What is Predictive Maintenance?

Predictive maintenance is trying to predict when equipment and machinery is likely to fail so you can use it to avoid or minimise downtime. It’s a data-driven approach which uses predictive data analytics to measure various equipment conditions, which can indicate potential failure of machinery or equipment.

The aim of predictive maintenance is to reduce repair and maintenance costs, whilst reducing downtime wherever possible by alleviating the chances of catastrophic equipment failure. The strategy is executed during normal operation to minimise disruption during day-to-day operations.

Rather than focusing on constant maintenance, predictive maintenance achieves the  minimum of maintenance whilst keeping assets in operation and minimising the costs of reactive maintenance. It relies on metrics to determine when maintenance should happen and how often, using statistical analysis to help make decisions.

For a real world example, a lathe is scheduled for maintenance every 6 months ,4 months in the grease reservoir level needs checking but it’s not related to any other immediate concerns. The best predictive maintenance models are those which are the most effective at predicting failures and giving ample advanced warning of forthcoming downtime to avoid disruptions, and will alert you to the problem stated above before something worse happens. 

Types of predictive maintenance:

• Infrared: Infrared cameras can be used to recognise abnormally high temperature levels.
• Acoustic analysis: using sonic or ultrasound tests, this analysis is performed to find gas and liquid leaks.
• Vibration: sensors are used to find degradation in the performance of equipment including pumps and motors.
• Oli analysis: this establishes asset wear by tracking an asset’s number and size of particles.

How SMEs can use predictive maintenance

One of the biggest uses of predictive maintenance is for operation-critical machinery and equipment. For example, data servers holding a business’s most important information have to constantly be monitored and checked to make sure organisational infrastructure isn’t at risk of collapsing or downtime.

In manufacturing, vital machinery has to be just as closely monitored and tracked to avoid downtime and save maintenance costs. In both of these scenarios, it’s unrealistic to expect employees to dedicate their time to just one piece of equipment. This is why predictive maintenance is used instead, helping to keep operations running without rising costs.

In factories, manufacturing analytics are linked to predictive maintenance suites providing real-time data on production levels and machinery conditions so production remains consistent. It’s an area where the The Internet of Things (IoT) has started to take on a prominent role in the industry because of its interconnectivity and ability to communicate data. IoT devices are used to provide real-time data insights so businesses can benefit from improved and more consistent maintenance. Predictive processes are agile too, and can be used in electrical systems and circuitry when fluctuations can be detected where currents are being affected. 

What are the benefits? 

It’s been highlighted businesses spend around 80% of their day-to-day responding to issues rather than preventing them proactively. Predictive maintenance helps keep you ahead of those problems because it identifies those failures before they happen. It’s monitoring performance continuously. With time and costs saved, businesses committing to this strategy will see huge improvements in the reliability of their assets.

Key stats:

• Up to 25% boost in production
• A minimum of 25% reduction for maintenance costs
• 70-75% of breakdowns eliminated
• At least 35% reduction in downtime

Predictive or preventive maintenance?

Whilst the best maintenance programs leverage both predictive maintenance and preventive maintenance, they are different approaches. Preventive uses the average life cycle of an asset, whereas predictive makes decisions when identifying their condition. Predictive is more complex to set up preventive but it can be more effective for businesses wanting to save time and money.  Sensors can be used in a multitude of industries to provide readings on equipment such as refrigeration. In this scenario sensors provide a better understanding of fuel levels and power being used, the data subsequently being utilised to show how much power is needed for maximum efficiency whilst using the lowest power. Businesses can use that data to save money by not overworking their equipment.  

Next steps

Whether you need to measure assets via vibration, temperature, oil or acoustics, the tools within CMMS systems and the OpsBase platform can assist in developing the predictions you need to determine how to proceed with a piece of equipment. If you would like to find out more about how these technologies can help your business, sign up for your free trial and demo.