses IoT data from predictive maintenance sensors and other sources to provide real-time visibility into the performance of critical assets. This allows manufacturers to prioritize maintenance tasks based on urgency and reduce unplanned downtime.
Predictive analytics dashboard
A predictive maintenance dashboard visually represents data collected from various sources, such as sensors, maintenance logs, and historical data to provide insights into equipment health, performance, and potential failures.
One example of a predictive maintenance dashboard is the Fluke Connect™ Condition Monitoring software. This software collects data from sensors and uses machine learning algorithms to identify patterns and anomalies in equipment performance. The dashboard provides real-time alerts and predictive analytics to help maintenance teams make data-driven decisions and prevent equipment failures.
Machine learning models for anomaly detection
Machine learning models are trained to detect patterns and anomalies in data. In predictive maintenance, machine learning analyzes vast amounts of operational data and identifies early signs of equipment failure. The models learn from historical data to make more accurate predictions over time.
For example, General Electric uses machine learning models to predict equipment failure in their jet engines. These models analyze data from thousands of sensors on the engine and can detect anomalies that could lead to a breakdown. This allows GE to schedule maintenance at the most optimal time and prevent costly delays.
Integration with IoT platforms
Predictive maintenance relies heavily on data collected from sensors and other IoT devices. Integrating these devices with predictive maintenance software allows for real-time monitoring and analysis of equipment performance. This enables manufacturers to address potential malfunctions and prevent disruptions in production proactively.
For example, a leading firm in the camera, image, and printing industry faced a challenge with their printing factories. Each printerwas valued at over 1 million dollars and produced large prints and banners. They collected various parameters through IoT sensors but needed a way to optimize their maintenance strategy. They could monitor printer performance in real-time by integrating their devices with an IoT platform.
The platform used predictive analytics to identify potential malfunctions and optimize maintenance, minimizing production disruptions. As a result, the company reduced equipment downtime and increased overall productivity.
Remote monitoring and diagnostics tools
Remote monitoring and diagnostics tools allow manufacturers to monitor their equipment remotely from anywhere worldwide. These predictive maintenance tools use IoT sensors, artificial intelligence systems, and predictive analytics to identify potential issues and diagnose problems before they cause unplanned downtime.
The tool can also connect directly to the manufacturer's system in Industry 4.0 for remote monitoring, providing real-time alerts and diagnostics to resolve issues quickly.
What is the Difference Between Predictive Maintenance and Preventive Maintenance?
Predictive maintenance and preventive maintenance are two approaches used in manufacturing to maintain equipment and prevent unplanned downtime. While both aim to avoid unexpected breakdowns, their methods and effectiveness differ.
Preventive maintenance involves regularly scheduled maintenance tasks, such as replacing parts or conducting inspections, regardless of the equipment's condition.
On the other hand, predictive maintenance uses real-time data from sensors and other sources to anticipate potential failures and schedule maintenance accordingly. This allows for more targeted and efficient maintenance, reducing costs and downtime and predicting and minimizing money loss.
Benefits of Predictive Maintenance for Manufacturing
Predictive maintenance offers several significant benefits for the manufacturing industry, including:
Minimized downtime
Predictive maintenance allows for proactive identification and prevention of equipment failures. This, in turn, minimizes unplanned downtime, which can be costly for manufacturers in terms of lost productivity and revenue.
For industries with critical equipment, such as in the medical technology sector, this can also prevent life-threatening situations caused by equipment malfunctions.
Improved equipment reliability
Predictive maintenance work helps improve the equipment's overall reliability by detecting potential issues before they happen. This reduces the likelihood of unexpected breakdowns and malfunctions. Additionally, addressing problems early on can also extend the lifespan of equipment.
A study by the US Department of Energy found that predictive maintenance can reduce equipment downtime by up to 35% to 45% and increase production (plant loading) by up to 20% to 25%. This highlights the significant impact of predictive maintenance on equipment reliability and overall output.
Optimized maintenance costs
With predictive maintenance technology, manufacturers can schedule maintenance and repairs more efficiently. By addressing potential issues before they escalate, companies can avoid costly emergency repairs or replacements. The optimized maintenance schedule also reduces the time and resources spent on unnecessary maintenance tasks, ultimately leading to cost savings for the manufacturer.
Data-driven decision making
The more data that predictive maintenance systems analyze, the more accurate their predictions become. Various machine learning algorithms and deep learning techniques learn from past data to improve future predictions. Manufacturers can use this data-driven approach to make informed decisions about equipment maintenance and replacement, leading to more efficient operations. Additionally, this data-driven approach allows for better adaptation to future growth and scalability needs.
Increased operational efficiency
With real-time monitoring and predictive analytics, manufacturers can identify equipment inefficiencies and make necessary adjustments. When equipment operates optimally, overall operational efficiency increases, leading to higher productivity and reduced costs.
In addition, industrial predictive maintenance can also help identify patterns of equipment malfunctions or failures, allowing manufacturers to take proactive measures to prevent them in the future. This further improves overall operational efficiency.
Better safety
Health and safety are critical concerns in manufacturing, where faulty equipment can pose a safety risk to workers. Predictive maintenance helps prevent accidents and malfunctions by identifying and addressing potential issues before they become hazards.
In cases where safety recalls are necessary, predictive maintenance can also help manufacturers quickly and efficiently address the problem before it leads to harm.
How To Implement Predictive Maintenance?
Implementing predictive maintenance requires thorough planning, proper execution, and continuous improvement.
Take into consideration that the problem is very extensive, and there are no ready-made solutions. However, by following these general steps, manufacturers can successfully implement predictive maintenance methods in their facilities.
Here are some steps to follow when implementing a predictive maintenance program:
1. Identify critical assets: Identify the most critical equipment that impacts production or has a high potential for failure.
2. Gather data: Collect data from sensors, equipment logs, and other sources to establish a baseline for average equipment performance.
3. Verify data privacy regulations: Icorporate interpretations, verifications, and validations regarding various data privacy regulations such as GDPR, PII, and RODO. These actions are typically carried out by a Data Officer as part of data governance.
4. Analyze the data: Use machine learning algorithms and analytics tools to analyze the collected data and identify potential failure patterns or anomalies.
5. Set up thresholds: Based on the analysis, set up thresholds for equipment performance that will trigger a maintenance action.
6. Implement predictive maintenance schedule: Use the data and thresholds to create a predictive maintenance schedule, incorporating both planned and unplanned maintenance tasks.
7. Monitor and adjust: Continuously monitor the equipment and adjust thresholds and maintenance schedules as needed based on new data.
8. Utilize predictive maintenance software: Consider using specialized predictive maintenance software to automate data collection, analysis, and scheduling tasks.
Other Types of Maintenance in Manufacturing
While predictive maintenance offers significant benefits, it is not the only type of maintenance used in manufacturing facilities. Other common types of predictive maintenance include reactive, corrective, preventive, and condition-based maintenance.
Each type serves a different purpose and can be used in conjunction with predictive maintenance.
Reactive maintenance
Reactive maintenance, or "run-to-failure" or "breakdown" maintenance, involves only repairing equipment after failure. This type of maintenance is typically used when the cost of downtime and repairs is lower than the cost of implementing a preventive maintenance program.
Corrective maintenance
Corrective maintenance involves repairing equipment after showing signs of deterioration or malfunction. This type of maintenance is often used as a short-term solution to keep production running while a more permanent fix can be implemented.
For example, if a piece of equipment is making strange noises, corrective maintenance may involve replacing a faulty component to keep it running until it can be taken offline for more extensive repairs.
Preventive maintenance
Preventive maintenance involves regularly scheduled maintenance tasks to prevent equipment failures. These can include routine inspections, cleaning, and replacing parts before they fail. A preventive maintenance schedule is based on the manufacturer's recommendations and can help extend equipment lifespan and prevent unexpected downtime.
Condition-based maintenance
Condition-based maintenance, often a blend of reactive, corrective, and preventive maintenance, involves monitoring equipment in real-time to detect potential failures or changes in performance. This type of maintenance uses sensors and other technology to measure things like temperature, vibration, and fluid levels to identify issues before they lead to breakdowns.
With condition-based maintenance, maintenance tasks are only performed when necessary based on the equipment's actual condition rather than a predetermined schedule. This can help save time and resources by avoiding unnecessary maintenance tasks.
Implementing a combination of these maintenance types, including predictive maintenance, can improve overall operational efficiency and reduce manufacturer downtime.
Maximize Uptime and Minimize Your Costs with Predictive Maintenance with iRonin.IT
By implementing predictive maintenance, manufacturers can maximize uptime and minimize equipment failures and downtime costs. With real-time monitoring and data analysis, manufacturers can proactively address potential issues before they lead to costly breakdowns.
In addition, by identifying maintenance needs in advance, manufacturers can better plan for maintenance tasks and predict the risk of unexpected downtime. This can ultimately lead to increased productivity, improved product quality, and higher profits.
Don't wait any longer to implement predictive maintenance in your facility. By following the steps outlined above and utilizing advanced technology, you can control your equipment's performance and optimize and scale your maintenance processes. Our iRonin.IT can help you with that.
