Predictive Maintenance (PdM) is dedicated to one major mission – to prevent an issue before it becomes real trouble. The global predictive maintenance market stood at $4 billion in 2020 and is forecast by Marketsandmarkets to reach $12.3 billion in 2025. The growth in investments in this industry is not accidental – the effective use of this tool helps to save a lot of unnecessary costs.
The goal of PdM is simple – evaluating equipment for timely maintenance. By 2021, business is already well aware of the role and capabilities of the Internet of Things, therefore investments in the discussed technology are increasing according to demand. Evidently, innovation implies more optimized solutions than preventive maintenance (PM).
Organizations using PdM can make predictions up to 20 times faster and with greater accuracy than those using threshold systems. The numerous sensors supplied to the PdM service bring a number of other benefits:
The equipment is always influenced by more factors than can be calculated, therefore measuring the actual condition will always be more useful than assumptions and predictions based on past experience or averages. Knowing about the actual condition, the owner can accurately calculate the required parts, consumables, and predict the duration of downtime.
This makes it possible to avoid costs in the form of unnecessary equipment, delays in working capital, therefore, it will help to save a significant amount of money. Add to this the fact that PdM often relies on hardware rather than personnel, which also leads to optimization.
It is not entirely correct to directly compare PdM and PM, since preventive maintenance in many respects is the predecessor of PdM, and not a direct competitor and it is too early to write it off. After all, installing PdM is still a complex process, the system itself requires skill in handling and is not without errors.
In order to create an efficient PdM, you will need:
Condition monitoring sensors can read the temperature, vibration, noise, and so on, the parameter read depends on the type of sensor. With the help of the Internet of Things, a huge amount of data received from sensors, while still random, is processed through predictive machine learning algorithms. After transforming this data into digital, the owner gets the opportunity to monitor the state in real time and perform other functions of data analysis. Over time, predictive machine learning algorithms will further learn to spot anomalies.
With predictive maintenance in place, in the event of threats, the employee will timely spot them and correct the situation relying on the work of algorithms. Going back to the PM comparison, it’s easy to see that workers, urgent replacements, and downtime are, eventually, more expensive than predictive services.
At the same time, this does not mean at all that the latter is cheap, you need a team of professionals and technology that will put everything described above into action, this requires a significant investment. But it’s not about a specific price here and now, it’s about risk control – unexpected situations always happen at the wrong time. Accordingly, the choice of predictive maintenance is fully justified in the case when the equipment requires significant costs for full repair or replacement.
We’ve looked at some of the obvious benefits, you should look at real-world examples of the technology to see the rest. There are areas where downtime is downright dangerous, like utilities. Already, drones with sensors are used in this area. A specialized predictive maintenance program, for instance, helps (via machine learning) to see trees that can fall on the rails in time – Sharper Shape is used for this. It is 30% cheaper than human labor and, of course, faster – it is impossible to effectively prevent the problem if the check takes place once a month. Predictive maintenance solutions in this area now include a daily check.
Another example where predictive maintenance solutions are equally important is trucking. From air travel to road and water transport, the Internet of Things is taking over these industries too. For the owner of the car fleet, information about the location of the vehicle is invaluable. Previously, it could only be found upon arrival at a predetermined point. Now, the owner, with the help of the predictive maintenance program, can know the location of the vehicle, the supply of gasoline, and the general condition of the vehicle, including its load.
Predictive services also provide an invaluable bonus – insight. $30 every hour – this amount was saved by a company that carries out cargo transportation by sea. Using predictive machine learning algorithms, they found that using a large number of generators at a lower power would be more cost-effective than using multiple generators at maximum power. Note that saving $30 per ship per year translates into a total of $650,000 for the company.
The question of safety immediately arises because utilities or water transport are examples where the issue of safety stands squarely. Indeed, many companies are worried about predictive maintenance solutions’ weak security measures. There is also the likelihood, albeit low, of errors in analysis, forcing some organizations not to abandon other support methods altogether.
It should also be added that there is another known issue – personnel training. Training can slow down the business for a while and lead to downtime, but the benefits are also obvious. The estimated benefits depend on the scale, specifics, and other characteristics of a particular business.
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