While operating a huge plant with a bunch of heavy machinery doing so many things, mechanical breakdowns are one thing you expect the first time. Surely you might do some routine servicing in order to keep everything on the mark but still, things can go out of hand any minute.

Not to mention how costly routine Maintenance is on a yearly basis especially when you realize mostly you find nothing wrong. It’s not that you would like to find faults but don’t you think it would be much better if you would have been better if you only needed to do service only when it is needed? Just try to think how much money you would have saved if you do so. Let us bring the concept of Remote Monitoring for predictivemaintenance.

It is the result of combining artificial intelligence and the internet of things with heavy industrial machinery to build remote monitoring for Predictive Maintenance software. You can receive notifications on when you should probably check the systems out or whether you need any kind of maintenance at all or not but most importantly you get to know if something is about to go wrong and alert you how soon it needs to get maintenance. All these things come to gather as the product of Remote Monitoring for Predictive Maintenance

remote monitoring for Predictive maintenance with the help of IoT and AI has been tried and tested in various industries in many countries around the world like Japan, Germany and Singapore and it is predicted that by the end of 2030 IoT industry specifically for Remote Monitoring for Predictive Maintenance would reach 64.3 Billion USD.

In this article, we will discuss everything about predictive maintenance and how it is linked to IoT and A.I. and many more things about predictive maintenance and remote monitoring so let’s get started.

What Remote Monitoring for Predictive Maintenance at Its core?

Predictive Maintenance is exactly how it sounds like where A.I. is taking information from the IoT and based on the collected data it got from IoT and then it predicts when exactly it needs to be maintained and alerting only then.

Previously maintenance activities used to happen in two main ways (1) At routine intervals and (2) when the machine breaks down. For example, most cars have regular service intervals at around 3,000 miles or so. But in the case of some motor belts or bulbs, often they’re replaced only when they break down. 

Here we are not going to discuss which is better as both of them has some kind of pros and cons. On one hand, you spent too much money and on the other hand, you risk downtime and risk harming a machine more. Remote Monitoring for Predictive Maintenance brings the best of both worlds.

It reduces unnecessary expenses and also significantly slices down the risk of unexpected downtime. The system here is installed with various kinds of sensors and actuators that are connected together and shares data between themselves. From the data, AI takes control and gets information out of this and then decides what would be the optimum time to get your system serviced.

For example, your predictive analytics solution and the system measures the vibrations, temperature and performance of it. Once it sees any kind of changes in any of these things it usually means they’ve experienced some wear and tear. And using this data, these solutions raise alerts when a piece of machinery needs maintenance. The time wasted in maintenance and the money saved from predictive maintenance can be used for making overall production and employee security better.

Role of IoT in Predictive Maintenance

IoT is a network of sensors and actuators it connects light & heavy machinery to the Internet and allows technicians to control and monitor their performance and functionality from a remote place. Organizations on every level can easily bring state-of-the-art industrial quality control to their products and machinery if they can implement IoT into their system.

Besides the data gathered currently from the equipment, enterprises can add more sensors to collect even more data points for comprehensive health monitoring. In the above situation, you can add vibration sensors or mics to the quality control equipment. 

The data from the Remote Monitoring for Predictive Maintenance gets sent to the Predictive maintenance solution which often includes some machine learning technology to predict outcomes that gathered data from the predictive maintenance software. The accuracy of the Ml solution depends on the amount of data available to train a machine learning model. The outcome generally improves over time as more and more data points become available.

Here Is Your Predictive Maintenance Checklist

Start With Single Sector: One of the most important parts of growing big is starting small. Giants like Amazon Apple and Ford all started small. As with any major initiative, you need buy-in from all parties for predictive maintenance to be successful. The technology is still new and still has some time to mature. how it is executed or implemented can vary with industries.

Don’t try to implement predictive maintenance for all the departments in your organization. Start small, measure the success, and scale up from there. Once it gets mature enough you would have enough time to implement that into each system. You might ask why should we even bother about it right now the thing here is the competition. It’s best to keep up with modern technology and stay ahead of competitors, doesn’t it?

RoI is More Important Than You Think: Measuring the different aspects of different parts of your business is the best thing you can have in your arsenal. You should measure the status of your all-over aspects stated above before and after the implementation of your predictive maintenance solution in order to measure the RoI properly. Some of it might look intangible at the beginning but in the long run, things like fewer uninterrupted workflows are bound to improve employee satisfaction bringing the RoI for themselves.

Build a Place for gathering Feedback: You can’t get everything right the very first time even if you do there is a slight chance that you might be not monitoring performance or not collecting the feedback properly. All the information you gather about your machines from IoT won’t be wasted you can still use them to improvise your systems.

Like when a solution alert about something there is probably equipment that’s faulty. Your organization must have a person who monitors the predictive analytics solution and measures if something is getting broken again and again and finds out why exactly this is happening and solve that problem all throughout from the grass root level. This way of gathering information brings a more holistic approach to making the overall production line superior.

Proper Training Is Very much Needed: One thing about IoT is that you will have to make sure that your employees know how to use this predictive analytics solution and more importantly they need to be trained to do so. They also need to be told about the benefits it has over the organization and the people working in it. Informed and well-trained employees are crucial for a successful deployment