Remote Predictive Maintenance with IoT Apps

How do you beat unplanned downtime, inefficient employee routines, and recurring production errors? In a typical industrial setting, you react. Once an error is discovered, it is often too late to avoid process interruptions. Service fees are high and the relationship between you and your customers gets strained. But how about a scenario where you can do remote preventive maintenance and predictiive analytics using IoT apps?

Why IoT apps – an overview

With remote predictive maintenance, you gain instant actionable insights into equipment behavior and resolve any potential issues before they occur. How does this happen? You simply work with IoT apps installed on your devices – IPCs, sensors, legacy machines, and other industrial equipment. In other words, you use complex machine learning models packaged as lightweight IoT applications. Usually, an IoT app is a lightweight predictive maintenance algorithm, an easy-to-manage software bundle you install on gateways or directly on machines at the IoT edge. These apps, in turn, can run on devices instantly to serve a specific predictive maintenance task. Further, they can be updated over the air with no need to do any work on the IoT devices.  

This is how you set the foundation for remote maintenance in all its aspects: preventive maintenance, reactive maintenance, proactive mainenance, corrective maintenance, condition monitoring, maintenance management, and other advanced analytics strategies. Why bother? With the right predictive maintenance strategy in place, you beat equipment failure, get out of failure mode and instead of just reacting to events, you schedule planned downtime to have a closer look at asset performance, oversee IoT sensors at all times, and keep to a predictable maintenance schedule that contributes to more affective asset management.

With the right predictive maintenance software (in our case, IoT apps as lightweight artificial intelligence bundles), planned maintenance becomes not simply a tool that delivers an insight into equipment performance but also a management tool that helps companies to better incorporate maintenance activity (e.g. condition based maintenance) in their overall performance strategy.

What can you achieve?

• Increased uptime: Dispose of complex detection mechanisms to spot issues and solve problems before costly machine failures occur.
• Better products: Harvested machine and sensor data can be used to perfect existing products and make them even more customer-centric.
• Reduced maintenance cost: With predictive maintenance technology in place, there will be less need to send repair teams on-site and perform costly service work.
• Safer environments for employees: Remote predictive maintenance has been a proven tool for avoiding work hazards such as handling potentially malfunctioning equipment.
• Increase in employee productivity: With a streamlined production cycle and functional equipment, employees can fully focus on their core activities.

Remote Predictive Maintenance: Some Tangible Benefits

Now, machine learning is often considered difficult. Way too often, there is a lack of internal know-how to drive IoT projects. For this reason, many organizations decide to opt out of IoT initiatives at the pilot stage.

With the right IoT product, a lightweight low-code infrastructure for IoT launches, and expert guidance, organizations are well-positioned to successfully carry out predictive maintenance initiatives and get results.

And once a remote maintenance solution is in place, the benefits are numerous. With the right kind of IoT product and a strong partnership, downtime can be reduced by up to 30%. Employee efficiency is predicted to rise by up to 20%. Further, on-site visits to perform manual equipment checkups will no longer be necessary, resulting in an up to 70% decrease in time spent on site.  

Remote maintenance can go even further. With machine learning algorithms installed on mission-critical edge devices, companies are now able to cut production errors by up to 60%. Specifically, Vision AI coupled with complex detection technology can spot faults in product assembly, evaluate parts coming from third-party vendors, and even make predictions about the next needed checkup for a product before it leaves the assembly hall.

The Implementation Roadmap

So how do you get there? With Record Evolution, you implement, roll out, and scale remote maintenance solutions within weeks. In using complex ML algorithms packaged as IoT apps, companies are able to implement various use cases at the IoT edge without alterations to the existing machine landscape or IoT infrastructure. Thanks to the robust infrastructure that comes with the IoT app store, organizations are now able to use, create, install, roll out, and update IoT apps over the air on any number of edge devices, from anywhere, at any time.

Once a remote maintenance strategy is in place, you can go in many directions. As a first step, you can identify a number of mission-critical machines. Then you map out the machine conditions that matter mostly to you or your customers or will cause a major disruption in the event of malfunction. Second, you will need a strategy to access the machine data and decide what data do you need to combat potential downtime.

Third, you need to look around for a suitable IoT product and partner up with a service. These will help with the implementation of the remote predictive maintenance case. Here, look for an IoT product with a robust support infrastructure and an expert team behind the wheel.

The steps to building a remote predictive maintenance solution

If expertise is lacking in your ogranization, you will work together with the product team to do any of the following:

• Select the data that you need to extract
• Identify ready-made IoT apps or build a custom ML model from scratch
• Map out a suitable data collection and data management strategy
• Identify an analytics solution
• Prepare for implementation and rollout
• Scale the solution across your organization to address enterprise-wise issues

With these in place, you are well-positioned to monitor your predictive model over time. You do this by gathering historical data of the condition of your machines and continually updating the predictive model on the basis of that data.

In combining suitable machine data with machine learning, remote predictive maintenance helps in identifying issues before they can have an impact on operations, efficiently scheduling on-site checkups and visits, eliminating disruptions caused by faulty machinery, and continually perfecting machine performance. All these become a tangible reality thanks to IoT connectivity coupled with remotely controlled IoT apps.

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