What is the minimum amount of maintenance work you need to do in order to keep your equipment in peak condition and avoid costly delays and breakdowns? The answer can be found by exploring predictive maintenance.
According to a predictive maintenance report by Market Research Future, the global predictive maintenance market is expected to grow to USD 23 billion by 2025. Here, we look at what predictive maintenance is and how it works, how the manufacturing industry is benefiting from using this system, and some advantages and disadvantages of this method of maintenance.
What is predictive maintenance?
Predictive maintenance is a pre-emptive maintenance approach that uses data analysis tools and techniques to identify anomalies in your operations and potential defects in your assets so that any problems can be repaired before they result in failure. The main aim of this method is to carry out maintenance at a scheduled point in time when it is most cost effective, thereby preventing any setbacks or malfunctions. Predictive maintenance differs from preventive maintenance as it takes into account the current condition of the equipment, instead of the average or expected life statistics, to determine when maintenance will be needed.
It works by using condition-monitoring sensors that send real-time machine health data, and Internet of Things technology which allows communication between equipment, software solutions and the cloud, all of which is then fed into predictive data models which analyse any possible failures.
How is the manufacturing industry profiting from using predictive maintenance?
With predictive maintenance, manufacturers are increasingly collecting big data from sensors in their equipment and products, then using algorithms on this data to detect warning signs of potentially detrimental failures before they happen. A report by PwC estimates that employing predictive maintenance in the manufacturing industry reduces costs by 12% and increases uptime by 9%. In addition, it also extends the lifetime of older assets by 20%, and lowers safety, environmental, quality, and health risks by up to 14%.
Some advantages and disadvantages of predictive maintenance
Predictive maintenance programmes have been shown to
By using this method, maintenance is only performed on machines when it is necessary, leading to significant cost savings, including cutting maintenance time, minimising production hours lost to maintenance, and decreasing the cost of spare parts and supplies.
As with all maintenance systems, predictive maintenance also has its drawbacks. The main reason this method is not used more extensively is that it can be expensive to install a complete IoT system with sensors, transmission and analysis. Some condition monitoring systems also require specialist knowledge to provide data analysis, which often has to be outsourced. In addition, predictive analysis may not consider background information, such as how old the equipment is or environmental factors.
However, even taking into account the higher implementation costs, predictive maintenance tends to pay off in the long run. As Forrester analyst Paul Miller puts it, predictive maintenance “can eliminate unplanned failures which often provide direct savings in maintenance but, just as importantly, by taking a train out of service before it breaks – that means better customer service and happier customers.”
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