Like suppliers for traditional automotive manufacturing, suppliers for the electric vehicle (EV) industry use robots for their manufacturing processes. Yes, robots are a substantial investment, but these assets provide significant manufacturing advantages: precision and consistency, safety and ergonomics, and flexibility and adaptability to ultimately result in increased production of high-quality components. And since these suppliers have invested heavily in robots, it makes good business sense to look after their investment by ensuring good asset health and long asset life through superior maintenance practices.

That said, the established maintenance method is time-based where routine maintenance is performed at fixed time intervals, regardless of the asset condition. But there is a much more advantageous, efficient, and cost-effective approach to maintenance, one that brings major benefits such as improved asset health and extended asset life – predictive maintenance.

Predictive Maintenance Defined

First, to understand the business justification for predictive maintenance, I think it's best to start with its definition. Predictive maintenance is the maintenance strategy that uses sensors and monitoring systems to continuously capture operational data. This data in turn is used for assessing asset performance and condition to identify operational patterns and anomalies that can be used to indicate potential failures.

More importantly, maintenance is triggered by data insights that show that the asset’s performance is deviating from its normal operating condition or deviating from specific parameters to suggest imminent failure. Maintenance teams are then able to schedule and perform crucial maintenance tasks only when indications of impending asset failure exist.

OK, now you know what predictive maintenance is. Let's look at how suppliers for the EV industry can achieve this strategy. The key is a combination of using artificial intelligence (AI) predictive analytics like Mitsubishi Electric’s MaiLab data science tool and automation software like ICONICS’ suite of automation software.

How Mitsubishi Electric’s AI Predictive Analytics & ICONICS’ Automation Software Bring About Predictive Maintenance

To predict the failure of an asset, predictive maintenance solutions analyze captured operational/performance data like asset operational conditions, average service lives of components, frequency of asset motion patterns, and real-time drive data. More importantly, besides calculating different metrics like wear and consumption, AI analytics can identify patterns/trends that suggest that components are close to failure. Then to ease understanding and interpretation of the results, automation software can be used to visualize and display the results in a centralized, customized dashboard.

The value of this technology is the data-driven actionable insight it provides. But not to worry, suppliers for the EV industry can achieve predictive maintenance through an A-team combination of Mitsubishi Electric’s AI predictive analytics and ICONICS’ automation software. Let me explain.

Fortunately, Mitsubishi Electric industrial robots are now designed with embedded AI functionalities in the company’s MELFA SmartPlus card for its FR-series intelligent robots. Built for robot controllers, the solution provides:

  • Consumption degree calculation - which determines when robotic parts, such as ball screws and ball splines, gears, bearings, and belts, are likely to need replacement and sends notifications when maintenance is needed.  
  • Maintenance simulations - which estimate robot service life, considering robot operating conditions and activities performed, and suggest a maintenance schedule that optimizes maintenance costs.

This is where ICONICS’ automation software comes into play, providing value on many levels but specifically these levels for predictive maintenance:

  • Data collection, integration, and visualization: ICONICS’ automation software can gather and integrate data from various manufacturing and operational sources including sensors, assets/equipment, and production lines into a centralized dashboard to provide a holistic view of the entire operational system. 
  • Alerts and notifications:ICONICS’ automation software will contextualize and visualize the MELFA SmartPlus card predictive information in easy-to-understand dashboards and generate alerts and notifications when deviations or anomalies are detected in asset performance or conditions. With prompt notification of potential issues, maintenance teams can quickly act thus significantly reducing response times.

Through predictive maintenance, suppliers for the EV industry can optimize operational efficiency and productivity which means considerable value of increased speed to market.

The Benefits of Adopting a Predictive Maintenance Strategy 

Some of you might be asking why you should invest in predictive maintenance. You probably know predictive maintenance is the way to go. But you still might not be sure, especially if you are using extremely reliable high-quality robots, like Mitsubishi Electric industrial robots. These assets have such excellent engineering and robust design, breakdown or even failure does not often occur. So, what’s the point?

Well, the point is that even the highest quality vehicles still require oil changes, replacement of windshield wiper blades, air filters, and other routine maintenance at certain intervals. Robots and robot parts are quite similar, so predictive maintenance pays dividends with many solid benefits. Below, are several of those benefits:

Minimizing downtime and maximizing productivity: Robot controllers provide telemetry data such as positions of robot axes, servo motor loads, controller temperatures, etc., and the automation software displays this information in centralized dashboards. It also sends alarms for any abnormal conditions, for example if the servo load is greater than a set value or has been greater than a set value for too long. The maintenance teams can be alerted to inspect the robot and act if needed. And for predictive maintenance, a robot controller can be outfitted with a MELFA SmartPlus card that can provide predictive information such as grease usage per axis or timing belt wear per axis. The automation software can then deploy logic to present this information visually. This approach allows maintenance teams to address any issues before breakdowns that can lead to costly unplanned downtime.

Cost effectiveness and resource optimization: Robot assets and systems are intricate and require specific maintenance procedures. Predictive maintenance alerts the maintenance teams about components that need servicing or replacing as opposed to time-based maintenance, that might lead to unnecessary part replacements or servicing when the components are still in good working condition. As a result, predictive maintenance reduces operational costs and the unnecessary consumption of valuable resources.

Enhanced safety and quality control: For any manufacturing industry including the EV industry and its suppliers, safety and product quality are paramount. An unexpected asset failure during production can lead to unsafe conditions and subquality vehicle/component assembly. Predictive maintenance enables operations teams to proactively address potential malfunctions to minimize safety risks and ensure product consistency and quality.

Transform Your Operations through Predictive Maintenance & Automation Software Technology

The EV industry is booming and is projected to continue growing especially given increasing environmental concerns, governmental incentives, EV infrastructure development, advancement in battery technology, and heightened consumer awareness and interest. This means suppliers for the industry are tasked to meet the rocketing demand.

One way to ensure these suppliers can rise to the occasion is to realize predictive maintenance. As a data-driven strategy that uses real-time insights to predict failures, predictive maintenance optimizes maintenance efforts allowing teams to perform maintenance only when necessary. And this strategy has incredible payback with clear advantages.

Ultimately, adopting predictive maintenance means suppliers are looking after their asset investments. The technology and expertise exist for EV suppliers to transform their operations through predictive maintenance. And the time is now.