A Guide to Predictive Maintenance Strategy

Used for everything from assessing the condition of remote offshore pipelines in oil and gas to monitoring the health of components on jumbo jets and preventing power lines from going down, there are many predictive maintenance examples.

Predictive maintenance solutions allow engineers to diagnose problems before they become critical and result in machine downtime, unplanned maintenance – and increased costs. They assess actual machine health, using sensors and other technologies to collect data and provide continuous condition monitoring of equipment. The aim is to diagnose faults before they cause machine breakdown. Understanding when and where corrective maintenance is needed is key to your predictive maintenance strategy’s success.

Read on to learn more about predictive maintenance. Explore the benefits and challenges of implementing a predictive maintenance strategy, some of the latest technologies, and how to implement a predictive maintenance solution.

So, what’s the difference between predictive and traditional preventative maintenance? Preventative maintenance is routine maintenance of equipment to keep it running. For example, a manufacturer may specify that maintenance is carried out regularly. So, preventative maintenance is planned or scheduled according to the manufacturer’s recommendations. As with predictive maintenance, the aim is to avoid component failure and minimise downtime.

The key difference is that predictive maintenance actively monitors the condition of a machine. It alerts the engineer to any anomalies that could mean failure is on its way. By intervening and performing corrective maintenance, this outcome can be avoided. Predictive maintenance relies on many different technologies including vibration monitoring, in which sensors pick up vibrations from the machine that may indicate something is awry. This is often used on high-speed machinery with rotating parts. Cutting-edge technologies such as machine learning and artificial intelligence (AI) are also used to predict problems before they occur. Learn how RS can help you with predictive maintenance and condition monitoring.

Despite all these advantages, predictive maintenance has a couple of drawbacks. 

First, it can be expensive to set up. For example, vibration analysis requires a sensor network in your machinery (part of the industrial Internet of Things). Sensors used may include piezoelectric sensors or microelectromechanical models. The cost of installing these sensors must be factored against the potential costs of machinery failure. You may also need to invest in a specialised team for predictive maintenance. This could mean paying for training or hiring new employees who understand how to do it.

Second, if you’re collecting data you don’t understand, or the wrong type of data, it’s difficult to know what it means in terms of potential failure. A typical manufacturing plant will have so many systems that adding sensors to each and collecting all the data is costly and impractical. When looking at equipment, it’s necessary to ask what parameters need to be measured. For example, it may be necessary to monitor air leakage from a compressor using acoustic sensors or vibration monitoring to evaluate the health of a machine featuring rotating equipment. Data analytics also needs a consistent stream of data to detect anomalies. Any inconsistencies in how data is captured can make it difficult to spot problems.

There are several steps to take when implementing predictive maintenance solutions. You need to select the technologies you intend to use and install sensor networks on the assets to be monitored. It’s also essential that engineers understand the baseline condition of equipment and what constitutes an anomaly. It’s spotting anomalies that allows them to know that maintenance should be carried out. Additionally, if you are not already using predictive maintenance software like a computerised maintenance management system, you will need one to schedule maintenance. 

Implementing a predictive maintenance strategy can have major benefits for manufacturing businesses, which is why the field has become mainstream over the past few years. Of course, minimising downtime and maximising productivity while reducing costs are welcomed by manufacturers everywhere. However, by making manufacturing more efficient, predictive maintenance is also expected to help reduce industrial energy usage across the world, helping combat climate change. 

If you’re ready to start your workplace maintenance journey, RS can help.