Michio Kaku in his famous book The Physics of Future wrote that we won’t need doctors to diagnoze 95% of health conditions. Instead, our mirror in the bathroom or, say, toilet will be equipped with DNA-microchips that will alert you if you have at least hundreds of cancer cells. Sounds surreal, right? Even though our healthcare still can’t work such sort of magic, what we do know is how to take care of our equipment’s health before any serious problem pops up.
IoT predictive maintenance allows manufacturing or fleet management companies to foresee when an equipment is close to failure, so that they can timely replace the parts or make repairs. Such an IoT device brings a tremendous value to businesses, saving their money and preventing downtime. In this blog post, the JatApp team is going to explore the benefits of predictive maintenance software, reveal technologies needed to build such a solution, and give a relevant example from our software development practice.
What is predictive maintenance?
Predictive maintenance refers to the process of detecting equipment deficiencies to prevent its failures. This often entails analyzing an asset to foresee when it will break down before it actually happens, as well as identifying possible risks to minimize or eliminate them. Also, predictive maintenance includes scheduling routine checkups to make sure that the machinery is functioning properly.
The concept of predictive maintenance is opposed to reactive maintenance. The latter refers to the process of repairing the equipment after it has broken down. Using reactive maintenance as the key strategy leads to significant downtime, which presents a serious concern for businesses. Every hour when a piece of equipment is out of service can cost manufacturers thousands of dollars.
Why is there so much fuss around predictive maintenance?
Traditionally, the most common maintenance approach was preventative maintenance, which means scheduling regular repairs to reduce the likelihood of machine failure. However, there’s a problem with that — the aging equipment doesn’t necessarily require repairs. Even though a piece of equipment could have been used much longer, it was replaced or repaired after a specific period of time.
The research evidence suggests that this maintenance strategy is the best option only for 18% of plant machinery, while the remaining 82% breaks down due to other random factors. Eventually, this leads to decreased employee productivity and unexpected downtime. Unforeseen failure may not only cause equipment damage, but also compromise workers’ safety. Unexpected downtime also means urgent revisions, which translates into higher expenses.
As preventative maintenance is becoming more and more dinosaur-y, IoT-based predictive maintenance enters the scene. This approach to maintenance includes the use of sensors, analytics, data collection, and machine learning to track machinery in real-time and foresee breakdown with surgical precision. Furthermore, in the future, some types of equipment will be able to conduct self-maintenance, thereby significantly reducing costs associated with human labor.
Major benefits of using IoT for predictive maintenance
As you can see, IoT predictive maintenance has a number of merits, like reduced costs and employees’ safety risks. The benefits don’t end there, however. Let’s have a closer look at some of the major pros of using IoT for predictive maintenance.
If the equipment does break down, it’s likely to take less time to repair in case predictive maintenance technologies were in place compared to when the failure was not expected. Continued inspection of equipment health lets one better understand when the breakdown is inevitable and prepare for such an event. One of the recent studies shows that the mean time to repair equipment was reduced by 60% in 500 plants only after the year of implementing predictive maintenance.
Prolonging equipment life
Thanks to both quantitative and qualitative metrics, predictive maintenance allows businesses to prolong equipment life. IoT sensors can offer companies valuable insights into machine health to schedule repairs as well as renovate them for better performance. By foreseeing possible breakdowns, companies can solve any problem prior to it reaching the point of no return. In this way, it’s possible to keep equipment running at the maximum efficiency for longer periods of time.
Improving production quality
The recent research shows that predictive maintenance contributes to a positive return on investment in 83% of the cases. This is because predictive maintenance solutions are able to identify deficiencies in real-time. If the issues with equipment are effectively handled, such things as costs, downtime, or safety concerns, don’t affect businesses that much any longer. When IoT provides real-time data on a regular basis, maintenance teams can schedule repairs instead of being rushed to fix malfunctioning equipment.
Detecting areas of inefficiency
Advanced algorithms and sensor data can show the problematic issues, so that businesses can address inefficiencies early on. Moreover, real-time data can also highlight the areas for improvement, which allows companies to enhance equipment performance, if necessary. For instance, IoT-based sensors coupled with artificial intelligence can help a plant significantly cut down energy consumption.
Besides, predictive maintenance can save workers’ lives by allowing managers to identify defects and immediately report them. For example, employees can be in charge of detecting anomalies in electric poles. This process, however, is both tedious and time-consuming, so performing this task repetitively can make us, mere humans, sleepy and prone to errors. Meanwhile, predictive maintenance solutions can identify whether the pole is leaking in a matter of seconds, leaving the maintenance guys without a job (but no worry about them: they’ll have more creative tasks to do).
Caution. Tech information ahead! How does predictive maintenance work?
Predictive maintenance is a sort of a blend of all the latest buzzwords in the world of technology: connectivity, cloud computing, big data, machine learning, and edge computing. To enable connectivity, an engineer normally needs wired and wireless solutions, sensors, batteries, antennas, and connectors. It’s worth admitting that these core products should be designed with regard to the industrial environments, in which damages are not rare.
Sensor data can be retrieved from assets, like drives, motors, and actuators, processed via field gateways, and transmitted to the cloud through wireless connectivity. Then, the data from sensors is structured and filtered at a big data warehouse into valuable insights about different performance indicators, like temperature or vibration. Machine learning can be used to analyze this information to detect any deficiencies before they cause downtime. What’s more, predictive models become even more accurate over time, being trained by machine learning algorithms.
But here’s a thing: it’s necessary to make sure that the right data is gathered and the right datasets are processed. IoT data may be used to determine the equipment condition, while other static information might illustrate the details of the configuration or model. The use of history data is equally important, as it helps to enhance predictive outcomes and improve model efficiency.
Real-life examples of predictive maintenance
For you to better understand the concept of predictive maintenance, we’re going to explore one of our most recent relevant cases. JatApp built a software solution that integrates with electric vehicle (EV) charging stations. Users can not only monitor electricity expenditures and operate these stations, but also control their performance. More specifically, this web application enables clients to respond to failures faster and more efficiently as well as address specific issues remotely.
The technology is used by three kinds of users — managers that rented or purchased charging stations, support administrators, and people who bought the EV charging stations for their personal use. Importantly, all types of users can respond to problems and ensure stations’ maintenance. Managers can diagnose the stations and report different issues to administrators. At the same time, administrators can reboot the hardware and software without having to travel to the place where these stations are installed.
Moreover, all users can view the statistics of a charging station, but with different levels of permission. For instance, the clients can access daily reports on the engine health, while administrators can make use of the information related to the station transaction logs.
Functionality of software for EV charging station
A perfect match: digital twins and predictive maintenance
Digital twin technology is expected to be in the spotlight in the next few years. The solution refers to creation of IoT hardware in the digital form. The technology has a number of important benefits that make the predictive maintenance market sour today. Namely, the key advantages are:
- Improved speed of data analysis
Thanks to digital twin algorithms, users can conduct analysis of equipment health faster than a maintenance workers’ eyes can see. Moreover, it provides clients with a myriad of suggestions for effective response.
- Endless testing possibilities
Users can simulate various maintenance problems and receive detailed reports on expected outcomes. Instead of waiting for a major issue with a machine to happen, managers can no longer waste their precious time and test potential issues without causing any equipment damage.
Components of a digital twin
Help businesses predict, not prevent problems
Large companies need predictive maintenance solutions in place to decrease the likelihood of downtime, workers’ safety issues, undesired expenses, and, let’s admit it, tons of stress! IoT solutions are offering much-needed relief to these businesses, helping them to plan their future with more certainty. Besides, in times when the world is moving towards sustainable use, manufacturers can show their corporate social responsibility by prolonging the life of their machinery in every possible way.
Given all these trends and benefits, the demand for predictive maintenance isn’t likely to decrease any time soon. If you’re considering building IoT predictive maintenance technology, it’s better to hurry up and kick off your project. JatApp can offer you great technical expertise in this regard. Our talented team can assist you in creating the solution that helps companies no longer wait patiently for their machinery to become out of service. Instead, we’re delivering cutting-edge technologies that allow users to enjoy remote diagnostics and problem reporting before the equipment problem starts to cause too much trouble.
In fact, we’ve been building industrial IoT predictive maintenance software for more than seven years now. Not without a reason, our company has a five-star rating on Clutch and was recognized as one of the top Ukrainian custom software development companies in 2022.
Want to cooperate with us? Don’t hesitate to leave us a note and we’ll get back to you as soon as possible.