From Vibration Analysis to AI: Exploring the Cutting-Edge Types of Predictive Maintenance

Predictive maintenance has revolutionized the way industries manage their equipment and assets. The business world is moving away from reactive measures and towards proactive in every aspect.

For operations and maintenance, predictive maintenance is leading the way with advanced techniques that utilize cutting-edge technologies like machine learning (ML), the Internet of Things (IoT), and remote monitoring.

Organizations in various different sectors are leveraging advanced technologies like vibration analysis and artificial intelligence (AI) to detect and address potential issues before they result in costly failures or downtime.

In this article, we will delve into the cutting-edge types of predictive maintenance, exploring their benefits, examples, and many use cases.

What is predictive maintenance?

Predictive maintenance (PdM) is a maintenance strategy that uses data and analytics to predict when equipment is likely to fail.

This allows organizations to take preventive action before a failure occurs, which helps save time, money, and unplanned downtime.

Types of predictive maintenance

There are many different types of predictive maintenance techniques available, each with its own strengths and weaknesses.

Some of the most common techniques include:

• Vibration analysis: This technique uses sensors to measure the vibration of equipment. Changes in vibration can indicate potential problems, such as bearing wear or loose components.
• Oil analysis: This technique involves sampling and analyzing the oil in equipment. Changes in the oil's condition can indicate potential problems, such as wear, contamination, or overheating.
• Thermal imaging: This technique uses infrared cameras to measure the temperature of equipment. Hotspots can indicate potential problems, such as overheating or electrical arcing.
• Motor circuit analysis: This technique involves analyzing the electrical current and voltage in motors. Changes in the motor's electrical signature can indicate potential problems, such as bearing wear or winding insulation breakdown.
• Acoustic analysis: This technique uses sensors to measure the sound emitted by equipment. Changes in sound can indicate potential problems, such as bearing wear or loose components.
• Optical analysis: This technique uses sensors to measure the light emitted by equipment. Changes in light can indicate potential problems, such as wear, contamination, or overheating.
• Current signature analysis: This technique involves analyzing the electrical current in equipment. Changes in the current signature can indicate potential problems, such as bearing wear or winding insulation breakdown.
• Particle analysis: This technique involves sampling and analyzing the particles in the oil in equipment. Changes in the particle count or particle size can indicate potential problems, such as wear, contamination, or overheating.
• Data analytics: This technique uses a variety of data sources, such as sensor data, historical data, and weather data, to identify potential problems.
• Condition-based monitoring: This technique uses a variety of sensors to monitor the condition of equipment. The data from these sensors is then analyzed to identify potential problems.
• Machine learning: This technique uses artificial intelligence to analyze data from sensors and predict when equipment is likely to fail.

Machine learning is one of the most cutting-edge types of predictive maintenance techniques. Machine learning algorithms can learn from historical data to identify patterns that indicate potential problems. This allows organizations to predict failures even before they occur.

One example of a company that is using machine learning for predictive maintenance is Rolls-Royce. Rolls-Royce uses machine learning to monitor the condition of its jet engines. The company has been able to reduce the number of engine failures by a huge margin using this technology.

As machine learning continues to develop, predictive maintenance will become even more powerful.

The increasing need for PdM techniques for enterprises

Primarily, predictive maintenance allows businesses to shift from reactive maintenance to a proactive approach.

As organizations detect potential equipment failures in advance, they can plan and schedule maintenance activities more efficiently, minimizing unexpected breakdowns, reducing downtime, and optimizing the use of resources, leading to significant cost savings.

• Increased complexity of equipment: As equipment becomes more complex, it becomes more difficult to identify and diagnose problems before they cause a failure.
• Increased cost of downtime: Downtime can be very costly for enterprises, both in terms of lost productivity, product loss, and lost revenue and damaged reputation.
• Increased focus on sustainability: Enterprises are increasingly focused on sustainability, and predictive maintenance can help to reduce the environmental impact of their operations. By preventing failures, predictive maintenance can help to reduce the amount of energy and resources that are wasted.
• Advances in technology: Advances in technology, such as the IoT and AI, are making predictive maintenance more feasible and affordable. These technologies can collect and analyze large amounts of data, which enables proactive action for assets across multiple sites and portfolio-scale optimization.

Further, predictive maintenance relies on the collection and analysis of vast amounts of data from various sources, such as sensors, monitoring systems, and historical records.

This data provides valuable insights into equipment performance, patterns, and potential failure modes. Enterprises can optimize maintenance strategies, identify trends, and continuously improve their operations by leveraging these data-driven decision-making processes.

Enterprises that embrace predictive maintenance gain a competitive edge by reducing downtime, improving customer satisfaction through uninterrupted service, and optimizing their maintenance budgets.

This strategic advantage allows businesses to allocate resources more effectively, invest in innovation, and stay ahead of their competitors.

Is predictive maintenance the right choice for your business?

Predictive maintenance is a powerful tool that can help businesses improve the reliability and availability of their equipment. However, it is not right for every business.

Here are some questions to ask yourself when determining if predictive maintenance is right for your business:

• What are the critical assets in my business?
• How much downtime can I afford?
• What is the cost of repairs?
• How much data do I have available?
• Do I have the expertise to implement predictive maintenance?

If you are considering predictive maintenance, it is important to weigh the benefits and costs carefully. If you believe that predictive maintenance can help you to improve the reliability and availability of your assets, then it may be right for your business.

If you can answer these questions positively, then predictive maintenance may be a good fit for your business.

Further, you need a software platform like Facilio to bring your vision to life.

Picking the right maintenance management software platform for your PdM program

The right software platform can make a big difference in the success of a predictive maintenance program. Here are some of the key reasons why:

• Accuracy: The software platform should be able to predict when equipment is likely to fail accurately. This is essential for preventing unplanned downtime and costly repairs.
• Scalability: The software platform should be able to scale as your business grows if you plan to implement predictive maintenance for a large number of assets.
• Ease of use: The software platform should be easy to use for both technical and non-technical users, ensuring that the program is adopted and used by everyone in the organization.
• Integration: The software platform should be able to integrate with other systems, such as your asset management system and your manufacturing execution system, to ensure that the data from the software platform is available to everyone who needs it.
• Support: The software platform should be supported by a team of experts who can help you to implement, monitor, optimize, and scale the program. This is important for ensuring that you get the most out of the software platform.

Here are some of the factors to consider when selecting a software platform for predictive maintenance:

• The type of equipment you are monitoring: The software platform should be able to monitor the specific type of equipment you are using. For example, if you are monitoring rotating equipment, you will need a software platform that is designed for this type of equipment or a provider who will build out what's necessary.
• The amount of data you are collecting: The software platform should be able to handle the amount of data you are collecting. If you are collecting a lot of data, you will need a software platform that is scalable.
• Your budget: The cost of the software platform will vary depending on the features and capabilities you need. For obvious reasons, it is important to set a budget before you start shopping for a software platform.

Once you have considered these factors, you can start to narrow down your choices. Take the time to select the right platform to ensure that you get the most out of your investment.

Facilio is the most advanced CMMS that utilizes the power of IoT and machine learning and a platform model that accommodates almost any use case and type of equipment for implementing advanced maintenance management techniques like predictive maintenance or condition-based maintenance.  

Interested in how PdM can benefit your business and what it will take to implement it at scale? Talk to our product experts today!