Equipment reliability directly impacts a business’s profitability, and unplanned downtime is one of the biggest drains on profits. Downtime can cost millions of dollars per hour in service-intensive industries like manufacturing, aviation and healthcare. In addition, disrupted production runs, financial impacts and compromised quality are some of the undesirable aftereffects of relying solely on schedule maintenance.
That’s exactly where predictive maintenance, aka condition-based maintenance comes in. Predictive maintenance is a practice in industrial operations that uses sophisticated algorithms and software to predict when a machine is at risk of breaking down and proactively take measures to prevent it.
Furthermore, it helps you take pre-emptive measures to reduce the risk of failures while helping preserve machines so they last longer. It’s an effective process that saves time, resources, and money.
Do you want to optimize your production yield without increasing your maintenance budget? Join us on this journey and get acquainted!
An introduction to predictive maintenance Â
Predictive maintenance is a technique that deals with the proactive maintenance of equipment and systems. Using data, we can predict when a piece of equipment is likely to fail and schedule maintenance in advance based on this principle. Â
There are multiple predictive maintenance algorithms, but they all share the same goal: use data to identify patterns and predict future failures. The most common approach is to use historical data and train a machine-learning model. Although a relatively new field, the potential benefits of predictive maintenance are clear, and it is likely to become increasingly important in the years to come.
Benefits of predictive maintenance
- Increased equipment uptime: By proactively addressing potential equipment failures, you can reduce the risk of unexpected downtime.
- Reduced maintenance costs: By scheduling maintenance in advance, you can avoid the costs associated with emergency repairs and unplanned downtime.
- Improved equipment performance: Predictive maintenance can help you to identify and address equipment issues before they become significant problems.
- Increased safety: Predictive maintenance can help you identify and address potential safety hazards before they occur.
- Improved asset utilization: By optimizing your maintenance schedules, you can maximize the value of your assets.
- Enhanced customer experience: Customers experience and loyal can be enhanced by reducing downtime and improving reliability of supply.
How predictive maintenance works – methods & approachÂ
Predictive maintenance uses a variety of methods, including:Â
- Vibration analysis: Using sensors to measure the vibrations of equipment and analyzing the data to identify any abnormal patterns that could indicate a potential failure.
- Oil analysis: This involves collecting and analyzing lubricating oil samples to detect any contaminants or abnormal wear that could indicate a potential failure.
- Thermal imaging: This method uses thermal cameras to detect hotspots on equipment, which could indicate an impending failure.
- Ultrasonic testing: This involves using high-frequency sound waves to inspect equipment conditions and identify any issues.
- Electric motor testing: An approach which utilizes specialized equipment to test the condition of electric motors and identify any issues.
Implementing a predictive maintenance program, involves three crucial steps –
1. Gathering data on equipment performanceÂ
Equipment is outfitted with sensors that collect data on various parameters, such as vibration, temperature, and lubrication levels. This data is then transmitted to a central location, where it can be analyzed to identify any potential issues. In addition to sensors, other data sources like maintenance and repair records, machine logs and production data work as an alternative. This data can provide valuable insights into equipment performance and help identify potential issues.Â
2. Analyzing data using machine learning algorithmsÂ
Once the data has been collected, it is analyzed using machine learning algorithms to identify any trends or patterns that may indicate a potential failure. These algorithms can analyze large amounts of data and identify subtle patterns that may not be apparent to the human eye. The analysis results are used to create a predictive model that can forecast when equipment is likely to fail. This model is then used to schedule maintenance tasks at the optimal time rather than on a fixed schedule or after a failure has occurred.Â
3. Predicting when equipment is likely to failÂ
Organizations can ensure that their equipment operates optimally by predicting these issues and addressing them promptly. This can be particularly important in industries where equipment failures can have serious consequences, such as in the aerospace or nuclear power sectors. Â
Step-by-step process to implementing a predictive maintenance program
- Determine which equipment will benefit most from a predictive maintenance program. This might include equipment that is critical to your operations, has a high failure rate or is costly to repair.
- Collect data on the equipment you have selected. This can include data on vibration levels, temperature, lubricant conditions and other factors that can indicate the health of the equipment.
- Invest in an analytics solution that helps you analyze the data to identify trends and patterns indicating when equipment is likely to fail.
- Develop a schedule for monitoring the equipment based on the data analysis. This might involve conducting inspections or tests at regular intervals or setting up sensors to monitor the equipment continuously.
- Train your maintenance team on the predictive maintenance techniques and tools they will use. This might include training on using sensors, software tools or interpreting the data collected.
- Implement the predictive maintenance program and begin monitoring the equipment according to your developed schedule.
- Review the results of the program regularly and make adjustments as needed. This might involve modifying the monitoring schedule or changing your techniques.
- Continuously collect and analyze data on the equipment to identify any potential issues before they occur.
In conclusion, the future of predictive maintenance looks bright in the age of Industry 4.0. Predictive maintenance has already proven to be an effective way to reduce equipment downtime, improve efficiency and lower maintenance costs across industries. However, the potential for further cost and efficiency improvements through integration with other technologies, such as the Internet of Things, is vast. Companies can thus create a more holistic approach to maintenance. As technology evolves, it will be interesting to see how predictive maintenance and other innovations will further shape the future of maintenance and asset management.
